diff --git a/.nojekyll b/.nojekyll
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--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-4c8b8e7f
\ No newline at end of file
+3e1b1e7c
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diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html b/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
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--- a/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
+++ b/notebooks/datasets/OISSS_L4_multimission_monthly_v1.html
@@ -731,7 +731,7 @@
Direct S3 Data Access tutorial (Multi-Mission Optimally Interp
This tutorial only works in a jupyterhub hosted at AWS US-WEST-2.
-
+
- User guide: http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdf
- DOI 10.5067/SMP10-4UMCS
diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/a41a7c24-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png b/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/bb1b61aa-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
similarity index 100%
rename from notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/a41a7c24-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
rename to notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/bb1b61aa-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png
diff --git a/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/a41a7c24-2-419d3300-7fdc-404c-afae-9e97155fb347.png b/notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/bb1b61aa-2-419d3300-7fdc-404c-afae-9e97155fb347.png
similarity index 100%
rename from notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/a41a7c24-2-419d3300-7fdc-404c-afae-9e97155fb347.png
rename to notebooks/datasets/OISSS_L4_multimission_monthly_v1_files/figure-html/bb1b61aa-2-419d3300-7fdc-404c-afae-9e97155fb347.png
diff --git a/search.json b/search.json
index 1124e3b4..50120be1 100644
--- a/search.json
+++ b/search.json
@@ -1,514 +1,465 @@
[
{
- "objectID": "quarto_text/OPERA.html",
- "href": "quarto_text/OPERA.html",
- "title": "OPERA",
- "section": "",
- "text": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) product suite is designed to collect data from satellite radar and optical instruments to generate three products:\n\na near-global Dynamic Surface Water Extent (DSWx) product suite\na near-global land-surface Disturbance (DIST) product suite\na North America land-surface Displacement (DISP) product suite\n\nOnly DSWx products will be distributed through PO.DAAC. More information can be found on PO.DAAC’s OPERA webpage."
- },
- {
- "objectID": "quarto_text/OPERA.html#background",
- "href": "quarto_text/OPERA.html#background",
- "title": "OPERA",
+ "objectID": "notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.html",
+ "href": "notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.html",
+ "title": "Use Case: Co-locate satellite and in-situ data for cross-validation",
"section": "",
- "text": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) product suite is designed to collect data from satellite radar and optical instruments to generate three products:\n\na near-global Dynamic Surface Water Extent (DSWx) product suite\na near-global land-surface Disturbance (DIST) product suite\na North America land-surface Displacement (DISP) product suite\n\nOnly DSWx products will be distributed through PO.DAAC. More information can be found on PO.DAAC’s OPERA webpage."
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
},
{
- "objectID": "quarto_text/OPERA.html#data-resources-tutorials",
- "href": "quarto_text/OPERA.html#data-resources-tutorials",
- "title": "OPERA",
- "section": "Data Resources & Tutorials",
- "text": "Data Resources & Tutorials\n\nImagery Exploration\nSOTO by Worldview - explore OPERA imagery in a GUI\nVideo Tutorial: Exploring OPERA Surface Water Extent Data in NASA Worldview\n\n\nSearch & Download\nVia Graphical User Interface:\nFind/download OPERA data on Earthdata Search\nVia Command Line - PO.DAAC subscriber/downloader example:\npodaac-data-subscriber -c OPERA_L3_DSWX-HLS_PROVISIONAL_V1 -d ./data/OPERA_L3_DSWX-HLS_PROVISIONAL_V1 --start-date 2023-04-04T00:00:00Z -e .tif\n\nSee how to Download/Subscribe for more information on how to use the PO.DAAC subscriber/downloader.\n\n\n\nWorkflow tutorials: Access & Visualization\nAWS Cloud: OPERA Dynamic Surface Water Extent (DSWx) Data - How to search for, download, visualize, and mosaic OPERA data over lake Powell while working in the cloud.\nLocal Machine: OPERA Dynamic Surface Water Extent (DSWx) Data - How to search for, download, visualize, and mosaic OPERA data over lake Powell while working on a local machine.\n\n\nGIS workflows\nStoryMap: Exploring Water Surface Extent with Satellite Data"
+ "objectID": "notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.html#access-temperature-profiles-from-argovis-api",
+ "href": "notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.html#access-temperature-profiles-from-argovis-api",
+ "title": "Use Case: Co-locate satellite and in-situ data for cross-validation",
+ "section": "Access temperature profiles from ArgoVis API",
+ "text": "Access temperature profiles from ArgoVis API\nArgoVis is an API and visualization service that provides access to Argo float profiles. The endpoint for requesting profile data is given in the cell below:\n\nargo_api_endpoint = 'https://argovis.colorado.edu/selection/profiles/?'\n\nprint(argo_api_endpoint)\n\nhttps://argovis.colorado.edu/selection/profiles/?\n\n\nCreate the AOI polygon in required XY format, make it a string, and collect the dictionary of API parameters:\n\nargo_api_aoi = [[[aoi_minlon, aoi_minlat], \n [aoi_minlon, aoi_maxlat], \n [aoi_maxlon, aoi_maxlat],\n [aoi_maxlon, aoi_minlat],\n [aoi_minlon, aoi_minlat]]]\n\nargo_api_params = {\n 'startDate': start_date.replace(\"-0\",\"-\"), # 1.\n 'endDate': end_date.replace(\"-0\",\"-\"), # 1. No leading zeros in start/end dates\n 'shape': str(argo_api_aoi).replace(\" \",\"\"), # 2. Array of XY vertices for AOI polygon\n #'presRange': \"[0,30]\" # 3. We wont limit by pressure range\n}\n\nargo_api_params\n\n{'startDate': '2019-1-1',\n 'endDate': '2019-1-31',\n 'shape': '[[[-26.0,30.0],[-26.0,40.0],[-12.0,40.0],[-12.0,30.0],[-26.0,30.0]]]'}\n\n\nSubmit the request parameters to the Argovis API. You should receive a JSON response back. Print the number of profiles inside our AOI:\n\nargo_api_response = requests.get(url=argo_api_endpoint, params=argo_api_params)\n\n# Load the response from JSON if the response status is 200:\nif argo_api_response.status_code == 200:\n argo_profiles = argo_api_response.json()\n print(len(argo_profiles))\nelse:\n # Otherwise dump the text for more clues:\n print(argo_api_response.text)\n\n41\n\n\n\nPrepare profile data for further analysis\nConcatenate the list of metadata dictionaries returned for the argos into a table and update a few of its columns with Pythonic types:\n\nargo_df = pd.DataFrame(argo_profiles).sort_values(\"date\")\n\n# Add a column with pandas datetime objects for easier indexing\nargo_df['datetime'] = pd.to_datetime(argo_df['date'])\n# And then replace the original date column with Python dates\nargo_df['date'] = argo_df.datetime.apply(lambda x: x.date).tolist()\n\n# Add two columns of sanitized lats/lons to the data frame\nargo_df['lat'] = argo_df['roundLat'].astype(float).tolist()\nargo_df['lon'] = argo_df['roundLon'].astype(float).tolist()\n\nargo_df.info()\n\n<class 'pandas.core.frame.DataFrame'>\nInt64Index: 41 entries, 40 to 0\nData columns (total 36 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 _id 41 non-null object \n 1 POSITIONING_SYSTEM 41 non-null object \n 2 DATA_CENTRE 41 non-null object \n 3 PI_NAME 41 non-null object \n 4 WMO_INST_TYPE 41 non-null object \n 5 VERTICAL_SAMPLING_SCHEME 41 non-null object \n 6 DATA_MODE 41 non-null object \n 7 PLATFORM_TYPE 41 non-null object \n 8 measurements 41 non-null object \n 9 station_parameters 41 non-null object \n 10 pres_max_for_TEMP 41 non-null float64 \n 11 pres_min_for_TEMP 41 non-null float64 \n 12 pres_max_for_PSAL 41 non-null float64 \n 13 pres_min_for_PSAL 41 non-null float64 \n 14 max_pres 41 non-null float64 \n 15 date 41 non-null object \n 16 date_added 41 non-null object \n 17 date_qc 41 non-null int64 \n 18 lat 41 non-null float64 \n 19 lon 41 non-null float64 \n 20 geoLocation 41 non-null object \n 21 position_qc 41 non-null int64 \n 22 cycle_number 41 non-null int64 \n 23 dac 41 non-null object \n 24 platform_number 41 non-null int64 \n 25 station_parameters_in_nc 41 non-null object \n 26 nc_url 41 non-null object \n 27 DIRECTION 41 non-null object \n 28 BASIN 41 non-null int64 \n 29 core_data_mode 41 non-null object \n 30 roundLat 41 non-null object \n 31 roundLon 41 non-null object \n 32 strLat 41 non-null object \n 33 strLon 41 non-null object \n 34 formatted_station_parameters 41 non-null object \n 35 datetime 41 non-null datetime64[ns, UTC]\ndtypes: datetime64[ns, UTC](1), float64(7), int64(5), object(23)\nmemory usage: 11.9+ KB\n\n\nYou can download profiles in netCDF format from the FTP link stored in the nc_url fields of the response. Here’s the URL for the first of the profiles:\n\nprint(argo_df.iloc[0].nc_url)\n\nftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis/6902663/profiles/R6902663_124.nc\n\n\nDisplay a table summarizing the space/time characteristics of eaach profile:\n\nargo_df[['platform_number', 'cycle_number', 'datetime', 'lon', 'lat']] #, 'measurements']]\n\n\n\n\n\n\n\n\nplatform_number\ncycle_number\ndatetime\nlon\nlat\n\n\n\n\n40\n6902663\n124\n2019-01-01 20:14:00+00:00\n-17.383\n35.601\n\n\n39\n6901260\n49\n2019-01-02 05:43:00+00:00\n-12.812\n37.707\n\n\n38\n6901143\n228\n2019-01-02 09:22:20+00:00\n-21.083\n32.254\n\n\n37\n6902664\n124\n2019-01-02 20:28:00+00:00\n-18.411\n34.985\n\n\n36\n3901643\n43\n2019-01-04 06:13:00+00:00\n-22.429\n37.556\n\n\n35\n3901942\n48\n2019-01-05 20:23:30+00:00\n-15.286\n35.316\n\n\n34\n6901262\n22\n2019-01-06 05:42:59.999000+00:00\n-17.967\n34.228\n\n\n33\n3901932\n22\n2019-01-06 05:49:00+00:00\n-19.904\n33.428\n\n\n32\n1901688\n288\n2019-01-07 03:11:53+00:00\n-23.134\n34.258\n\n\n31\n6901260\n50\n2019-01-07 05:26:00+00:00\n-12.887\n37.905\n\n\n30\n1901688\n289\n2019-01-08 03:15:16+00:00\n-23.137\n34.248\n\n\n29\n6902552\n167\n2019-01-08 12:50:00+00:00\n-23.798\n33.144\n\n\n28\n1901688\n290\n2019-01-09 03:19:48+00:00\n-23.148\n34.233\n\n\n27\n1901688\n291\n2019-01-10 03:27:35+00:00\n-23.158\n34.218\n\n\n26\n6901273\n11\n2019-01-10 05:25:00+00:00\n-12.473\n32.219\n\n\n25\n6902663\n125\n2019-01-11 20:15:00+00:00\n-17.071\n35.830\n\n\n24\n6901260\n51\n2019-01-12 05:18:00+00:00\n-12.982\n38.047\n\n\n23\n6901143\n229\n2019-01-12 07:05:12+00:00\n-21.206\n32.474\n\n\n22\n6902664\n125\n2019-01-12 20:16:00+00:00\n-18.198\n35.057\n\n\n21\n3901643\n44\n2019-01-14 05:58:00+00:00\n-22.706\n37.542\n\n\n20\n3901942\n49\n2019-01-15 20:37:30+00:00\n-15.739\n34.976\n\n\n19\n6901262\n23\n2019-01-16 05:39:00+00:00\n-17.725\n34.223\n\n\n18\n6902785\n54\n2019-01-16 05:56:00+00:00\n-25.507\n38.293\n\n\n17\n3901932\n23\n2019-01-16 06:04:00+00:00\n-19.081\n34.101\n\n\n16\n6901260\n52\n2019-01-17 05:28:00+00:00\n-13.092\n38.266\n\n\n15\n6902552\n168\n2019-01-18 13:02:00+00:00\n-23.575\n33.225\n\n\n14\n1901688\n292\n2019-01-19 20:13:54.002000+00:00\n-23.272\n34.136\n\n\n13\n6901273\n12\n2019-01-20 05:31:00+00:00\n-12.447\n32.181\n\n\n12\n6902663\n126\n2019-01-21 20:21:00+00:00\n-16.892\n35.982\n\n\n11\n6901260\n53\n2019-01-22 05:33:00+00:00\n-13.146\n38.378\n\n\n10\n6901143\n230\n2019-01-22 09:01:03+00:00\n-21.136\n32.880\n\n\n9\n6902664\n126\n2019-01-22 20:23:00+00:00\n-18.099\n35.114\n\n\n8\n3901643\n45\n2019-01-24 06:11:00+00:00\n-23.115\n37.358\n\n\n7\n3901942\n50\n2019-01-25 20:21:30+00:00\n-14.963\n35.402\n\n\n6\n6901262\n24\n2019-01-26 05:47:59.999000+00:00\n-17.474\n34.302\n\n\n5\n3901932\n24\n2019-01-26 06:00:00+00:00\n-18.151\n34.375\n\n\n4\n6902785\n55\n2019-01-26 06:10:00+00:00\n-25.212\n38.213\n\n\n3\n6901260\n54\n2019-01-27 05:38:00+00:00\n-13.265\n38.484\n\n\n2\n6902552\n169\n2019-01-28 12:45:00+00:00\n-23.267\n33.294\n\n\n1\n1901688\n293\n2019-01-29 13:03:27.001000+00:00\n-23.403\n34.206\n\n\n0\n6901273\n13\n2019-01-30 05:27:00+00:00\n-12.737\n32.602\n\n\n\n\n\n\n\nNow plot argo profile locations on an interactive map.\nThis plot uses folium/leaflet. Hover/click the clusters (which correspond to specific Argo float platforms) to zoom to the groups of individual profiles and display metadata about them:\n\ndef _get_tooltip(profile: dict):\n return \"\"\"<b>Date</b>: {date}<br>\n <b>Profile ID</b>: {_id}<br>\n <b>Platform ID</b>: {platform_number}<br>\n <b>Latitude</b>: {lat}<br>\n <b>Longitude</b>: {lon}<br>\"\"\".format(**profile)\n\n\nm = folium.Map(location=[argo_df['lat'].mean(), argo_df['lon'].mean()], \n tiles=\"Stamen Terrain\",\n zoom_start=5, )\n\n# Loop over list of unique platform_numbers (floats)\nunique_argo_platform_numbers = argo_df.platform_number.unique().tolist()\n\nfor i, platform in enumerate(unique_argo_platform_numbers):\n # Get row(s) for the current platform\n p = argo_df[argo_df['platform_number']==platform]\n # Make an empty marker cluster to add to the map widget\n cluster = MarkerCluster(name=p['platform_number'])\n # Make markers in a loop and add to the cluster:\n for c in p['cycle_number'].tolist():\n # Select the row for the current profile ('cycle')\n profile = p[p['cycle_number']==c].iloc[0]\n # Create a new marker and add it to the cluster\n cluster.add_child(folium.Marker(\n location=[profile['lat'], profile['lon']],\n tooltip=_get_tooltip(profile.to_dict())))\n m.add_child(cluster)\n\ndisplay(m)\n\nMake this Notebook Trusted to load map: File -> Trust Notebook\n\n\n\nReformat profile data into data frames\nThe in situ measurements temperature, pressure, and salinity readings collected during each profile are returned inside the JSON response.\nThe format of the measurements field is perfect for conversion to pandas data frames. Apply pandas.DataFrame over the entire measurements column to make a pandas.Series of data frames, and replace the existing content in the measurements column:\n\nargo_df['measurements'] = argo_df['measurements'].apply(pd.DataFrame).tolist()\n\n# Print statistical summary of the table content:\nargo_df.iloc[0].measurements.describe()\n\n\n\n\n\n\n\n\ntemp\npres\npsal\n\n\n\n\ncount\n105.000000\n105.000000\n105.000000\n\n\nmean\n11.579429\n794.390476\n35.832990\n\n\nstd\n4.726514\n655.512828\n0.433002\n\n\nmin\n4.053000\n6.000000\n35.073000\n\n\n25%\n8.096000\n146.000000\n35.597000\n\n\n50%\n10.885000\n713.000000\n35.765000\n\n\n75%\n15.750000\n1363.000000\n36.128000\n\n\nmax\n18.418000\n2010.000000\n36.504000\n\n\n\n\n\n\n\nPlot temperature at the minimum pressure for each profile\nThis cell applies a lambda over the measurements column to slice the row corresponding to the minimum pressure bin for each profile and returns the corresponding temperature measurement:\n\ndef _get_prof_temp_at_pres_min(x):\n return x[x['pres']==x['pres'].min()]['temp'].item()\n\n# Apply the fuunction over the column of measurements tables\nargo_df['temp_at_pres_min'] = argo_df['measurements'].apply(_get_prof_temp_at_pres_min).tolist()\n\n# Plot temperature measured nearest to the sea surface for each profile \nargo_df.plot.scatter(x=\"datetime\", y=\"temp_at_pres_min\", figsize=(16, 4))\nplt.title(\"~Daily temperature at minimum pressure across ~40 argo profiles\")\nplt.xlabel(None)\nplt.ylabel(\"Temperature (degrees C)\")\nplt.ylim(15.5, 20.5)\nplt.grid(alpha=0.25)\n\n\n\n\n\n\nSelect an Argo of Interest and its platform_number\nSee which floats had the most profiles within our timeframe/area of interest:\n\nargo_df.groupby(\"platform_number\").count()['cycle_number']\n\nplatform_number\n1901688 6\n3901643 3\n3901932 3\n3901942 3\n6901143 3\n6901260 6\n6901262 3\n6901273 3\n6902552 3\n6902663 3\n6902664 3\n6902785 2\nName: cycle_number, dtype: int64\n\n\nChoose a float with six profiles to study further during the remainder of the notebook.\n\ntarget_argo = 6901260\n\n# Select rows (profiles) for the desired platform:\nargo_skinny = argo_df[argo_df.platform_number==target_argo].copy()\n\nargo_skinny.describe()\n\n\n\n\n\n\n\n\npres_max_for_TEMP\npres_min_for_TEMP\npres_max_for_PSAL\npres_min_for_PSAL\nmax_pres\ndate_qc\nlat\nlon\nposition_qc\ncycle_number\nplatform_number\nBASIN\ntemp_at_pres_min\n\n\n\n\ncount\n6.000000\n6.0\n6.000000\n6.0\n6.000000\n6.0\n6.000000\n6.000000\n6.0\n6.000000\n6.0\n6.0\n6.000000\n\n\nmean\n1992.666667\n6.0\n1992.666667\n6.0\n1992.666667\n1.0\n38.131167\n-13.030667\n1.0\n51.500000\n6901260.0\n1.0\n16.921000\n\n\nstd\n21.500388\n0.0\n21.500388\n0.0\n21.500388\n0.0\n0.297238\n0.168997\n0.0\n1.870829\n0.0\n0.0\n0.495337\n\n\nmin\n1961.000000\n6.0\n1961.000000\n6.0\n1961.000000\n1.0\n37.707000\n-13.265000\n1.0\n49.000000\n6901260.0\n1.0\n16.153000\n\n\n25%\n1980.500000\n6.0\n1980.500000\n6.0\n1980.500000\n1.0\n37.940500\n-13.132500\n1.0\n50.250000\n6901260.0\n1.0\n16.643250\n\n\n50%\n1994.500000\n6.0\n1994.500000\n6.0\n1994.500000\n1.0\n38.156500\n-13.037000\n1.0\n51.500000\n6901260.0\n1.0\n17.014000\n\n\n75%\n2010.750000\n6.0\n2010.750000\n6.0\n2010.750000\n1.0\n38.350000\n-12.910750\n1.0\n52.750000\n6901260.0\n1.0\n17.263250\n\n\nmax\n2014.000000\n6.0\n2014.000000\n6.0\n2014.000000\n1.0\n38.484000\n-12.812000\n1.0\n54.000000\n6901260.0\n1.0\n17.479000"
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{
- "objectID": "quarto_text/OPERA.html#additional-resources",
- "href": "quarto_text/OPERA.html#additional-resources",
- "title": "OPERA",
- "section": "Additional Resources",
- "text": "Additional Resources\nNASA Mission Page"
+ "objectID": "notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.html#access-sea-surface-temperature-from-modis",
+ "href": "notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.html#access-sea-surface-temperature-from-modis",
+ "title": "Use Case: Co-locate satellite and in-situ data for cross-validation",
+ "section": "Access sea surface temperature from MODIS",
+ "text": "Access sea surface temperature from MODIS\nThe user guide for MODIS Level 2 Sea Surface Temperature (SST) from GHRSST is available on the PO.DAAC Drive: https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf\nWe will access L2 SST data for our AOI and time period of interest by submitting two subset requests to the Harmony API.\nRedefine the AOI to the minimum XY bounds of selected profiles\nSimply replace the aoi_* Python variables with min/max of the lat and lon columns in the new argo_skinny data frame:\n\naoi_minlon = argo_skinny.lon.min()\naoi_maxlon = argo_skinny.lon.max()\naoi_minlat = argo_skinny.lat.min()\naoi_maxlat = argo_skinny.lat.max()\n\naoi_minlon, aoi_minlat, aoi_maxlon, aoi_maxlat\n\n(-13.265, 37.707, -12.812, 38.484)\n\n\nSearch the Common Metadata Repository (CMR) for its unique concept-id\nThe API requires a dataset identifier that we must obtain from CMR. In the next cell, submit a request to the CMR API to grab the metadata for to the dataset/collection.\n\nmodis_results = requests.get(\n url='https://cmr.earthdata.nasa.gov/search/collections.umm_json', \n params={'provider': \"POCLOUD\",\n 'ShortName': \"MODIS_A-JPL-L2P-v2019.0\",\n 'token': _token}\n).json()\n\n# Select the first/only record in the JSON response:\nmodis_coll = modis_results['items'][0]\n\n# Select the 'concept-id' from the 'meta' dictionary:\nmodis_ccid = modis_coll['meta']['concept-id']\n\nmodis_ccid\n\n'C1940473819-POCLOUD'\n\n\n\nRequest subsets from the Harmony API\nWe will submit two requests to the Harmony API. The API is under active development, and it’s therefore recommended that you test your input parameters in the Swagger API interface.\nThe next cell joins the base url for the API to the concept-id obtained above. Run the cell and print the complete url to confirm:\n\nharmony_url = \"https://harmony.earthdata.nasa.gov\"\nharmony_url_modis = f\"{harmony_url}/{modis_ccid}/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?\"\n\nprint(harmony_url_modis)\n\nhttps://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?\n\n\nMake a dictionary of subset parameters and format the values to meet requirements of the Harmony API. (See the Swagger UI linked above for more information about those requirements.)\nNote how I’ve commented out the time parameter for the second half of January. I requested the first 15 days and then the second 15 days in two requests to get the whole month.\nHere we print the parameters for the first request:\n\nharmony_params_modis1 = {\n 'time': f'(\"{start_date}T00:00:00.000Z\":\"2019-01-15T23:59:59.999Z\")',\n 'lat': f'({aoi_minlat}:{aoi_maxlat})',\n 'lon': f'({aoi_minlon}:{aoi_maxlon})',\n}\n\nharmony_params_modis2 = {\n 'time': f'(\"2019-01-16T00:00:00.000Z\":\"{end_date}T23:59:59.999Z\")',\n 'lat': f'({aoi_minlat}:{aoi_maxlat})',\n 'lon': f'({aoi_minlon}:{aoi_maxlon})',\n}\n\nharmony_params_modis1\n\n{'time': '(\"2019-01-01T00:00:00.000Z\":\"2019-01-15T23:59:59.999Z\")',\n 'lat': '(37.707:38.484)',\n 'lon': '(-13.265:-12.812)'}\n\n\nComplete the url by formatting the query portion using the parameters dictionary:\n\nrequest_url_modis1 = harmony_url_modis+\"subset=time{time}&subset=lat{lat}&subset=lon{lon}\".format(**harmony_params_modis1)\nrequest_url_modis2 = harmony_url_modis+\"subset=time{time}&subset=lat{lat}&subset=lon{lon}\".format(**harmony_params_modis2)\n\nprint(request_url_modis1)\n\nhttps://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?subset=time(\"2019-01-01T00:00:00.000Z\":\"2019-01-15T23:59:59.999Z\")&subset=lat(37.707:38.484)&subset=lon(-13.265:-12.812)\n\n\n\n\nSubmit the request parameters to the Harmony API endpoint\nI’ve already submitted the two requests required to obtain full coverage for our region and timeframe of interest (the two urls in the job_status list below). To submit a new request, or to submit these two MODIS requests again, comment out the two items in the list like this:\njob_status = [\n #'https://...'\n #'https://...\n]\nIt should trigger new requests in the subsequent cells.\n\njob_status = [ \n# \"https://harmony.earthdata.nasa.gov/jobs/512ca343-3bfe-48c5-a480-9281b7348761\", # First time slice\n# \"https://harmony.earthdata.nasa.gov/jobs/5b29414d-3856-4e94-9568-01b32b02a951\", # Second time slice\n]\n\nThe next cell should download a JSON for your new request or from the first request that I submitted while I developed this notebook.\nPrint the message field of the JSON response:\n\nrequest_urls_for_modis = [request_url_modis1, request_url_modis2]\n\nif len(job_status)==0:\n # Loop over the list of request urls:\n for r in request_urls_for_modis:\n # Submit the request and decode the response from json string to dict:\n response_modis = requests.get(r)\n # If the response came back with something other than '2xx', raise an error:\n if not response_modis.status_code // 100 == 2: \n raise Exception(response_modis.text)\n else:\n response_data = response_modis.json()\n # Append the status endpoint to the list of 'job_status' urls:\n job_status.append(response_data['links'][0]['href'])\nelse:\n response_data = requests.get(job_status[0]).json()\n\nresponse_data['message']\n\n'The job is being processed'\n\n\nSuccessful requests to the API will respond with a JSON that starts like this:\n{\n \"username\": \"jmcnelis\",\n \"status\": \"running\",\n \"message\": \"The job is being processed\",\n \"progress\": 0,\n \"createdAt\": \"2021-02-25T02:09:35.972Z\",\n \"updatedAt\": \"2021-02-25T02:09:35.972Z\",\n ...\nThe example above is truncated to the first several lines for the sake of space.\nMonitor the status of an in-progress job\nSelect the status URL(s) from the list(s) of links:\n\nif len(job_status)==0:\n try:\n job_status = [l['href'] for l in response_data['links'] if l['title']==\"Job Status\"]\n except (KeyError, IndexError) as e:\n raise e\n\nprint(job_status)\n\n['https://harmony.earthdata.nasa.gov/jobs/558426d1-3df4-4cc2-80dc-943d03ac5810', 'https://harmony.earthdata.nasa.gov/jobs/dafd8c06-89b5-4dd6-af1d-cacb12512101']\n\n\nRun the next cell to monitor the status of as many requests as you need.\nIt will loop over the job_status list and wait for all the requests to finish processing. (It terminates when the status field of the JSON response does not contain the string \"running\".)\n\nwait = 10 # The number of seconds to wait between each status check\ncompleted = {} # A dict of JSON responses for completed jobs\n\n# Loop repeatedly to check job status. Wait before retrying.\nwhile True:\n for j in job_status: # Iterate over list of job urls\n if j in completed: # Skip if completed.\n continue\n # Get the current job's status as a JSON object.\n job_data = requests.get(j).json()\n if job_data['status']!='running':\n completed[j] = job_data # Add to 'completed' if finished\n # Break loop if 'completed' dictionary contains all jobs.\n if len(completed)==2:\n break\n # If still processing, print a status update and wait ten seconds.\n print(f\"# Job(s) in progress ({len(completed)+1}/{len(job_status)})\")\n time.sleep(wait)\n \nprint(f\"\\n{'&'*40}\\n%\\t\\tDONE!\\n{'&'*40}\\n\")\n\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (2/2)\n\n&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n% DONE!\n&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n\n\n\nThe final response(s) are massive whenever your subset results in a large number of output granules. Print everything but the links here:\n\nprint(json.dumps({k:v for k, v in job_data.items() if k!=\"links\"}, indent=2))\n\n{\n \"username\": \"jmcnelis\",\n \"status\": \"successful\",\n \"message\": \"The job has completed successfully\",\n \"progress\": 100,\n \"createdAt\": \"2021-03-15T21:08:45.844Z\",\n \"updatedAt\": \"2021-03-15T21:10:51.310Z\",\n \"request\": \"https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?subset=time(%222019-01-16T00%3A00%3A00.000Z%22%3A%222019-01-31T23%3A59%3A59.999Z%22)&subset=lat(37.707%3A38.484)&subset=lon(-13.265%3A-12.812)\",\n \"numInputGranules\": 55,\n \"jobID\": \"dafd8c06-89b5-4dd6-af1d-cacb12512101\"\n}\n\n\nNow look at the first url that points to a subset file (skip the first two because they point to other stuff about the order):\n\nprint(json.dumps(job_data['links'][2], indent=2))\n\n{\n \"href\": \"https://harmony.earthdata.nasa.gov/service-results/harmony-prod-staging/public/podaac/l2-subsetter/80c8503e-c958-4825-b072-ccdee3f7863b/20190116023001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4\",\n \"title\": \"20190116023001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4\",\n \"type\": \"application/x-netcdf4\",\n \"rel\": \"data\",\n \"bbox\": [\n -13.3,\n 37.7,\n -12.8,\n 38.5\n ],\n \"temporal\": {\n \"start\": \"2019-01-16T02:30:01.000Z\",\n \"end\": \"2019-01-16T02:34:59.000Z\"\n }\n}\n\n\nThis cell collects all the output links (Python dicts) from our requests in a list and prints the total number of outputs:\n\njob_links = []\n\nfor j in list(completed.values()):\n for l in j['links']:\n if l['href'].endswith(\"subsetted.nc4\"):\n job_links.append(l)\n\nprint(len(job_links))\n\n74\n\n\n\nPrepare subset data for further analysis\nGet the subset metadata as pandas.DataFrame. We can use apply logic to calculate stats over the time series in subsequent steps. Print the number of rows to confirm. (Should match above)\n\nsubsets_df = pd.DataFrame(data=[{**l, **l['temporal']} for l in job_links])\n\nprint(subsets_df.index.size)\n\n74\n\n\nSelect day/drop night observations\nAdd a day/night flag column to the table. Apply a function over the href column to check the source filename for a string indicating day/night for the swath:\n\nsubsets_df['daytime'] = subsets_df['href'].apply(lambda x: 'MODIS_A-N' not in x)\n\nsubsets_df.info()\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 74 entries, 0 to 73\nData columns (total 9 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 href 74 non-null object\n 1 title 74 non-null object\n 2 type 74 non-null object\n 3 rel 74 non-null object\n 4 bbox 74 non-null object\n 5 temporal 74 non-null object\n 6 start 74 non-null object\n 7 end 74 non-null object\n 8 daytime 74 non-null bool \ndtypes: bool(1), object(8)\nmemory usage: 4.8+ KB\n\n\nAnd finally, reformat the start timestamps as a new column containing pandas datetime objects instead of strings. Then, add one more column containing a date object (rather than the full datetime timestamp) which we’ll use to aggregate the data before plotting.\n\n# Add new 'datetime' column so that we aren't working with strings:\nsubsets_df['datetime'] = pd.to_datetime(subsets_df['start'])\n\n# Add new 'date' column for aggregation during the final steps of the workflow:\nsubsets_df['date'] = subsets_df.datetime.apply(lambda x: x.date()).tolist()\n\nsubsets_df.date.iloc[0]\n\ndatetime.date(2019, 1, 1)\n\n\n\n\n\nAccessing outputs from your subset request\nNow we will download all the netCDF subsets to the local workspace. (I’m inside AWS as I develop this ipynb.) Set a target directory and create it if needed:\n\ntarget_dir = f\"resources/data/\"\n\n!mkdir -p $target_dir\n\nThis function should handle downloads reliably–test by downloading the first netCDF subset from our table (subsets_df):\n\ndef download_target_file(url: str, force: bool=False):\n # Determine the target path for the download\n target_file = join(target_dir, basename(url))\n if isfile(target_file) and force is False:\n print(f\"# File already exists. Skipping...\\n({basename(url)})\\n\")\n return\n print(f\"# File downloading...\\n({basename(url)})\\n\")\n # Open a remote connection for download stream/write to disk:\n with requests.get(url) as r:\n # Raise exception if response has status other than '2xx':\n if not r.status_code // 100 == 2: \n raise Exception(r.text)\n else:\n # Otherwise write the file to disk:\n with open(target_file, 'wb') as f:\n for chunk in r.iter_content(chunk_size=1024):\n if chunk:\n f.write(chunk)\n \n\n# Test the download function by passing the URL for the first subset in the `subsets` table:\ndownload_target_file(url=subsets_df['href'].iloc[0])\n\n# Join the string path to the target file that should have just downloaded.\ntest_nc4 = join(target_dir, basename(subsets_df['href'].iloc[0]))\n\nprint(\"The first file downloaded successfully:\", isfile(test_nc4))\n\n# File already exists. Skipping...\n(20190101031001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\nThe first file downloaded successfully: True\n\n\nMake sure you can dump the header of that file with ncdump. (The output below is truncated.)\n\n!ncdump -h $test_nc4 | head -20\n\nnetcdf \\20190101031001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted {\ndimensions:\n nj = 92 ;\n ni = 42 ;\n time = 1 ;\nvariables:\n float lat(nj, ni) ;\n lat:_FillValue = -999.f ;\n lat:long_name = \"latitude\" ;\n lat:standard_name = \"latitude\" ;\n lat:units = \"degrees_north\" ;\n lat:valid_min = -90.f ;\n lat:valid_max = 90.f ;\n lat:comment = \"geographical coordinates, WGS84 projection\" ;\n lat:coverage_content_type = \"coordinate\" ;\n float lon(nj, ni) ;\n lon:_FillValue = -999.f ;\n lon:long_name = \"longitude\" ;\n lon:standard_name = \"longitude\" ;\n lon:units = \"degrees_east\" ;\n\n\nNetCDF file format errors indicate that the download was not successful. cat the file for more clues. Read and plot the sea_surface_temperature variable:\n\nwith xr.open_dataset(test_nc4) as ds:\n ds.sea_surface_temperature[0].plot()\n\n\n\n\n\nDownload all the netCDF subsets\nGet the links in the href column in a loop:\n\nfor u in subsets_df['href'].tolist():\n download_target_file(u)\n\n# File already exists. Skipping...\n(20190101031001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190101141501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190102021501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190102132001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190103030000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190103140501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104020501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104034001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104034501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104131001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104144501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190105025001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190105135000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190106033000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190106143501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190107023501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190107134001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190108032000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190108142000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190109022501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190109132501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190110030501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190110141001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190111021000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190111131500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190111145500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190112025501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190112140001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113020001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113033501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113130500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113144001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190114024000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190114134500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190115032501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190115143001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190116023001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. 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Skipping...\n(20190127021001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190127131500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190127145500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190128025500-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190128140001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190129020001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190129033501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190129130501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190129144001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190130024001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190130134500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190131032501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190131143001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n\n\nThe next cell adds a column of absolute paths to the netCDF files to the data frame subsets_df:\n\nsubsets_df['path'] = subsets_df['href'].apply(lambda x: abspath(join(target_dir, basename(x))))\n\nisfile(subsets_df['path'].iloc[0])\n\nTrue\n\n\n\n\nLimit to daytime MODIS observations\nSelect just the daytime observations into a new data frame. (Remember we added a daytime column during a previous step.)\n\nsubsets_day = subsets_df[subsets_df.daytime==True].copy()\n\nprint(subsets_day.index.size, \"of\", subsets_df.index.size, \"MODIS acquisitions were collected during daytime\")\n\n37 of 74 MODIS acquisitions were collected during daytime\n\n\n\n\nData quality\nThe quality_level variable describes the observation quality for each pixel in the L2 swaths. Values are assigned between 1 and 6 corresponding to these quality levels:\n\nno_data\nbad_data\nworst_quality\nlow_quality\nacceptable_quality\nbest_quality\n\nThe next cell plots the masked SST grid for the first daytime observations:\n\nwith xr.open_dataset(subsets_day.iloc[0].path) as ds:\n\n # Create a mask for pixels that are \n quality_mask = ds.quality_level[0]==5\n\n # Fill pixels where ###### with np.nan:\n masked_ds = ds.where(quality_mask)\n\n # Plot the resulting array of sea surface temperature:\n masked_ds.sea_surface_temperature[0].plot()\n\n\n\n\n\n\n\nPlot time series from multiple data sources\nRoll the logic above into a few map-able functions that group the SST data by day to produce (up to) one daily mean.\n\nApply filter and mean in two functions\nget_user_stat reads the input netCDF and applies some user-specified function to the dataset to render the desired output, then closes the file.\nThe second function _masked_mean filters and calculates the XY mean of the sea_surface_temperature variable. (You could replace this function with your own to do something different.)\n\nTest the combined routine against the first file in the daytime MODIS table:\n\nsubsets_day['path'].iloc[0]\n\n'/Users/jmcnelis/tmp/appscitmp/tutorials/notebooks/SWOT-EA-2021/resources/data/20190101141501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4'\n\n\n\nimport warnings\n\ndef get_user_stat(netcdf, function):\n with xr.open_dataset(netcdf) as ds: \n output = function(ds)\n return output\n\n\ndef _masked_mean(ds):\n '''Produce any output stat/object you want in this function'''\n # Create a mask for pixels that are \n quality_mask = ds.quality_level[0]>=5\n # Fill pixels with np.nan where quality_level is less than 4:\n masked_ds = ds.where(quality_mask)\n # Ignore warnings about calculating mean over an empty array:\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n # Calculate mean over the XY dimensions (nj, ni in this case)\n value = masked_ds['sea_surface_temperature'].mean(['nj', 'ni']).data.item()\n #value = np.nanmean(masked_sst)\n return value\n\nval = get_user_stat(subsets_day['path'].iloc[0], function=_masked_mean)\n\nval-273.15 # subtract 273.15 to convert Kelvin to Celsius\n\n16.743005371093773\n\n\nThat should give a reasonable value in degrees Celsius.\n\nGet means for the filtered MODIS SST time series in a new column\nApply the _masked_mean function over the column of subsets (i.e. netCDF4 files) to get the time series in a new column sst_mean:\n\nsubsets_day['sst_mean'] = subsets_day['path'].apply(get_user_stat, args=(_masked_mean,))-273.15\n\nsubsets_day['sst_mean'].describe()\n\ncount 15.000000\nmean 16.404915\nstd 0.566561\nmin 15.284357\n25% 15.921838\n50% 16.546533\n75% 16.833688\nmax 17.222162\nName: sst_mean, dtype: float64\n\n\nWe may need to group by the date:\n\nsubsets_day_means = subsets_day.groupby(\"date\", as_index=False).mean()\n\nsubsets_day_means.describe()\n\n\n\n\n\n\n\n\nsst_mean\n\n\n\n\ncount\n15.000000\n\n\nmean\n16.404915\n\n\nstd\n0.566561\n\n\nmin\n15.284357\n\n\n25%\n15.921838\n\n\n50%\n16.546533\n\n\n75%\n16.833688\n\n\nmax\n17.222162\n\n\n\n\n\n\n\nNow plot the two time series along the same date axis for visual comparison:\n\nfig, ax = plt.subplots(figsize=(16, 4))\n\n# Plot mean sea surface temperature from MODIS SST from GHRSST\nsubsets_day_means.plot.scatter(\n x=\"date\",\n y=\"sst_mean\", \n label=\"SST observed by MODIS\",\n s=100,\n ax=ax\n)\n\n# Plot mean sea surface temperature from the Argo floats\nargo_skinny.plot.scatter(\n x=\"date\",\n y=\"temp_at_pres_min\",\n s=100,\n color=\"orange\",\n marker=\"v\",\n label=\"SST measured by Argo floats\",\n ax=ax\n)\n\n# Matplotlib aesthetic treatments starting from here -->\nax.set_ylabel(\"Temperature (deg C)\")\nax.set_ylim(15.0, 18.0)\nax.grid(alpha=0.25)\n\n\n\n\n\n\n\nMUR Level 4 SST from AWS Open Registry\nTry plotting the summarized time series for the two datasets against MUR L4 SST from AWS Open Registry: https://registry.opendata.aws/mur/\n\nimport fsspec\nimport xarray as xr\nfrom dask.distributed import Client\n\n# Reference the MUR L4 SST data on the AWS Open Registry\nurl = 's3://mur-sst/zarr'\n\n# Open the remote dataset from its S3 endpoint (pre-consolidated)\nds = xr.open_zarr(fsspec.get_mapper(url, anon=True), consolidated=True)\n\n# Slice the dataset along its X, Y, and T dimensions:\nmur_L4_subset = ds['analysed_sst'].sel(\n time=slice('2019-01-01','2019-01-31'),\n lat=slice(aoi_minlat, aoi_maxlat), \n lon=slice(aoi_minlon, aoi_maxlon),\n).persist()\n\n# Aggregate the spatial dimensions to compute the one-dimensional time series of means:\nmur_L4_subset_means = mur_L4_subset.groupby(\"time\").mean([\"lon\", \"lat\"])-273.15\n\nprint(mur_L4_subset_means)\n\n<xarray.DataArray 'analysed_sst' (time: 31)>\ndask.array<sub, shape=(31,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2019-01-01T09:00:00 ... 2019-01-31T09:00:00\n\n\nAdd the MUR time series to the subsets table so that they share the same time axis with the L2 time series:\n\nsubsets_day_means['L4_MUR_SST'] = mur_L4_subset_means.compute().data\n\nPlot the result alongside our data processed throughout the notebook:\n\nfig, ax = plt.subplots(figsize=(16, 5))\n\n# Plot the L4 SST from MUR (hosted by AWS Open Registry)\nsubsets_day_means.plot.line(\n x=\"date\",\n y=\"L4_MUR_SST\",\n color=\"red\",\n label=\"L4 MUR SST (AWS Open Registry)\",\n ax=ax,\n)\n\n# Plot the L2 SST from GHRSST (subset through Harmony API)\nsubsets_day_means.plot.scatter(\n x=\"date\",\n y=\"sst_mean\", \n label=\"L2 MODIS SST (EOSDIS Cloud)\",\n s=100,\n ax=ax\n)\n\n# Plot the in situ temps measured at the surface during Argo profiles (accessed from ArgoVis)\nargo_skinny.plot.scatter(\n x=\"date\",\n y=\"temp_at_pres_min\",\n s=100,\n color=\"orange\",\n marker=\"v\",\n label=\"In situ measurements (ArgoVis API)\",\n ax=ax\n)\n\n# Matplotlib aesthetic treatments starting from here -->\nplt.xticks(rotation=15)\nax.set_xlabel(None)\nax.set_xlim(subsets_day_means.date.iloc[0], subsets_day_means.date.iloc[-1])\nax.set_ylabel(\"Temperature (deg C)\")\nax.set_ylim(15.0, 18.0)\nax.grid(alpha=0.25)\nax.set_title(\"Daily SST from L2 MODIS, L4 MUR, and in situ measurements (January 2019)\")\n\nText(0.5, 1.0, 'Daily SST from L2 MODIS, L4 MUR, and in situ measurements (January 2019)')"
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+ "href": "notebooks/GIS/SWOTsample_CSVconversion.html",
+ "title": "SWOT Shapefile Data Conversion to CSV",
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- "text": "Tutorials highlighting Cloud Optimized Formats.\n\nExample: Zarr Access for NetCDF4 Files\nThis tutorial teaches about the Zarr format and library for accessing data in the cloud, building on prior knowledge from CMR and Earthdata Login tutorials, working through an example of using the EOSDIS Zarr Store to access data using XArray.\nZarr Hackathon Tutorial\n\n\nExample: Zarr Dataset\nThis tutorial opens PO.DAAC MUR dataset in a zarr format.\nZarr-eosdid-store Library\n\n\nExample: Opening NetCDF’s in Zarr Format\nThis tutorial leverages the Zarr reformatter service (available through Harmony API) to access ocean bottom pressure (OBP) data from ECCO V4r4 in Zarr format (instead of native netCDF4 file format).\nZarr2netCDF Example"
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
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- "text": "These instructions are for anyone that would like to contribute tutorials that utilize (in part) NASA Earthdata to the PO.DAAC Cookbook. If you have cloned the PO.DAAC tutorials GitHub repository with all of the PO.DAAC Cookbook Notebooks, you can make changes on your copy of the tutorials and then create a pull request that will be reviewed by our team to potentially add your content. Follow these intructions on how to create a pull request in GitHub. If adding a new tutorial, within the Pull Request, state the section of the Cookbook you think your content would fit best and a member of PO.DAAC may link the tutorial so it renders within the Cookbook.\nAdded content will only be accepted if it follows these guidlines:\n\nFor Jupyter Notebook Tutorials, follow this template as a standard.\nFor tutorials outside of Jupyter Notebooks, the format must have the following sections:\n\nTitle\nAuthor Name/Affiliation\nSummary\nRequirements (the compute environment used, requirements to run the notebook (needed packages etc.))\nLearning Objectives\n\nThe tutorial must be tested successfully and reviewed by a PO.DAAC member non-author. (This will be done before merging the pull request.)"
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+ "href": "notebooks/GIS/SWOTsample_CSVconversion.html#before-you-start",
+ "title": "SWOT Shapefile Data Conversion to CSV",
+ "section": "Before you start",
+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata\nIn this notebook, we will be calling the authentication in the below cell, a work around if you do not yet have a netrc file.\n\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nfrom getpass import getpass\nimport netrc\nfrom platform import system\nfrom os.path import join, isfile, basename, abspath, expanduser\n\ndef setup_earthdata_login_auth(endpoint: str='urs.earthdata.nasa.gov'):\n netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n try:\n username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n print('Please provide your Earthdata Login credentials for access.')\n print('Your info will only be passed to %s and will not be exposed in Jupyter.' % (endpoint))\n username = input('Username: ')\n password = getpass('Password: ')\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n \nsetup_earthdata_login_auth('urs.earthdata.nasa.gov')\n\nPlease provide your Earthdata Login credentials for access.\nYour info will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter.\n\n\nUsername: nickles\nPassword: ···········\n\n\n\nSearch Common Metadata Repository (CMR) for SWOT sample data links by Shapefile\nWe want to find the SWOT sample files that will cross over our region of interest. For this tutorial, we use a shapefile of the United States, finding 44 total granules. Each dataset has it’s own unique collection ID. For the SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1 dataset, we can find the collection ID here.\n\n# the URL of the CMR service\ncmr_url = 'https://cmr.earthdata.nasa.gov/search/granules.json'\n\n#The shapefile we want to use in our search\nshp_file = open('../resources/US_shapefile.zip', 'rb')\n\n#need to declare the file and the type we are uploading\nfiles = {'shapefile':('US_shapefile.zip',shp_file, 'application/shapefile+zip')}\n\n#used to define parameters such as the concept-id and things like temporal searches\nparameters = {'collection_concept_id':'C2263384307-POCLOUD',\n 'page_size': 2000}#, #default will only return 10 granules, so we set it to the max\n #'bounding_box':\"-124.848974,24.396308,-66.885444,49.384358\"} #could also use a bounding box\n\n#request the granules from this collection that align with the shapefile\nresponse = requests.post(cmr_url, params=parameters, files=files)\n\nif len(response.json()['feed']['entry'])>0:\n print(len(response.json()['feed']['entry'])) #print out number of files found\n #print(dumps(response.json()['feed']['entry'][0], indent=2)) #print out the first file information\n\n44\n\n\n\n\nGet Download links from CMR search results\n\ndownloads = []\nfor r in response.json()['feed']['entry']:\n for l in r['links']:\n #if the link starts with the following, it is the download link we want\n if 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/' in l['href']: \n #if the link has \"Reach\" instead of \"Node\" in the name, we want to download it for the swath use case\n if 'Reach' in l['href']:\n downloads.append(l['href'])\nprint(len(downloads)) #should end up with half the number of files above since we only need reach files, not node files\n\n22\n\n\n\n\nDownload the Data into a folder\n\n#Create folder to house downloaded data \nfolder = Path(\"SWOT_sample_files\")\n#newpath = r'SWOT_sample_files' \nif not os.path.exists(folder):\n os.makedirs(folder)\n\n\nfor f in downloads:\n urlretrieve(f, f\"{folder}/{os.path.basename(f)}\")\n\n\n\nUnzip shapefiles in existing folder\n\nfor item in os.listdir(folder): # loop through items in dir\n if item.endswith(\".zip\"): # check for \".zip\" extension\n zip_ref = zipfile.ZipFile(f\"{folder}/{item}\") # create zipfile object\n zip_ref.extractall(folder) # extract file to dir\n zip_ref.close() # close file\n\n\n\nMerging two seperate shapefiles into one\n\n# Read shapefiles\nSWOT_1 = gpd.read_file(folder / 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.shp')\nSWOT_2 = gpd.read_file(folder / 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.shp')\n \n# Merge/Combine multiple shapefiles into one\nSWOT_Merge = gpd.pd.concat([SWOT_1, SWOT_2])\n \n#Export merged geodataframe into shapefile\nSWOT_Merge.to_file(folder / 'SWOT_Merge.shp')\n\n\n\nMerging multiple shapefiles from within a folder\n\n# State filename extension to look for within folder, in this case .shp which is the shapefile\nshapefiles = folder.glob(\"*.shp\")\n\n# Merge/Combine multiple shapefiles in folder into one\ngdf = pd.concat([\n gpd.read_file(shp)\n for shp in shapefiles\n]).pipe(gpd.GeoDataFrame)\n\n# Export merged geodataframe into shapefile\ngdf.to_file(folder / 'SWOTReaches.shp')\n\n\n\nConverting to CSV\nConverting merged geodataframe into a csv file.\n\ngdf.to_csv(folder / 'csvmerge.csv')\n\n\n\nQuerying a Shapefile\nIf you want to search for a specific reach id or a specific length of river reach that is possible through a spatial query using Geopandas.\nUtilizing comparison operators (>, <, ==, >=, <=).\nYou can zoom into a particular river reach by specifying by it’s reach_id or looking for duplicate overlapping river reaches.\n\nreach = gdf.query(\"reach_id == '74292500301'\")\nreach\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n2\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING (-98.50490 40.06789, -98.50525 40.0...\n\n\n308\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING (-98.50490 40.06789, -98.50525 40.0...\n\n\n262\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING 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75')\nWSE\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n263\n77158000011\n7.132750e+08\n7.132750e+08\n2022-08-08T11:5628Z\n25.297171\n-108.473158\nno_data\n123.71461\n-1.000000e+12\n0.00000\n...\n69.5\n1719.195048\n49\n9731.610\n9731.609922\n-1.000000e+12\n0\n2\n1\nLINESTRING (-108.49317 25.28405, -108.49287 25...\n\n\n119\n73282800021\n7.134418e+08\n7.134418e+08\n2022-08-10T10:1658Z\n33.634414\n-87.209808\nno_data\n88.18387\n-1.000000e+12\n4.26350\n...\n211.5\n3285.033201\n57\n687962.665\n11346.636403\n-1.000000e+12\n0\n2\n1\nLINESTRING (-87.23478 33.62552, -87.23452 33.6...\n\n\n630\n74267700121\n7.134419e+08\n7.134419e+08\n2022-08-10T10:1834Z\n38.778477\n-84.107260\nno_data\n134.81383\n-1.000000e+12\n2.68570\n...\n669.0\n2311.101872\n57\n2560861.191\n11466.933285\n-1.000000e+12\n0\n2\n1\nLINESTRING (-84.17021 38.79320, -84.16986 38.7...\n\n\n34\n73290000041\n7.145118e+08\n7.145118e+08\n2022-08-22T19:3017Z\n30.597928\n-88.626436\nno_data\n118.64166\n-1.000000e+12\n15.47494\n...\n105.0\n754.311517\n52\n67960.800\n10424.745294\n-1.000000e+12\n0\n2\n1\nLINESTRING (-88.60566 30.58840, -88.60597 30.5...\n\n\n242\n74253000021\n7.145118e+08\n7.145117e+08\n2022-08-22T19:2912Z\n34.018836\n-90.967538\nno_data\n91.37639\n-1.000000e+12\n4.93354\n...\n968.0\n67506.844891\n50\n1108109.937\n9988.011659\n-1.000000e+12\n0\n4\n1\nLINESTRING (-91.01678 33.99997, -91.01645 34.0...\n\n\n658\n74291500071\n7.145117e+08\n7.145116e+08\n2022-08-22T19:2746Z\n38.843434\n-92.441821\nno_data\n76.10944\n-1.000000e+12\n9.76231\n...\n408.0\n4018.894985\n59\n2280021.224\n11890.852274\n-1.000000e+12\n0\n2\n1\nLINESTRING (-92.39134 38.81822, -92.39168 38.8...\n\n\n\n\n6 rows × 111 columns\n\n\n\n\n\nConverting to CSV\nConverting querried variable into a csv file.\n\nreach.to_csv(folder / 'reach.csv')\n\n\nWSE.to_csv(folder / 'WSE.csv')"
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- "text": "These instructions are for anyone that would like to contribute tutorials that utilize (in part) NASA Earthdata to the PO.DAAC Cookbook. If you have cloned the PO.DAAC tutorials GitHub repository with all of the PO.DAAC Cookbook Notebooks, you can make changes on your copy of the tutorials and then create a pull request that will be reviewed by our team to potentially add your content. Follow these intructions on how to create a pull request in GitHub. If adding a new tutorial, within the Pull Request, state the section of the Cookbook you think your content would fit best and a member of PO.DAAC may link the tutorial so it renders within the Cookbook.\nAdded content will only be accepted if it follows these guidlines:\n\nFor Jupyter Notebook Tutorials, follow this template as a standard.\nFor tutorials outside of Jupyter Notebooks, the format must have the following sections:\n\nTitle\nAuthor Name/Affiliation\nSummary\nRequirements (the compute environment used, requirements to run the notebook (needed packages etc.))\nLearning Objectives\n\nThe tutorial must be tested successfully and reviewed by a PO.DAAC member non-author. (This will be done before merging the pull request.)"
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
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- "text": "Instructions for PO.DAAC Contributors\n\nHow the Cookbook was Built\nThe PO.DAAC Cookbook is built using Quarto. To build this particular Quarto book, we used these instructions from NASA Openscapes. It’s a Quarto book tutorial on how to copy (fork or download) an existing Quarto website like this one and adapt it for your own uses.\n\n\nSetting up your Work Station\nAdmittedly, there are many ways to set up your work station to effectively accomplish contributing to or editing the PO.DAAC Cookbook quarto website. Steps 1, 2, 4, and 6 are universal to edit the cookbook, but steps 3 and 5 can have variations. The following is a method we have found to be helpful:\n1. Creat a GitHub Account - If you do not have one already, create a GitHub account here: https://github.com/.\n2. Join a team on the podaac repository - Once you have a GitHub account, in the podaac repository, request to join the PO.DAAC team that best suits your position here. This will give you the permissions required to contribute as a PO.DAAC Member.\n3. Set Up your Coding Workspace - On a local machine, I tend to deploy Jupyter Lab (where I do most of my coding) from Anaconda Navigator. RStudio, VS Code, etc. could also work, depending on which you prefer. The workflow would also work on a cloud environment, where I also tend to use the Jupyter Lab interface.\n4. Install Quarto - download and install Quarto’s latest version here.\n5. Pick your Prefered Method to Interface with GitHub in your Workspace - GitHub Desktop makes it easy for me to track my changes and create Pull Requests without needing to remember commands in a the command line interface. If you prefer to use the command line, this is a comprehensive and maintained list of git commands that may be useful.\n6. Clone the podaac/tutorials GitHub Repository - Instructions to do this are in the Tech Guides Section of this Cookbook. If using GitHub Desktop, say no when it asks you if you would like to fork the repository. A fork creates a completely independent copy of the repository while a clone creates a linked copy that will continue to synchronize with the tutorials repository.\nIf you’ve completed the above, you should have all the necessary ingredients to contribute to the PO.DAAC Cookbook!\n\n\nTutorials Repository Organization\nEach chapter in our Cookbook is a separate .qmd markdown file within the quarto_text directory. The notebooks directory holds all of our internal tutorials that get rendered in the Cookbook. images contains all figures embedded within the Cookbook files.\nThe Cookbook structure (i.e. the order of sections and chapters) is determined in the _quarto.yml file in the root directory. We can shuffle chapter order by editing the _quarto.yml file, and add new chapters by adding to the _quarto.yml and creating a new file in the appropriate sub-directory that is indicated in _quarto.yml.\n\n\nHow to Edit Cookbook Content\n1. Create a New Branch in GitHub - In a GitHub repository, the main source of code for that repo is deployed in what is called the “Master” branch. This is the branch that the PO.DAAC Cookbook is rendered from. To edit, creating a new branch is important so changes are not overwritten on the master branch if another individual is working on the same file as you. It helps to have your own branch to work on (named however you like) to make changes and then merge those changes with the master branch after changes are done. After merging, it is common practice to delete the branch you created. I like to create a new branch from GitHub Desktop like so:\n\n\n\n\n\n2. Navigate to the tutorials Folder in your Coding Workspace - once you’ve created a new branch, any code you modify in your coding workspace from the repository you cloned should have tracked changes in your new branch. You can open any file in the tutorials folder and start editing! Here is what my Jupyter Lab workspace looks like:\n\n\n\n\n\n3. Tip for Previewing Changes in the Cookbook - To implement changes in the actual Cookbook, usually those changes need to be committed to your new branch and pulled into the master branch using a Pull Request (outlined below). Most of the time though, it is nice to see what your changes would look like visually in the Cookbook before you commit to them. To open up a preview page of the cookbook from your workspace, open up the terminal and change the directory to your tutorials folder location. Once there, type in quarto preview and another tab should open up in your browser that changes every time you save a change to your files. Here is a screenshot of my terminal opening the preview session:\n\n\n\n\n\nNote: the warnings about external files are fine. We do not host the external files in our repo, but link to them from other repos around GitHub, so they will not be rendered in the preview session. They will render in the actual Cookbook when your branch is merged with the master branch.\n4. Open the File you Wish to Edit - Most text within the Cookbook can be found in a .qmd file within the quarto_text folder\n5. Make Edits - GitHub should track your changes automatically. For example below, I have opened the ‘Contribute.qmd’ file in my Jupyter Lab and in the GitHub Desktop application, it shows all of the changes I have made in green and the old version in red. Here, I changed the text describing tutorial guidelines. Here is a helpful guide for Markdown Basics in Quarto.\n\n\n\n\n\n6. Commit Changes to your Branch and Push to Origin - I like to use GitHub Desktop for this, but you can also use the terminal using git commands.\n\n\n\n\n\n7. Create a Pull Request to Merge your Branch with the Master Branch - From the GitHub Desktop, you can then select “Create Pull Request” and it should open a browser window taking you to the tutorials repository in GitHub. In that browser window, if the information is not already populated from your commit, Add a descriptive title, outline any changes made, add reviewers within PO.DAAC that you think would be able to review your notebook, and then press “Create pull request.” A reviewer will look over your changes and either give feedback on improvements to be made before merging is enabled or accept the changes and merge your branch into the master branch.\n\n\n\n\n\n8. Delete your Branch after Merge is Complete - it is common practice to delete old branches and start again with new branches for new edits.\n\n\nHow to Add Tutorials and Display them in the Cookbook\nAdding tutorials to the podaac/tutorials GitHub repository as a PO.DAAC Contributor should follow the same instructions as those outside of PO.DAAC. See above.\nAfter a tutorial has been added to the repository, however, in order for it to display in the Cookbook, a couple more files need to be updated:\n1. The _quarto.yml file - This file is essentially the table of contents of the PO.DAAC Cookbook, telling quarto where to place a tutorial or file in the Cookbook. Write the path of the added tutorial in the appropriate desired location.\n2. The specific landing page .qmd file - This is the .qmd file that houses the section the tutorial will be in. I usually link the added tutorial on this homepage for the section.\nFor Example, here is a screenshot of the current ECCO portion of the _quarto.yml file and the ECCO.qmd file. The Use Case Demo notebook is hightlighted in both places it is linked. The notebook sits under multiple sections, first and formost, the “Tutorials” Section, and within that, the “Dataset Specific Examples” Section and finally, the “ECCO” page. In the .yml file, we gave the tutorial a title after the “text:” portion, which will be visible on the left hand side table of contents in the rendered Cookbook. Underneath the title, the notebook GitHub path is written out after “href:” as shown. The ECCO.qmd file hosts the information regarding the available ECCO tutorials, and somewhere within this page, the new tutorial should be linked. Note: this link may have a slightly different path starting point than the .yml file because the .qmd files are within a subfolder of the tutorials repo. You will likely need to add a “../” before the path in the .qmd file.\n\n\n\n\n\n\nGuidance for Dataset Specific Tutorials Section\nOnce a couple tutorials have been created for a particular mission, it is useful to add a page under the “Dataset Specific Tutorials” Section in the Cookbook for the tutorials. To add one, create a .qmd file in the quarto_text folder with the mission name as the file name. A good example for this would be the ECCO.qmd file or the SWOT.qmd file. Each Dataset Specific Landing Page should have the following sections:\n\nTitle of Mission\nBackground - a brief over view of the mission and products that links to the PO.DAAC webpage for the mission\nData Resources & Tutorials - this section can have sub-sections grouping resources)\nAdditional Resources - links to workshops or other useful materials relating to the mission)\n\n\n\n\nHow to Link to Notebook Tutorials Hosted in Other Repositories\nWe can include remote notebooks in the Cookbook by explicitly importing them with the following steps. This will create a local copy of them that have additional preamble inserted that includes the original urls and attribution for the notebook.\n\nNavigate to the _import directory.\nOpen assets.json in a text editor. Copy an existing example and use the same structure to indicate the remote notebook you’d like to include. You can write Markdown in the preamble.\n\ntitle: this will be the new title of the notebook\npreamble: this text will be inserted into the notebook below the new title. It should include any description and attribution of the notebook. The preamble is followed by two URL fields (next):\nsource: the url landing page of the specific notebook.\nurl: the raw url of the notebook. (i.e. it usually starts with https://raw.githubusercontent.com/ and can be found by clicking the raw button at the top of a GitHub file)\ntarget: the local filename to give the notebook. The notebook will be saved in the external folder in the root directory.\nprocess: true or false: whether or not to include the entire entry when running the quarto_import.py script\n\nAfter these updates to _import/assets.json, do the following in the terminal, which will return a confirmation of the file that has been processed:\n\ncd _import\nconda env update -f environment.yml\nconda activate quarto-import\npython quarto_import.py -f assets.json\n\nThen update _quarto.yml by adding your file (external/<target>) to the appropriate location in the Cookbook. Also link the external notebook in any .qmd file landing pages that are necessary (See “How to Add Tutorials and Display them in the Cookbook” above)."
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+ "text": "Finding ways to visualize SWOT Simulated Shapefile Dataset\n\nLearning Objectives:\n\nAccessing SWOT shapefile hydrology dataset and visualizing it locally.\nAccessing & visualizing dataset through the use of Geopandas & Matplotlib.\n\nThis tutorial is looking to explore geospatial libraries and visualizing vector datasets without the use of a GIS desktop software.\n\n\nDataset:\nSWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1:\n\nDOI: https://doi.org/10.5067/KARIN-2RSP1\n\nThis SWOT simulated river data includes shapefiles of River Nodes and River Reaches. Shapefiles of SWOT sample data can be downloaded here. The single file this notebook will perform most analysis on can be downloaded here.\n\n\nSetting up Environments\n\nPrior to running the notebook, the environments must be set correctly.\nThis notebook can be ran using both Python 3.9 and 3.10 as long as the libraries are correctly installed.\nUtilizing Anaconda Navigator to create your enviroments. Accessing the Conda-Forge channel to install geopsatial libraries.\nGDAL and GeoPandas will direct and install majority of the libraries you will need, but some libraries will need to be installed by searching them individually.\n\n\n\nLibraries Needed\n\nimport os\nimport pandas as pd\nimport numpy as np\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport contextily as cx\n\n\n\nOpening a Single Shapefile\nUsing Geopandas to open & read a single shapefile. (Change the path to your pre-downloaded shapefile)\n\nRiver = gpd.read_file('C:\\SWOT\\SWOT_River_Reaches\\SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.shp')\nRiver\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n0\n74225000301\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0806Z\n33.916062\n-95.560044\nno_data\n3.495055e+01\n-1.000000e+12\n2.515300e-01\n...\n156.0\n3097.993819\n54\n1461998.230\n10753.251601\n-1.000000e+12\n0\n2\n1\nLINESTRING (-95.53934 33.88031, -95.53966 33.8...\n\n\n1\n74225000311\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0806Z\n33.906781\n-95.654646\nno_data\n3.405308e+01\n-1.000000e+12\n5.495000e-02\n...\n175.0\n3701.568090\n79\n1477859.906\n15861.676389\n-1.000000e+12\n0\n2\n1\nLINESTRING (-95.59665 33.94170, -95.59665 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30.5...\n\n\n483\n75165000241\n-1.000000e+12\n-1.000000e+12\nno_data\n30.560974\n-96.417506\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n80.0\n373.408224\n49\n451539.432\n9841.670412\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.39119 30.53292, -96.39150 30.5...\n\n\n484\n75165000251\n-1.000000e+12\n-1.000000e+12\nno_data\n30.591646\n-96.453558\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n84.0\n551.788513\n49\n461358.159\n9818.727520\n-1.000000e+12\n0\n2\n1\nLINESTRING (-96.42988 30.58532, -96.42977 30.5...\n\n\n\n\n485 rows × 111 columns\n\n\n\n\n\nConverting a Shapefile\nIf you want a more in depth view of the datasets attributes you can convert it to a CSV.\nAlso if you would like to open up the dataset as a GeoJSON, Geopandas can help transform the dataset.\n\nRiver.to_csv(\"C:\\SWOT\\SWOT_Attributes.csv\")\n\n\nRiver.to_file(\"C:\\SWOT\\SWOT.json\", driver='GeoJSON')\n\n\n\nPlotting a Shapefile\nUsing Matplotlib to plot the shapefiles, then adding a basemap for context using the library Contextily.\nContextily offers a list of basemap providers that can be easily visualized.\nhttps://contextily.readthedocs.io/en/latest/intro_guide.html\n\nfig, ax = plt.subplots(figsize=(25,15))\nRiver.plot(ax=ax, color='black')\ncx.add_basemap(ax, crs=River.crs, source=cx.providers.OpenTopoMap)\n\n\n\n\n\n\nShapefile Attribute Visualization\nShapefiles have various attributes or variables with each column signifiying individual data values.\nPreviously we plotted by showcasing all the river reaches of that shapefile on the map.\nYou can also plot a shapefile based on a specific variable.\nWithin Matplotlib you can specifiy the column parameter based on the column within the data’s attributes.\nFor the example below, we will look at the column ‘wse’ which stands for water surface elevation.\n\n#First, we set all -999999999999 values to nan so that the color variation shows for the simulated values\nRiver[\"wse\"] = River.wse.apply(lambda x: x if x > -10 else np.nan)\n\n\nfig, ax = plt.subplots(figsize=(15,25))\nRiver.plot(column='wse', ax=ax, legend=True, cmap='viridis')\n\n<AxesSubplot:>\n\n\n\n\n\n\n\nYou can also specifiy which row of attributes you would like to plot using Pandas ‘.loc’ or ‘.iloc’.\n\nfig, ax = plt.subplots(figsize=(25,15))\nRiver.loc[1:5].plot(column='wse',ax=ax, legend=True)\n\n<AxesSubplot:>\n\n\n\n\n\n\n\nQuerying a Shapefile\nIf you want to search for a specific reach id or a specific length of river reach that is possible through a spatial query using Geopandas.\nUtilizing comparison operators (>, <, ==, >=, <=).\nYou can zoom into a particular river reach by specifying it’s row of attributes. 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42.7...\n\n\n386\n74295800141\n7.138263e+08\n7.138263e+08\n2022-08-14T21:0507Z\n43.699405\n-100.216124\nno_data\n38.30095\n-1.000000e+12\n10.35277\n...\n127.0\n1214.735618\n47\n3695005.134\n9362.384395\n-1.000000e+12\n0\n2\n1\nLINESTRING (-100.18767 43.70568, -100.18804 43...\n\n\n441\n75140100401\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0849Z\n31.378090\n-95.684602\nno_data\n44.96841\n-1.000000e+12\n0.00208\n...\n80.0\n375.277777\n82\n399697.389\n16375.887635\n-1.000000e+12\n0\n1\n1\nLINESTRING (-95.65686 31.34060, -95.65687 31.3...\n\n\n455\n75140300011\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0839Z\n31.899540\n-96.012439\nno_data\n35.29524\n-1.000000e+12\n0.00000\n...\n67.0\n511.629791\n79\n538860.706\n15891.038850\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.00043 31.86864, -96.00043 31.8...\n\n\n473\n75165000141\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0907Z\n30.226423\n-96.104246\nno_data\n52.37545\n-1.000000e+12\n5.08629\n...\n92.0\n858.963903\n48\n351257.312\n9672.898906\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.12550 30.21370, -96.12531 30.2...\n\n\n\n\n19 rows × 111 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(25,15))\nWSE.plot(ax=ax, color='black')\ncx.add_basemap(ax, crs=River.crs, source=cx.providers.Esri.NatGeoWorldMap)"
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- "text": "Questions about the contribute process?\nCreate an issue on our tutorials Issues GitHub page."
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+ "text": "Bonus\nOpening a folder with multiple shapefiles\n\nIf you have multiple River Reaches or Nodes in a folder, it is possible to visualize all on one map.\nUtilizing both Glob and Pathlib libraries to read the folder, then using Pandas concat to merge the reaches to its own variable.\nMatplotlib Basemap offers the customization ability to create your own basemap.\n\nhttps://matplotlib.org/basemap/users/geography.html\n\nimport glob\nfrom pathlib import Path\nimport pandas as pd\n\n# Direct folder path of shapefiles\nfolder = Path(\"C:\\\\SWOT\\\\SWOT_River_Reaches\")\n\n# State filename extension to look for within the folder, in this case .shp which is the shapefile\nshapefiles = folder.glob(\"*.shp\")\n\n# Merge/Combine multiple shapefiles in folder into one\ngdf = pd.concat([\n gpd.read_file(shp)\n for shp in shapefiles\n]).pipe(gpd.GeoDataFrame)\n\n\nfrom mpl_toolkits.basemap import Basemap \n\nfig, ax = plt.subplots(figsize=(25,15))\ngdf.plot(ax=ax, legend=True, color = 'black')\nmap = Basemap(llcrnrlon=-130, llcrnrlat=20, urcrnrlon=-65.,urcrnrlat=52., lat_0 = 40., lon_0 = -80)\nmap.drawmapboundary(fill_color='lightblue', color=\"black\")\nmap.fillcontinents(color='tan',lake_color='lightblue')\nmap.drawcountries(color='grey', linewidth=1)\nmap.drawstates(color='lightgrey', linewidth=1)\n\n<matplotlib.collections.LineCollection at 0x251e1a4ce80>"
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- "text": "Here are some cheatsheets and guides helping visualize what working with NASA Earthdata Cloud data looks like, and how to get started!\n\nNASA Earthdata Cloud Overview\nCloud Access Pathways\nGetting Started Roadmaps: Cloud & Local Workflows\nTools & Services Roadmap\nCloud Terminology 101\nWorkflow Cheatsheet\nCheatsheet Terminology\n\n\n\nNASA Earthdata Cloud is the NASA archive of Earth observations and is hosted in Amazon Web Services (AWS) cloud with DAAC tools and services built for use “next to the data.” The NASA DAACs (data centers) are currently transitioning to this cloud-based environment. All PO.DAAC data will be housed in the cloud, and can be accessed through AWS. The cloud offers a scalable and effective way to address storage, network, and data movement concerns while offering a tremendous amount of flexibility to the user. Particularly if working with large data volumes, data access and processing would be more efficient if workflows are taking place in the cloud, avoiding having to download large data volumes. Data download will continue to be freely available to users, from the Earthdata Cloud archive.\n\n\nPublished Google Slide\n\n\n\nThree pathway examples, to interact and access data (and services) from and within the NASA Earthdata Cloud, are illustrated in the diagram. Green arrows and icons indicate working locally, after downloading data to your local machine, servers, or compute/storage space. Orange arrows and icons highlight a workflow within the cloud, setting up your own AWS EC2 cloud instance, or virtual machine, in the cloud next to the data. Blue arrows and icons also indicate a within the cloud workflow, through shareable cloud environments such as Binder or JupyterHub set up in an AWS cloud region. Note that each of these may have a range of cost models. EOSDIS data are being stored in the us-west-2 region of AWS cloud; we recommend setting up your cloud computing environment in the same region as the data for free and easy in-cloud access.\n\n\nPublished Google Slide\nA note on costing: What is free and what do I have to budget for, now that data is archived in the cloud?\n\nDownloading data from the Earthdata Cloud archive in AWS, to your local computer environment or local storage (e.g. servers) is and will continue to be free for the user.\nAccessing the data directly in the cloud (from us-west-2 S3 region) is free. Users will need a NASA Earthdata Login account and AWS credentials to access, but there is no cost associated with these authentication steps, which are in place for security reasons.\nAccessing data in the cloud via EOSDIS or DAAC cloud-based tools and services such as the CMR API, Harmony API, OPenDAP API (from us-west-2 S3 region) is free to the user. Having the tools and services “next to the data” in the cloud enables DAACs to support data reduction and transformation, more efficiently, on behalf of the user, so users only access the data they need.\nCloud computing environments (i.e. virtual machines in the cloud) for working with data in the cloud (beyond direct or via services provided access) such as data analysis or running models with the data, is user responsibility, and should be considered in budgeting. I.e. User would need to set up a cloud compute environment (such as an EC2 instance or JupyterLab) and are responsible for any storage and computing costs.\n\nThis means that even though direct data access in the cloud is free to the user, they would first need to have a cloud computing environment/machine to execute the data access step from, and then continue their analysis.\nDepending on whether that cloud environment is provided by the user themselves, user’s institution, community hubs like Pangeo or NASA Openscapes JupyterLab sandbox, this element of the workflow may require user accountability, budgeting and user financial maintenance.\n\n\n\n\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using an in-cloud workflow (i.e. bringing user code into the cloud, avoiding data download and performing data workflows “next to the data”).\n\n\nPublished Google Slide\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using a local machine (e.g. laptop) workflow, as data storage and computational work.\n\n\nPublished Google Slide\n\n\n\n\nBelow is a practical guide for learning about and selecting helpful tools or services for a given use case, focusing on how to find and access NASA Earthdata Cloud-archived data from local compute environment (e.g. laptop) or from a cloud computing workspace, with accompanying example tutorials. Once you follow your desired pathway, click on the respective blue notebook icon to get to the example tutorial. Note: these pathways are not exhaustive, there are many ways to accomplish these common steps, but these are some of our recommendations.\n\n\nPublished Google Slide\n\n\n\nCloud Terminology 101 for those new to commonly used cloud computing terms and phrases.\n\n\nPublished Google Slide\n\n\n\nThe following is a practical reference guide with links to tutorials and informational websites for users who are starting to take the conceptual pieces and explore and implement in their own workflows.\n\n\nPublished Google Slide\n\n\n\nTerminology cheatsheet to explain terms commonly used in cloud computing and those located on the NASA Earthdata Cloud Cheatsheet. See also NASA Earthdata Glossary.\n\n\nPublished Google Slide"
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+ "section": "Next Steps",
+ "text": "Next Steps\n\nThis notebook has helped showcase how to visualize shapefile data without the use of a GIS desktop software.\nShowcasing different ways of plotting based on variables and adding context to the map.\nLocal visualization was the first step, but the next goal is to move towards utilizing the cloud."
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+ "href": "notebooks/Podaac_CMR_Shapefile_Search.html",
+ "title": "This Notebook is no longer up to date, a newer version exists here",
"section": "",
- "text": "Here are some cheatsheets and guides helping visualize what working with NASA Earthdata Cloud data looks like, and how to get started!\n\nNASA Earthdata Cloud Overview\nCloud Access Pathways\nGetting Started Roadmaps: Cloud & Local Workflows\nTools & Services Roadmap\nCloud Terminology 101\nWorkflow Cheatsheet\nCheatsheet Terminology\n\n\n\nNASA Earthdata Cloud is the NASA archive of Earth observations and is hosted in Amazon Web Services (AWS) cloud with DAAC tools and services built for use “next to the data.” The NASA DAACs (data centers) are currently transitioning to this cloud-based environment. All PO.DAAC data will be housed in the cloud, and can be accessed through AWS. The cloud offers a scalable and effective way to address storage, network, and data movement concerns while offering a tremendous amount of flexibility to the user. Particularly if working with large data volumes, data access and processing would be more efficient if workflows are taking place in the cloud, avoiding having to download large data volumes. Data download will continue to be freely available to users, from the Earthdata Cloud archive.\n\n\nPublished Google Slide\n\n\n\nThree pathway examples, to interact and access data (and services) from and within the NASA Earthdata Cloud, are illustrated in the diagram. Green arrows and icons indicate working locally, after downloading data to your local machine, servers, or compute/storage space. Orange arrows and icons highlight a workflow within the cloud, setting up your own AWS EC2 cloud instance, or virtual machine, in the cloud next to the data. Blue arrows and icons also indicate a within the cloud workflow, through shareable cloud environments such as Binder or JupyterHub set up in an AWS cloud region. Note that each of these may have a range of cost models. EOSDIS data are being stored in the us-west-2 region of AWS cloud; we recommend setting up your cloud computing environment in the same region as the data for free and easy in-cloud access.\n\n\nPublished Google Slide\nA note on costing: What is free and what do I have to budget for, now that data is archived in the cloud?\n\nDownloading data from the Earthdata Cloud archive in AWS, to your local computer environment or local storage (e.g. servers) is and will continue to be free for the user.\nAccessing the data directly in the cloud (from us-west-2 S3 region) is free. Users will need a NASA Earthdata Login account and AWS credentials to access, but there is no cost associated with these authentication steps, which are in place for security reasons.\nAccessing data in the cloud via EOSDIS or DAAC cloud-based tools and services such as the CMR API, Harmony API, OPenDAP API (from us-west-2 S3 region) is free to the user. Having the tools and services “next to the data” in the cloud enables DAACs to support data reduction and transformation, more efficiently, on behalf of the user, so users only access the data they need.\nCloud computing environments (i.e. virtual machines in the cloud) for working with data in the cloud (beyond direct or via services provided access) such as data analysis or running models with the data, is user responsibility, and should be considered in budgeting. I.e. User would need to set up a cloud compute environment (such as an EC2 instance or JupyterLab) and are responsible for any storage and computing costs.\n\nThis means that even though direct data access in the cloud is free to the user, they would first need to have a cloud computing environment/machine to execute the data access step from, and then continue their analysis.\nDepending on whether that cloud environment is provided by the user themselves, user’s institution, community hubs like Pangeo or NASA Openscapes JupyterLab sandbox, this element of the workflow may require user accountability, budgeting and user financial maintenance.\n\n\n\n\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using an in-cloud workflow (i.e. bringing user code into the cloud, avoiding data download and performing data workflows “next to the data”).\n\n\nPublished Google Slide\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using a local machine (e.g. laptop) workflow, as data storage and computational work.\n\n\nPublished Google Slide\n\n\n\n\nBelow is a practical guide for learning about and selecting helpful tools or services for a given use case, focusing on how to find and access NASA Earthdata Cloud-archived data from local compute environment (e.g. laptop) or from a cloud computing workspace, with accompanying example tutorials. Once you follow your desired pathway, click on the respective blue notebook icon to get to the example tutorial. Note: these pathways are not exhaustive, there are many ways to accomplish these common steps, but these are some of our recommendations.\n\n\nPublished Google Slide\n\n\n\nCloud Terminology 101 for those new to commonly used cloud computing terms and phrases.\n\n\nPublished Google Slide\n\n\n\nThe following is a practical reference guide with links to tutorials and informational websites for users who are starting to take the conceptual pieces and explore and implement in their own workflows.\n\n\nPublished Google Slide\n\n\n\nTerminology cheatsheet to explain terms commonly used in cloud computing and those located on the NASA Earthdata Cloud Cheatsheet. See also NASA Earthdata Glossary.\n\n\nPublished Google Slide"
+ "text": "CMR allows the upload of ESRI Shapefiles via command line for granule and collection level search. To use this functionality from the command line, we can use curl or python to do our search. This example will run through the curl command line program and a python request for doing shapefile search.\nPrerequisites:\n\na valid ESRI Shapefile\n(optional) Collection identifier (concept-id) for granule level search\n\nWe will use a shapefile located in the github/podaac source repository for this search: https://github.com/podaac/tutorials/blob/master/notebooks/resources/gulf_shapefile.zip\nFor more information on collections, granules, and concept-ids, please refer to the following tutorial:\nhttps://github.com/podaac/tutorials/blob/master/notebooks/podaac_cmr_tutorial.ipynb\nFor our tutorial, we will be using a TEST collection available at the cmr uat environment. This is an environment, open to the public, where new functionality is released and users can submit feedback on it before it makes its way to the operational system.\nThis collection is an L2 collection titled AMSR2-REMSS-L2P-v8a, from the Advanced Microwave Scanning Radiometer 2. It has the concept id:\nC1225996408-POCUMULUS"
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- "text": "The Surface Water and Ocean Topography (SWOT) mission aims to provide valuable data and information about the world’s oceans and its terrestrial surface water such as lakes, rivers, and wetlands. SWOT is being developed jointly by NASA and Centre National D’Etudes Spatiales (CNES), with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA). The satellite launched on December 16, 2022 and data is expected to be released to the public no earlier than Fall 2023. PO.DAAC is the NASA archive for the SWOT mission, and will be making data available via the NASA Earthdata Cloud (hosted in AWS) with direct download capabilities available. More information can be found on PO.DAAC’s SWOT webpage.\nPO.DAAC will host a variety of SWOT data products. Their product description documents can be found in the chart listing each dataset. Before these SWOT products are available, we have SWOT simulated datasets encompassing both oceanography and hydrology sample data. This data is not for analysis, but rather to become comfortable with future SWOT products data formats and access mechanisms."
+ "text": "CMR allows the upload of ESRI Shapefiles via command line for granule and collection level search. To use this functionality from the command line, we can use curl or python to do our search. This example will run through the curl command line program and a python request for doing shapefile search.\nPrerequisites:\n\na valid ESRI Shapefile\n(optional) Collection identifier (concept-id) for granule level search\n\nWe will use a shapefile located in the github/podaac source repository for this search: https://github.com/podaac/tutorials/blob/master/notebooks/resources/gulf_shapefile.zip\nFor more information on collections, granules, and concept-ids, please refer to the following tutorial:\nhttps://github.com/podaac/tutorials/blob/master/notebooks/podaac_cmr_tutorial.ipynb\nFor our tutorial, we will be using a TEST collection available at the cmr uat environment. This is an environment, open to the public, where new functionality is released and users can submit feedback on it before it makes its way to the operational system.\nThis collection is an L2 collection titled AMSR2-REMSS-L2P-v8a, from the Advanced Microwave Scanning Radiometer 2. It has the concept id:\nC1225996408-POCUMULUS"
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- "text": "The Surface Water and Ocean Topography (SWOT) mission aims to provide valuable data and information about the world’s oceans and its terrestrial surface water such as lakes, rivers, and wetlands. SWOT is being developed jointly by NASA and Centre National D’Etudes Spatiales (CNES), with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA). The satellite launched on December 16, 2022 and data is expected to be released to the public no earlier than Fall 2023. PO.DAAC is the NASA archive for the SWOT mission, and will be making data available via the NASA Earthdata Cloud (hosted in AWS) with direct download capabilities available. More information can be found on PO.DAAC’s SWOT webpage.\nPO.DAAC will host a variety of SWOT data products. Their product description documents can be found in the chart listing each dataset. Before these SWOT products are available, we have SWOT simulated datasets encompassing both oceanography and hydrology sample data. This data is not for analysis, but rather to become comfortable with future SWOT products data formats and access mechanisms."
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+ "text": "Python tutorial shapefile search\nThe following snippet will use the ‘requests’ library along with the shapefile available at github to perform a shapefile search on the CMR. It will return values that overlap or intersect the shapefile provided.\n\nimport requests\nimport json\nimport pprint\n\n# the URL of the CMR searvice\nurl = 'https://cmr.uat.earthdata.nasa.gov/search/granules.json'\n\n#The shapefile we want to use in our search\nshp_file = open('resources/gulf_shapefile.zip', 'rb')\n\n#need to declare the file and the type we are uploading\nfiles = {'shapefile':('gulf_shapefile.zip',shp_file, 'application/shapefile+zip')}\n\n#used to define parameters such as the concept-id and things like temporal searches\nparameters = {'echo_collection_id':'C1225996408-POCUMULUS'}\n\nresponse = requests.post(url, files=files, params=parameters)\npp = pprint.PrettyPrinter(indent=2)\npp.pprint(response.json())\n\n{ 'feed': { 'entry': [ { 'browse_flag': False,\n 'collection_concept_id': 'C1225996408-POCUMULUS',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCUMULUS',\n 'dataset_id': 'PODAAC-GHAM2-2PR8A',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': 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'http://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/SeaSurfaceTemperature',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://podaac-tools.jpl.nasa.gov/hitide/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/R/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHAM2-2PR8A',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://www.remss.com/missions/amsr/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/python/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '-74.22 12.67 -72.07 -9.88 -69.9 '\n '-19.92 -67.08 -28.4 -63.4 -35.9 '\n '-58.95 -42.2 -53.53 -47.66 -47.65 '\n '-52.02 -34.56 -58.69 -17.31 -64.43 '\n '8.06 -70.3 67.96 -81.39 77.65 -84.51 '\n '83.02 -88.37 86.38 -95.57 88.07 '\n '-108.72 89.12 -180 74.365 -180 74.38 '\n '-164.66 73.43 -150.84 71.69 -139.02 '\n '68.87 -127.94 65.36 -119.27 60.86 '\n '-111.89 55.65 -106 48.87 -100.59 '\n '35.47 -93.58 18.07 -87.75 -9.48 '\n '-81.48 -70.19 -70.63 -79.47 -67.68 '\n '-84.1 -64.24 -86.49 -59.54 -87.96 '\n '-51.37 -87.37 -51.91 -86.7 -51.35 '\n '-84.71 -46.76 -79.14 -25.78 -75.63 '\n '-5.06 -74.22 12.67'],\n [ '89.12 180 88.7 139.61 87.61 118.89 '\n '85.45 108.25 81.36 102.44 76.26 '\n '99.59 67.2 96.89 2.66 84.87 -26.48 '\n '77.33 -38.2 72.8 -48.19 67.35 -55.84 '\n '61.23 -61.53 54.49 -67.4 43.27 '\n '-71.52 28.58 -73.78 11.11 -74.43 '\n '-16.08 -76.59 -36.18 -82.49 -63.69 '\n '-85.68 -73.58 -87.61 -76.1 -88.52 '\n '-68.49 -89.11 -47.72 -89.02 43.7 '\n '-87.75 68.92 -86.53 75.08 -84.68 '\n '79.14 -81.03 82.51 -73.53 85.44 9.28 '\n '101.12 29.92 107.08 45.19 113.93 '\n '52.33 118.74 58.08 124.12 62.55 '\n '129.96 66.35 136.99 69.31 144.96 '\n '71.84 155.45 74.365 180 89.12 180']],\n 'time_end': '2018-01-02T19:48:16.000Z',\n 'time_start': '2018-01-02T18:10:08.000Z',\n 'title': '20180102181008-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29943-v02.0-fv01.0.nc'},\n { 'browse_flag': False,\n 'collection_concept_id': 'C1225996408-POCUMULUS',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCUMULUS',\n 'dataset_id': 'PODAAC-GHAM2-2PR8A',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '1.0242048E7',\n 'id': 'G1226019419-POCUMULUS',\n 'links': [ { 'href': 's3://podaac-dev-l2ss-samples/20180102181008-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_v08_r29943-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'http://data.nodc.noaa.gov/cgi-bin/nph-dods/ghrsst/GDS2/L2P/AMSR2/REMSS/v8a/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/AMSR2/REMSS/v8a/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/GDS2/L2P/AMSR2/REMSS/v8a',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://podaac-ftp.jpl.nasa.gov/allData/ghrsst/data/GDS2/L2P/AMSR2/REMSS/v8a',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/IDL/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://suzaku.eorc.jaxa.jp/GCOM_W/w_amsr2/whats_amsr2.html',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/SeaSurfaceTemperature',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://podaac-tools.jpl.nasa.gov/hitide/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/R/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHAM2-2PR8A',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://www.remss.com/missions/amsr/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/python/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '-74.22 12.67 -72.07 -9.88 -69.9 '\n '-19.92 -67.08 -28.4 -63.4 -35.9 '\n '-58.95 -42.2 -53.53 -47.66 -47.65 '\n '-52.02 -34.56 -58.69 -17.31 -64.43 '\n '8.06 -70.3 67.96 -81.39 77.65 -84.51 '\n '83.02 -88.37 86.38 -95.57 88.07 '\n '-108.72 89.12 -180 74.365 -180 74.38 '\n '-164.66 73.43 -150.84 71.69 -139.02 '\n '68.87 -127.94 65.36 -119.27 60.86 '\n '-111.89 55.65 -106 48.87 -100.59 '\n '35.47 -93.58 18.07 -87.75 -9.48 '\n '-81.48 -70.19 -70.63 -79.47 -67.68 '\n '-84.1 -64.24 -86.49 -59.54 -87.96 '\n '-51.37 -87.37 -51.91 -86.7 -51.35 '\n '-84.71 -46.76 -79.14 -25.78 -75.63 '\n '-5.06 -74.22 12.67'],\n [ '89.12 180 88.7 139.61 87.61 118.89 '\n '85.45 108.25 81.36 102.44 76.26 '\n '99.59 67.2 96.89 2.66 84.87 -26.48 '\n '77.33 -38.2 72.8 -48.19 67.35 -55.84 '\n '61.23 -61.53 54.49 -67.4 43.27 '\n '-71.52 28.58 -73.78 11.11 -74.43 '\n '-16.08 -76.59 -36.18 -82.49 -63.69 '\n '-85.68 -73.58 -87.61 -76.1 -88.52 '\n '-68.49 -89.11 -47.72 -89.02 43.7 '\n '-87.75 68.92 -86.53 75.08 -84.68 '\n '79.14 -81.03 82.51 -73.53 85.44 9.28 '\n '101.12 29.92 107.08 45.19 113.93 '\n '52.33 118.74 58.08 124.12 62.55 '\n '129.96 66.35 136.99 69.31 144.96 '\n '71.84 155.45 74.365 180 89.12 180']],\n 'time_end': '2018-01-02T19:48:16.000Z',\n 'time_start': '2018-01-02T18:10:08.000Z',\n 'title': '20180102181008-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_v08_r29943-v02.0-fv01.0.nc'}],\n 'id': 'https://cmr.uat.earthdata.nasa.gov:443/search/granules.json',\n 'title': 'ECHO granule metadata',\n 'updated': '2020-05-18T21:41:56.223Z'}}"
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- "text": "SWOT Data Resources & Tutorials\n\nSearch & Download\nVia Graphical User Interface:\n\nFind/download simulated SWOT data on Earthdata Search\n\nProgrammatically: ie. within Python code workflows\n\nSearch and Download via earthaccess\nwith unique SWORD river reach ID\nwith unique Hydrologic Unit Code (HUC) basin ID\n\nVia Command Line - PO.DAAC subscriber/downloader examples:\nHydrology: These examples will download either the river vector files or the raster files for a location in Texas with multiple passes:\npodaac-data-downloader -c SWOT_SIMULATED_NA_CONTINENT_L2_HR_RiverSP_V1 -d ./SWOT_SIMULATED_NA_CONTINENT_L2_HR_RiverSP_V1 -start-date 2022-08-02T00:00:00Z --end-date 2022-08-22T00:00:00Z -b=\"-97,32.5,-96.5,33\"\npodaac-data-downloader -c SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 -d ./SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 --start-date 2022-08-02T00:00:00Z --end-date 2022-08-22T00:00:00Z -b=\"-97,32.5,-96.5,33\"\nOceanography: This example will download modeled sea surface heights:\npodaac-data-subscriber -c SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1 -d ./data/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1 --start-date 2015-12-30T00:00:00Z\npodaac-data-downloader -c SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1 -d ./data/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1 --start-date 2011-11-20T00:00:00Z --end-date 2011-11-20T12:00:00Z\n\nSee how to Download/Subscribe for more information on how to use the PO.DAAC subscriber/downloader.\n\n\n\nIn-cloud Access & Visualization\nAccess sample SWOT Hydrology data in the cloud\nAccess sample SWOT Oceanography data in the cloud\n\n\nGIS workflows\nSWOT: Through a GIS Lens StoryMap\nGIS shapefile exploration\nNetCDF to Geotiff Conversion\n\n\nTransform\nTransform SWOT Hydrology river reach shapefiles into time series"
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+ "href": "notebooks/Podaac_CMR_Shapefile_Search.html#curl-command-line-syntax",
+ "title": "This Notebook is no longer up to date, a newer version exists here",
+ "section": "Curl command line syntax",
+ "text": "Curl command line syntax\nThis command submits the same request as the Python example above, returning search results in JSON format for Granules that share spatial coverage with the input shapefile (resources/gulf_shapefile.zip) and belong to the target Collection (echo_collection_id=C1225996408-POCUMULUS):\ncurl -XPOST \"https://cmr.uat.earthdata.nasa.gov/search/granules.json\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1225996408-POCUMULUS\" -F \"pretty=true\"\nThe (truncated) results:\n{\n \"feed\" : {\n \"updated\" : \"2020-05-18T22:09:58.452Z\",\n \"id\" : \"https://cmr.uat.earthdata.nasa.gov:443/search/granules.json\",\n \"title\" : \"ECHO granule metadata\",\n \"entry\" : [ {\n \"time_start\" : \"2018-01-01T05:56:16.000Z\",\n \"granule_size\" : \"1.0242048E7\",\n \"online_access_flag\" : true,\n \"id\" : \"G1226019017-POCUMULUS\",\n \"day_night_flag\" : \"UNSPECIFIED\",\n \"browse_flag\" : false,\n \"time_end\" : \"2018-01-01T07:34:24.000Z\",\n \"coordinate_system\" : \"CARTESIAN\",\n \"polygons\" : [ [ \"-74.21 -163.74 -72.95 -180 -75.43 -180 -74.21 -163.74\" ], [ \"-72.95 180 -69.22 161.56 -63.56 148.24 -59.74 142.63 -54.76 137.29 -43.19 129.21 -28.81 123 -9.39 117.38 70.28 101.89 78.71 98.84 83.8 94.5 85.75 90.5 87.14 84.43 88.59 63.9 89.12 2.26 88.28 -48.29 87.12 -61.14 85.38 -68.1 82.54 -72.63 77.71 -75.93 63.62 -80.05 9.8 -89.82 -15.54 -95.55 -35.84 -102.3 -50.51 -110.4 -56.3 -115.37 -60.99 -120.88 -64.95 -127.37 -68.22 -135.08 -70.76 -143.97 -72.76 -155.11 -74.35 -180 -89.345 -180 -88.32 -113.33 -86.69 -101.61 -83.86 -95.95 -79.32 -92.73 -70.2 -89.86 -10.6 -79.24 16.19 -73.27 33.1 -67.88 46.23 -61.6 52.76 -57.05 57.86 -52.26 62.64 -46.04 66.5 -38.81 69.5 -30.55 71.71 -21.38 74.26 1.26 73.75 29.71 72.1 42.61 69.64 53.42 66.25 62.72 62.36 69.82 57.32 76.19 51.2 81.62 37.2 89.55 18.32 96.04 -10.4 102.56 -70.39 113.28 -79.49 116.19 -83.94 119.43 -86.33 123.83 -87.89 131.86 -86.97 131.68 -85.71 133.93 -81.4 147.95 -77.84 164.04 -75.43 180 -72.95 180\" ], [ \"-74.35 180 -74.81 162.65 -77.45 142.63 -82.89 118.89 -85.78 110.18 -87.46 107.84 -88.13 109.51 -88.85 123.7 -89.345 180 -74.35 180\" ] ],\n \"original_format\" : \"UMM_JSON\",\n \"collection_concept_id\" : \"C1225996408-POCUMULUS\",\n \"data_center\" : \"POCUMULUS\",\n \"links\" : [ {\n \"rel\" : \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\" : \"en-US\",\n \"href\" : \"s3://podaac-dev-l2ss-samples/20180101055616-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29921-v02.0-fv01.0.nc\"\n },\n ...\nThis command gets the same listing again with curl, this time returning the search results in their native xml format:\ncurl -XPOST \"https://cmr.uat.earthdata.nasa.gov/search/granules\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1225996408-POCUMULUS\" -F \"pretty=true\"\nThe (truncated) results:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<results>\n <hits>1912</hits>\n <took>970</took>\n <references>\n <reference>\n <name>20180101055616-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29921-v02.0-fv01.0.nc</name>\n <id>G1226019017-POCUMULUS</id>\n <location>https://cmr.uat.earthdata.nasa.gov:443/search/concepts/G1226019017-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n <reference>\n <name>20180101055616-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_v08_r29921-v02.0-fv01.0.nc</name>\n <id>G1226019025-POCUMULUS</id>\n <location>https://cmr.uat.earthdata.nasa.gov:443/search/concepts/G1226019025-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n <reference>\n <name>20180101073424-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29922-v02.0-fv01.0.nc</name>\n <id>G1226019035-POCUMULUS</id>\n <location>https://cmr.uat.earthdata.nasa.gov:443/search/concepts/G1226019035-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n ..."
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- "href": "quarto_text/SWOT.html#additional-resources",
- "title": "SWOT",
- "section": "Additional Resources",
- "text": "Additional Resources\n\n2022 SWOT Ocean Cloud Workshop\nhttps://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/\nThe goal of the workshop was to enable the (oceanography) science team to be ready for processing and handling the large volumes of SWOT SSH data in the cloud. Learning objectives focus on how to access the simulated SWOT L2 SSH data from Earthdata Cloud either by downloading or accessing the data on the cloud."
+ "objectID": "notebooks/Cloud L2SS subset and plot - JH.html",
+ "href": "notebooks/Cloud L2SS subset and plot - JH.html",
+ "title": "This Notebook is no longer supported, a newer version exists here.",
+ "section": "",
+ "text": "This will demonstrate how to subset swath/L2 data with the data and services hosted on the cloud."
},
{
- "objectID": "quarto_text/ECCO.html",
- "href": "quarto_text/ECCO.html",
- "title": "ECCO",
+ "objectID": "notebooks/Cloud L2SS subset and plot - JH.html#cloud-level-2-subsetter-l2ss-api",
+ "href": "notebooks/Cloud L2SS subset and plot - JH.html#cloud-level-2-subsetter-l2ss-api",
+ "title": "This Notebook is no longer supported, a newer version exists here.",
"section": "",
- "text": "The Estimating the Circulation and Climate of the Ocean (ECCO) project makes the best possible estimates of ocean circulation and its role in climate. ECCO combines state-of-the-art ocean circulation models with global ocean data sets. More information can be found on PO.DAAC’s ECCO webpage."
+ "text": "This will demonstrate how to subset swath/L2 data with the data and services hosted on the cloud."
},
{
- "objectID": "quarto_text/ECCO.html#background",
- "href": "quarto_text/ECCO.html#background",
- "title": "ECCO",
- "section": "",
- "text": "The Estimating the Circulation and Climate of the Ocean (ECCO) project makes the best possible estimates of ocean circulation and its role in climate. ECCO combines state-of-the-art ocean circulation models with global ocean data sets. More information can be found on PO.DAAC’s ECCO webpage."
+ "objectID": "notebooks/Cloud L2SS subset and plot - JH.html#before-beginning",
+ "href": "notebooks/Cloud L2SS subset and plot - JH.html#before-beginning",
+ "title": "This Notebook is no longer supported, a newer version exists here.",
+ "section": "Before Beginning",
+ "text": "Before Beginning\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\n\nLearning Objective:\n\nSubset a specific file/granule that has already been found using the podaac L2 subsetter\n\n\nfrom harmony import BBox, Client, Collection, Request, Environment, LinkType\nfrom IPython.display import display, JSON\nimport tempfile\nimport shutil\nimport xarray as xr\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom pandas.plotting import register_matplotlib_converters\nimport numpy as np"
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- "title": "ECCO",
- "section": "Data Resources & Tutorials",
- "text": "Data Resources & Tutorials\n\nData Access\n\nIn-cloud - Direct Access to ECCO V4r4 Datasets in the Cloud\nIn this notebook, you will learn to 1) identify Amazon Web Services (AWS) S3 endpoints corresponding to two ECCO datasets of interest, 2) retrieve your AWS credentials which provide access to PO.DAAC data in AWS, 3) load the target netCDF files into two multi-file datasets with xarray, and 4) slice and plot the datasets as animated time series using matplotlib and cartopy. The notebook finishes by writing the animations to disk as MP4 files. The two variables analyzed in this example are global monthly sea surface height (SSH) data and monthly ocean temperature flux (TFLUX) data over the Gulf of Mexico.\n\n\nLocal Machine Download - Access to ECCO V4r4 Datasets on a Local Machine\nThis is a modified version of the In-cloud Access python notebook above to batch download ECCO data on a local machine.\n\n\n\nUse Case Demo\n\nECCO Science Use Case Jupyter Notebook Demonstration\nThis tutorial will use data from the ECCO model to derive spatial correlations between sea surface temperature anomaly and sea surface height anomaly through time for two regions of the Indian Ocean. The goal is to investigate the correlative characteristics of the Indian Ocean Dipole and how the east and west regions behave differently."
+ "objectID": "notebooks/Cloud L2SS subset and plot - JH.html#subset-of-a-po.daac-granule",
+ "href": "notebooks/Cloud L2SS subset and plot - JH.html#subset-of-a-po.daac-granule",
+ "title": "This Notebook is no longer supported, a newer version exists here.",
+ "section": "Subset of a PO.DAAC Granule",
+ "text": "Subset of a PO.DAAC Granule\nWe build onto the root URL in order to actually perform a transformation. The first transformation is a subset of a selected granule. At this time, this requires discovering the granule id from CMR. That information can then be appended to the root URL and used to call Harmony with the help of the request library.\nNotes: The L2 subsetter current streams the data back to the user, and does not stage data in S3 for redirects. This is functionality we will be adding over time.\nCreate a Harmony-py client\n\nharmony_client = Client(env=Environment.PROD)\n\nWith the client created, we can contruct and validate the request. As this is a subsetting + concatenation request, we specify options on the request that define spatial bounds, variables we are interested in, temporal bounds, and indicated the result should be concatenated.\n\ncollection = Collection(id='C1940471193-POCLOUD') #Jason-1 GDR SSHA version E NetCDF\n\nrequest = Request(\n collection=collection,\n spatial=BBox(0,0,1,1), # 1 degree box\n granule_id='G1969371708-POCLOUD' #JA1_GPR_2PeP374_173_20120303_121639_20120303_125911.nc\n)\n\nrequest.is_valid()\n\nTrue\n\n\nNow that we have a valid request we simply need to call the submit function using the client we created earlier and pass in the request as a parameter.\nTip: if you want to see the request before submitting it, use the request_as_curl function on the client to get an equivalent curl command for the request that will be submitted.\n\nprint(harmony_client.request_as_curl(request))\njob_id = harmony_client.submit(request)\nprint(f'Job ID: {job_id}')\n\ncurl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=0c2f471216e220fc8ef81d7f18a5ddfb' -H 'User-Agent: Windows/10 CPython/3.8.12 harmony-py/0.4.2 python-requests/2.25.1' 'https://harmony.earthdata.nasa.gov/C1940471193-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%280%3A1%29&subset=lon%280%3A1%29&granuleId=G1969371708-POCLOUD'\nJob ID: 8fad49e8-c95f-4a98-8e99-d5b053d86de7\n\n\n\nprint(harmony_client.status(job_id))\n\nprint('\\nWaiting for the job to finish')\nresults = harmony_client.result_json(job_id, show_progress=True)\n\n{'status': 'running', 'message': 'The job is being processed', 'progress': 0, 'created_at': datetime.datetime(2022, 10, 25, 17, 5, 0, 76000, tzinfo=tzutc()), 'updated_at': datetime.datetime(2022, 10, 25, 17, 5, 0, 438000, tzinfo=tzutc()), 'created_at_local': '2022-10-25T10:05:00-07:00', 'updated_at_local': '2022-10-25T10:05:00-07:00', 'data_expiration': datetime.datetime(2022, 11, 24, 17, 5, 0, 76000, tzinfo=tzutc()), 'data_expiration_local': '2022-11-24T09:05:00-08:00', 'request': 'https://harmony.earthdata.nasa.gov/C1940471193-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(0%3A1)&subset=lon(0%3A1)&granuleId=G1969371708-POCLOUD', 'num_input_granules': 1}\n\nWaiting for the job to finish\n\n\n [ Processing: 0% ] | | [|]\n\n\nConnectionError: ('Connection aborted.', TimeoutError(10060, 'A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond', None, 10060, None))\n\n\n\ntemp_dir = tempfile.mkdtemp()\nfutures = harmony_client.download_all(job_id, directory=temp_dir, overwrite=True)\nfile_names = [f.result() for f in futures]\nfile_names\n\n\nds = xr.open_dataset(file_names[0])\nds\n\n\nds.ssha.plot()"
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{
- "objectID": "quarto_text/ECCO.html#additional-resources",
- "href": "quarto_text/ECCO.html#additional-resources",
- "title": "ECCO",
- "section": "Additional Resources",
- "text": "Additional Resources\nECCO Project Website"
+ "objectID": "notebooks/Cloud L2SS subset and plot - JH.html#verify-the-subsetting-worked",
+ "href": "notebooks/Cloud L2SS subset and plot - JH.html#verify-the-subsetting-worked",
+ "title": "This Notebook is no longer supported, a newer version exists here.",
+ "section": "Verify the subsetting worked",
+ "text": "Verify the subsetting worked\nBounds are defined earlier\n\nlat_max = ds.lat.max()\nlat_min = ds.lat.min()\n\nlon_min = ds.lon.min()\nlon_max = ds.lon.max()\n\n\nif lat_max < bblat_max and lat_min > bblat_min:\n print(\"Successful Latitude subsetting\")\nelse:\n assert false\n\n \nif lon_max < bblon_max and lon_min > bblon_min:\n print(\"Successful Longitude subsetting\")\nelse:\n assert false"
},
{
- "objectID": "quarto_text/DatasetSpecificExamples.html",
- "href": "quarto_text/DatasetSpecificExamples.html",
- "title": "Dataset Specific",
- "section": "",
- "text": "ECCO - Estimating the Circulation and Climate of the Ocean\nGHRSST - Group for High Resolution Sea Surface Temperature\nOPERA - Observational Products for End-Users from Remote Sensing Analysis\nSentinel-6A Michael Freilich Jason-CS\nSMAP - Soil Moisture Active Passive\nS-MODE - Submesoscale Ocean Dynamics and Vertical Transport Experiment\nSWOT - Surface Water and Ocean Topography"
+ "objectID": "notebooks/Cloud L2SS subset and plot - JH.html#plot-swath-onto-a-map",
+ "href": "notebooks/Cloud L2SS subset and plot - JH.html#plot-swath-onto-a-map",
+ "title": "This Notebook is no longer supported, a newer version exists here.",
+ "section": "Plot swath onto a map",
+ "text": "Plot swath onto a map\n\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\n\nplt.scatter(ds.lon, ds.lat, lw=2, c=ds.ssha)\nplt.colorbar()\nplt.clim(-0.3, 0.3)\n\nplt.show()"
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- "objectID": "quarto_text/DatasetSpecificExamples.html#missions-with-dataset-specific-examples",
- "href": "quarto_text/DatasetSpecificExamples.html#missions-with-dataset-specific-examples",
- "title": "Dataset Specific",
+ "objectID": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html",
+ "href": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html",
+ "title": "Shapefile Search in the Common Metadata Repository (CMR)",
"section": "",
- "text": "ECCO - Estimating the Circulation and Climate of the Ocean\nGHRSST - Group for High Resolution Sea Surface Temperature\nOPERA - Observational Products for End-Users from Remote Sensing Analysis\nSentinel-6A Michael Freilich Jason-CS\nSMAP - Soil Moisture Active Passive\nS-MODE - Submesoscale Ocean Dynamics and Vertical Transport Experiment\nSWOT - Surface Water and Ocean Topography"
+ "text": "CMR allows the upload of ESRI Shapefiles via command line for granule and collection level search. To use this functionality from the command line, we can use python or curl to do our search. This example will run through a python request and a curl command line program for doing shapefile search.\nPrerequisites:\nWe will use a shapefile located in the github/podaac source repository for this search: https://github.com/podaac/tutorials/blob/master/notebooks/resources/gulf_shapefile.zip\nFor more information on collections, granules, and concept-ids, please refer to the following tutorial:\nhttps://github.com/podaac/tutorials/blob/master/notebooks/podaac_cmr_tutorial.ipynb\nThis collection is the MODIS_A-JPL-L2P-v2019.0 Level 2 collection from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra satellite. In the CMR environment it has the collection id:\nC1940473819-POCLOUD"
},
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- "href": "quarto_text/DatasetSpecificExamples.html#gis-storymaps-of-select-datasets",
- "title": "Dataset Specific",
- "section": "GIS StoryMaps of Select Datasets:",
- "text": "GIS StoryMaps of Select Datasets:\nPO.DAAC GIS StoryMap Collection Page"
+ "objectID": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#python-tutorial-shapefile-search",
+ "href": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#python-tutorial-shapefile-search",
+ "title": "Shapefile Search in the Common Metadata Repository (CMR)",
+ "section": "Python tutorial shapefile search",
+ "text": "Python tutorial shapefile search\nThe following snippet will use the ‘requests’ library along with the shapefile available at github to perform a shapefile search on the CMR. It will return values that overlap or intersect the shapefile provided.\n\nimport requests\nimport json\nimport pprint\n\n# the URL of the CMR searvice\nurl = 'https://cmr.earthdata.nasa.gov/search/granules.json'\n\n#The shapefile we want to use in our search\nshp_file = open('resources/gulf_shapefile.zip', 'rb')\n\n#need to declare the file and the type we are uploading\nfiles = {'shapefile':('gulf_shapefile.zip',shp_file, 'application/shapefile+zip')}\n\n#used to define parameters such as the concept-id and things like temporal searches\nparameters = {'echo_collection_id':'C1940473819-POCLOUD'}\n\nresponse = requests.post(url, files=files, params=parameters)\npp = pprint.PrettyPrinter(indent=2)\npp.pprint(response.json())\n\n{ 'feed': { 'entry': [ { 'boxes': ['28.481 -83.616 49.941 -51.077'],\n 'browse_flag': True,\n 'collection_concept_id': 'C1940473819-POCLOUD',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 2P Global Sea Surface '\n 'Skin Temperature from the Moderate '\n 'Resolution Imaging Spectroradiometer '\n '(MODIS) on the NASA Aqua satellite '\n '(GDS2)',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '9.34600830078125E-5',\n 'id': 'G1966128926-POCLOUD',\n 'links': [ { 'href': 's3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/s3#',\n 'title': 'This link provides direct '\n 'download access via S3 to the '\n 'granule.'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc.md5',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc.md5'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'Download '\n '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.cmr.json',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.cmr.json'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'api endpoint to retrieve '\n 'temporary credentials valid '\n 'for same-region direct s3 '\n 'access'},\n { 'href': 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%202P%20Global%20Sea%20Surface%20Skin%20Temperature%20from%20the%20Moderate%20Resolution%20Imaging%20Spectroradiometer%20(MODIS)%20on%20the%20NASA%20Aqua%20satellite%20(GDS2)/granules/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'OPeNDAP request URL'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sea_surface_temperature.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.quality_level.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_bias.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_standard_deviation.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 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True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/forum/viewforum.php?f=18&sid=e2d67e5a01815fc6e39fcd2087ed8bc8',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '75.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/MODIS_L2P_SST_DataCube.ipynb',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '49.94093 -81.73856 31.93185 -83.6126 '\n '30.8223 -71.67589 28.48104 -59.75119 '\n '37.68658 -55.70623 45.51698 '\n '-51.07733 48.7527 -66.02296 49.94093 '\n '-81.73856']],\n 'time_end': '2002-07-04T06:39:59.000Z',\n 'time_start': '2002-07-04T06:35:00.000Z',\n 'title': '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0',\n 'updated': '2020-11-12T11:02:35.998Z'},\n { 'boxes': ['10.927 -86.378 32.007 -59.736'],\n 'browse_flag': True,\n 'collection_concept_id': 'C1940473819-POCLOUD',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 2P Global Sea Surface '\n 'Skin Temperature from the Moderate '\n 'Resolution Imaging 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'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705090006-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_bias.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705090006-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_standard_deviation.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/flag/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/reprocessing/r2019/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst4/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/forum/viewforum.php?f=18&sid=e2d67e5a01815fc6e39fcd2087ed8bc8',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '75.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/MODIS_L2P_SST_DataCube.ipynb',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '39.455 -118.21204 21.43644 '\n '-120.66811 18.2618 -98.75371 '\n '35.69537 -92.21542 38.28696 '\n '-105.14129 39.455 -118.21204']],\n 'time_end': '2002-07-05T09:05:00.000Z',\n 'time_start': '2002-07-05T09:00:01.000Z',\n 'title': '20020705090006-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0',\n 'updated': '2020-11-12T08:04:19.259Z'},\n { 'boxes': ['17.991 -91.581 39.125 -63.246'],\n 'browse_flag': True,\n 'collection_concept_id': 'C1940473819-POCLOUD',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 2P Global Sea Surface '\n 'Skin Temperature from the Moderate '\n 'Resolution Imaging Spectroradiometer '\n '(MODIS) on the NASA Aqua satellite '\n '(GDS2)',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '22.20408535003662',\n 'id': 'G1966080708-POCLOUD',\n 'links': [ { 'href': 's3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/s3#',\n 'title': 'This link provides direct '\n 'download access via S3 to the '\n 'granule.'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'Download '\n '20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.cmr.json',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.cmr.json'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'api endpoint to retrieve '\n 'temporary credentials valid '\n 'for same-region direct s3 '\n 'access'},\n { 'href': 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%202P%20Global%20Sea%20Surface%20Skin%20Temperature%20from%20the%20Moderate%20Resolution%20Imaging%20Spectroradiometer%20(MODIS)%20on%20the%20NASA%20Aqua%20satellite%20(GDS2)/granules/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'OPeNDAP request URL'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sea_surface_temperature.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.quality_level.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_bias.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_standard_deviation.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/flag/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/reprocessing/r2019/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst4/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/forum/viewforum.php?f=18&sid=e2d67e5a01815fc6e39fcd2087ed8bc8',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '75.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/MODIS_L2P_SST_DataCube.ipynb',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '21.10151 -63.24635 39.12532 '\n '-65.68633 37.97627 -78.84272 35.4481 '\n '-91.57713 17.99084 -85.09664 '\n '21.10151 -63.24635']],\n 'time_end': '2002-07-05T18:25:00.000Z',\n 'time_start': '2002-07-05T18:20:01.000Z',\n 'title': '20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0',\n 'updated': '2020-11-12T08:49:28.019Z'}],\n 'id': 'https://cmr.earthdata.nasa.gov:443/search/granules.json',\n 'title': 'ECHO granule metadata',\n 'updated': '2022-10-24T23:43:37.549Z'}}"
},
{
- "objectID": "quarto_text/Workshops.html",
- "href": "quarto_text/Workshops.html",
- "title": "Workshops",
- "section": "",
- "text": "We develop tutorials for teaching events that each have their own e-book. We often do this in collaboration with NASA OpenScapes. Tutorials are developed to teach open science and Cloud workflows for specific audiences. They are a snapshot in time as workflows with NASA Earthdata Cloud emerge and evolve."
+ "objectID": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#curl-command-line-syntax",
+ "href": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#curl-command-line-syntax",
+ "title": "Shapefile Search in the Common Metadata Repository (CMR)",
+ "section": "Curl command line syntax",
+ "text": "Curl command line syntax\nThis command submits the same request as the Python example above, returning search results in JSON format for Granules that share spatial coverage with the input shapefile (resources/gulf_shapefile.zip) and belong to the target Collection (echo_collection_id=C1940473819-POCLOUD):\ncurl -XPOST \"https://cmr.earthdata.nasa.gov/search/granules.json\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1940473819-POCLOUD\" -F \"pretty=true\"\nThe (truncated) results:\n{\n \"feed\" : {\n \"updated\" : \"2020-05-18T22:09:58.452Z\",\n \"id\" : \"https://cmr.earthdata.nasa.gov:443/search/granules.json\",\n \"title\" : \"ECHO granule metadata\",\n \"entry\" : [ {\n \"time_start\" : \"2002-07-04T06:35:00.000Z\",\n \"granule_size\" : \"9.34600830078125E5\",\n \"online_access_flag\" : true,\n \"id\" : \"G1966128926-POCLOUD\",\n \"day_night_flag\" : \"UNSPECIFIED\",\n \"browse_flag\" : true,\n \"time_end\" : \"2002-07-04T06:39:59.000Z\",\n \"coordinate_system\" : \"CARTESIAN\",\n \"polygons\" : [ [ \"-74.21 -163.74 -72.95 -180 -75.43 -180 -74.21 -163.74\" ], [ \"-72.95 180 -69.22 161.56 -63.56 148.24 -59.74 142.63 -54.76 137.29 -43.19 129.21 -28.81 123 -9.39 117.38 70.28 101.89 78.71 98.84 83.8 94.5 85.75 90.5 87.14 84.43 88.59 63.9 89.12 2.26 88.28 -48.29 87.12 -61.14 85.38 -68.1 82.54 -72.63 77.71 -75.93 63.62 -80.05 9.8 -89.82 -15.54 -95.55 -35.84 -102.3 -50.51 -110.4 -56.3 -115.37 -60.99 -120.88 -64.95 -127.37 -68.22 -135.08 -70.76 -143.97 -72.76 -155.11 -74.35 -180 -89.345 -180 -88.32 -113.33 -86.69 -101.61 -83.86 -95.95 -79.32 -92.73 -70.2 -89.86 -10.6 -79.24 16.19 -73.27 33.1 -67.88 46.23 -61.6 52.76 -57.05 57.86 -52.26 62.64 -46.04 66.5 -38.81 69.5 -30.55 71.71 -21.38 74.26 1.26 73.75 29.71 72.1 42.61 69.64 53.42 66.25 62.72 62.36 69.82 57.32 76.19 51.2 81.62 37.2 89.55 18.32 96.04 -10.4 102.56 -70.39 113.28 -79.49 116.19 -83.94 119.43 -86.33 123.83 -87.89 131.86 -86.97 131.68 -85.71 133.93 -81.4 147.95 -77.84 164.04 -75.43 180 -72.95 180\" ], [ \"-74.35 180 -74.81 162.65 -77.45 142.63 -82.89 118.89 -85.78 110.18 -87.46 107.84 -88.13 109.51 -88.85 123.7 -89.345 180 -74.35 180\" ] ],\n \"original_format\" : \"UMM_JSON\",\n \"collection_concept_id\" : \"C1940473819-POCLOUD\",\n \"data_center\" : \"POCLOUD\",\n \"links\" : [ {\n \"rel\" : \"http://esipfed.org/ns/fedsearch/1.1/s3#\",\n \"hreflang\" : \"en-US\",\n \"href\" : \"s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc\"\n },\n ...\nThis command gets the same listing again with curl, this time returning the search results in their native xml format:\ncurl -XPOST \"https://cmr.earthdata.nasa.gov/search/granules\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1940473819-POCLOUD\" -F \"pretty=true\"\nThe (truncated) results:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<results>\n <hits>1912</hits>\n <took>970</took>\n <references>\n <reference>\n <name>20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc</name>\n <id>G1966128926-POCLOUD</id>\n <location>https://cmr.earthdata.nasa.gov:443/search/concepts/G1226019017-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n ..."
},
{
- "objectID": "quarto_text/Workshops.html#s-mode-open-data-workshop",
- "href": "quarto_text/Workshops.html#s-mode-open-data-workshop",
- "title": "Workshops",
- "section": "2022 S-MODE Open Data Workshop",
- "text": "2022 S-MODE Open Data Workshop\nhttps://espo.nasa.gov/s-mode/content/S-MODE_2022_Open_Data_Workshop - Recordings and Presentations - Github tutorials\nThe Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) science team is hosted a virtual Open Data Workshop on 1 December 2022 from 11:00am – 1:00pm ET to share about the S-MODE mission, to learn about its instrumentation, and find out how to access and use its data products."
+ "objectID": "notebooks/l2-regridding/reprojection notebook.html",
+ "href": "notebooks/l2-regridding/reprojection notebook.html",
+ "title": "Harmony API Introduction",
+ "section": "",
+ "text": "This notebook provides an overview of the capabilities offered through the Harmony API and SWOT L2 Reproject tool. While written for SWOT L2 data, it works with most any level 2 data for projecting to a normal grid. In this tutorial we will use MODIS L2 data to show the native file projected to equal-area-cylindracal projection using both Nearest Neighbor and Bi-linear interpolation.\nStanding on the shoulders of previous authors: Amy Steiker, Patrick Quinn"
},
{
- "objectID": "quarto_text/Workshops.html#swot-ocean-cloud-workshop",
- "href": "quarto_text/Workshops.html#swot-ocean-cloud-workshop",
- "title": "Workshops",
- "section": "2022 SWOT Ocean Cloud Workshop",
- "text": "2022 SWOT Ocean Cloud Workshop\nhttps://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/\nThe goal of the workshop is to get ready for Surface Water and Ocean Topography (SWOT) and enable the (oceanography) science team to be ready for processing and handling the large volumes of SWOT SSH data in the cloud. Learning objectives focus on how to access the simulated SWOT L2 SSH data from Earthdata Cloud either by downloading or accessing the data on the cloud. PO.DAAC is the NASA archive for the SWOT mission, and once launched will be making data available via the NASA Earthdata Cloud, hosted in AWS."
+ "objectID": "notebooks/l2-regridding/reprojection notebook.html#before-you-start",
+ "href": "notebooks/l2-regridding/reprojection notebook.html#before-you-start",
+ "title": "Harmony API Introduction",
+ "section": "Before you start",
+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
},
{
- "objectID": "quarto_text/Workshops.html#cloud-agu-workshop",
- "href": "quarto_text/Workshops.html#cloud-agu-workshop",
- "title": "Workshops",
- "section": "2021 Cloud AGU Workshop",
- "text": "2021 Cloud AGU Workshop\nhttps://nasa-openscapes.github.io/2021-Cloud-Workshop-AGU\nThe 2021 Cloud Workshop at AGU: Enabling Analysis in the Cloud Using NASA Earth Science Data is a virtual half-day collaborative open science learning experience aimed at exploring, learning, and promoting effective cloud-based science and applications workflows using NASA Earthdata Cloud data, tools, and services (among others), in support of Earth science data processing and analysis in the era of big data."
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+ "section": "Import packages",
+ "text": "Import packages\n\nfrom urllib import request, parse\nfrom http.cookiejar import CookieJar\nimport getpass\nimport netrc\nimport os\nimport requests\nimport json\nimport pprint\nfrom osgeo import gdal\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport rasterio\nfrom rasterio.plot import show\nimport numpy as np\nimport os\nimport time\nfrom netCDF4 import Dataset\n%matplotlib inline"
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- "text": "2021 Cloud Hackathon\nhttps://nasa-openscapes.github.io/2021-Cloud-Hackathon\nThe Cloud Hackathon: Transitioning Earthdata Workflows to the Cloud is a virtual 5-day (4 hours per day) collaborative open science learning experience aimed at exploring, creating, and promoting effective cloud-based science and applications workflows using NASA Earthdata Cloud data, tools, and services (among others), in support of Earth science data processing and analysis in the era of big data. Its goals are to:\n\nIntroduce Earth science data users to NASA Earthdata cloud-based data products, tools and services in order to increase awareness and support transition to cloud-based science and applications workflows.\nEnable science and applications workflows in the cloud that leverage NASA Earth Observations and capabilities (services) from within the NASA Earthdata Cloud, hosted in Amazon Web Services (AWS) cloud, thus increasing NASA Earthdata data utility and meaningfulness for science and applications use cases.\nFoster community engagement utilizing Earthdata cloud tools and services in support of open science and open data."
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+ "title": "Harmony API Introduction",
+ "section": "Identify a data collection of interest",
+ "text": "Identify a data collection of interest\nA CMR collection ID is needed to request services through Harmony. The collection ID can be determined using the CMR API. We will query the corresponding ID of a known collection short name, MODIS_A-JPL-L2P-v2019.0.\n\nparams = {\n 'short_name': 'MODIS_A-JPL-L2P-v2019.0',\n 'provider_id': 'POCLOUD'\n} # parameter dictionary with known CMR short_name\n\ncmr_collections_url = 'https://cmr.earthdata.nasa.gov/search/collections.json'\ncmr_response = requests.get(cmr_collections_url, params=params)\ncmr_results = json.loads(cmr_response.content) # Get json response from CMR collection metadata\n\ncollectionlist = [el['id'] for el in cmr_results['feed']['entry']]\nharmony_collection_id = collectionlist[0]\nprint(harmony_collection_id)\n\nC1940473819-POCLOUD\n\n\nWe can also view the MODIS_A-JPL-L2P-v2019.0 collection metadata to glean more information about the collection:\n\npprint.pprint(cmr_results)\n\n{'feed': {'entry': [{'archive_center': 'NASA/JPL/PODAAC',\n 'associations': {'services': ['S1962070864-POCLOUD',\n 'S2004184019-POCLOUD',\n 'S2153799015-POCLOUD',\n 'S2227193226-POCLOUD'],\n 'tools': ['TL2108419875-POCLOUD',\n 'TL2092786348-POCLOUD'],\n 'variables': ['V1997812737-POCLOUD',\n 'V1997812697-POCLOUD',\n 'V2112014688-POCLOUD',\n 'V1997812756-POCLOUD',\n 'V1997812688-POCLOUD',\n 'V1997812670-POCLOUD',\n 'V1997812724-POCLOUD',\n 'V2112014684-POCLOUD',\n 'V1997812701-POCLOUD',\n 'V1997812681-POCLOUD',\n 'V2112014686-POCLOUD',\n 'V1997812663-POCLOUD',\n 'V1997812676-POCLOUD',\n 'V1997812744-POCLOUD',\n 'V1997812714-POCLOUD']},\n 'boxes': ['-90 -180 90 180'],\n 'browse_flag': True,\n 'cloud_hosted': True,\n 'collection_data_type': 'SCIENCE_QUALITY',\n 'consortiums': ['GEOSS', 'EOSDIS'],\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 2P Global Sea Surface Skin '\n 'Temperature from the Moderate Resolution '\n 'Imaging Spectroradiometer (MODIS) on the '\n 'NASA Aqua satellite (GDS2)',\n 'has_formats': True,\n 'has_spatial_subsetting': True,\n 'has_temporal_subsetting': True,\n 'has_transforms': False,\n 'has_variables': True,\n 'id': 'C1940473819-POCLOUD',\n 'links': [{'href': 'https://podaac.jpl.nasa.gov/Podaac/thumbnails/MODIS_A-JPL-L2P-v2019.0.jpg',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#'},\n {'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/flag/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/reprocessing/r2019/sst/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst4/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac.jpl.nasa.gov/forum/viewforum.php?f=18&sid=e2d67e5a01815fc6e39fcd2087ed8bc8',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'length': '75.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n {'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/MODIS_L2P_SST_DataCube.ipynb',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'}],\n 'online_access_flag': True,\n 'orbit_parameters': {'inclination_angle': '98.1',\n 'number_of_orbits': '1.0',\n 'period': '98.4',\n 'swath_width': '2330.0'},\n 'organizations': ['NASA/JPL/PODAAC'],\n 'original_format': 'UMM_JSON',\n 'platforms': ['Aqua'],\n 'processing_level_id': '2',\n 'service_features': {'esi': {'has_formats': False,\n 'has_spatial_subsetting': False,\n 'has_temporal_subsetting': False,\n 'has_transforms': False,\n 'has_variables': False},\n 'harmony': {'has_formats': True,\n 'has_spatial_subsetting': True,\n 'has_temporal_subsetting': True,\n 'has_transforms': False,\n 'has_variables': True},\n 'opendap': {'has_formats': True,\n 'has_spatial_subsetting': True,\n 'has_temporal_subsetting': True,\n 'has_transforms': False,\n 'has_variables': True}},\n 'short_name': 'MODIS_A-JPL-L2P-v2019.0',\n 'summary': 'NASA produces skin sea surface temperature '\n '(SST) products from the Infrared (IR) '\n 'channels of the Moderate-resolution Imaging '\n 'Spectroradiometer (MODIS) onboard the Aqua '\n 'satellite. Aqua was launched by NASA on May '\n '4, 2002, into a sun synchronous, polar orbit '\n 'with a daylight ascending node at 1:30 pm, '\n 'formation flying in the A-train with other '\n 'Earth Observation Satellites (EOS), to study '\n 'the global dynamics of the Earth atmosphere, '\n 'land and oceans. MODIS captures data in 36 '\n 'spectral bands at a variety of spatial '\n 'resolutions. Two SST products can be present '\n 'in these files. The first is a skin SST '\n 'produced for both day and night (NSST) '\n 'observations, derived from the long wave IR '\n '11 and 12 micron wavelength channels, using a '\n 'modified nonlinear SST algorithm intended to '\n 'provide continuity of SST derived from '\n 'heritage and current NASA sensors. At night, '\n 'a second SST product is generated using the '\n 'mid-infrared 3.95 and 4.05 micron wavelength '\n 'channels which are unique to MODIS; the SST '\n 'derived from these measurements is identified '\n 'as SST4. The SST4 product has lower '\n 'uncertainty, but due to sun glint can only be '\n 'used at night. MODIS L2P SST data have a 1 km '\n 'spatial resolution at nadir and are stored in '\n '288 five minute granules per day. Full global '\n 'coverage is obtained every two days, with '\n 'coverage poleward of 32.3 degree being '\n 'complete each day. The production of MODIS '\n 'L2P SST files is part of the Group for High '\n 'Resolution Sea Surface Temperature (GHRSST) '\n 'project and is a joint collaboration between '\n 'the NASA Jet Propulsion Laboratory (JPL), the '\n 'NASA Ocean Biology Processing Group (OBPG), '\n 'and the Rosenstiel School of Marine and '\n 'Atmospheric Science (RSMAS). Researchers at '\n 'RSMAS are responsible for SST algorithm '\n 'development, error statistics and quality '\n 'flagging, while the OBPG, as the NASA ground '\n 'data system, is responsible for the '\n 'production of daily MODIS ocean products. JPL '\n 'acquires MODIS ocean granules from the OBPG '\n 'and reformats them to the GHRSST L2P netCDF '\n 'specification with complete metadata and '\n 'ancillary variables, and distributes the data '\n 'as the official Physical Oceanography Data '\n 'Archive (PO.DAAC) for SST. The R2019.0 '\n 'supersedes the previous R2014.0 datasets '\n 'which can be found at '\n 'https://doi.org/10.5067/GHMDA-2PJ02',\n 'time_start': '2002-07-04T00:00:00.000Z',\n 'title': 'GHRSST Level 2P Global Sea Surface Skin '\n 'Temperature from the Moderate Resolution '\n 'Imaging Spectroradiometer (MODIS) on the NASA '\n 'Aqua satellite (GDS2)',\n 'updated': '2019-12-02T22:59:24.849Z',\n 'version_id': '2019.0'}],\n 'id': 'https://cmr.earthdata.nasa.gov:443/search/collections.json?short_name=MODIS_A-JPL-L2P-v2019.0&provider_id=POCLOUD',\n 'title': 'ECHO dataset metadata',\n 'updated': '2022-10-25T21:32:46.472Z'}}\n\n\nNext we get a granule ID from this collection, G2525170359-POCLOUD.\n\ncmr_url = \"https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=\"+harmony_collection_id+\"&sort_key=-start_date\"\n\nresponse = requests.get(cmr_url)\n\ngid=response.json()['items'][0]['meta']['concept-id']\nprint(gid)\n\nG2525170359-POCLOUD"
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- "text": "The Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) is a NASA Earth Venture Suborbital (EVS-3) mission that utilizes research aircraft equipped with state-of-the-art remote sensing instruments, a research vessel, Wave Gliders, Saildrones, and many other in situ assets. These instruments contribute to an unprecedented view of the physics of submesoscale eddies and fronts, and their effects on vertical transport in the upper ocean. The S-MODE investigation is composed of a Pilot Campaign (Fall 2021) and two Intensive Operating Periods (IOP-1 and IOP-2, Fall 2022 and Spring 2023). Each field campaign is between 3-4 weeks in duration. The scientific area of interest is located about 150 miles off the coast of San Francisco\nMore information can be found on PO.DAAC’s SMODE webpage."
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- "text": "The Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) is a NASA Earth Venture Suborbital (EVS-3) mission that utilizes research aircraft equipped with state-of-the-art remote sensing instruments, a research vessel, Wave Gliders, Saildrones, and many other in situ assets. These instruments contribute to an unprecedented view of the physics of submesoscale eddies and fronts, and their effects on vertical transport in the upper ocean. The S-MODE investigation is composed of a Pilot Campaign (Fall 2021) and two Intensive Operating Periods (IOP-1 and IOP-2, Fall 2022 and Spring 2023). Each field campaign is between 3-4 weeks in duration. The scientific area of interest is located about 150 miles off the coast of San Francisco\nMore information can be found on PO.DAAC’s SMODE webpage."
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- "text": "Data Resources & Tutorials\n\nScience Case Study Airborne\nThe DownloadDopplerScattData.ipynb notebook walks through creating the .netrc file and downloading the Dopplerscatt data used in this case study. The VisualizeDopplerScattData.ipynb notebook contains the Airborne Science Case Study data visualization and discussion. Instructions for installing the airborne material in a conda environment are contained in this Airborne Case Study README.\n\n\nScience Case Study In Situ\nThe insitu_dataviz_demo.ipynb notebook contains the In Situ Science Case Study data visualization and discussion. This notebook also contains sample code to run the PO.DAAC Data Downloader to download Saildrone data. Instructions for installing the necessary Python packages, and more information on obtaining S-MODE data are in the In Situ Case Study README."
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+ "title": "Harmony API Introduction",
+ "section": "Access reprojected data",
+ "text": "Access reprojected data\nThe Harmony API accepts reprojection requests with a given coordinate reference system using the outputCrs keyword. According to the Harmony API documentation, this keyword “recognizes CRS types that can be inferred by gdal, including EPSG codes, Proj4 strings, and OGC URLs (http://www.opengis.net/def/crs/…)”."
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- "text": "Additional Resources\n\n2022 S-MODE Open Data Workshop\nhttps://espo.nasa.gov/s-mode/content/S-MODE_2022_Open_Data_Workshop - Recordings and Presentations\nThe Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) science team is hosted a virtual Open Data Workshop on 1 December 2022 from 11:00am – 1:00pm ET to share about the S-MODE mission, to learn about its instrumentation, and find out how to access and use its data products."
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+ "href": "notebooks/l2-regridding/reprojection notebook.html#the-practice-datasets-below-used-for-this-tutorial-are-no-longer-supported-for-details-about-the-harmony-api-see-this-tutorial-from-the-2021-cloud-hackathon-or-this-tutorial-introducing-the-harmony-py-library.",
+ "title": "Harmony API Introduction",
+ "section": "The practice datasets below used for this tutorial are no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "text": "The practice datasets below used for this tutorial are no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.\nTwo examples below demonstrate inputting an EPSG code and Proj4 string using the global test granule from previous examples. First, let’s view the projection information of the granule in the native projection, using the variable subset example:"
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- "title": "In Development/Experimental",
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- "text": "Content coming soon!"
+ "objectID": "notebooks/l2-regridding/reprojection notebook.html#access-level-2-swath-regridded-data",
+ "href": "notebooks/l2-regridding/reprojection notebook.html#access-level-2-swath-regridded-data",
+ "title": "Harmony API Introduction",
+ "section": "Access Level 2 swath regridded data",
+ "text": "Access Level 2 swath regridded data\nMoving outside of the harmony/gdal service, we will now request regridding from the sds/swot-reproject service using the C1940473819-POCLOUD.\nThe Harmony API accepts several query parameters related to regridding and interpolation in addition to the reprojection parameters above:\ninterpolation=<String> - Both near and bilinear are valid options\nscaleSize=x,y - 2 comma separated numbers as floats\nscaleExtent=xmin,ymin,xmax,ymax - 4 comma separated numbers as floats\nwidth=<Float>\nheight=<Float>\nAn error is returned if both scaleSize and width/height parameters are both provided (only one or the other can be used).\nRequest reprojection to Europe Lambert Conformal Conic with a new scale extent and nearest neighbor interpolation:\n\nharmony_root = 'https://harmony.earthdata.nasa.gov'\n\n# URL encode string using urllib parse package\nproj_string = '+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs' # proj4 of WGS 84 / NSIDC EASE-Grid 2.0 Global projection\n#l2proj_string = '+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'\nl2proj_encode = parse.quote(proj_string)\n\nregridConfig = {\n 'l2collection_id': 'C1940473819-POCLOUD',\n 'ogc-api-coverages_version': '1.0.0',\n 'variable': 'all',\n 'granuleid': 'G1234734747-POCLOUD',\n 'outputCrs': l2proj_encode,\n 'interpolation': 'near',\n 'width': 1000,\n 'height': 1000\n}\n\nregrid_url = harmony_root+'/{l2collection_id}/ogc-api-coverages/{ogc-api-coverages_version}/collections/{variable}/coverage/rangeset?&granuleid={granuleid}&outputCrs={outputCrs}&interpolation={interpolation}&height={height}&width={width}'.format(**regridConfig)\nprint('Request URL', regrid_url)\nregrid_response = request.urlopen(regrid_url)\nregrid_results = regrid_response.read()\n\nRequest URL https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?&granuleid=G1234734747-POCLOUD&outputCrs=%2Bproj%3Dcea%20%2Blon_0%3D0%20%2Blat_ts%3D30%20%2Bx_0%3D0%20%2By_0%3D0%20%2Bellps%3DWGS84%20%2Btowgs84%3D0%2C0%2C0%2C0%2C0%2C0%2C0%20%2Bunits%3Dm%20%2Bno_defs&interpolation=near&height=1000&width=1000\n\n\nHTTPError: HTTP Error 401: Unauthorized\n\n\nThis reprojected and regridded output is downloaded to the Harmony outputs directory and we can inspect a variable to check for projection and grid dimension:\n\nregrid_file_name = 'regrid-near.nc'\nregrid_filepath = str(regrid_file_name)\nfile_ = open(regrid_filepath, 'wb')\nfile_.write(regrid_results)\nfile_.close()\n\n\nharmony_root = 'https://harmony.earthdata.nasa.gov'\n\n# URL encode string using urllib parse package\nproj_string = '+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs' # proj4 of WGS 84 / NSIDC EASE-Grid 2.0 Global projection\n#l2proj_string = '+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'\nl2proj_encode = parse.quote(proj_string)\n\nharmony_root = 'https://harmony.uat.earthdata.nasa.gov'\n\nregridConfig = {\n 'l2collection_id': 'C1234724470-POCLOUD',\n 'ogc-api-coverages_version': '1.0.0',\n 'variable': 'all',\n 'granuleid': 'G1234734747-POCLOUD',\n 'outputCrs': l2proj_encode,\n 'interpolation': 'bilinear',\n 'width': 1000,\n 'height': 1000\n}\n\nregrid_bi_url = harmony_root+'/{l2collection_id}/ogc-api-coverages/{ogc-api-coverages_version}/collections/{variable}/coverage/rangeset?&granuleid={granuleid}&outputCrs={outputCrs}&interpolation={interpolation}&height={height}&width={width}'.format(**regridConfig)\nprint('Request URL', regrid_bi_url)\nregrid_bi_response = request.urlopen(regrid_bi_url)\nregrid_bi_results = regrid_bi_response.read()\n\n\nregrid_bi_file_name = 'regrid-bi.nc'\nregrid_bi_filepath = str(regrid_bi_file_name)\nfile_ = open(regrid_bi_filepath, 'wb')\nfile_.write(regrid_bi_results)\nfile_.close()\n\nPrint the x and y dimensions to confirm that the output matches the requested scale extent in meters:\n\nimport xarray as xr\nreproject_ds = xr.open_dataset(regrid_filepath, drop_variables='time')\nprint(reproject_ds)\n\n\nimport xarray as xr\nreproject_bi_ds = xr.open_dataset(regrid_bi_filepath, drop_variables='time')\nprint(reproject_bi_ds)\n\n\noriginal_ds = xr.open_dataset('20200131234501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc')\nprint(original_ds)\n\n\ng = reproject_ds.sea_surface_temperature.plot(robust=True)\ng.axes.set_title(\"Nearest Neighbor Interpolation\")\n\n\ng= reproject_bi_ds.sea_surface_temperature.plot(robust=True)\ng.axes.set_title(\"Bilinear Interpolation\")\n\n\ng = original_ds.sea_surface_temperature.plot(robust=True)\ng.axes.set_title(\"Native File\")\n\n\ng= original_ds.sea_surface_temperature.plot(x=\"lon\", y=\"lat\", robust=True)\ng.axes.set_title(\"Native, projected to Lat/Lon\")"
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- "title": "SMAP",
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+ "title": "A newer version of this Notebook exists here.",
"section": "",
- "text": "The Soil Moisture Active Passive (SMAP) satellite is designed to principally measure soil moisture and freeze/thaw state from space for all non-liquid water surfaces globally within the top layer of the Earth. The mission additionally provides a value-added Level 4 terrestrial carbon dataset derived from SMAP observations. More information can be found on PO.DAAC’s SMAP webpage."
+ "text": "This Jupyter Notebook contains examples related to querying the HUC Feature Translation Service (FTS), previewing (viusualizing) the queried region of interest, and using FTS results to query NASA’s Common Metadata Repository (CMR).\nExample Use Case: Check if data is available over my region of interest using HUCs. In this example we are using FTS to geospatially define our region of interest, namely the Upper Tuolumne River Basin in the San Joaquin River Basin in California’s Sierra Nevada, searching by HUC or region name, and then using those geospatial bounds (coordinates) to query Sentinel-1 data in CMR. 1. use FTS to define geographic region of interest (query by partial or exact HUC or HUC region name) 2. preview query 3. use coordinates returned by FTS to query Sentinel-1 data in CMR, by polygon or bounding box.\nResources\nUSGS Hydrologic unit map to help identifiy region of interest (e.g. HUC value or name) can be found here: https://water.usgs.gov/GIS/regions.html\n\n%%capture\n\nimport sys\n!{sys.executable} -m pip install bs4 requests\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport json\n\ncloudfront_url = \"https://d3fu1wb0xptl0v.cloudfront.net\""
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- "objectID": "quarto_text/SMAP.html#background",
- "href": "quarto_text/SMAP.html#background",
- "title": "SMAP",
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#huc-feature-translation-service-fts-examples",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#huc-feature-translation-service-fts-examples",
+ "title": "A newer version of this Notebook exists here.",
"section": "",
- "text": "The Soil Moisture Active Passive (SMAP) satellite is designed to principally measure soil moisture and freeze/thaw state from space for all non-liquid water surfaces globally within the top layer of the Earth. The mission additionally provides a value-added Level 4 terrestrial carbon dataset derived from SMAP observations. More information can be found on PO.DAAC’s SMAP webpage."
+ "text": "This Jupyter Notebook contains examples related to querying the HUC Feature Translation Service (FTS), previewing (viusualizing) the queried region of interest, and using FTS results to query NASA’s Common Metadata Repository (CMR).\nExample Use Case: Check if data is available over my region of interest using HUCs. In this example we are using FTS to geospatially define our region of interest, namely the Upper Tuolumne River Basin in the San Joaquin River Basin in California’s Sierra Nevada, searching by HUC or region name, and then using those geospatial bounds (coordinates) to query Sentinel-1 data in CMR. 1. use FTS to define geographic region of interest (query by partial or exact HUC or HUC region name) 2. preview query 3. use coordinates returned by FTS to query Sentinel-1 data in CMR, by polygon or bounding box.\nResources\nUSGS Hydrologic unit map to help identifiy region of interest (e.g. HUC value or name) can be found here: https://water.usgs.gov/GIS/regions.html\n\n%%capture\n\nimport sys\n!{sys.executable} -m pip install bs4 requests\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport json\n\ncloudfront_url = \"https://d3fu1wb0xptl0v.cloudfront.net\""
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- "objectID": "quarto_text/SMAP.html#data-resources-tutorials",
- "href": "quarto_text/SMAP.html#data-resources-tutorials",
- "title": "SMAP",
- "section": "Data Resources & Tutorials",
- "text": "Data Resources & Tutorials\n\nVisualizing Ocean Salinity as Powerful Storms Wreak Havoc in California - Data in Action Use Case Notebook Tutorial with a local machine workflow\n\nThe Data in Action Story\n\n\n\nMonitoring Changes in the Arctic Using SMAP Satellite data and Saildrone - AWS cloud tutorial that compares salinity from the SMAP satellite and Saildrone in-situ measurements"
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-partial-region-matches",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-partial-region-matches",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Search Feature Translation Service for Partial Region Matches",
+ "text": "Search Feature Translation Service for Partial Region Matches\nIf you are unsure what the corresponding HUC is for your region of interest, you can query the FTS for partial region matches.\n\n###################\n\n# Querying partial matches with region \"San Joaquin\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"San Jo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with HUC 1805000301\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 11,\n \"time\": \"5.689 ms.\",\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false\n },\n \"results\": {\n \"San Joaquin\": {\n \"HUC\": \"1804\",\n \"Bounding Box\": 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},
{
- "objectID": "quarto_text/SMAP.html#additional-resources",
- "href": "quarto_text/SMAP.html#additional-resources",
- "title": "SMAP",
- "section": "Additional Resources",
- "text": "Additional Resources\nNASA Mission Page"
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-exact-huc-matches",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-exact-huc-matches",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Search Feature Translation Service for Exact HUC Matches",
+ "text": "Search Feature Translation Service for Exact HUC Matches\nHere we can define a HUC, or hydrologic unit code, and use this to query the HUC FTS. By defining the parameter EXACT = True, we tell the query to not search for partial matches.\nBased on the partial response in the previous step, we can now do an exact search for SJRB, using its HUC (1804).\n\n###################\n\n# Querying exact matches for HUC \"1804\" = San Joaquin RB\n\nHUC = \"1804\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.452 ms.\",\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true\n },\n \"results\": {\n \"1804\": {\n \"Region Name\": \"San Joaquin\",\n \"Bounding Box\": \"-121.93679916804501,36.36688239563472,-118.65438684397327,38.757297326299295\",\n \"Convex Hull Polygon\": 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\"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/1804.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}"
},
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- "objectID": "external/NASA_Earthdata_Authentication.html",
- "href": "external/NASA_Earthdata_Authentication.html",
- "title": "How to Authenticate for NASA Earthdata Programmatically",
- "section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository."
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-partial-region-matches-1",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-partial-region-matches-1",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Search Feature Translation Service for Partial Region Matches",
+ "text": "Search Feature Translation Service for Partial Region Matches\nBut we are specifically interested in Tuolumne RB within the San Joaquin, so let’s do a partial search for “Upper Tuo”, given we may not know the exact region name.\n\n###################\n\n# Querying partial matches with region \"Upper Tuo\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"Upper Tuo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with HUC 1805000301\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.549 ms.\",\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false\n },\n \"results\": {\n \"Upper Tuolumne\": {\n \"HUC\": \"18040009\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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- "objectID": "external/NASA_Earthdata_Authentication.html#summary",
- "href": "external/NASA_Earthdata_Authentication.html#summary",
- "title": "How to Authenticate for NASA Earthdata Programmatically",
- "section": "Summary",
- "text": "Summary\nThis notebook creates a hidden .netrc file (_netrc for Window OS) with Earthdata login credentials in your home directory. This file is needed to access NASA Earthdata assets from a scripting environment like Python.\n\nEarthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nAuthentication via netrc File\nYou will need a netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A netrc file can be created manually within text editor and saved to your home directory. An example of the required content is below.\nmachine urs.earthdata.nasa.gov\nlogin <USERNAME>\npassword <PASSWORD>\n<USERNAME> and <PASSWORD> would be replaced by your actual Earthdata Login username and password respectively."
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-exact-named-region-matches",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-exact-named-region-matches",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Search Feature Translation Service for Exact Named Region Matches",
+ "text": "Search Feature Translation Service for Exact Named Region Matches\nGiven the above response, or that we already know an exact region name or HUC in USGS’s Watershed Boundary Dataset (WBD), we can use this instead of a partial search. Below is an example of exact matches by HUC (18040009), and then by region name (“Upper Tuolumne”).\n\n###################\n\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.516 ms.\",\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true\n },\n \"results\": {\n \"18040009\": {\n \"Region Name\": \"Upper Tuolumne\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"Visvalingam Polygon\": 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\"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}\n\n\n\n###################\n\n# Querying exact matches with region \"Upper Tuolumne\"\n\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\n# Note the change in endpoint from \"/prod/huc\" to \"/prod/region\"\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exact matches with region \"Woods Creek-Skykomish River\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.665 ms.\",\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": true\n },\n \"results\": {\n \"Upper Tuolumne\": {\n \"HUC\": \"18040009\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}"
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{
- "objectID": "external/NASA_Earthdata_Authentication.html#import-required-packages",
- "href": "external/NASA_Earthdata_Authentication.html#import-required-packages",
- "title": "How to Authenticate for NASA Earthdata Programmatically",
- "section": "Import Required Packages",
- "text": "Import Required Packages\n\nfrom netrc import netrc\nfrom subprocess import Popen\nfrom platform import system\nfrom getpass import getpass\nimport os\n\nThe code below will:\n\ncheck what operating system (OS) is being used to determine which netrc file to check for/create (.netrc or _netrc)\ncheck if you have an netrc file, and if so, varify if those credentials are for the Earthdata endpoint\ncreate a netrc file if a netrc file is not present.\n\n\nurs = 'urs.earthdata.nasa.gov' # Earthdata URL endpoint for authentication\nprompts = ['Enter NASA Earthdata Login Username: ',\n 'Enter NASA Earthdata Login Password: ']\n\n# Determine the OS (Windows machines usually use an '_netrc' file)\nnetrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n\n# Determine if netrc file exists, and if so, if it includes NASA Earthdata Login Credentials\ntry:\n netrcDir = os.path.expanduser(f\"~/{netrc_name}\")\n netrc(netrcDir).authenticators(urs)[0]\n\n# Below, create a netrc file and prompt user for NASA Earthdata Login Username and Password\nexcept FileNotFoundError:\n homeDir = os.path.expanduser(\"~\")\n Popen('touch {0}{2} | echo machine {1} >> {0}{2}'.format(homeDir + os.sep, urs, netrc_name), shell=True)\n Popen('echo login {} >> {}{}'.format(getpass(prompt=prompts[0]), homeDir + os.sep, netrc_name), shell=True)\n Popen('echo \\'password {} \\'>> {}{}'.format(getpass(prompt=prompts[1]), homeDir + os.sep, netrc_name), shell=True)\n # Set restrictive permissions\n Popen('chmod 0600 {0}{1}'.format(homeDir + os.sep, netrc_name), shell=True)\n\n # Determine OS and edit netrc file if it exists but is not set up for NASA Earthdata Login\nexcept TypeError:\n homeDir = os.path.expanduser(\"~\")\n Popen('echo machine {1} >> {0}{2}'.format(homeDir + os.sep, urs, netrc_name), shell=True)\n Popen('echo login {} >> {}{}'.format(getpass(prompt=prompts[0]), homeDir + os.sep, netrc_name), shell=True)\n Popen('echo \\'password {} \\'>> {}{}'.format(getpass(prompt=prompts[1]), homeDir + os.sep, netrc_name), shell=True)\n\n\nSee if the file was created\nIf the file was created, we’ll see a .netrc file (_netrc for Window OS) in the list printed below. To view the contents from a Jupyter environment, click File on the top toolbar, select Open from Path…, type .netrc, and click Open. The .netrc file will open within the text editor.\n\n!!! Beware, your password will be visible if the .netrc file is opened in the text editor.\n\n\n!ls -al ~/"
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#function-for-visualization",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#function-for-visualization",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Function for Visualization",
+ "text": "Function for Visualization\nBelow is a function created specifically to visualize the output of the HUC Feature Translation Service.\n\ndef visualize(fts_response):\n \n regions = []\n bounding_boxes = []\n convex_hull_polygons = []\n visvalingam_polygons = []\n for element in fts_response['results']:\n for heading in fts_response['results'][element]:\n if heading == \"Bounding Box\":\n bounding_boxes.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Convex Hull Polygon\":\n convex_hull_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Visvalingam Polygon\":\n visvalingam_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"HUC\":\n regions.append(\"Region Name: \" + element + \"\\n\" + \"HUC: \" + fts_response['results'][element][heading])\n elif heading == \"Region Name\":\n regions.append(\"Region Name: \" + fts_response['results'][element][heading] + \"\\n\" + \"HUC: \" + element)\n else:\n continue\n\n bounding_boxes = [box(e[0], e[1], e[2], e[3]) for e in bounding_boxes]\n convex_hull_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in convex_hull_polygons]\n visvalingam_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in visvalingam_polygons]\n \n for i in range(len(bounding_boxes)):\n ax = gpd.GeoSeries(bounding_boxes[i]).plot(alpha=0.2, cmap='Pastel1', figsize=(10,10))\n gpd.GeoSeries(convex_hull_polygons[i]).plot(ax = ax, cmap='Pastel2')\n gpd.GeoSeries(visvalingam_polygons[i]).plot(alpha=0.5, ax=ax, cmap='viridis')\n\n plt.title(regions[i])\n plt.show()"
},
{
- "objectID": "external/ECCO_download_data.html",
- "href": "external/ECCO_download_data.html",
- "title": "Access to ECCO V4r4 Datasets on a Local Machine",
- "section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.\nDuped+slightly modified version of the s3 access ipynb. Tested on JPL-issued macbook and my linux box. It starts by setting up a most trusted strategy for batch downloads behind URS ussing curl/wget. Will attempt to add line(s) to your netrc file if needed btw; then it writes your urs cookies to a local file that should effectively “pre-authenticate” future download sessions for those sub domains."
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#visualization",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#visualization",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Visualization",
+ "text": "Visualization\nWe can take that response and pass it to the visualize() function created above.\n\n#visualize FTS response\nvisualize(response)\n\n\n\n\n\n###################\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.556 ms.\",\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true\n },\n \"results\": {\n \"18040009\": {\n \"Region Name\": \"Upper Tuolumne\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"Visvalingam Polygon\": 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8332,-119.31574230232172,37.96621302648555,-119.30533157629623,38.02416955035392,-119.34927245018639,38.08565116171684,-119.35845034913046,38.08266815651314,-119.39810467927725,38.1068175096006,-119.43127595110076,38.11332130542388,-119.4403859021283,38.09636985024184,-119.46399499479998,38.09838383773871,-119.4692413104168,38.12798441894279,-119.48819819267908,38.132729004352086,-119.50246159786525,38.159339980352456,-119.50459633952858,38.140964939755975,-119.54763344883679,38.14419101891764,-119.54624260196397,38.15397065015242,-119.5773162810824,38.15780512931315,-119.57980050712018,38.17791634178195,-119.62908996641869,38.196015076128845,-119.62508146642494,38.22905559795254,-119.65612154137676,38.229472830243594\",\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}\n\n\n\n#visualize FTS response\nvisualize(response)"
},
{
- "objectID": "external/ECCO_download_data.html#quick-start",
- "href": "external/ECCO_download_data.html#quick-start",
- "title": "Access to ECCO V4r4 Datasets on a Local Machine",
- "section": "Quick Start",
- "text": "Quick Start\nA key takeaway in this notebook… follow these instructions on the Earthdata Wiki to authenticate and store your URS cookies in a local file. You can batch download really efficiently this way, effectively “pre-authenticated” through your previous session.\nAvoid lines that begin with % or %% in code cells. Those are IPython “magic functions” that tell a line or cell to evaluate in some special mode like by bash instead of py3.\n\nConfigure your .netrc file\nGood idea to back up your existing netrc file, if you have one. And while youre at it check for these entries because they might exist in there already:\n\n%cp ~/.netrc ~/bak.netrc\n\n%cat ~/.netrc | grep '.earthdata.nasa.gov' | cut -f-5 -d\" \"\n\nmachine urs.earthdata.nasa.gov login jmcnelis password\nmachine uat.urs.earthdata.nasa.gov login jmcnelis password\nmachine opendap.earthdata.nasa.gov login jmcnelis password\n\n\n\nAdd entries to your netrc for these two earthdata.nasa.gov sub domains, at a minimum:\nmachine urs.earthdata.nasa.gov login jmcnelis password ***\nmachine opendap.earthdata.nasa.gov login jmcnelis password ***\nand replace jmcnelis and *** with your Earthdata Login username and password, respectively…\n\nReplace jmcnelis and *** with your Earthdata username and password, and then run the cell to append these two lines to your netrc file, if one exists. Otherwise write them to a new one. (all set up by -a)\n\n%%file -a ~/.netrc\nmachine urs.earthdata.nasa.gov login jmcnelis password ***\nmachine opendap.earthdata.nasa.gov login jmcnelis password ***\n\nAppending to /Users/jmcnelis/.netrc\n\n\nDump the netrc again sans passwords to confirm that it worked:\n\n!cat ~/.netrc | grep '.earthdata.nasa.gov' | cut -f-5 -d\" \"\n\nmachine urs.earthdata.nasa.gov login jmcnelis password\nmachine uat.urs.earthdata.nasa.gov login jmcnelis password\nmachine opendap.earthdata.nasa.gov login jmcnelis password\nmachine urs.earthdata.nasa.gov login jmcnelis password\nmachine opendap.earthdata.nasa.gov login jmcnelis password\n\n\nFinally, you need to make sure to limit access to the netrc file because it stores your plain text password. Simple on MacOS and Linux:\n\n!chmod 0600 ~/.netrc\n\nNo outputs expected.\n\n\nDownload a sample data file, get your URS cookies, and write them to a local file\nNow I’ll download a random file that’s protected by URS/Earthdata Login authentication so that I can grab my URS cookies.\nI chose to download a file containing ECCO grid geometries for the 0.5-degree latitude/longitude grid. It’s small and it may prove useful for downstream analysis of the SSH data. (Again, any protected data file will work.)\n\n%%bash\nwget --no-verbose \\\n --no-clobber \\\n --load-cookies ~/.urs_cookies \\\n --save-cookies ~/.urs_cookies \\\n --keep-session-cookies \\\n https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_GEOMETRY_05DEG_V4R4/GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg.nc\n\nI used –quiet mode so wget would not dump tons of garbage into the notebook. Confirm that the cookies file exists in your home directory:\n\n!ls ~/.urs_cookies\n\n/Users/jmcnelis/.urs_cookies\n\n\nAnd see if the file downloaded successfully to double-confirm it worked as expected (and be aware that the ncdump output is truncated to the first 50 lines):\n\n!ncdump -h GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg.nc | head -25\n\nnetcdf GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg {\ndimensions:\n Z = 50 ;\n latitude = 360 ;\n longitude = 720 ;\n nv = 2 ;\nvariables:\n float Z(Z) ;\n Z:axis = \"Z\" ;\n Z:bounds = \"Z_bnds\" ;\n Z:comment = \"Non-uniform vertical spacing.\" ;\n Z:coverage_content_type = \"coordinate\" ;\n Z:long_name = \"depth of grid cell center\" ;\n Z:positive = \"up\" ;\n Z:standard_name = \"depth\" ;\n Z:units = \"m\" ;\n float latitude(latitude) ;\n latitude:axis = \"Y\" ;\n latitude:bounds = \"latitude_bnds\" ;\n latitude:comment = \"uniform grid spacing from -89.75 to 89.75 by 0.5\" ;\n latitude:coverage_content_type = \"coordinate\" ;\n latitude:long_name = \"latitude at grid cell center\" ;\n latitude:standard_name = \"latitude\" ;\n latitude:units = \"degrees_north\" ;\n float longitude(longitude) ;\n\n\nAnd that’s it! You should now be able to use wget and curl to download URS-protected data from PODAAC cloud without providing your creds each time.\n\n\nPrepare a list of files to download\nNow the only step that remains is to get a list of URLs to pass to wget or curl for downloading. There’s a lot of ways to do this – even more so for ECCO V4r4 data because the files/datasets follow well-structured naming conventions – but we will rely on Earthdata Search to do this from the browser for the sake of simplicity.\n1. Find the collection/dataset of interest in Earthdata Search.\nStart from this complete list of ECCO collections in Earthdata Search (79 in total), and refine the results until you see your dataset of interest. In this case we want monthly sea surface height grids provided at 0.5-degree cell resolution on an interpolated latitude/longitude grid.\n2. Pick your collection, then click the green Download All button on the next page.\nClick the big green button identified by the red arrow/box in the screenshot below.\n\nThat will add all the granules in the collection to your “shopping cart” and then redirect you straight there and present you with the available options for customizing the data prior to download. We will ignore those because they’re mostly in active development and because we want to download all data in the collection.\n\n\nThe screenshot above shows the download customization interface (i.e. “shopping cart”)\n\n3. Click Download Data to get your list of download urls (bottom-left, another green button)\nThe Download Data button takes you to one final page that provides the list of urls from which to download the files matching your search parameters and any customization options that you selected in the steps that followed. This page will be retained in your User History in case you need to return to it later.\n\nThere are several ways that you could get the list of urls into a text file that’s accessible from Jupyter or your local shell. I simply clicked the save button in my browser and downloaded them as a text file to a subdirectory called resources inside this workspace. (You could also copy them into a new notebook cell and write them to a file like we did with the netrc file above.)\n\n\nDownload files in a batch with GNU Wget\nI find wget options to be convenient and easy to remember. There are only a handful that I use with any regularity.\nThe most important wget option for our purpose is set by the -i argument, which takes a path to the input text file containing our download urls. Another nice feature of wget is the ability to continue downloads where you left of during a previously-interuppted download session. That option is turned on by passing the -c argument.\nNow run wget against a list of files retrieve from Earthdata Search and see what happens.\n\n!ls resources/*.txt\n\nresources/5237392644-download.txt\n\n\nGo ahead and create a data/ directory to keep the downloaded files, and then start the downloads into that location by including the -P argument:\n\n%%bash\n\nmkdir -p data\n\nwget --no-verbose \\\n --no-clobber \\\n --continue \\\n -i resources/5237392644-download.txt -P data/\n\nWait a long time if you have to… then count the number of netCDF files in the data directory:\n\n!ls -1 data/*.nc | wc -l\n\n 312\n\n\nAdd a folder for outputs, if needed:\n\n!mkdir -p outputs/\n\nGet a list of netCDF files at the data directory and print the count + the first filename in the list:\n\nfiles = !ls data/*.nc\n\nlen(files), files[0]\n\n(312, 'data/SEA_SURFACE_HEIGHT_mon_mean_1992-01_ECCO_V4r4_latlon_0p50deg.nc')\n\n\nThis is the first time you’ll need any Python in this notebook… Install Python 3 requirements (I am on version 3.8.)\n\n#!conda install -c conda-forge numpy dask netCDF4 xarray, cartopy, ffmpeg\nimport xarray as xr\nimport matplotlib.animation as animation\nimport matplotlib.pyplot as plt\nimport cartopy.feature as cfeat\nimport cartopy.crs as ccrs\nimport cartopy\n\nOpen the netCDFs as one multi-file dataset with xarray:\n\nds = xr.open_mfdataset(\n paths=files,\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks={'latitude': 360, 'longitude': 720, 'time': 5}\n)\n\nssh = ds.SSH\n\nprint(ssh)\n\n<xarray.DataArray 'SSH' (time: 312, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(312, 360, 720), dtype=float32, chunksize=(1, 360, 720), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 1992-01-16T18:00:00 ... 2017-12-16T06:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: -1.8805772066116333\n valid_max: 1.4207719564437866\n\n\n\n\nPlot gridded sea surface height time series\nBut only the timesteps beginning in 2015:\n\nssh_after_201x = ssh[ssh['time.year']>=2015,:,:]\nprint(ssh_after_201x)\n\n<xarray.DataArray 'SSH' (time: 36, latitude: 360, longitude: 720)>\ndask.array<getitem, shape=(36, 360, 720), dtype=float32, chunksize=(1, 360, 720), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-16T12:00:00 ... 2017-12-16T06:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: -1.8805772066116333\n valid_max: 1.4207719564437866\n\n\nPlot the grid for the first time step using a Robinson projection. Define a helper function for consistency throughout the notebook:\n\ndef make_figure(proj):\n fig = plt.figure(figsize=(16,6))\n ax = fig.add_subplot(1, 1, 1, projection=proj)\n ax.add_feature(cfeat.LAND)\n ax.add_feature(cfeat.OCEAN)\n ax.add_feature(cfeat.COASTLINE)\n ax.add_feature(cfeat.BORDERS, linestyle='dotted')\n return fig, ax\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\nssh_after_201x.isel(time=0).plot(ax=ax, transform=ccrs.PlateCarree(), cmap='Spectral_r')\n\n<cartopy.mpl.geocollection.GeoQuadMesh at 0x1a344a0a0>\n\n\n\n\n\nNow plot the whole time series (post-2010) in an animation and write it to an mp4 file called ecco_monthly_ssh_grid_2015_to_x.mp4:\n\ndef get_animation(var, cmap: str=\"Spectral_r\"):\n \"\"\"Get time series animation for input xarray dataset\"\"\"\n\n def draw_map(i: int, add_colorbar: bool):\n data = var[i]\n m = data.plot(ax=ax, \n transform=ccrs.PlateCarree(),\n add_colorbar=add_colorbar,\n vmin=var.valid_min, \n vmax=var.valid_max,\n cmap=cmap)\n plt.title(str(data.time.values)[:7])\n return m\n\n def init():\n return draw_map(0, add_colorbar=True)\n \n def animate(i):\n return draw_map(i, add_colorbar=False)\n\n return init, animate\n\nNow make the animation using the function:\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\ninit, animate = get_animation(ssh_after_201x)\n\nani = animation.FuncAnimation(fig=fig, \n func=animate, \n frames=ssh_after_201x.time.size, \n init_func=init, \n interval=0.2, \n blit=False, \n repeat=False)\n\n# Now save the animation to an MP4 file:\nani.save('outputs/ecco_monthly_ssh_grid_2015_to_x.mp4', writer=animation.FFMpegWriter(fps=8))\n\nplt.close(fig)\n\nRender the animation in the ipynb:\n\n#HTML(ani.to_html5_video())"
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-polygon",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-polygon",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Query CMR by Polygon",
+ "text": "Query CMR by Polygon\nHere is a more useful example of the Feature Translation Service. We can use results obtained from the FTS to then directly and automatically query CMR. Below I’m extracting the polygon representing Upper Tuolumne River Basin within the San Joaquin River Basin, and using it to search for granules available through the Sentinel-1 mission.\n\n\n###################\n\nCOLLECTION_ID = \"C1522341104-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain convex hull polygon from response\npolygon = response['results'][REGION]['Convex Hull Polygon']\n#polygon = response['results'][REGION]['Visvalingam Polygon']\n\n# Query CMR\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?polygon={}&echo_collection_id={}&pretty=True\".format(polygon, COLLECTION_ID))\n\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2019-11-26T18:24:02.850Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?polygon=-121.105517801627,37.57291785522102,-120.51777999837259,37.58160878749919,-119.26845687218679,37.73942430183757,-119.26095827844847,37.741190162251485,-119.26079495969867,37.74128122475133,-119.25581474616479,37.7450598684955,-119.25563206491506,37.74520087891193,-119.25521361804067,37.745555179953044,-119.20452512020273,37.79316755800414,-119.20311483687158,37.794898117376476,-119.20297581291345,37.79511513091779,-119.20108320354137,37.801137019450096,-119.20096521291657,37.803876760070864,-119.19927543166921,37.88483115890352,-119.19931234937746,37.885001276611604,-119.20064394937538,37.88738135160793,-119.31090541587093,38.044980644071586,-119.3277000731365,38.0651666159153,-119.32796109605277,38.06544024091488,-119.34908448143665,38.08655395234041,-119.62508146642494,38.22905559795254,-119.65624842470987,38.22952896670182,-119.65829346949835,38.22947615316025,-119.79473757241158,38.21799358859471,-119.99491475022586,38.196920114669126,-120.38613654232694,38.056378609678916,-121.15444382863438,37.62884831659255,-121.15500076925849,37.6284224540932,-121.15993039529252,37.62332076451776,-121.16822139007132,37.61386883849076,-121.17452907235321,37.605445134337174,-121.17462853797804,37.60522817287921,-121.17469632131127,37.60502320725453,-121.17471004943627,37.60496802808791,-121.17476593797784,37.604743358296616,-121.17472602131124,37.60443736142207,-121.1743974786034,37.603737121839856,-121.17385444318757,37.603213931215635,-121.12495024430518,37.575249448967384,-121.1206057318119,37.57340581772024,-121.1184699109819,37.573299354178744,-121.105517801627,37.57291785522102&echo_collection_id=C1522341104-NSIDC_ECS&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5\",\n \"time_start\": \"2015-04-01T01:19:59.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:141648389\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:31.000Z\",\n \"id\": \"G1565814828-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8805513382\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.5845451 -121.9242706 36.5554848 -121.9242706 36.5554848 -118.6670151 38.5845451 -118.6670151 38.5845451 -121.9242706\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2018.10.26/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5.iso.xml\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/SMAP/SPL2SMAP_S.002/\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C1522341104-NSIDC_ECS&m=-38.109375!23.34375!1!1!0!0%2C2&tl=1518545080!4!!&q=SPL2SMAP_S\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R16010_001.h5\",\n \"time_start\": \"2015-04-01T14:55:27.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:141648391\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T14:56:00.000Z\",\n \"id\": \"G1565814832-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.2027692795\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"40.1287079 -122.2977142 38.0572586 -122.2977142 38.0572586 -118.9470978 40.1287079 -118.9470978 40.1287079 -122.2977142\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": 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\"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C1522341104-NSIDC_ECS&m=-38.109375!23.34375!1!1!0!0%2C2&tl=1518545080!4!!&q=SPL2SMAP_S\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R16010_001.h5\",\n \"time_start\": \"2015-05-24T14:43:43.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:142137676\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-05-24T14:44:16.000Z\",\n \"id\": \"G1568678063-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.3608856201\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"37.9581909 -122.8578873 35.6854019 -122.8578873 35.6854019 -119.6006241 37.9581909 -119.6006241 37.9581909 -122.8578873\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.05.24/SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R16010_001.h5\"\n },\n {\n \"rel\": 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\"https://search.earthdata.nasa.gov/search/granules?p=C1522341104-NSIDC_ECS&m=-38.109375!23.34375!1!1!0!0%2C2&tl=1518545080!4!!&q=SPL2SMAP_S\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150604T145527_20150605T020716_122W37N_R16010_001.h5\",\n \"time_start\": \"2015-06-04T14:55:27.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:142097232\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-06-04T14:56:00.000Z\",\n \"id\": \"G1567779794-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"4.5870676041\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"39.0352478 -124.071579 36.9062119 -124.071579 36.9062119 -120.8143158 39.0352478 -120.8143158 39.0352478 -124.071579\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.06.04/SMAP_L2_SM_SP_1AIWDV_20150604T145527_20150605T020716_122W37N_R16010_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2018.10.31/SMAP_L2_SM_SP_1AIWDV_20150604T145527_20150605T020716_122W37N_R16010_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.06.04/SMAP_L2_SM_SP_1AIWDV_20150604T145527_20150605T020716_122W37N_R16010_001.h5.iso.xml\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/SMAP/SPL2SMAP_S.002/\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C1522341104-NSIDC_ECS&m=-38.109375!23.34375!1!1!0!0%2C2&tl=1518545080!4!!&q=SPL2SMAP_S\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150611T141840_20150612T015904_120W37N_R16010_001.h5\",\n \"time_start\": \"2015-06-11T14:18:40.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:142102425\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-06-11T14:19:43.000Z\",\n \"id\": \"G1567889264-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.6879024506\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.9448853 -122.0176315 36.9062119 -122.0176315 36.9062119 -118.760376 38.9448853 -118.760376 38.9448853 -122.0176315\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.06.11/SMAP_L2_SM_SP_1AIWDV_20150611T141840_20150612T015904_120W37N_R16010_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2018.11.01/SMAP_L2_SM_SP_1AIWDV_20150611T141840_20150612T015904_120W37N_R16010_001.qa\"\n },\n {\n \"rel\": 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- "title": "Data Subscriber: Continual, Scripted Access to PODAAC data",
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- "text": "The PO.DAAC Data subscriber is a python-based tool for continuously downloading data from the PO.DAAC archive. Use this script if you want to constantly download the newest data from PO.DAAC as it comes in.\nFor installation and dependency information, please see the top-level README.\n$> podaac-data-subscriber -h\n-h, --help show this help message and exit\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\n-f, --force Flag to force downloading files that are listed in CMR query, even if the file exists and checksum matches\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time after which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time before which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from the command line.\n Default: \"-180,-90,180,90\".\n-dc Flag to use cycle number for directory where data products will be downloaded.\n-dydoy Flag to use start time (Year/DOY) of downloaded data for directory where data products will be downloaded.\n-dymd Flag to use start time (Year/Month/Day) of downloaded data for directory where data products will be downloaded.\n-dy Flag to use start time (Year) of downloaded data for directory where data products will be downloaded.\n--offset OFFSET Flag used to shift timestamp. Units are in hours, e.g. 10 or -10.\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs.\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\n--process PROCESS_CMD\n Processing command to run on each downloaded file (e.g., compression). Can be specified multiple times.\n--version Display script version information and exit.\n--verbose Verbose mode.\n-p PROVIDER, --provider PROVIDER\n Specify a provider for collection search. Default is POCLOUD.\n--dry-run Search and identify files to download, but do not actually download them\n\n\nUsage:\nusage: PO.DAAC data subscriber [-h] -c COLLECTION -d OUTPUTDIRECTORY [-f] [-sd STARTDATE] [-ed ENDDATE] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-m MINUTES] [-e EXTENSIONS] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs. \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --minutes must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the subscriber.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nEvery time the script runs successfully (that is, no errors), a .update__<collectionname> file is created in your download directory with the last run timestamp. This timestamp will be used the next time the script is run. It will look for data between the timestamp in that file and the current time to determine new files to download.\n\n\n\nThere are numerous ‘times’ available to query on in CMR. For the default subscriber, we look at the ‘created at’ field, which will look for when a granule file was ingested into the archive. This means as PO.DAAC gets data, your subscriber will also get data, regardless of the time range within the granule itself.\n\n\n\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token\n\n\n\n\n\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the subscriber is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the subscriber will skip downloading that file.\nThis can drastically reduce the time for the subscriber to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the subscriber to always download matching files, by using –force/-f.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -f\n\n\n\nTo automatically run and update a local file system with data files from a collection, one can use a syntax like the following:\n10 * * * * podaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d /path/to/data/VIIRS_N20-OSPO-L2P-v2.61 -e .nc -e .h5 -m 60 -b=\"-180,-90,180,90\" --verbose >> ~/.subscriber.log\n\nThis will run every hour at ten minutes passed, and output will be appended to a local file called ~/.subscriber.log\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\"\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
+ "objectID": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-bounding-box",
+ "href": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-bounding-box",
+ "title": "A newer version of this Notebook exists here.",
+ "section": "Query CMR by Bounding Box",
+ "text": "Query CMR by Bounding Box\nInstead of querying via polygon, we can extract the bounding box of the region and use this to query CMR. Similarly to above, we’re extracting information (this time the bounding box) from the Upper Tuolumne River Basin and using this to search for granules available through the Sentinel-1 mission.\nHere we query by region in these two examples, however it would be equally valid to query by HUC.\n\n###################\n\nCOLLECTION_ID = \"C1522341104-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\nREGION = \"Upper Tuolumne\"\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain bounding box from response\nbbox = response['results'][REGION]['Bounding Box']\n\n# Query CMR\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?bounding_box={}&echo_collection_id={}&pretty=True\".format(bbox, COLLECTION_ID))\n\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2019-11-26T18:24:03.927Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?bounding_box=-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182&echo_collection_id=C1522341104-NSIDC_ECS&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5\",\n \"time_start\": \"2015-04-01T01:19:59.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:141648389\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:31.000Z\",\n \"id\": \"G1565814828-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8805513382\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.5845451 -121.9242706 36.5554848 -121.9242706 36.5554848 -118.6670151 38.5845451 -118.6670151 38.5845451 -121.9242706\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2018.10.26/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": 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\"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R16010_001.h5\",\n \"time_start\": \"2015-04-01T14:55:27.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:141648391\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T14:56:00.000Z\",\n \"id\": \"G1565814832-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.2027692795\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"40.1287079 -122.2977142 38.0572586 -122.2977142 38.0572586 -118.9470978 40.1287079 -118.9470978 40.1287079 -122.2977142\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R16010_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2018.10.26/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R16010_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R16010_001.h5.iso.xml\"\n },\n {\n \"inherited\": true,\n 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\"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R16010_001.h5\",\n \"time_start\": \"2015-05-24T14:43:43.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:142137676\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-05-24T14:44:16.000Z\",\n \"id\": \"G1568678063-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.3608856201\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"37.9581909 -122.8578873 35.6854019 -122.8578873 35.6854019 -119.6006241 37.9581909 -119.6006241 37.9581909 -122.8578873\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.05.24/SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R16010_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2018.11.04/SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R16010_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.05.24/SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R16010_001.h5.iso.xml\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/SMAP/SPL2SMAP_S.002/\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C1522341104-NSIDC_ECS&m=-38.109375!23.34375!1!1!0!0%2C2&tl=1518545080!4!!&q=SPL2SMAP_S\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150604T145527_20150605T020716_122W37N_R16010_001.h5\",\n \"time_start\": 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"section": "",
- "text": "Usage:\nusage: PO.DAAC data subscriber [-h] -c COLLECTION -d OUTPUTDIRECTORY [-f] [-sd STARTDATE] [-ed ENDDATE] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-m MINUTES] [-e EXTENSIONS] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs. \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --minutes must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the subscriber.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nEvery time the script runs successfully (that is, no errors), a .update__<collectionname> file is created in your download directory with the last run timestamp. This timestamp will be used the next time the script is run. It will look for data between the timestamp in that file and the current time to determine new files to download."
+ "text": "This notebook will demonstrate how to subset Level 2 data using a sea surface temperature product from the following collection: MODIS_A-JPL-L2P-v2019.0, GHRSST Level 2P Global Sea Surface Skin Temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua satellite (GDS2)."
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- "text": "There are numerous ‘times’ available to query on in CMR. For the default subscriber, we look at the ‘created at’ field, which will look for when a granule file was ingested into the archive. This means as PO.DAAC gets data, your subscriber will also get data, regardless of the time range within the granule itself."
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+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\n\nfrom harmony import BBox, Client, Collection, Request, Environment, LinkType\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nimport tempfile\nimport getpass\nimport netrc\nimport json\nimport requests\nimport sys\nimport shutil\nimport xarray as xr\n\n\nFind a granule for subsetting\nBelow we call out a specific granule (G2524986524-POCLOUD) on which we will use the podaac L2 subsetter. Finding this information would complicate the tutorial- but po.daac has a tutorial available for using the CMR API to find collections and granules of interest. Please see this tutorial for that information.\n\ncollection = 'C1940473819-POCLOUD'\nvariable = 'sea_surface_temperature'\nvenue = 'prod'\n\n\n# Defaults\ncmr_root = 'cmr.earthdata.nasa.gov'\nharmony_root = 'https://harmony.earthdata.nasa.gov'\nedl_root = 'urs.earthdata.nasa.gov'"
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- "text": "The netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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+ "title": "Harmony EOSS L2SS API Tutorial",
+ "section": "Subset of a PO.DAAC Granule",
+ "text": "Subset of a PO.DAAC Granule\nWe can now build onto the root URL in order to actually perform a transformation. The first transformation is a subset of a selected granule. At this time, this requires discovering the granule id from CMR. That information can then be appended to the root URL and used to call Harmony with the help of the request library.\nAbove we show how to find a granule id for processing.\nNotes: The L2 subsetter current streams the data back to the user, and does not stage data in S3 for redirects. This is functionality we will be adding over time. It doesn’t work with URS backed files, which is coming in the next few weeks it only works on the show dataset, but\n\ncmr_url = \"https://\"+cmr_root+\"/search/granules.umm_json?collection_concept_id=\"+collection+\"&sort_key=-start_date&bounding_box=-90,-45.75,90,-45\"\n\nresponse = requests.get(cmr_url)\n\ngid=response.json()['items'][0]['meta']['concept-id']\nprint(response.json()['items'][0])\nprint(gid)\n\n{'meta': {'concept-type': 'granule', 'concept-id': 'G2525170373-POCLOUD', 'revision-id': 3, 'native-id': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0', 'provider-id': 'POCLOUD', 'format': 'application/vnd.nasa.cmr.umm+json', 'revision-date': '2022-10-25T21:12:59.142Z'}, 'umm': {'TemporalExtent': {'RangeDateTime': {'EndingDateTime': '2022-10-25T18:54:58.000Z', 'BeginningDateTime': '2022-10-25T18:50:01.000Z'}}, 'MetadataSpecification': {'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.4', 'Name': 'UMM-G', 'Version': '1.6.4'}, 'GranuleUR': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0', 'ProviderDates': [{'Type': 'Insert', 'Date': '2022-10-25T21:10:17.165Z'}, {'Type': 'Update', 'Date': '2022-10-25T21:10:17.165Z'}], 'SpatialExtent': {'HorizontalSpatialDomain': {'Geometry': {'BoundingRectangles': [{'WestBoundingCoordinate': -85.928, 'NorthBoundingCoordinate': -26.101, 'EastBoundingCoordinate': -54.117, 'SouthBoundingCoordinate': -47.37}], 'GPolygons': [{'Boundary': {'Points': [{'Longitude': -54.11689, 'Latitude': -43.06013}, {'Longitude': -58.42233, 'Latitude': -35.30318}, {'Longitude': -62.29779, 'Latitude': -26.1022}, {'Longitude': -73.93747, 'Latitude': -28.35286}, {'Longitude': -85.92834, 'Latitude': -29.50335}, {'Longitude': -84.04758, 'Latitude': -47.36956}, {'Longitude': -68.62455, 'Latitude': -46.17143}, {'Longitude': -54.11689, 'Latitude': -43.06013}]}}]}}}, 'DataGranule': {'ArchiveAndDistributionInformation': [{'SizeUnit': 'MB', 'Size': 20.64744758605957, 'Checksum': {'Value': 'e2d56b12c5fcbe7f10af1653834d76f6', 'Algorithm': 'MD5'}, 'SizeInBytes': 21650418, 'Name': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc'}, {'SizeUnit': 'MB', 'Size': 9.34600830078125e-05, 'Checksum': {'Value': 'a3c500f11b4d0af678f9e8de11397c97', 'Algorithm': 'MD5'}, 'SizeInBytes': 98, 'Name': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5'}], 'DayNightFlag': 'Unspecified', 'ProductionDateTime': '2022-10-25T21:06:25.000Z'}, 'CollectionReference': {'Version': '2019.0', 'ShortName': 'MODIS_A-JPL-L2P-v2019.0'}, 'RelatedUrls': [{'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc', 'Description': 'Download 20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc', 'Type': 'GET DATA'}, {'URL': 's3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc', 'Description': 'This link provides direct download access via S3 to the granule', 'Type': 'GET DATA VIA DIRECT ACCESS'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5', 'Description': 'Download 20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5', 'Type': 'EXTENDED METADATA'}, {'URL': 's3://podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5', 'Description': 'This link provides direct download access via S3 to the granule', 'Type': 'EXTENDED METADATA'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/s3credentials', 'Description': 'api endpoint to retrieve temporary credentials valid for same-region direct s3 access', 'Type': 'VIEW RELATED INFORMATION'}, {'URL': 'https://opendap.earthdata.nasa.gov/collections/C1940473819-POCLOUD/granules/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0', 'Type': 'USE SERVICE API', 'Subtype': 'OPENDAP DATA', 'Description': 'OPeNDAP request URL'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sea_surface_temperature.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.quality_level.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_bias.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_standard_deviation.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}]}}\nG2525170373-POCLOUD\n\n\n\nharmony_client = Client(env=Environment.PROD)\n\ncollection_id = Collection(collection) \n\nrequest = Request(\n collection=collection_id,\n spatial=BBox(-90,-45.75,90,45), # lat: (-45.75:45), lon: (-90:90)\n granule_id=gid \n)\n\nrequest.is_valid()\n\nTrue\n\n\n\n# sumbit request and monitor job\njob_id = harmony_client.submit(request)\nprint('\\n Waiting for the job to finish. . .\\n')\nresponse = harmony_client.result_json(job_id, show_progress=True)\nprint(\"\\n. . .DONE!\")\n\n\n Waiting for the job to finish. . .\n\n\n. . .DONE!\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\n\ntemp_dir = tempfile.mkdtemp()\nfutures = harmony_client.download_all(job_id, directory=temp_dir, overwrite=True)\nfile_names = [f.result() for f in futures]\nfile_names\n\n['C:\\\\Users\\\\nickles\\\\AppData\\\\Local\\\\Temp\\\\tmp2hna47qz\\\\20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4']\n\n\n\nds = xr.open_dataset(file_names[0])\nds\n\nlat_var = None\nlon_var = None\n\n# Determine the lat/lon coordinate names\nfor coord_name, coord in ds.coords.items():\n if 'units' not in coord.attrs:\n continue\n if coord.attrs['units'] == 'degrees_north':\n lat_var = coord_name\n if coord.attrs['units'] == 'degrees_east':\n lon_var = coord_name\n \n# If the lat/lon coordinates could not be determined, use l2ss-py get_coord_variable_names\nif not lat_var or not lon_var:\n from podaac.subsetter import subset\n lat_var_names, lon_var_names = subset.get_coord_variable_names(ds)\n lat_var = lat_var_names[0]\n lon_var = lon_var_names[0]\n\nprint(f'lat_var={lat_var}')\nprint(f'lon_var={lon_var}')\n\nlat_var=lat\nlon_var=lon\n\n\n\nif ds[variable].size == 0:\n print(\"No data in subsetted region. Exiting\")\n sys.exit(0)\n\n\nimport matplotlib.pyplot as plt\nimport math\n\nfig, axes = plt.subplots(ncols=3, nrows=math.ceil((len(ds.data_vars)/3)))\nfig.set_size_inches((15,15))\n\nfor count, xvar in enumerate(ds.data_vars):\n if ds[xvar].dtype == \"timedelta64[ns]\":\n continue\n #ds[xvar].astype('timedelta64[D]').plot(ax=axes[int(count/3)][count%3])\n ds[xvar].plot(ax=axes[int(count/3)][count%3])"
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- "text": "Use the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the subscriber is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the subscriber will skip downloading that file.\nThis can drastically reduce the time for the subscriber to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the subscriber to always download matching files, by using –force/-f.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -f\n\n\n\nTo automatically run and update a local file system with data files from a collection, one can use a syntax like the following:\n10 * * * * podaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d /path/to/data/VIIRS_N20-OSPO-L2P-v2.61 -e .nc -e .h5 -m 60 -b=\"-180,-90,180,90\" --verbose >> ~/.subscriber.log\n\nThis will run every hour at ten minutes passed, and output will be appended to a local file called ~/.subscriber.log\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\"\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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+ "href": "notebooks/harmony subsetting/Harmony L2 Subsetter.html#verify-the-subsetting-worked",
+ "title": "Harmony EOSS L2SS API Tutorial",
+ "section": "Verify the subsetting worked",
+ "text": "Verify the subsetting worked\nBounds used were:\n‘lat’: ‘(-45.75:45)’, ‘lon’: ‘(-90:90)’\n\nvar_ds = ds[variable]\nmsk = xr.ufuncs.logical_not(xr.ufuncs.isnan(var_ds.data.squeeze()))\n\nllat = ds[lat_var].where(msk)\nllon = ds[lon_var].where(msk)\n\nlat_max = llat.max()\nlat_min = llat.min()\n\nlon_min = llon.min()\nlon_max = llon.max()\n\nlon_min = (lon_min + 180) % 360 - 180\nlon_max = (lon_max + 180) % 360 - 180\n\nprint(lon_min)\nprint(lon_max)\nprint(lat_min)\nprint(lat_max)\n\nif lat_max <= 45 and lat_min >= -45.75:\n print(\"Successful Latitude subsetting\")\nelif xr.ufuncs.isnan(lat_max) and xr.ufuncs.isnan(lat_min):\n print(\"Partial Lat Success - no Data\")\nelse:\n assert False\n\n\nif lon_max <= 90 and lon_min >= -90:\n print(\"Successful Longitude subsetting\")\nelif xr.ufuncs.isnan(lon_max) and xr.ufuncs.isnan(lon_min):\n print(\"Partial Lon Success - no Data\")\nelse:\n assert False\n \n\n<xarray.DataArray 'lon' ()>\narray(-85.92834473)\n<xarray.DataArray 'lon' ()>\narray(-54.11688995)\n<xarray.DataArray 'lat' ()>\narray(-45.74999237)\n<xarray.DataArray 'lat' ()>\narray(-27.91536331)\nSuccessful Latitude subsetting\nSuccessful Longitude subsetting"
},
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- "href": "external/July_2022_Earthdata_Webinar.html",
- "title": "Earthdata Webinar",
- "section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club"
+ "objectID": "notebooks/Harmony API.html#before-you-start",
+ "href": "notebooks/Harmony API.html#before-you-start",
+ "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "section": "Before you start",
+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
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- "objectID": "external/July_2022_Earthdata_Webinar.html#abstract",
- "href": "external/July_2022_Earthdata_Webinar.html#abstract",
- "title": "Earthdata Webinar",
- "section": "Abstract",
- "text": "Abstract\n\nNearly a petabyte of NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) data products have been moved to NASA’s Earthdata Cloud—hosted in the Amazon Web Services (AWS) cloud. To maximize the full potential of cloud computing on the big Data, one needs to be familiar with not only the data products and their access methods, but also a new set of knowledge for working in a cloud environment. This can be a daunting task for the majority of the science community, who may be familiar with high-performance computing, but not with AWS services. To aid end users in learning and to be successful during this paradigm shift, the PO.DAAC team has been exploring pathways toward practical solutions to help research groups migrate their workflow into cloud.\nDuring this webinar we will explain basic concepts of working in the cloud and use a simple science use case to demonstrate the workflow. Participants do not need prior knowledge of AWS services and the Earthdata Cloud. This is a step-by-step walkthrough of exploring and discovering PO.DAAC data and applying AWS cloud computing to analyze global sea level rise from altimetry data and Estimating the Circulation and Climate of the Ocean (ECCO) products.\nWe hope that you can start to practice cloud computing using AWS and PODAAC/Earthdata cloud products by following the 6 steps in this tutorial without investing a large amount of time."
+ "objectID": "notebooks/Harmony API.html#build-the-eoss-root-url",
+ "href": "notebooks/Harmony API.html#build-the-eoss-root-url",
+ "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "section": "Build the EOSS Root URL",
+ "text": "Build the EOSS Root URL\nNext we will build a URL for the EOSS service for a given granule. To get data using the service, you need a CMR collection ID for a supported collection and the ID of a granule within that collection.\nBy convention, all Harmony services are accessed through <harmony_root>/<collection_id>/<service_name>\n\nharmony_root = 'https://harmony.earthdata.nasa.gov'\nconfig = {\n 'collection_id': 'C1233800302-EEDTEST',\n 'eoss_version': '0.1.0'\n}\neoss_collection_root = harmony_root+'/{collection_id}/eoss/{eoss_version}/items/'.format(**config)\nprint(eoss_collection_root)\n\nhttps://harmony.earthdata.nasa.gov/C1233800302-EEDTEST/eoss/0.1.0/items/"
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- "href": "external/July_2022_Earthdata_Webinar.html#motivation",
- "title": "Earthdata Webinar",
- "section": "Motivation",
- "text": "Motivation\n\nIt is expected the NASA Earthdata will grow to >250 PB in 2025,\nCloud computing has a big potential\nThe path to the cloud computing is unclear for majority of the science and application community\nThe science community are often perplexed at the start line by a new language related to cloud computing and the large amount of different AWS tools and services, such as CloudFront, EC2, VPC, AMI, IAM, Bucket, Glacier, Snowcone, Snowball, Snowmobile, data lakes, just to name a few.\nWe aim to share our experience of passing the start line and start to run cloud computing, demonstrate a use case assuming zero knowledge of AWS cloud\nThe global mean sea level used here is an important climate indicator and relatively easy to calculate from the PODAAC data in the cloud."
+ "objectID": "notebooks/Harmony API.html#variable-subset-of-a-granule",
+ "href": "notebooks/Harmony API.html#variable-subset-of-a-granule",
+ "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "section": "Variable Subset of a Granule",
+ "text": "Variable Subset of a Granule\nWe can now build onto the root URL in order to actually perform a transformation. The first transformation is a variable subset of a selected granule. At this time, this requires discovering the granule id and variable id from CMR. That information can then be appended to the root URL and used to call Harmony with the help of the request library.\nHarmony stages transformed data in S3 to make it easy to do additional processing in the cloud. The response that Harmony returns is actually a redirect to the S3 location where your data is staged. Should you call Harmony in a tool that follows redirects, like your web browser, your file will be seamlessly downloaded locally for you. However, should you desire to do additional processing in AWS, you have that option as well by simply looking at the redirected URL. The code snippet below uses “geturl()” to show the URL of your staged data.\n\nvarSubsetConfig = {\n 'granule_id' : 'G2524192900-POCLOUD',\n 'variable_id' : 'red_var'\n}\neoss_var_subset_url = eoss_collection_root+'{granule_id}/?rangeSubset={variable_id}'.format(**varSubsetConfig)\n\nprint('Request URL', eoss_var_subset_url)\n\nwith request.urlopen(eoss_var_subset_url) as response:\n print('URL for data staged in S3:', response.geturl())"
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- "objectID": "external/July_2022_Earthdata_Webinar.html#objectives",
- "href": "external/July_2022_Earthdata_Webinar.html#objectives",
- "title": "Earthdata Webinar",
- "section": "Objectives",
- "text": "Objectives\n\n\nSet up a cloud computing environment from scrath\nRun the global mean sea level code in the cloud\nBuild a webpage server using the same cloud computer\n\n\nTarget audience\n\nScience- and application-oriented group who\n\nhas interest in cloud computing;\nis familiar with python, conda, and jupyter-notebook;\nbut with limited knowledge of NASA Earthdata and IT;\nand zero knowledge of AWS cloud. *** ## Outline: the steps toward running in-cloud analysis\n\n\n\nGet an AWS account\nStart an AWS cloud computer (Elastic Computer Cloud, EC2)\n\nexplain AWS console, EC2 instance\n\nConfigure the EC2 with the necessary software\n\nWhile waiting, explain the global mean sea level analysis code\n\nConfigure a jupyter-lab on the EC2 and connect to it from browser\nDemo the code (this notebook) in the cloud and save the figure\nSet up an apache server (hosting website)\n\nCreate a static html webpage to show the result\n\n\n\nThere are many ways to achieve this goal. Many alternatives are much smarter but they usually involves a set of new knowledge related to cloud and/or AWS that steepens the learning curve and sometimes makes the process intimidating. The following steps are suggested here because it is believed to involve a minimum amount of specilized knowledge beyond our common practice on our own computer.\n\n\n\nImportant terms\n\n\n\n\n\n\n\n\nAWS terminology\nLong name\nMeaning\n\n\n\n\nAWS Region\n\nAWS facility. There are many of them. NASA Earth Data are in US-WEST-2, somewhere in Oregon.\n\n\nEC2\nElastic Computer Cloud\nA computer in one of the AWS regions. It is a common practice that you should use an EC2 in the region where you data is hosted.\n\n\nAWS console\n\nA web-based control panel for all AWS tools and services. You can start an EC2, create a storage disk (S3 bucket) and much more.\n\n\nKey Pair\n\nAn SSH key generated for accessing the EC2, e.g., through SSH. Anyone who has your key can connect to your EC2. It means that you can share the same EC2 with others just through sharing a Key Pair file."
+ "objectID": "notebooks/Harmony API.html#add-on-a-spatial-subset",
+ "href": "notebooks/Harmony API.html#add-on-a-spatial-subset",
+ "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "section": "Add on a spatial subset",
+ "text": "Add on a spatial subset\nThe second transformation is a spatial subset of a selected granule. This can be combined with the request we already built above by simply specifying a bounding box.\n\nspatialSubsetConfig = {\n 'west' : '-128',\n 'south' : '23',\n 'east' : '-63',\n 'north' : '47'\n}\neoss_spatial_subset_url = eoss_var_subset_url+'&bbox={west},{south},{east},{north}'.format(**spatialSubsetConfig)\n\nprint('Request URL', eoss_spatial_subset_url)\n\nwith request.urlopen(eoss_spatial_subset_url) as response:\n print('URL for data staged in S3:', response.geturl())"
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- "objectID": "external/July_2022_Earthdata_Webinar.html#step-1-get-an-aws-account",
- "href": "external/July_2022_Earthdata_Webinar.html#step-1-get-an-aws-account",
- "title": "Earthdata Webinar",
- "section": "Step 1 – Get an AWS account",
- "text": "Step 1 – Get an AWS account\n\nIf you already have an AWS account, skip to Step 2. ### Choices: 1. Look for institutional support (recommended) 1. Engage in NASA-funded programs (e.g., openscapes) 1. Apply a free AWS account (today’s focus) 1. It is free for a year but only offers small computers (1 CPU, 1GB memory) 1. With the offer of 750 hours per month, a free-tier EC2 can be on all time for a year. 1. Need your personal information including credit card\n(https://aws.amazon.com)\nThis page explains the five steps to create an AWS account. > https://progressivecoder.com/creating-an-aws-account-a-step-by-step-process-guide/. ***"
+ "objectID": "notebooks/Harmony API.html#reprojection",
+ "href": "notebooks/Harmony API.html#reprojection",
+ "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "section": "Reprojection",
+ "text": "Reprojection\nThe third transformation is a reprojection of the data. This can be combined with the requests we already built above by simply specifying a coordinate reference system. Coordinate reference systems are identified by a common name, EPSG code, or URI. Today, this is based on reference systems supported by gdal. Examples include: ‘CRS:84’, ‘EPSG:32611’.\n\nreprojectionConfig = {\n 'crs' : 'EPSG:32611'\n}\neoss_reprojection_url = eoss_spatial_subset_url+'&crs={crs}'.format(**reprojectionConfig)\n\nprint('Request URL', eoss_reprojection_url)\n\nwith request.urlopen(eoss_reprojection_url) as response:\n print('URL for data staged in S3:', response.geturl())"
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- "objectID": "external/July_2022_Earthdata_Webinar.html#step-2---start-an-ec2",
- "href": "external/July_2022_Earthdata_Webinar.html#step-2---start-an-ec2",
- "title": "Earthdata Webinar",
- "section": "Step 2 - Start an EC2",
- "text": "Step 2 - Start an EC2\n\nLog in through aws console https://aws.amazon.com/console/\nStart an EC2 (AWS jargon: launch an instance, layman’s interpretation: start a remote server hosted by AWS)\n\nName and Tags: earthdata_webinar\nApplication and OS Images (Amazon Machine Image): >Red Hat Enterprise Linux 8 (HVM) SSD Volume type (free-tier elegible)\nInstance type: t2.micro (1CPU, 1Gb memory) (If you have a institution- or project-supported AWS account, try to use a bigger computer with >4G memory.)\nKey pair (login): “Create new key pair”\n\nenter a name, e.g., “aws_ec2_jupyter” -> create key pair\nlook for the .pem file in the Download folder, move it to .ssh folder. > mv ~/Downloads/aws_ec2_jupyter.pem .ssh/\nchange permission to 400 using > chmod 400 aws_ec2_jupyter.pem\n\ncheck the two boxes for HTTP and HTTPS for the webserver\nAdd storage: 10 Gb should be fine for prototyping and testing. You have total 30Gb free storage, which can be split among three EC2s for example.\nClick “Launch Instance” button\n\n\n\nReference\n\nAWS get set up for amazon EC2: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/get-set-up-for-amazon-ec2.html\nAWS Get started with AWS EC2: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EC2_GetStarted.html ***"
+ "objectID": "notebooks/Harmony API.html#reformatting",
+ "href": "notebooks/Harmony API.html#reformatting",
+ "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "section": "Reformatting",
+ "text": "Reformatting\nNext is a reformatting of the output file of the data. This can be combined with the requests we already built above by simply specifying a format. Examples include: image/tiff’, ‘image/png’\n\nreformattingConfig = {\n 'format' : 'image/png'\n}\neoss_reformatting_url = eoss_reprojection_url+'&format={format}'.format(**reformattingConfig)\n\nprint('Request URL', eoss_reformatting_url)\n\nwith request.urlopen(eoss_reformatting_url) as response:\n print('URL for data staged in S3:', response.geturl())"
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- "objectID": "external/July_2022_Earthdata_Webinar.html#step-3---login-and-install-the-necessary-software-and-prepare-earthdata-login-.netrc",
- "href": "external/July_2022_Earthdata_Webinar.html#step-3---login-and-install-the-necessary-software-and-prepare-earthdata-login-.netrc",
- "title": "Earthdata Webinar",
- "section": "Step 3 - Login and install the necessary software and prepare EarthData Login (.netrc)",
- "text": "Step 3 - Login and install the necessary software and prepare EarthData Login (.netrc)\n\nFind the public IP from your EC2 dashboard. (The EC2 dashboard example: link)\nFirst connect to the instance via ssh.\n\n ssh -i \"~/.ssh/aws_ec2_jupyter.pem\" ec2-user@The_public_ip_address -L 9889:localhost:9889\n\nRemember to set the following parameters appropriately: * -i points the ssh client on your local machine at your pem key to authenticate * -L tunnels traffic on port 9889 between the ec2 instance and your local machine. This port number can be any value between 1024 and 32767. 1. Update packages. Optionally install wget, git etc. for downloading this notebook from github.com\n\n > ```sudo yum update -y && sudo yum install wget -y```\n\nDownload miniconda install script into tmp/ and execute it with bash. Then, activate the base environment.\n\n mkdir -p tmp\n wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O tmp/miniconda.sh && \\\n bash tmp/miniconda.sh -b -p $HOME/conda && \\\n source ~/conda/bin/activate\n\nCreate a new environment called jupyter running Python 3.7; activate it; install JupyterLab and other required packages.\n\n conda create -n jupyter python=3.7 -y && \\\n conda activate jupyter && \\\n conda install requests tqdm numpy pandas -y && \\\n conda install matplotlib netCDF4 -y &&\\\n conda install xarray jupyterlab s3fs hdf5 scipy -y &&\\\n conda install pyproj -y\n\nWarning: the free-tier EC2 has only 1Gb memory. Make sure to monitor the installation to avoid memory errors. If the installation is aborted due to lack of memory, repeat the installation again to pass the problem or considering install the packages one by one.\n\n\nAn EarthData Login (EDL) account is needed for accessing NASA Eartdata regardless the location of the data, either in the Earthdata cloud or on-premise from DAACs.\n\nRun the following line in the EC2 terminal: >bash echo \"machine urs.earthdata.nasa.gov\\n login your_earthdata_username\\n password your_earthdata_account_password\" > ~/.netrc\nUse a text editor to replace your_earthdata_username with your EDL username and your_earthdata_account_password with your EDL password. > shell vi ~/.netrc\nChange .netrc file permission: >shell chmod 400 ~/.netrc *** ### Advanced approach using “User data” box to install softwares while launching the EC2 (replacing step 3.3)\n\n\nThe following replaces step 3.3, but is not required in this tutorial.\nThe system software updates can be done by inserting the following bash script into the “User data” box during the Launch Instance step (Step 2). It replaces Steps 3.3.\n#!/bin/bash\n sudo yum update -y\n sudo yum install wget -y\n sudo yum install httpd -y\n sudo service httpd start"
+ "objectID": "notebooks/Harmony API.html#continue-exploring",
+ "href": "notebooks/Harmony API.html#continue-exploring",
+ "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
+ "section": "Continue Exploring",
+ "text": "Continue Exploring\nHarmony’s specification is available online. Feel free to read more and continue exploring how to use Harmony. https://harmony.uat.earthdata.nasa.gov/docs/eoss/0.1.0/spec"
},
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- "objectID": "external/July_2022_Earthdata_Webinar.html#step-4---set-up-a-jupyter-lab",
- "href": "external/July_2022_Earthdata_Webinar.html#step-4---set-up-a-jupyter-lab",
- "title": "Earthdata Webinar",
- "section": "Step 4 - Set up a jupyter-lab",
- "text": "Step 4 - Set up a jupyter-lab\nJupyterlab is a web-based interactive development environment for python and other languages. It is a perfect tool for accessing the computing resources on an EC2 through SSH tunneling. Jupyterlab and the associated software are install in Step 3. Here is two steps to start and connect to a jupyterlab server on the EC2.\n\nUse Python to generate and store a hashed password as a shell variable: >\n\nPW=\"$(python3 -c 'from notebook.auth import passwd; import getpass; print(passwd(getpass.getpass(), algorithm=\"sha256\"))')\"\n\nStart jupyter lab instance with the following parameters: >\n\njupyter lab --port=9889 --ip='127.0.0.1' --NotebookApp.token='' --NotebookApp.password=\"$PW\" --notebook-dir=\"$HOME\" --no-browser```\n\n1. Access the server through your web browser: http://127.0.0.1:9889/\n\n<div class=\"alert alert-block alert-success\">\n<b>Optional for convinence</b> </br> \nYou can use tmux to start a screen to keep the jupyterlab running on the EC2 even after logging.</br> \n\nDetach the screen by pressing CTRL + b -> d. \n</div>\n\n\n#### Reference\n\n* https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html#conda\n* https://requests.readthedocs.io/en/master/user/install/\n* https://matplotlib.org/stable/#installation\n* https://shapely.readthedocs.io/en/latest/\n***\n\n## Step 5 - Run the code (this notebook) in the cloud and save the figure\n\n## Data products\n\n1. MEaSURES-SSH version JPL1812\n - short name: ```SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812```\n - [landing page](https://podaac.jpl.nasa.gov/dataset/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812) (Newer version is available)\n1. GMSL\n - short name: ```JPL_RECON_GMSL```\n - [landing page](https://podaac.jpl.nasa.gov/dataset/JPL_RECON_GMSL)\n1. ECCO global mean sea level (used in the reader's exercise)\n - short name: `ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4`\n - [landing page](https://doi.org/10.5067/ECTSM-MSL44)\n\n::: {.cell execution_count=28}\n``` {.python .cell-code}\n#load python modules\n\nimport xarray as xr\nimport numpy as np\nimport pylab as plt\nimport pandas as pd\n#Short_name is used to identify a specific dataset in NASA Earthdata. \nshort_name='SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812'\n:::"
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+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "",
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis Jupyter Notebook contains examples related to geospatial search using the PO.DAAC HUC Feature Translation Service (FTS), previewing (viusualizing) the queried region of interest, and using FTS results to query data through NASA’s Common Metadata Repository (CMR).\nExample Use Case: Check if data is available over my region of interest using HUCs. In this example we are using the FTS-HUC API (https://fts.podaac.earthdata.nasa.gov/) to geospatially define our region of interest, namely the Upper Tuolumne River Basin in the San Joaquin River Basin in California’s Sierra Nevada Mountains, searching by HUC or region name, and then using those geospatial bounds (coordinates) to query Sentinel-1 data in CMR. 1. use FTS to define geographic region of interest (query by partial or exact HUC or HUC region name) 2. preview query 3. use coordinates returned by FTS to query Sentinel-1 data in CMR, by polygon or bounding box.\nResources\nUSGS Hydrologic unit map to help identifiy region of interest (e.g. HUC value or name) can be found here: https://water.usgs.gov/GIS/regions.html"
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- "title": "Earthdata Webinar",
- "section": "The interface to the AWS Simple Storage Service (S3) file system (stores all NASA Earthdata)",
- "text": "The interface to the AWS Simple Storage Service (S3) file system (stores all NASA Earthdata)\nPO.DAAC cloud (POCLOUD) is a part of Earthdata Cloud. The data are hosted in a S3 bucket on AWS US-West-2. “US-West-2” is a term that refers to the AWS center in Oregon. In this case, the so-called ‘Direct-S3 access’ only works on the machines hosted in the US-West-2.\ns3fs is a pythonic file interface to S3 built on top of botocore. s3fs allows typical file-system style operations like cp, mv, ls, du, glob, and put/get of local files to/from S3. Details can be find on its website https://s3fs.readthedocs.io/en/latest/.\nIt is important that you set up the .netrc file correctly in order to enable the following init_S3FileSystem module. The .netrc file should be placed in your home folder. A typical .netrc file has the following content:\nmachine urs.earthdata.nasa.gov\n login your_earthdata_username\n password your_earthdata_account_password\nMake sure the permission of the .netrc file is set to 400: chmod 400 ~/.netrc\nIf you do not have or do not remember your Earthdata Login information, go here to register or here to reset password.\n\nAWS credentials with EDL\nAWS requires security credentials to access AWS S3.\nWith your EDL, you can obtain a temporay S3 credential through https://archive.podaac.earthdata.nasa.gov/s3credentials. It is a ‘digital key’ to access the Earthdata in AWS cloud. Here is an example:\n\n{“accessKeyId”: “ASIATNGJQBXBOPDTNBBD”, “secretAccessKey”: “odLdojElxfKDU5nw49+hPawe9oKUkR+ZXQqBcs5g”, “sessionToken”: “FwoGZXIvYXdzECgaDB4IzakIEQUrg/N3MiLdASJm6nrFYJ6SCZN5jPlfO4X3NBQTTSwIetjIU1BO0l863AmtL4D/4q8g2HQwgV351qpN3kp1v6yifKRfZ6T1oDtauSTizxnjQ7LislVVaxmwFqqH1oEbu4HKvi+0AmEUSzz2IwcJPgY5L9D8P2N8ccevIwgKLcvkWcIM0zMtp0TRsvdBE0W+NTDxc6RZlCQdclKtvf3jPqreJtigSH/MSePzORwR7FaFxXZYQpXLP+MRNmDMdrDzwFpaZKd9pgCBfnUkAL8w/ub+9WfVjh4lCfNuNUiGNLi2cS9VBeYtKKL16pYGMi17j1gp08JS6p9nD2egc3LyIL2vSIZouhNrJzisZqbLH8yZTq3rCG2pPsPcrFk=”, “expiration”: “2022-07-22 15:56:34+00:00”}\n\nFurther reading: https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html\n\ndef init_S3FileSystem():\n \"\"\"\n This routine automatically pull your EDL crediential from .netrc file and use it to obtain an AWS S3 credential through a podaac service accessable at https://archive.podaac.earthdata.nasa.gov/s3credentials\n \n Return:\n =======\n \n s3: an AWS S3 filesystem\n \"\"\"\n import requests,s3fs\n creds = requests.get('https://archive.podaac.earthdata.nasa.gov/s3credentials').json()\n s3 = s3fs.S3FileSystem(anon=False,\n key=creds['accessKeyId'],\n secret=creds['secretAccessKey'], \n token=creds['sessionToken'])\n return s3"
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- "title": "Earthdata Webinar",
- "section": "Use s3fs.glob to get all file names",
- "text": "Use s3fs.glob to get all file names\nThe S3FileSystem allows typical file-system style operations like cp, mv, ls, du, glob. Once the s3fs file system is established, we can use ‘glob’ to get all file names from a collection. In this case, the collection S3 path is\ns3://podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/\nUsing the following will get a list netcdf filenames:\nfns=s3sys.glob(\"s3://podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/*.nc\")\n\ns3sys=init_S3FileSystem()\n\ns3path=\"s3://podaac-ops-cumulus-protected/%s/\"%short_name\nfns=s3sys.glob(s3path+\"*.nc\")\nprint(fns[0])\n#Set the time stamps associated with the files\ntime=pd.date_range(start='1992-10-02',periods=len(fns),freq='5D') \n\npodaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/ssh_grids_v1812_1992100212.nc\n\n\n\nprint('There are %i files.'%len(fns))\n\nThere are 1922 files.\n\n\nHere is an example file.\n\nd=xr.open_dataset(s3sys.open(fns[0]))\nd\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (Longitude: 2160, nv: 2, Latitude: 960, Time: 1)\nCoordinates:\n * Longitude (Longitude) float32 0.08333 0.25 0.4167 ... 359.6 359.8 359.9\n * Latitude (Latitude) float32 -79.92 -79.75 -79.58 ... 79.58 79.75 79.92\n * Time (Time) datetime64[ns] 1992-10-02T12:00:00\nDimensions without coordinates: nv\nData variables:\n Lon_bounds (Longitude, nv) float32 -1.388e-17 0.1667 ... 359.8 360.0\n Lat_bounds (Latitude, nv) float32 -80.0 -79.83 -79.83 ... 79.83 79.83 80.0\n Time_bounds (Time, nv) datetime64[ns] 1992-09-02T12:00:00 1992-11-01T12:...\n SLA (Time, Longitude, Latitude) float32 ...\n SLA_ERR (Time, Longitude, Latitude) float32 ...\nAttributes: (12/13)\n Conventions: CF-1.6\n ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n Institution: Jet Propulsion Laboratory\n geospatial_lat_min: -79.916664\n geospatial_lat_max: 79.916664\n geospatial_lon_min: 0.083333336\n ... ...\n time_coverage_start: 1992-10-02\n time_coverage_end: 1992-10-02\n date_created: 2019-02-11T20:19:57.736094\n version_number: 1812\n summary: Sea level anomaly grids from altimeter data using...\n title: Sea Level Anormaly Estimate based on Altimeter Dataxarray.DatasetDimensions:Longitude: 2160nv: 2Latitude: 960Time: 1Coordinates: (3)Longitude(Longitude)float320.08333 0.25 0.4167 ... 359.8 359.9standard_name :longitudeunits :degrees_eastpoint_spacing :evenlong_name :longitudeaxis :Xbounds :Lon_boundsarray([8.333334e-02, 2.500000e-01, 4.166667e-01, ..., 3.595833e+02,\n 3.597500e+02, 3.599167e+02], dtype=float32)Latitude(Latitude)float32-79.92 -79.75 ... 79.75 79.92standard_name :latitudeunits :degrees_northpoint_spacing :evenlong_name :latitudeaxis :Ybounds :Lat_boundsarray([-79.916664, -79.75 , -79.583336, ..., 79.583336, 79.75 ,\n 79.916664], dtype=float32)Time(Time)datetime64[ns]1992-10-02T12:00:00standard_name :timelong_name :Timebounds :Time_boundsaxis :Tarray(['1992-10-02T12:00:00.000000000'], dtype='datetime64[ns]')Data variables: (5)Lon_bounds(Longitude, nv)float32...units :degrees_eastcomment :longitude values at the west and east bounds of each pixel.array([[-1.387779e-17, 1.666667e-01],\n [ 1.666667e-01, 3.333333e-01],\n [ 3.333333e-01, 5.000000e-01],\n ...,\n [ 3.595000e+02, 3.596667e+02],\n [ 3.596667e+02, 3.598333e+02],\n [ 3.598333e+02, 3.600000e+02]], dtype=float32)Lat_bounds(Latitude, nv)float32...units :degrees_northcomment :latitude values at the north and south bounds of each pixel.array([[-80. , -79.833336],\n [-79.833336, -79.666664],\n [-79.666664, -79.5 ],\n ...,\n [ 79.5 , 79.666664],\n [ 79.666664, 79.833336],\n [ 79.833336, 80. ]], dtype=float32)Time_bounds(Time, nv)datetime64[ns]...comment :Time bounds for each time value, same value as time variable. The time variable is defined on points instead of on bounding boxes.array([['1992-09-02T12:00:00.000000000', '1992-11-01T12:00:00.000000000']],\n dtype='datetime64[ns]')SLA(Time, Longitude, Latitude)float32...units :mlong_name :Sea Level Anomaly Estimatestandard_name :sea_surface_height_above_sea_levelalias :sea_surface_height_above_sea_level[2073600 values with dtype=float32]SLA_ERR(Time, Longitude, Latitude)float32...units :mlong_name :Sea Level Anomaly Error Estimate[2073600 values with dtype=float32]Attributes: (13)Conventions :CF-1.6ncei_template_version :NCEI_NetCDF_Grid_Template_v2.0Institution :Jet Propulsion Laboratorygeospatial_lat_min :-79.916664geospatial_lat_max :79.916664geospatial_lon_min :0.083333336geospatial_lon_max :359.91666time_coverage_start :1992-10-02time_coverage_end :1992-10-02date_created :2019-02-11T20:19:57.736094version_number :1812summary :Sea level anomaly grids from altimeter data using Kriging interpolation, which gives best linear prediction based upon prior knowledge of covariance. title :Sea Level Anormaly Estimate based on Altimeter Data\n\n\n\nPlot an example\n\nplt.figure(figsize=(15,7))\nplt.contourf(d['Longitude'],d['Latitude'],d['SLA'][0,...].T,levels=np.arange(-0.5,0.6,0.05))\nplt.ylabel('Latitude')\nplt.xlabel('Longitude')\nplt.title('Sea Level Anomaly %s'%d.time_coverage_start)\n\nText(0.5, 1.0, 'Sea Level Anomaly 1992-10-02')"
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- "title": "Earthdata Webinar",
- "section": "Calculate the global mean SSHA",
- "text": "Calculate the global mean SSHA\nThe global mean SSH is calculated as follows.\n\\(SSH_{mean} = \\sum \\eta(\\phi,\\lambda)*A(\\phi)\\), where \\(\\phi\\) is latitude, \\(\\lambda\\) is longitude, \\(A\\) is the area of the grid at latitude \\(\\phi\\), and \\(\\eta(\\phi,\\lambda)*A(\\phi)\\) is the weighted SLA at \\((\\phi,\\lambda)\\).\nThe following routine area pre-calculates the area as a function of latitude for the 1/6-degree resolution grids.\n\ndef area(lats):\n \"\"\"\n Calculate the area associated with a 1/6 by 1/6 degree box at latitude specified in 'lats'. \n \n Parameter\n ==========\n lats: a list or numpy array of size N\n the latitudes of interest. \n \n Return\n =======\n out: Array (N)\n area values (unit: m^2)\n \"\"\"\n # Modules:\n from pyproj import Geod\n # Define WGS84 as CRS:\n geod = Geod(ellps='WGS84')\n dx=1/12.0\n c_area=lambda lat: geod.polygon_area_perimeter(np.r_[-dx,dx,dx,-dx], lat+np.r_[-dx,-dx,dx,dx])[0]\n out=[]\n for lat in lats:\n out.append(c_area(lat))\n return np.array(out)\n\ndef global_mean(fn_s3,s3sys,ssh_area):\n \"\"\"\n Calculate the global mean given an s3 file of SSH, a s3fs.S3FileSystem, \n and the ssh_area, which is precalculated to save computing time. \n Parameter:\n ===========\n fn_s3: S3 file name, e.g., s3://podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/ssh_grids_v1812_1992100212.nc\n s3sys: generated by s3fs.S3FileSystem\n ssh_area: the area size associated with MEaSURES-SSH 1/6-degree resolution product. \n \n Return\n =======\n dout: scalar\n The global mean sea level (default unit from MEaSURES-SSH: meter)\n \"\"\"\n with xr.open_dataset(s3sys.open(fn_s3))['SLA'] as d:\n dout=((d*ssh_area).sum()/(d/d*ssh_area).sum()).values\n return dout\n\n\nd=xr.open_dataset(s3sys.open(fns[0]))\n#pre-calculate the area for reuse\nssh_area=area(d.Latitude.data).reshape(1,-1)\n\n\nprint('The global mean sea level from %s is %7.5f meters.'%(fns[0],global_mean(fns[0],s3sys,ssh_area) ) )\n\nThe global mean sea level from podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/ssh_grids_v1812_1992100212.nc is -0.00632 meters."
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- "title": "Earthdata Webinar",
- "section": "Demonstrate using a single thread",
- "text": "Demonstrate using a single thread\n\nBenchmark: using a single thread takes about 17 min to calculate all 1922 files. Here the program is sped up by skipping every 360 days (72 steps). The small EC2 can handle the computation because it involves one file per step.\n\n\n%%time\n\n#Loop 26-year 5-daily SSH fields (1922 files)\n#Skip every 72 files to speed up\n\nresult=[]\nt_local=time[::72]\nfor fn in fns[::72]:\n result.append(global_mean(fn,s3sys,ssh_area)*1e3 )\nresult=np.array(result)\n\nCPU times: user 4.35 s, sys: 1.26 s, total: 5.61 s\nWall time: 45 s\n\n\n\nfrom scipy.stats import linregress\n\nplt.figure(figsize=(14,5))\nplt.plot(t_local,result-10,'r-o')\ntyr=(t_local-t_local[0])/np.timedelta64(1,'Y') #convert the number of years\nmsk=np.isnan(result)\ntyr=tyr[~msk]\nresult=result[~msk]\n\n#Calculate the linear trend using linear regression `linregress`\nrate=linregress(tyr[1:],result[1:]) \nprint('The estimated sea level rise rate between 1993 and 2018: %5.1fmm/year.'%(rate[0]) )\nplt.text(t_local[0],10, 'Linear trend: %5.1fmm/year'%(rate[0]),fontsize=16)\nplt.xlabel('Time (year)',fontsize=16)\nplt.ylabel('Global Mean SLA (mm)',fontsize=16)\n\nplt.grid(True)\nplt.show()\n\nThe estimated sea level rise rate between 1993 and 2018: 2.5mm/year.\n\n\n\n\n\n\nQuiz The global sea level trend from altimetry should be 3.0mm/year. Why did we get 2.5mm/year from the above analysis? Can you get 3.0mm/year by modifying the above code?\nHint: The above analysis is aliased.\n\n\nAdd the GMSL from Frederikse et al. https://podaac.jpl.nasa.gov/dataset/JPL_RECON_GMSL\n\nfrom scipy.stats import linregress\n\nplt.figure(figsize=(14,5))\nplt.plot(t_local,result-10,'r-o',label='altimetry')\n\nplt.xlabel('Time (year)',fontsize=16)\nplt.ylabel('Global Mean SLA (meter)',fontsize=16)\nplt.grid(True)\n\n# Add GMSL from \n\nd1=xr.open_dataset('https://opendap.jpl.nasa.gov/opendap/allData/homage/L4/gmsl/global_timeseries_measures.nc')\nprint(d1)\nd1['global_average_sea_level_change'].plot(label='in-situ')\nplt.legend()\n\nplt.savefig('gmsl.png')\n\n<xarray.Dataset>\nDimensions: (time: 119)\nCoordinates:\n * time (time) datetime64[ns] ...\nData variables: (12/21)\n global_average_sea_level_change (time) float32 ...\n global_average_sea_level_change_upper (time) float32 ...\n global_average_sea_level_change_lower (time) float32 ...\n glac_mean (time) float32 ...\n glac_upper (time) float32 ...\n glac_lower (time) float32 ...\n ... ...\n global_average_thermosteric_sea_level_change (time) float32 ...\n global_average_thermosteric_sea_level_change_upper (time) float32 ...\n global_average_thermosteric_sea_level_change_lower (time) float32 ...\n sum_of_contrib_processes_mean (time) float32 ...\n sum_of_contrib_processes_upper (time) float32 ...\n sum_of_contrib_processes_lower (time) float32 ...\nAttributes: (12/42)\n title: Global sea-level changes and contributing proc...\n summary: This file contains reconstructed global-mean s...\n id: 10.5067/GMSLT-FJPL1\n naming_authority: gov.nasa.jpl\n source: Frederikse et al. The causes of sea-level rise...\n project: NASA sea-level change science team (N-SLCT)\n ... ...\n time_coverage_start: 1900-01-01\n time_coverage_end: 2018-12-31\n time_coverage_duration: P119Y\n time_coverage_resolution: P1Y\n date_created: 2020-07-28\n date_modified: 2020-09-14\n\n\n\n\n\n\nQuiz The global sea level trend from tide-gauge reconstruction (3.5mm/year) is steeper than altimetry-based analysis (3.0mm/year). Why is that?\nHint: Altimetry-based analysis does not consider vertical land motion."
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+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#install-libraries",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Install libraries",
+ "text": "Install libraries\n\n%%capture\n\nimport sys\n!{sys.executable} -m pip install bs4 requests\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport json"
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- "title": "Earthdata Webinar",
- "section": "Step 6 – Run an apache web server to show the result",
- "text": "Step 6 – Run an apache web server to show the result\nBecause the EC2 was set up to allow HTTPS traffic, we can setup a simple website to host the GMSL result. This simple website should be an illustration using an EC2 to streamline a web application. You can set up a cron job to establish auto-update of the result.\nFrom your EC2 command line, install apache webserver:\n\n\n\nsudo yum install httpd -y\nStart the server and auto-start when stopped\n\n\n\nsudo service httpd start\nCopy and paste the following code to make a webpage index.html\n<html>\n<head>\n <center>\n <h1 style=\"font-size:30px\">The global mean sea level </h1>\n <h2 style=\"font-size:20px\">Hosted on my personal AWS EC2</h2>\n <img src=\"gmsl.png\" alt=\"Global Mean Sea Level\" width=\"700\">\n <h1 style=\"font-size:20px\">Diagnosed from MEaSURES-SSH (red) and JPL_RECON_GMSL (blue)</h1>\n <h1 style=\"font-size:20px\">Earthdata webinar, 07/27/2022</h1>\n <h1 style=\"font-size:20px;color=purple\">Cloud-based analysis is fun!</h1>\n <img src=\"https://chucktownfloods.cofc.edu/wp-content/uploads/2019/07/Earthdata-Logo.jpg\" width=\"200\">\n </center>\n</head>\n</html>\nMove index.html to the default location /var/www/html/ using >\nsudo cp index.html /var/www/html/\nMake sure to use cp not mv to change the ownership to root. Note, it is the default but not the good practice to use root for this. We use it to simplify the tutorial.\nCopy gmsl.png into /var/www/html/.\n\n```shell sudo cp gmsl.png /var/www/html/\n\nAccess the webpage through the EC2 IP address from browser."
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+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-partial-region-matches",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Search Feature Translation Service for Partial Region Matches",
+ "text": "Search Feature Translation Service for Partial Region Matches\nIf you are unsure what the corresponding HUC is for your region of interest, you can query the FTS for partial region matches, by setting EXACT = FALSE.\n\n###################\n\n# Querying partial matches with region \"San Joaquin\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"San Jo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with region \"San Jo\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"11.754 ms.\",\n \"hits\": 11,\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"San Joaquin\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/180400.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.93679916804501,36.36688239563472,-118.65438684397327,38.757297326299295\",\n \"Convex Hull Polygon\": 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\"HUC\": \"180500030306\"\n }\n }\n}"
},
{
- "objectID": "external/July_2022_Earthdata_Webinar.html#summary",
- "href": "external/July_2022_Earthdata_Webinar.html#summary",
- "title": "Earthdata Webinar",
- "section": "Summary",
- "text": "Summary\n\nOnline materials for using AWS cloud and Eararthdata are abundant but often oranized by topics.\nHere we focus on building a simple workflow from scratch to show how in-cloud analysis can be achieved with minimal knowledge of AWS cloud\nBy repeating these steps, one is anticipated to learn the basic concepts of AWS and in-cloud analysis as well as PODAAC/Earthdata cloud.\n\n\nConclusions\n\nApply cloud-computing is not difficult, but finding the right path is.\nRelying on free AWS account linked to personal finance is not sustainable. The community needs a clear official instruction on the channels of getting supported.\nSupport for small-size proposals are needed to advance cloud computing from early adopters to mainstream.\n\n\n\nLesson Learned\n\nLearn as a group\n\nSmall-size ‘coding-clubs’ with scienists and engineers is helpful to solve problems faster.\n\nStart from basics\nLearn cloud by solving a practical problem, for example:\n\nI would like to analyze global mean sea level in the cloud\nI would like to build a regional sea level rise indictor in the cloud and host the result realtime through a website\nI would like to build a notebook to show diverse satellite and in-situ data to support a field campaign in realtime.\n\nRestricted cyber environment needs more attention to the Virtual Private Cloud (VPC) configuration. (We wasted many months on this item.)\n\n\n\nFuture development (stay tuned)\n\nScale-up analysis in the cloud\n\nAWS Lambda\nAWS Batch\nAWS HPC"
+ "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-matches",
+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-matches",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Search Feature Translation Service for Exact HUC Matches",
+ "text": "Search Feature Translation Service for Exact HUC Matches\nHere we can set a HUC ID, or hydrologic unit code, and use this to query the HUC FTS. By defining the parameter EXACT = True, we tell the query to not search for partial matches.\nBased on the partial name response in the previous step, we can now do an exact search for San Joaquin River Basin, using its HUC ID (1804).\n\n###################\n\n# Querying exact matches for HUC \"1804\" = San Joaquin River Basin\n\nHUC = \"1804\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.791 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"1804\": {\n \"USGS Polygon\": {\n \"Object URL\": 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\"Region Name\": \"San Joaquin\"\n }\n }\n}"
},
{
- "objectID": "external/July_2022_Earthdata_Webinar.html#further-reading",
- "href": "external/July_2022_Earthdata_Webinar.html#further-reading",
- "title": "Earthdata Webinar",
- "section": "Further reading",
- "text": "Further reading\n\nUse Dask to speed up the computation\nCalculate the global mean sea level from ECCO"
+ "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-partial-region-matches-1",
+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-partial-region-matches-1",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Search Feature Translation Service for Partial Region Matches",
+ "text": "Search Feature Translation Service for Partial Region Matches\nBut in this case we are specifically interested in Tuolumne River Basin within the San Joaquin main basin, so let’s do a partial search for “Upper Tuo”, given we may not know the exact region name.\n\n###################\n\n# Querying partial matches with region \"Upper Tuo\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"Upper Tuo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with region \"Upper Tuo\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"4.244 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"Upper Tuolumne\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"HUC\": \"18040009\"\n }\n }\n}"
},
{
- "objectID": "external/zarr_access.html",
- "href": "external/zarr_access.html",
- "title": "Zarr Access for NetCDF4 files",
- "section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
+ "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-and-named-region-matches",
+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-and-named-region-matches",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Search Feature Translation Service for Exact HUC and Named Region Matches",
+ "text": "Search Feature Translation Service for Exact HUC and Named Region Matches\nGiven the above response, or that we already know an exact region name or HUC in USGS’s Watershed Boundary Dataset (WBD), we can use this instead of a partial search. Below is an example of searching by exact match using HUC ID (e.g. 18040009), and then by region name (“Upper Tuolumne”).\n\n###################\n\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"18040009\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.582 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"18040009\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"Region Name\": \"Upper Tuolumne\"\n }\n }\n}\n\n\n\n###################\n\n# Querying exact matches with region \"Upper Tuolumne\"\n\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\n# Note the change in endpoint from \"/huc\" to \"/region\"\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exact matches with region \"Upper Tuolumne\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.312 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"Upper Tuolumne\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"Visvalingam Polygon\": 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\"HUC\": \"18040009\"\n }\n }\n}"
},
{
- "objectID": "external/zarr_access.html#timing",
- "href": "external/zarr_access.html#timing",
- "title": "Zarr Access for NetCDF4 files",
- "section": "Timing:",
- "text": "Timing:\n\nExercise: 45 minutes"
+ "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#function-for-visualization",
+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#function-for-visualization",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Function for Visualization",
+ "text": "Function for Visualization\nBelow is a function created specifically to visualize the output of the HUC Feature Translation Service.\n\ndef visualize(fts_response):\n \n regions = []\n bounding_boxes = []\n convex_hull_polygons = []\n visvalingam_polygons = []\n for element in fts_response['results']:\n for heading in fts_response['results'][element]:\n if heading == \"Bounding Box\":\n bounding_boxes.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Convex Hull Polygon\":\n convex_hull_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Visvalingam Polygon\":\n visvalingam_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"HUC\":\n regions.append(\"Region Name: \" + element + \"\\n\" + \"HUC: \" + fts_response['results'][element][heading])\n elif heading == \"Region Name\":\n regions.append(\"Region Name: \" + fts_response['results'][element][heading] + \"\\n\" + \"HUC: \" + element)\n else:\n continue\n\n bounding_boxes = [box(e[0], e[1], e[2], e[3]) for e in bounding_boxes]\n convex_hull_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in convex_hull_polygons]\n visvalingam_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in visvalingam_polygons]\n \n for i in range(len(bounding_boxes)):\n ax = gpd.GeoSeries(bounding_boxes[i]).plot(alpha=0.2, cmap='Pastel1', figsize=(10,10))\n gpd.GeoSeries(convex_hull_polygons[i]).plot(ax = ax, cmap='Pastel2')\n gpd.GeoSeries(visvalingam_polygons[i]).plot(alpha=0.5, ax=ax, cmap='viridis')\n\n plt.title(regions[i])\n plt.show()"
},
{
- "objectID": "external/zarr_access.html#summary",
- "href": "external/zarr_access.html#summary",
- "title": "Zarr Access for NetCDF4 files",
- "section": "Summary",
- "text": "Summary\nZarr is an open source library for storing N-dimensional array data. It supports multidimensional arrays with attributes and dimensions similar to NetCDF4, and it can be read by XArray. Zarr is often used for data held in cloud object storage (like Amazon S3), because it is better optimized for these situations than NetCDF4.\nThe zarr-eosdis-store library allows NASA EOSDIS NetCDF4 files to be read more efficiently by transferring only file metadata and data needed for computation in a small number of requests, rather than moving the whole file or making many small requests. It works by making the files directly readable by the Zarr Python library and XArray across a network. To use it, files must have a corresponding metadata file ending in .dmrpp, which increasingly true for cloud-accessible EOSDIS data. https://github.com/nasa/zarr-eosdis-store\nThe zarr-eosdis-store library provides several benefits over downloading EOSDIS data files and accessing them using XArray, NetCDF4, or HDF5 Python libraries:\n\nIt only downloads the chunks of data you actually read, so if you don’t read all variables or the full spatiotemporal extent of a file, you usually won’t spend time downloading those portions of the file\nIt parallelizes and optimizes downloads for the portions of files you do read, so download speeds can be faster in general\nIt automatically interoperates with Earthdata Login if you have a .netrc file set up\nIt is aware of some EOSDIS cloud implementation quirks and provides caching that can save time for repeated requests to individual files\n\nIt can also be faster than using XArray pointing NetCDF4 files with s3:// URLs, depending on the file’s internal structure, and is often more convenient.\nConsider using this library when: 1. The portion of the data file you need to use is much smaller than the full file, e.g. in cases of spatial subsets or reading a single variable from a file containing several 1. s3:// URLs are not readily available 1. Code need to run outside of the AWS cloud or us-west-2 region or in a hybrid cloud / non-cloud manner 1. s3:// access using XArray seems slower than you would expect (possibly due to unoptimized internal file structure) 1. No readily-available, public, cloud-optimized version of the data exists already. The example we show is also available as an AWS Public Dataset: https://registry.opendata.aws/mur/ 1. Adding “.dmrpp” to the end of a data URL returns a file\n\nObjectives\n\nBuild on prior knowledge from CMR and Earthdata Login tutorials\nWork through an example of using the EOSDIS Zarr Store to access data using XArray\nLearn about the Zarr format and library for accessing data in the cloud"
+ "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#visualization",
+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#visualization",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Visualization",
+ "text": "Visualization\nWe can take that response and pass it to the visualize() function created above. The pink polygon is Bounding Box, the green is Convex Hull Polygon and the purple color is Visvalingam Polygon\n\n#visualize FTS response\nvisualize(response)\n\n\n\n\nHere we can visualize the FTS response using HUC ID search (18040009) instead of Region search of “Upper Tuolumne”.\n\n###################\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.61 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"18040009\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": \"-121.105517801627,37.57291785522102,-120.51777999837259,37.58160878749919,-119.26845687218679,37.73942430183757,-119.26095827844847,37.741190162251485,-119.26079495969867,37.74128122475133,-119.25581474616479,37.7450598684955,-119.25563206491506,37.74520087891193,-119.25521361804067,37.745555179953044,-119.20452512020273,37.79316755800414,-119.20311483687158,37.794898117376476,-119.20297581291345,37.79511513091779,-119.20108320354137,37.801137019450096,-119.20096521291657,37.803876760070864,-119.19927543166921,37.88483115890352,-119.19931234937746,37.885001276611604,-119.20064394937538,37.88738135160793,-119.31090541587093,38.044980644071586,-119.3277000731365,38.0651666159153,-119.32796109605277,38.06544024091488,-119.34908448143665,38.08655395234041,-119.62508146642494,38.22905559795254,-119.65624842470987,38.22952896670182,-119.65829346949835,38.22947615316025,-119.79473757241158,38.21799358859471,-119.99491475022586,38.196920114669126,-120.38613654232694,38.056378609678916,-121.15444382863438,37.62884831659255,-121.15500076925849,37.6284224540932,-121.15993039529252,37.62332076451776,-121.16822139007132,37.61386883849076,-121.17452907235321,37.605445134337174,-121.17462853797804,37.60522817287921,-121.17469632131127,37.60502320725453,-121.17471004943627,37.60496802808791,-121.17476593797784,37.604743358296616,-121.17472602131124,37.60443736142207,-121.1743974786034,37.603737121839856,-121.17385444318757,37.603213931215635,-121.12495024430518,37.575249448967384,-121.1206057318119,37.57340581772024,-121.1184699109819,37.573299354178744,-121.105517801627,37.57291785522102\",\n \"Visvalingam Polygon\": \"-119.65612154137676,38.229472830243594,-119.65887392887248,38.21611789588928,-119.68748882882807,38.20072575945488,-119.74423195478164,38.21583016359807,-119.79473757241158,38.21799358859471,-119.80777226405803,38.20387888444998,-119.83634931818034,38.19900379279085,-119.8794751608217,38.205164957364616,-119.92926093053609,38.1903575073876,-119.95874486382365,38.194300821964816,-119.9915583491894,38.18745202718378,-119.99491475022586,38.196920114669126,-120.05974666158357,38.15445730952666,-120.10528053234623,38.13442047414111,-120.12843912918527,38.10262073148215,-120.20521266552441,38.056065841971076,-120.26427926855774,38.061807551337154,-120.34283367885251,38.04442165553081,-120.36616220485797,38.05864021280041,-120.38138515275097,38.04379106074015,-120.39399451523144,38.00852514516987,-120.41557226103123,38.00413815246833,-120.4432863495299,37.97081393481176,-120.45616958284324,37.92842260675252,-120.46878266719864,37.921885701554345,-120.4624829120001,37.891430354726594,-120.479954646348,37.871879250590325,-120.48053337030541,37.82415609128935,-120.5431307066666,37.84913992354228,-120.55227857644405,37.86382160997783,-120.57047251079081,37.845455260006304,-120.5664340514221,37.82240133087544,-120.59755003054045,37.81161053401718,-120.64595511379866,37.78181727677179,-120.67580313979397,37.7973594465393,-120.7399643115694,37.77691864136273,-120.75337800946522,37.73604807163446,-120.78855502295232,37.75464512473059,-120.82306428227372,37.739964848711736,-120.82298294894053,37.72367104352867,-120.84114918537068,37.706087340431,-120.8673865186633,37.69917305085835,-120.87598703427494,37.68470263317249,-120.91724289983591,37.65975447800287,-120.94840845082916,37.657548763422994,-121.0014800642885,37.642007069697115,-121.06141474752877,37.632796530128076,-121.15500076925849,37.6284224540932,-121.17472602131124,37.60443736142207,-121.15475178384224,37.60555392496201,-121.12649717971942,37.5761612791743,-121.105517801627,37.57291785522102,-121.06195388086127,37.58432554999496,-121.051833277752,37.59549621664428,-120.9623811789325,37.616090631195675,-120.93801052688701,37.61182627286894,-120.86057590200721,37.62129079368759,-120.85730386034561,37.613155902033554,-120.78087282192257,37.61414406661538,-120.73817309594716,37.63053679471494,-120.70606299078867,37.6253131207647,-120.65312547628753,37.630845649922776,-120.64372085026048,37.617530275985075,-120.59897548366325,37.61838293640045,-120.58566304618392,37.6280897895104,-120.55437374519079,37.61949203744041,-120.53035751501977,37.62416281034979,-120.52267756711501,37.58373637916253,-120.47482846302262,37.59087335206817,-120.45815308909016,37.620518593688814,-120.4371221932895,37.63718915616295,-120.3849402642038,37.635048797832894,-120.3755883360933,37.65339462697108,-120.39417890064777,37.66818361028146,-120.392313883984,37.68359100504921,-120.3559603048738,37.67552716860342,-120.32610607262848,37.648966585311314,-120.30639988828403,37.66573735924362,-120.3455439350983,37.72512544352645,-120.31539807993676,37.733894229971156,-120.28665821956469,37.72927809560332,-120.28245683519623,37.74541342682829,-120.26069274460497,37.73358365601331,-120.25387803732389,37.749232223697334,-120.200135472824,37.76372567054983,-120.17357968640687,37.79608365070794,-120.12741327606187,37.78170633927192,-120.08906888341306,37.81273366005712,-120.07891344384547,37.82866939649068,-120.05533048450707,37.812757440265386,-120.02518823247055,37.81132716005931,-119.96359235131615,37.78073240906514,-119.94507216905322,37.76542058617224,-119.90693944932076,37.75781682576735,-119.86549272230178,37.77222938512,-119.85359450461186,37.758767389307536,-119.83160174943771,37.76963435595735,-119.80589665676928,37.75608133722835,-119.75060675164673,37.77341942574316,-119.7359645225028,37.78635874968137,-119.6978660861036,37.78960109342637,-119.64907657576265,37.81516770484501,-119.65722697262504,37.83230492565173,-119.59708603105173,37.86135878810666,-119.58892104668939,37.88872669535584,-119.57247042275657,37.8998297015886,-119.53527651760601,37.90190377762701,-119.49696578120711,37.86409281310239,-119.47533168332404,37.85892923602705,-119.45447817606475,37.871394138091034,-119.44539026566218,37.858937875610366,-119.4045319969756,37.85021429541558,-119.40293857510306,37.833821247524384,-119.37314428244099,37.83849664855876,-119.35155096789117,37.82452854545545,-119.3552763335104,37.812805713182,-119.32340220126821,37.79368655279501,-119.29229722319144,37.762878678884476,-119.2876598648653,37.74535544245333,-119.26052065344913,37.74159433725089,-119.24428567430766,37.76834668616766,-119.22055414726117,37.77924966531742,-119.20108320354137,37.801137019450096,-119.21738830351609,37.8183151860901,-119.20449054936944,37.82981189961396,-119.21625690560114,37.847411426669964,-119.21512584414461,37.87042564434256,-119.19927543166921,37.88483115890352,-119.23787860140095,37.911280908862466,-119.26486444510903,37.91263911719369,-119.26807310656238,37.92880876300194,-119.30948290962311,37.94616478068332,-119.31574230232172,37.96621302648555,-119.30533157629623,38.02416955035392,-119.34927245018639,38.08565116171684,-119.35845034913046,38.08266815651314,-119.39810467927725,38.1068175096006,-119.43127595110076,38.11332130542388,-119.4403859021283,38.09636985024184,-119.46399499479998,38.09838383773871,-119.4692413104168,38.12798441894279,-119.48819819267908,38.132729004352086,-119.50246159786525,38.159339980352456,-119.50459633952858,38.140964939755975,-119.54763344883679,38.14419101891764,-119.54624260196397,38.15397065015242,-119.5773162810824,38.15780512931315,-119.57980050712018,38.17791634178195,-119.62908996641869,38.196015076128845,-119.62508146642494,38.22905559795254,-119.65612154137676,38.229472830243594\",\n \"Region Name\": \"Upper Tuolumne\"\n }\n }\n}\n\n\n\n#visualize FTS response\nvisualize(response)"
},
{
- "objectID": "external/zarr_access.html#exercise",
- "href": "external/zarr_access.html#exercise",
- "title": "Zarr Access for NetCDF4 files",
- "section": "Exercise",
- "text": "Exercise\nIn this exercise, we will be using the eosdis-zarr-store library to aggregate and analyze a month of sea surface temperature for the Great Lakes region\n\nSet up\n\nImport Required Packages\n\n# Core libraries for this tutorial\n# Available via `pip install zarr zarr-eosdis-store`\nfrom eosdis_store import EosdisStore\nimport xarray as xr\n\n# Other Python libraries\nimport requests\nfrom pqdm.threads import pqdm\nfrom matplotlib import animation, pyplot as plt\nfrom IPython.core.display import display, HTML\n\n# Python standard library imports\nfrom pprint import pprint\n\nAlso set the width / height for plots we show\n\nplt.rcParams['figure.figsize'] = 12, 6\n\n\n\nSet Dataset, Time, and Region of Interest\nLook in PO.DAAC’s cloud archive for Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 Multiscale Ultrahigh Resolution (MUR) data\n\ndata_provider = 'POCLOUD'\nmur_short_name = 'MUR-JPL-L4-GLOB-v4.1'\n\nLooking for data from the month of September over the Great Lakes\n\nstart_time = '2021-09-01T21:00:00Z'\nend_time = '2021-09-30T20:59:59Z'\n\n# Bounding box around the Great Lakes\nlats = slice(41, 49)\nlons = slice(-93, -76)\n\n# Some other possibly interesting bounding boxes:\n\n# Hawaiian Islands\n# lats = slice(18, 22.5)\n# lons = slice(-161, -154)\n\n# Mediterranean Sea\n# lats = slice(29, 45)\n# lons = slice(-7, 37)\n\n\n\n\nFind URLs for the dataset and AOI\nSet up a CMR granules search for our area of interest, as we saw in prior tutorials\n\ncmr_url = 'https://cmr.earthdata.nasa.gov/search/granules.json'\n\nSearch for granules in our area of interest, expecting one granule per day of September\n\nresponse = requests.get(cmr_url, \n params={\n 'provider': data_provider,\n 'short_name': mur_short_name, \n 'temporal': f'{start_time},{end_time}',\n 'bounding_box': f'{lons.start},{lats.start},{lons.stop},{lats.stop}',\n 'page_size': 2000,\n }\n )\n\n\ngranules = response.json()['feed']['entry']\n\nfor granule in granules:\n print(granule['title'])\n\n20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210902090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210903090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210904090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210905090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210906090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210907090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210908090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210909090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210910090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210911090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210912090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210913090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210914090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210915090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210916090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210917090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210918090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210919090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210920090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210921090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210922090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210923090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210924090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210925090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210926090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210927090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210928090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210929090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210930090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n\n\n\npprint(granules[0])\n\n{'boxes': ['-90 -180 90 180'],\n 'browse_flag': False,\n 'collection_concept_id': 'C1996881146-POCLOUD',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 4 MUR Global Foundation Sea Surface Temperature '\n 'Analysis (v4.1)',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '9.059906005859375E-5',\n 'id': 'G2113241213-POCLOUD',\n 'links': [{'href': 's3://podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/s3#',\n 'title': 'This link provides direct download access via S3 to the '\n 'granule.'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'Download '\n '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.md5',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.md5'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'api endpoint to retrieve temporary credentials valid for '\n 'same-region direct s3 access'},\n {'href': 'https://opendap.earthdata.nasa.gov/collections/C1996881146-POCLOUD/granules/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/service#',\n 'title': 'OPeNDAP request URL'},\n {'href': 'https://github.com/nasa/podaac_tools_and_services/tree/master/subset_opendap',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://earthdata.nasa.gov/esds/competitive-programs/measures/mur-sst',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n {'href': 'http://journals.ametsoc.org/doi/abs/10.1175/1520-0426%281998%29015%3C0741:BSHWSS%3E2.0.CO;2',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://doi.org/10.1016/j.rse.2017.07.029',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://registry.opendata.aws/mur/#usageexa',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n {'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1996881146-POCLOUD ',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '300.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n {'href': ' '\n 'https://search.earthdata.nasa.gov/search/granules?p=C1996881146-POCLOUD ',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '700.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n {'href': 'https://podaac.jpl.nasa.gov/MEaSUREs-MUR',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.ipynb',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'time_end': '2021-09-01T21:00:00.000Z',\n 'time_start': '2021-08-31T21:00:00.000Z',\n 'title': '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'updated': '2021-09-10T07:29:40.511Z'}\n\n\n\nurls = []\nfor granule in granules:\n for link in granule['links']:\n if link['rel'].endswith('/data#'):\n urls.append(link['href'])\n break\npprint(urls)\n\n['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210902090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210903090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210904090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210905090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210906090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210907090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210908090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210909090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210910090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210911090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210912090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210913090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210914090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210915090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210916090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210917090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210918090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210919090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210920090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210921090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210922090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210923090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210924090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210925090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210926090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210927090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210928090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210929090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210930090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc']\n\n\n\n\nOpen and view our AOI without downloading a whole file\n\nCheck to see if we can use an efficient partial-access technique\n\nresponse = requests.head(f'{urls[0]}.dmrpp')\n\nprint('Can we use EosdisZarrStore and XArray to access these files more efficiently?')\nprint('Yes' if response.ok else 'No')\n\nCan we use EosdisZarrStore and XArray to access these files more efficiently?\nYes\n\n\nOpen our first URL using the Zarr library\n\nurl = urls[0]\n\nds = xr.open_zarr(EosdisStore(url), consolidated=False)\n\nThat’s it! No downloads, temporary credentials, or S3 filesystems. Hereafter, we interact with the ds variable as with any XArray dataset. We need not worry about the EosdisStore anymore.\nView the file’s variable structure\n\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 1, lat: 17999, lon: 36000)\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00\nData variables:\n analysed_sst (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n analysis_error (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n dt_1km_data (time, lat, lon) timedelta64[ns] dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n mask (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sea_ice_fraction (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sst_anomaly (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\nAttributes: (12/47)\n Conventions: CF-1.7\n title: Daily MUR SST, Final product\n summary: A merged, multi-sensor L4 Foundation SST anal...\n references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n institution: Jet Propulsion Laboratory\n history: created at nominal 4-day latency; replaced nr...\n ... ...\n project: NASA Making Earth Science Data Records for Us...\n publisher_name: GHRSST Project Office\n publisher_url: http://www.ghrsst.org\n publisher_email: ghrsst-po@nceo.ac.uk\n processing_level: L4\n cdm_data_type: gridxarray.DatasetDimensions:time: 1lat: 17999lon: 36000Coordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000'], dtype='datetime64[ns]')Data variables: (6)analysed_sst(time, lat, lon)float32dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanalysis_error\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated error standard deviation of analysed_sst\n\nunits :\n\nkelvin\n\nvalid_min :\n\n0\n\nvalid_max :\n\n32767\n\ncomment :\n\nuncertainty in \\\"analysed_sst\\\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\ndt_1km_data\n\n\n(time, lat, lon)\n\n\ntimedelta64[ns]\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime to most recent 1km data\n\nvalid_min :\n\n-127\n\nvalid_max :\n\n127\n\nsource :\n\nMODIS and VIIRS pixels ingested by MUR\n\ncomment :\n\nThe grid value is hours between the analysis time and the most recent MODIS or VIIRS 1km L2P datum within 0.01 degrees from the grid point. \\\"Fill value\\\" indicates absence of such 1km data at the grid point.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n4.83 GiB\n31.96 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n170 Tasks\n169 Chunks\n\n\nType\ntimedelta64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nmask\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea/land field composite mask\n\nvalid_min :\n\n1\n\nvalid_max :\n\n31\n\nflag_masks :\n\n[1, 2, 4, 8, 16]\n\nflag_meanings :\n\nopen_sea land open_lake open_sea_with_ice_in_the_grid open_lake_with_ice_in_the_grid\n\ncomment :\n\nmask can be used to further filter the data.\n\nsource :\n\nGMT \\\"grdlandmask\\\", ice flag from sea_ice_fraction data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n15.98 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n170 Tasks\n169 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsea_ice_fraction\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea ice area fraction\n\nstandard_name :\n\nsea_ice_area_fraction\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\nsource :\n\nEUMETSAT OSI-SAF, copyright EUMETSAT\n\ncomment :\n\nice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n15.98 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n170 Tasks\n169 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsst_anomaly\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSST anomaly from a seasonal SST climatology based on the MUR data over 2003-2014 period\n\nunits :\n\nkelvin\n\nvalid_min :\n\n-32767\n\nvalid_max :\n\n32767\n\ncomment :\n\nanomaly reference to the day-of-year average between 2003 and 2014\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (47)Conventions :CF-1.7title :Daily MUR SST, Final productsummary :A merged, multi-sensor L4 Foundation SST analysis product from JPL.references :http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SSTinstitution :Jet Propulsion Laboratoryhistory :created at nominal 4-day latency; replaced nrt (1-day latency) version.comment :MUR = \\\"Multi-scale Ultra-high Resolution\\\"license :These data are available free of charge under data policy of JPL PO.DAAC.id :MUR-JPL-L4-GLOB-v04.1naming_authority :org.ghrsstproduct_version :04.1uuid :27665bc0-d5fc-11e1-9b23-0800200c9a66gds_version_id :2.0netcdf_version_id :4.1date_created :20210910T072132Zstart_time :20210901T090000Zstop_time :20210901T090000Ztime_coverage_start :20210831T210000Ztime_coverage_end :20210901T210000Zfile_quality_level :3source :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAFplatform :Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Shipssensor :MODIS, AMSR2, AVHRR, in-situMetadata_Conventions :Unidata Observation Dataset v1.0metadata_link :http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1keywords :Oceans > Ocean Temperature > Sea Surface Temperaturekeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsouthernmost_latitude :-90.0northernmost_latitude :90.0westernmost_longitude :-180.0easternmost_longitude :180.0spatial_resolution :0.01 degreesgeospatial_lat_units :degrees northgeospatial_lat_resolution :0.009999999776geospatial_lon_units :degrees eastgeospatial_lon_resolution :0.009999999776acknowledgment :Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.creator_name :JPL MUR SST projectcreator_email :ghrsst@podaac.jpl.nasa.govcreator_url :http://mur.jpl.nasa.govproject :NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Programpublisher_name :GHRSST Project Officepublisher_url :http://www.ghrsst.orgpublisher_email :ghrsst-po@nceo.ac.ukprocessing_level :L4cdm_data_type :grid\n\n\n\nds.analysed_sst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 1, lat: 17999, lon: 36000)>\ndask.array<open_dataset-4d5a9a1e1fda090e80524b67b2e413c6analysed_sst, shape=(1, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 -89.96 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 1lat: 17999lon: 36000dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000'], dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\nsst = ds.analysed_sst.sel(lat=lats, lon=lons)\nsst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 1, lat: 801, lon: 1701)>\ndask.array<getitem, shape=(1, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 41.0 41.01 41.02 41.03 ... 48.97 48.98 48.99 49.0\n * lon (lon) float32 -93.0 -92.99 -92.98 -92.97 ... -76.02 -76.01 -76.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 1lat: 801lon: 1701dask.array<chunksize=(1, 200, 1536), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n5.20 MiB\n3.52 MiB\n\n\nShape\n(1, 801, 1701)\n(1, 601, 1536)\n\n\nCount\n329 Tasks\n4 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float3241.0 41.01 41.02 ... 48.99 49.0long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([41. , 41.01, 41.02, ..., 48.98, 48.99, 49. ], dtype=float32)lon(lon)float32-93.0 -92.99 ... -76.01 -76.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-93. , -92.99, -92.98, ..., -76.02, -76.01, -76. ], dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000'], dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\nsst.plot()\n\n<matplotlib.collections.QuadMesh at 0x7f2d9848d4c0>\n\n\n\n\n\n\n\n\nAggregate and analyze 30 files\nSet up a function to open all of our URLs as XArrays in parallel\n\ndef open_as_zarr_xarray(url):\n return xr.open_zarr(EosdisStore(url), consolidated=False)\n\ndatasets = pqdm(urls, open_as_zarr_xarray, n_jobs=30)\n\n\n\n\n\n\n\n\n\n\nCombine the individual file-based datasets into a single xarray dataset with a time axis\n\nds = xr.concat(datasets, 'time')\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 30, lat: 17999, lon: 36000)\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-3...\nData variables:\n analysed_sst (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n analysis_error (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n dt_1km_data (time, lat, lon) timedelta64[ns] dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n mask (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sea_ice_fraction (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sst_anomaly (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\nAttributes: (12/47)\n Conventions: CF-1.7\n title: Daily MUR SST, Final product\n summary: A merged, multi-sensor L4 Foundation SST anal...\n references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n institution: Jet Propulsion Laboratory\n history: created at nominal 4-day latency; replaced nr...\n ... ...\n project: NASA Making Earth Science Data Records for Us...\n publisher_name: GHRSST Project Office\n publisher_url: http://www.ghrsst.org\n publisher_email: ghrsst-po@nceo.ac.uk\n processing_level: L4\n cdm_data_type: gridxarray.DatasetDimensions:time: 30lat: 17999lon: 36000Coordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00 ... 2021-09-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000', '2021-09-02T09:00:00.000000000',\n '2021-09-03T09:00:00.000000000', '2021-09-04T09:00:00.000000000',\n '2021-09-05T09:00:00.000000000', '2021-09-06T09:00:00.000000000',\n '2021-09-07T09:00:00.000000000', '2021-09-08T09:00:00.000000000',\n '2021-09-09T09:00:00.000000000', '2021-09-10T09:00:00.000000000',\n '2021-09-11T09:00:00.000000000', '2021-09-12T09:00:00.000000000',\n '2021-09-13T09:00:00.000000000', '2021-09-14T09:00:00.000000000',\n '2021-09-15T09:00:00.000000000', '2021-09-16T09:00:00.000000000',\n '2021-09-17T09:00:00.000000000', '2021-09-18T09:00:00.000000000',\n '2021-09-19T09:00:00.000000000', '2021-09-20T09:00:00.000000000',\n '2021-09-21T09:00:00.000000000', '2021-09-22T09:00:00.000000000',\n '2021-09-23T09:00:00.000000000', '2021-09-24T09:00:00.000000000',\n '2021-09-25T09:00:00.000000000', '2021-09-26T09:00:00.000000000',\n '2021-09-27T09:00:00.000000000', '2021-09-28T09:00:00.000000000',\n '2021-09-29T09:00:00.000000000', '2021-09-30T09:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (6)analysed_sst(time, lat, lon)float32dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanalysis_error\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated error standard deviation of analysed_sst\n\nunits :\n\nkelvin\n\nvalid_min :\n\n0\n\nvalid_max :\n\n32767\n\ncomment :\n\nuncertainty in \\\"analysed_sst\\\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\ndt_1km_data\n\n\n(time, lat, lon)\n\n\ntimedelta64[ns]\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime to most recent 1km data\n\nvalid_min :\n\n-127\n\nvalid_max :\n\n127\n\nsource :\n\nMODIS and VIIRS pixels ingested by MUR\n\ncomment :\n\nThe grid value is hours between the analysis time and the most recent MODIS or VIIRS 1km L2P datum within 0.01 degrees from the grid point. \\\"Fill value\\\" indicates absence of such 1km data at the grid point.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n144.83 GiB\n31.96 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n10170 Tasks\n5070 Chunks\n\n\nType\ntimedelta64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nmask\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea/land field composite mask\n\nvalid_min :\n\n1\n\nvalid_max :\n\n31\n\nflag_masks :\n\n[1, 2, 4, 8, 16]\n\nflag_meanings :\n\nopen_sea land open_lake open_sea_with_ice_in_the_grid open_lake_with_ice_in_the_grid\n\ncomment :\n\nmask can be used to further filter the data.\n\nsource :\n\nGMT \\\"grdlandmask\\\", ice flag from sea_ice_fraction data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n15.98 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n10170 Tasks\n5070 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsea_ice_fraction\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea ice area fraction\n\nstandard_name :\n\nsea_ice_area_fraction\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\nsource :\n\nEUMETSAT OSI-SAF, copyright EUMETSAT\n\ncomment :\n\nice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n15.98 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n10170 Tasks\n5070 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsst_anomaly\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSST anomaly from a seasonal SST climatology based on the MUR data over 2003-2014 period\n\nunits :\n\nkelvin\n\nvalid_min :\n\n-32767\n\nvalid_max :\n\n32767\n\ncomment :\n\nanomaly reference to the day-of-year average between 2003 and 2014\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (47)Conventions :CF-1.7title :Daily MUR SST, Final productsummary :A merged, multi-sensor L4 Foundation SST analysis product from JPL.references :http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SSTinstitution :Jet Propulsion Laboratoryhistory :created at nominal 4-day latency; replaced nrt (1-day latency) version.comment :MUR = \\\"Multi-scale Ultra-high Resolution\\\"license :These data are available free of charge under data policy of JPL PO.DAAC.id :MUR-JPL-L4-GLOB-v04.1naming_authority :org.ghrsstproduct_version :04.1uuid :27665bc0-d5fc-11e1-9b23-0800200c9a66gds_version_id :2.0netcdf_version_id :4.1date_created :20210910T072132Zstart_time :20210901T090000Zstop_time :20210901T090000Ztime_coverage_start :20210831T210000Ztime_coverage_end :20210901T210000Zfile_quality_level :3source :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAFplatform :Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Shipssensor :MODIS, AMSR2, AVHRR, in-situMetadata_Conventions :Unidata Observation Dataset v1.0metadata_link :http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1keywords :Oceans > Ocean Temperature > Sea Surface Temperaturekeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsouthernmost_latitude :-90.0northernmost_latitude :90.0westernmost_longitude :-180.0easternmost_longitude :180.0spatial_resolution :0.01 degreesgeospatial_lat_units :degrees northgeospatial_lat_resolution :0.009999999776geospatial_lon_units :degrees eastgeospatial_lon_resolution :0.009999999776acknowledgment :Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.creator_name :JPL MUR SST projectcreator_email :ghrsst@podaac.jpl.nasa.govcreator_url :http://mur.jpl.nasa.govproject :NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Programpublisher_name :GHRSST Project Officepublisher_url :http://www.ghrsst.orgpublisher_email :ghrsst-po@nceo.ac.ukprocessing_level :L4cdm_data_type :grid\n\n\nLook at the Analysed SST variable metadata\n\nall_sst = ds.analysed_sst\nall_sst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 30, lat: 17999, lon: 36000)>\ndask.array<concatenate, shape=(30, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 -89.96 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 30lat: 17999lon: 36000dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00 ... 2021-09-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000', '2021-09-02T09:00:00.000000000',\n '2021-09-03T09:00:00.000000000', '2021-09-04T09:00:00.000000000',\n '2021-09-05T09:00:00.000000000', '2021-09-06T09:00:00.000000000',\n '2021-09-07T09:00:00.000000000', '2021-09-08T09:00:00.000000000',\n '2021-09-09T09:00:00.000000000', '2021-09-10T09:00:00.000000000',\n '2021-09-11T09:00:00.000000000', '2021-09-12T09:00:00.000000000',\n '2021-09-13T09:00:00.000000000', '2021-09-14T09:00:00.000000000',\n '2021-09-15T09:00:00.000000000', '2021-09-16T09:00:00.000000000',\n '2021-09-17T09:00:00.000000000', '2021-09-18T09:00:00.000000000',\n '2021-09-19T09:00:00.000000000', '2021-09-20T09:00:00.000000000',\n '2021-09-21T09:00:00.000000000', '2021-09-22T09:00:00.000000000',\n '2021-09-23T09:00:00.000000000', '2021-09-24T09:00:00.000000000',\n '2021-09-25T09:00:00.000000000', '2021-09-26T09:00:00.000000000',\n '2021-09-27T09:00:00.000000000', '2021-09-28T09:00:00.000000000',\n '2021-09-29T09:00:00.000000000', '2021-09-30T09:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\nCreate a dataset / variable that is only our area of interest and view its metadata\n\nsst = ds.analysed_sst.sel(lat=lats, lon=lons)\nsst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 30, lat: 801, lon: 1701)>\ndask.array<getitem, shape=(30, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 41.0 41.01 41.02 41.03 ... 48.97 48.98 48.99 49.0\n * lon (lon) float32 -93.0 -92.99 -92.98 -92.97 ... -76.02 -76.01 -76.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 30lat: 801lon: 1701dask.array<chunksize=(1, 200, 1536), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n155.93 MiB\n3.52 MiB\n\n\nShape\n(30, 801, 1701)\n(1, 601, 1536)\n\n\nCount\n19590 Tasks\n120 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float3241.0 41.01 41.02 ... 48.99 49.0long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([41. , 41.01, 41.02, ..., 48.98, 48.99, 49. ], dtype=float32)lon(lon)float32-93.0 -92.99 ... -76.01 -76.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-93. , -92.99, -92.98, ..., -76.02, -76.01, -76. ], dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00 ... 2021-09-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000', '2021-09-02T09:00:00.000000000',\n '2021-09-03T09:00:00.000000000', '2021-09-04T09:00:00.000000000',\n '2021-09-05T09:00:00.000000000', '2021-09-06T09:00:00.000000000',\n '2021-09-07T09:00:00.000000000', '2021-09-08T09:00:00.000000000',\n '2021-09-09T09:00:00.000000000', '2021-09-10T09:00:00.000000000',\n '2021-09-11T09:00:00.000000000', '2021-09-12T09:00:00.000000000',\n '2021-09-13T09:00:00.000000000', '2021-09-14T09:00:00.000000000',\n '2021-09-15T09:00:00.000000000', '2021-09-16T09:00:00.000000000',\n '2021-09-17T09:00:00.000000000', '2021-09-18T09:00:00.000000000',\n '2021-09-19T09:00:00.000000000', '2021-09-20T09:00:00.000000000',\n '2021-09-21T09:00:00.000000000', '2021-09-22T09:00:00.000000000',\n '2021-09-23T09:00:00.000000000', '2021-09-24T09:00:00.000000000',\n '2021-09-25T09:00:00.000000000', '2021-09-26T09:00:00.000000000',\n '2021-09-27T09:00:00.000000000', '2021-09-28T09:00:00.000000000',\n '2021-09-29T09:00:00.000000000', '2021-09-30T09:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\nXArray reads data lazily, i.e. only when our code actually needs it. Up to this point, we haven’t read any data values, only metadata. The next line will force XArray to read the portions of the source files containing our area of interest. Behind the scenes, the eosdis-zarr-store library is ensuring data is fetched as efficiently as possible.\nNote: This line isn’t strictly necessary, since XArray will automatically read the data we need the first time our code tries to use it, but calling this will make sure that we can read the data multiple times later on without re-fetching anything from the source files.\nThis line will take several seconds to complete, but since it is retrieving only about 50 MB of data from 22 GB of source files, several seconds constitutes a significant time, bandwidth, and disk space savings.\n\nsst.load();\n\nNow we can start looking at aggregations across the time dimension. In this case, plot the standard deviation of the temperature at each point to get a visual sense of how much temperatures fluctuate over the course of the month.\n\n# We expect a warning here, from finding the standard deviation of arrays that contain all N/A values.\n# numpy produces N/A for these points, though, which is exactly what we want.\nstdev_sst = sst.std('time')\nstdev_sst.name = 'stdev of analysed_sst [Kelvin]'\nstdev_sst.plot();\n\n/srv/conda/envs/notebook/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1670: RuntimeWarning: Degrees of freedom <= 0 for slice.\n var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n\n\n\n\n\n\nInteractive animation of a month of data\nThis section isn’t as important to fully understand. It shows us a way to get an interactive animation to see what we have retrieved so far\nDefine an animation function to plot the ith time step. We need to make sure each plot is using the same color scale, set by vmin and vmax so the animation is consistent\n\nsst_min = sst.min()\nsst_max = sst.max()\n\ndef show_time_step(i):\n plt.clf()\n res = sst[i].plot.imshow(vmin=sst_min, vmax=sst_max)\n return (res,)\n\nRender each time slice once and show it as an HTML animation with interactive controls\n\n#anim = animation.FuncAnimation(plt.gcf(), func=show_time_step, frames=len(sst))\n#display(HTML(anim.to_jshtml()))\n#plt.close()\n\n\n\n\nSupplemental: What’s happening here?\nFor EOSDIS data in the cloud, we have begun producing a metadata sidecar file in a format called DMR++ that extracts all of the information about arrays, variables, and dimensions from data files, as well as the byte offsets in the NetCDF4 file where data can be found. This information is sufficient to let the Zarr library read data from our NetCDF4 files, but it’s in the wrong format. zarr-eosdis-store knows how to fetch the sidecar file and transform it into something the Zarr library understands. Passing it when reading Zarr using XArray or the Zarr library lets these libraries interact with EOSDIS data exactly as if they were Zarr stores in a way that’s more optimal for reading data in the cloud. Beyond this, the zarr-eosdis-store library makes some optimizations in the way it reads data to help make up for situations where the NetCDF4 file is not internally arranged well for cloud-based access patterns."
+ "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#query-cmr-by-bounding-box",
+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#query-cmr-by-bounding-box",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Query CMR by Bounding Box",
+ "text": "Query CMR by Bounding Box\nHere is a more useful example of the Feature Translation Service. We can use results obtained from the FTS to then directly and automatically query on data using CMR. We’re extracting the bounding box representing Upper Tuolumne River Basin within the San Joaquin River Basin, and using it to search for granules available from the SMAP/Sentinel-1 missions, as an example.\n\n###################\n\nCOLLECTION_ID = \"C1931663473-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\nREGION = \"Upper Tuolumne\"\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain bounding box from response\nbbox = response['results'][REGION]['Bounding Box']\n\n# Query CMR by bounding box\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?bounding_box={}&echo_collection_id={}&pretty=True\".format(bbox, COLLECTION_ID))\n\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2021-08-04T19:04:43.269Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?bounding_box=-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182&echo_collection_id=C1931663473-NSIDC_ECS&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\",\n \"time_start\": \"2015-04-01T01:20:04.000Z\",\n \"updated\": \"2020-12-11T20:16:06.973Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197590772\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:44.000Z\",\n \"id\": \"G1978082559-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8861885071\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.5845451 -121.9242706 36.5554848 -121.9242706 36.5554848 -118.6670151 38.5845451 -118.6670151 38.5845451 -121.9242706\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/opendap/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": 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EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197590771\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T14:55:53.000Z\",\n \"id\": \"G1978082510-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.2115306854\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"40.1287079 -122.2977142 38.0572586 -122.2977142 38.0572586 -118.9470978 40.1287079 -118.9470978 40.1287079 -122.2977142\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"application/x-hdfeos\",\n 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},
{
- "objectID": "external/insitu_dataviz_demo.html",
- "href": "external/insitu_dataviz_demo.html",
- "title": "S-MODE Workshop: Science Case Study In Situ",
- "section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\nimport glob\nfrom netCDF4 import Dataset\nimport xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport gsw"
+ "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#query-cmr-by-polygon",
+ "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#query-cmr-by-polygon",
+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Query CMR by Polygon",
+ "text": "Query CMR by Polygon\nInstead of querying via bounding box from the FTS response, we can extract the polygon of the region and use this to query CMR. Similarly to above, we’re extracting information from the Upper Tuolumne River Basin and using this to search for granules available from the Sentinel-1 mission.\nHere we query by region in these two examples, however it would be equally valid to query by HUC ID.\n\n\n###################\n\nCOLLECTION_ID = \"C1931663473-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain Visvalingam polygon from response\n#polygon = response['results'][REGION]['Convex Hull Polygon']\npolygon = response['results'][REGION]['Visvalingam Polygon']\n\n# Query CMR by polygon\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?polygon={}&echo_collection_id={}&pretty=True\".format(polygon, COLLECTION_ID))\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2021-08-04T19:06:13.741Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?polygon=-119.65612154137676,38.229472830243594,-119.65887392887248,38.21611789588928,-119.68748882882807,38.20072575945488,-119.74423195478164,38.21583016359807,-119.79473757241158,38.21799358859471,-119.80777226405803,38.20387888444998,-119.83634931818034,38.19900379279085,-119.8794751608217,38.205164957364616,-119.92926093053609,38.1903575073876,-119.95874486382365,38.194300821964816,-119.9915583491894,38.18745202718378,-119.99491475022586,38.196920114669126,-120.05974666158357,38.15445730952666,-120.10528053234623,38.13442047414111,-120.12843912918527,38.10262073148215,-120.20521266552441,38.056065841971076,-120.26427926855774,38.061807551337154,-120.34283367885251,38.04442165553081,-120.36616220485797,38.05864021280041,-120.38138515275097,38.04379106074015,-120.39399451523144,38.00852514516987,-120.41557226103123,38.00413815246833,-120.4432863495299,37.97081393481176,-120.45616958284324,37.92842260675252,-120.46878266719864,37.921885701554345,-120.4624829120001,37.891430354726594,-120.479954646348,37.871879250590325,-120.48053337030541,37.82415609128935,-120.5431307066666,37.84913992354228,-120.55227857644405,37.86382160997783,-120.57047251079081,37.845455260006304,-120.5664340514221,37.82240133087544,-120.59755003054045,37.81161053401718,-120.64595511379866,37.78181727677179,-120.67580313979397,37.7973594465393,-120.7399643115694,37.77691864136273,-120.75337800946522,37.73604807163446,-120.78855502295232,37.75464512473059,-120.82306428227372,37.739964848711736,-120.82298294894053,37.72367104352867,-120.84114918537068,37.706087340431,-120.8673865186633,37.69917305085835,-120.87598703427494,37.68470263317249,-120.91724289983591,37.65975447800287,-120.94840845082916,37.657548763422994,-121.0014800642885,37.642007069697115,-121.06141474752877,37.632796530128076,-121.15500076925849,37.6284224540932,-121.17472602131124,37.60443736142207,-121.15475178384224,37.60555392496201,-121.12649717971942,37.5761612791743,-121.105517801627,37.57291785522102,-121.06195388086127,37.58432554999496,-121.051833277752,37.59549621664428,-120.9623811789325,37.616090631195675,-120.93801052688701,37.61182627286894,-120.86057590200721,37.62129079368759,-120.85730386034561,37.613155902033554,-120.78087282192257,37.61414406661538,-120.73817309594716,37.63053679471494,-120.70606299078867,37.6253131207647,-120.65312547628753,37.630845649922776,-120.64372085026048,37.617530275985075,-120.59897548366325,37.61838293640045,-120.58566304618392,37.6280897895104,-120.55437374519079,37.61949203744041,-120.53035751501977,37.62416281034979,-120.52267756711501,37.58373637916253,-120.47482846302262,37.59087335206817,-120.45815308909016,37.620518593688814,-120.4371221932895,37.63718915616295,-120.3849402642038,37.635048797832894,-120.3755883360933,37.65339462697108,-120.39417890064777,37.66818361028146,-120.392313883984,37.68359100504921,-120.3559603048738,37.67552716860342,-120.32610607262848,37.648966585311314,-120.30639988828403,37.66573735924362,-120.3455439350983,37.72512544352645,-120.31539807993676,37.733894229971156,-120.28665821956469,37.72927809560332,-120.28245683519623,37.74541342682829,-120.26069274460497,37.73358365601331,-120.25387803732389,37.749232223697334,-120.200135472824,37.76372567054983,-120.17357968640687,37.79608365070794,-120.12741327606187,37.78170633927192,-120.08906888341306,37.81273366005712,-120.07891344384547,37.82866939649068,-120.05533048450707,37.812757440265386,-120.02518823247055,37.81132716005931,-119.96359235131615,37.78073240906514,-119.94507216905322,37.76542058617224,-119.90693944932076,37.75781682576735,-119.86549272230178,37.77222938512,-119.85359450461186,37.758767389307536,-119.83160174943771,37.76963435595735,-119.80589665676928,37.75608133722835,-119.75060675164673,37.77341942574316,-119.7359645225028,37.78635874968137,-119.6978660861036,37.78960109342637,-119.64907657576265,37.81516770484501,-119.65722697262504,37.83230492565173,-119.59708603105173,37.86135878810666,-119.58892104668939,37.88872669535584,-119.57247042275657,37.8998297015886,-119.53527651760601,37.90190377762701,-119.49696578120711,37.86409281310239,-119.47533168332404,37.85892923602705,-119.45447817606475,37.871394138091034,-119.44539026566218,37.858937875610366,-119.4045319969756,37.85021429541558,-119.40293857510306,37.833821247524384,-119.37314428244099,37.83849664855876,-119.35155096789117,37.82452854545545,-119.3552763335104,37.812805713182,-119.32340220126821,37.79368655279501,-119.29229722319144,37.762878678884476,-119.2876598648653,37.74535544245333,-119.26052065344913,37.74159433725089,-119.24428567430766,37.76834668616766,-119.22055414726117,37.77924966531742,-119.20108320354137,37.801137019450096,-119.21738830351609,37.8183151860901,-119.20449054936944,37.82981189961396,-119.21625690560114,37.847411426669964,-119.21512584414461,37.87042564434256,-119.19927543166921,37.88483115890352,-119.23787860140095,37.911280908862466,-119.26486444510903,37.91263911719369,-119.26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\"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\",\n \"time_start\": \"2015-04-01T01:20:04.000Z\",\n \"updated\": \"2020-12-11T20:16:06.973Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197590772\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:44.000Z\",\n \"id\": \"G1978082559-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8861885071\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.5845451 -121.9242706 36.5554848 -121.9242706 36.5554848 -118.6670151 38.5845451 -118.6670151 38.5845451 -121.9242706\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/opendap/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2020.12.11/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5.iso.xml\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/SMAP/SPL2SMAP_S.003/\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C1931663473-NSIDC_ECS&pg[0][gsk]=-start_date&tl=1583080558!4!!\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/data/data-access-tool/SPL2SMAP_S/versions/3/\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R17000_001.h5\",\n \"time_start\": \"2015-04-01T14:55:15.000Z\",\n \"updated\": \"2020-12-11T20:16:06.985Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197590771\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T14:55:53.000Z\",\n \"id\": \"G1978082510-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.2115306854\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"40.1287079 -122.2977142 38.0572586 -122.2977142 38.0572586 -118.9470978 40.1287079 -118.9470978 40.1287079 -122.2977142\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/opendap/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": 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\"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/data/data-access-tool/SPL2SMAP_S/versions/3/\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R17000_001.h5\",\n \"time_start\": \"2015-05-24T14:43:34.000Z\",\n \"updated\": \"2020-12-12T23:08:12.173Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197692314\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-05-24T14:44:19.000Z\",\n \"id\": \"G1978421514-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.2886819839\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"37.9581909 -122.8578873 35.6854019 -122.8578873 35.6854019 -119.6006241 37.9581909 -119.6006241 37.9581909 -122.8578873\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.05.24/SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": 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- "title": "S-MODE Workshop: Science Case Study In Situ",
- "section": "Compare saildrone ADCP data with R/V Oceanus data",
- "text": "Compare saildrone ADCP data with R/V Oceanus data\nA major goal of the S-MODE Pilot was to compare saildrone ADCP data. Here’s one example of a simple saildrone-Oceanus velocity comparison.\n\n# this is not working for the Oceanus ADCP\nstart_time = '2021-10-20T00:00:00Z'\nstart_time = '2021-08-01T00:00:00Z'\nend_time = '2021-11-08T00:00:00Z'\n\nshort_name = 'SMODE_LX_SHIPBOARD_ADCP_V1'\n!podaac-data-downloader -c $short_name -d data/$short_name --start-date $start_time --end-date $end_time -e .nc4\n\n/Users/crocha/miniconda3/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.0.0)/charset_normalizer (2.0.4) doesn't match a supported version!\n warnings.warn(\"urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported \"\n[2022-11-28 19:46:35,848] {podaac_data_downloader.py:243} INFO - Found 2 total files to download\n[2022-11-28 19:46:35,885] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:46:35.885322 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_os75nb.nc4\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:46:35.967540 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:287} INFO - Downloaded Files: 0\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:288} INFO - Failed Files: 0\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:289} INFO - Skipped Files: 2\n[2022-11-28 19:46:36,245] {podaac_access.py:122} INFO - CMR token successfully deleted\n[2022-11-28 19:46:36,245] {podaac_data_downloader.py:299} INFO - END\n\n\n\n\n\nds = xr.open_dataset('data/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4',drop_variables='depth').isel(trajectory=0)\nds['depth'] = Dataset('data/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4')['depth'][0]\n\n\nsubset_oceanus = ds.where((ds.time>=t0)&(ds.time<=t1),drop=True) \n\n\n# interpolate to subset72 time\n# TODO: try to resample with averaging to 5 minutes before interpolating (e.g., subset_oceanus.resample(time='5min').mean())\nsubset_oceanus = subset_oceanus.interp({'time': subset72.time})\n\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imin = index.get_loc(minval, method=\"nearest\")\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imax = index.get_loc(maxval, method=\"nearest\")\n\n\n\n# interpolate to saildrone depth to oceanus depth\nsubset72['depth'] = subset72.cell_depth + 1.9 # add depth of the instrument 1.9\nsubset72 = subset72.swap_dims({'cell_depth': 'depth'})\nsubset72 = subset72.interp({'depth': subset_oceanus.depth})\n\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imin = index.get_loc(minval, method=\"nearest\")\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imax = index.get_loc(maxval, method=\"nearest\")\n\n\n\nVisual comparison\n\ndepth_bin = 1\nsc = 6\n\nfig, ax = plt.subplots(figsize=(12,8))\n\nq1 = plt.quiver(\n subset72.longitude,\n subset72.latitude,\n subset72.vel_east.isel(depth=depth_bin),\n subset72.vel_north.isel(depth=depth_bin),\n color='C00',scale=sc\n)\n\n\nq2 = plt.quiver(\n subset_oceanus.longitude,\n subset_oceanus.latitude,\n subset_oceanus.zonal_velocity_component.isel(depth=depth_bin),\n subset_oceanus.meridional_velocity_component.isel(depth=depth_bin),\n color='C02', scale=sc\n)\n\nplt.quiverkey(q1, .1, 0.815, .5, 'SD-1072',)\nplt.quiverkey(q2, .1, 0.620, .5, 'R/V Oceanus',)\n\nax.set_ylim(37.18,37.26)\nax.set_aspect(np.cos(37.22*np.pi/180))\n\n\n\n\n\nfig, axs = plt.subplots(2,1,figsize=(12,14))\n\nkw = {'vmin': -.4,'vmax': +.4,'cmap': 'RdBu_r'}\n\nsubset_oceanus.zonal_velocity_component.plot(x='time',y='depth',ax=axs[0],**kw)\nsubset72.vel_east.plot(x='time',y='depth',ax=axs[1])\n\naxs[0].set_title('R/V Oceanus')\naxs[1].set_title('SD-1072')\n\n[ax.set_ylim(80,0) for ax in axs]\n\n[(80.0, 0.0), (80.0, 0.0)]\n\n\n\n\n\n\n\nQuantitative comparison: calculate and plot velocity differences\n\nsubset_oceanus = subset_oceanus.where(subset_oceanus.depth<=70)\nsubset72 = subset72.where(subset_oceanus.depth<=70)\n\n\ndu = subset_oceanus.zonal_velocity_component-subset72.vel_east\ndv = subset_oceanus.meridional_velocity_component-subset72.vel_north\n\n\n_ = plt.hist(dv.values.flatten(),bins=np.arange(-.15,.156,.01))\n\n\n\n\n\ndu.mean().values, dv.mean().values\n\n(array(-0.01207606), array(0.00744287))\n\n\n\ndu.std().values, dv.std().values\n\n(array(0.03616049), array(0.04748247))"
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+ "title": "HUC Feature Translation Service (FTS) Examples",
+ "section": "Check with Earthdata Search",
+ "text": "Check with Earthdata Search\nHere we show how to check the CMR response with Earth Data Search. First, let’s get the number of granules from CMR response.\nNote that Earthdata Search query data granules (files) by Visvalingam polygon. So make sure to use Visvalingam polygon when query CMR with polygon\n\nnumber_of_granules=cmr_response.headers['CMR-Hits']\nprint(number_of_granules)\n\n1325\n\n\nTo find granules in Earthdata Search, we need to first search for the collection. You can search for SMAP/Sentinel in the top left corner to find the Soil Mositure dataset.Earthdata allows you to do an Advanced seach over a HUC region. You can search by HUC ID or HUC region. In our case, let’s search for “HUC Region” and “Upper Tuolumne” .\n\n\n\nAdvanced Search\n\n\nFinally, we can locate the total number of granules from the search which matches with the one we identified from CMR.\nAlso, our search in Earh Data has a unique url with a project ID. This url corresponds to SMAP/Sentinel Soil Mositure granules within the Upper Tuolumne:\nhttps://search.earthdata.nasa.gov/search/granules?projectId=3965468611\n\n\n\nEarthdata Granules"
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+ "href": "notebooks/datasets/OPERA_GIS_Notebook.html",
+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
"section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\n%load_ext autoreload\n%autoreload 2\nimport sys\nsys.path.append('../src')\nfrom matplotlib import pyplot as plt\n%matplotlib inline\nfrom pathlib import Path\nimport numpy as np\nimport rioxarray\nimport xarray as xr\nfrom plot_dopplerscatt_data import make_streamplot_image\nimport warnings\nwarnings.simplefilter('ignore')"
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- "title": "S-MODE Workshop: Science Case Study Airborne Part 2",
- "section": "Apply the good data mask for all current observations",
- "text": "Apply the good data mask for all current observations\nOnly accept estimates that use a minimum number of observations. The current recommended number is 4. Use the variable nobs_all_lines to make a mask and then mask all variables\n\ndef mask_velocity_all_lines(ds, minobs, data_vars, vthresh=0.1):\n \"\"\"Mask all measurements with fewer than minobs observations.\"\"\"\n bad = ( (ds.nobs_all_lines.data < minobs) |\n (ds.u_current_error_all_lines.data > vthresh) |\n (ds.v_current_error_all_lines.data > vthresh) )\n for v in data_vars:\n if np.issubdtype(ds[v].dtype, np.floating):\n ds[v].data[bad] = np.nan\n return ds\n\n\nminobs = 4\nvthresh =0.1\ndata_vars = [\n 'u_current_all_lines',\n 'v_current_all_lines',\n 'u_current_error_all_lines',\n 'v_current_error_all_lines']\nds_all = mask_velocity_all_lines(ds_all, minobs, data_vars, vthresh)"
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
},
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- "title": "Zarr Example",
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- "text": "imported on: 2023-07-05\n\nThis notebook is from NASA’s Zarr EOSDIS store notebook\n\n\nThe original source for this document is https://github.com/nasa/zarr-eosdis-store/blob/main/presentation/example.ipynb\n\n\nzarr-eosdis-store example\nInstall dependencies\n\nimport sys\n\n# zarr and zarr-eosdis-store, the main libraries being demoed\n!{sys.executable} -m pip install zarr zarr-eosdis-store\n\n# Notebook-specific libraries\n!{sys.executable} -m pip install matplotlib\n\nImportant: To run this, you must first create an Earthdata Login account (https://urs.earthdata.nasa.gov) and place your credentials in ~/.netrc e.g.:\n machine urs.earthdata.nasa.gov login YOUR_USER password YOUR_PASSWORD\nNever share or commit your password / .netrc file!\nBasic usage. After these lines, we work with ds as though it were a normal Zarr dataset\n\nimport zarr\nfrom eosdis_store import EosdisStore\n\nurl = 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210715090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'\n\nds = zarr.open(EosdisStore(url))\n\nView the file’s variable structure\n\nprint(ds.tree())\n\n/\n ├── analysed_sst (1, 17999, 36000) int16\n ├── analysis_error (1, 17999, 36000) int16\n ├── dt_1km_data (1, 17999, 36000) int16\n ├── lat (17999,) float32\n ├── lon (36000,) float32\n ├── mask (1, 17999, 36000) int16\n ├── sea_ice_fraction (1, 17999, 36000) int16\n ├── sst_anomaly (1, 17999, 36000) int16\n └── time (1,) int32\n\n\nFetch the latitude and longitude arrays and determine start and end indices for our area of interest. In this case, we’re looking at the Great Lakes, which have a nice, recognizeable shape. Latitudes 41 to 49, longitudes -93 to 76.\n\nlats = ds['lat'][:]\nlons = ds['lon'][:]\nlat_range = slice(lats.searchsorted(41), lats.searchsorted(49))\nlon_range = slice(lons.searchsorted(-93), lons.searchsorted(-76))\n\nGet the analysed sea surface temperature variable over our area of interest and apply scale factor and offset from the file metadata. In a future release, scale factor and add offset will be automatically applied.\n\nvar = ds['analysed_sst']\nanalysed_sst = var[0, lat_range, lon_range] * var.attrs['scale_factor'] + var.attrs['add_offset']\n\nDraw a pretty picture\n\nfrom matplotlib import pyplot as plt\n\nplt.rcParams[\"figure.figsize\"] = [16, 8]\nplt.imshow(analysed_sst[::-1, :])\nNone\n\n\n\n\nIn a dozen lines of code and a few seconds, we have managed to fetch and visualize the 3.2 megabyte we needed from a 732 megabyte file using the original archive URL and no processing services"
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+ "href": "notebooks/datasets/OPERA_GIS_Notebook.html#local-machine-download-version",
+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Local Machine Download Version",
+ "text": "Local Machine Download Version\n\nAuthor: Nicholas Tarpinian, PO.DAAC"
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- "text": "Author: Jinbo Wang Jinbo.Wang@jpl.nasa.gov, Jack McNelis jack.mcnelis@jpl.nasa.gov\nThis is a demonstration of accessing the ECCO-BASED PRE-SWOT NUMERICAL SIMULATION. The dataset can be found following https://search.earthdata.nasa.gov/search?q=pocloud%20pre-swot.\nimport s3fs\nimport requests\nimport xarray as xr\nimport pylab as plt\nfrom netrc import netrc\nfrom urllib import request\nfrom platform import system\nfrom getpass import getpass\nfrom http.cookiejar import CookieJar\nfrom os.path import expanduser, join\n\nShortName = \"MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0\"\ntarget_file = \"LLC4320_pre-SWOT_ACC_SMST_20111221.nc\"\nEarthdata Login\nAuthenticate with your Earthdata Login/URS credentials by configuring a .netrc file in your home directory.\nRun the next cell to authenticate. (You might be prompted for your Earthdata Login username and password.)\ndef setup_earthdata_login_auth(endpoint: str='urs.earthdata.nasa.gov'):\n netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n try:\n username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n print('Please provide your Earthdata Login credentials for access.')\n print('Your info will only be passed to %s and will not be exposed in Jupyter.' % (endpoint))\n username = input('Username: ')\n password = getpass('Password: ')\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n \nsetup_earthdata_login_auth()\n\nPlease provide your Earthdata Login credentials for access.\nYour info will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter.\n\n\nUsername: marscreature\nPassword: ·············\nYou should now be able to download the file at the following link:\nhttps_access = f\"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/{ShortName}/{target_file}\"\n\nprint(https_access)\n\nhttps://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0/LLC4320_pre-SWOT_ACC_SMST_2011122?.nc\nOpen the dataset\nRun the next cell to access/open the netCDF file with xarray:\ndef begin_s3_direct_access():\n \"\"\"Returns s3fs object for accessing datasets stored in S3.\"\"\"\n response = requests.get(\"https://archive.podaac.earthdata.nasa.gov/s3credentials\").json()\n return s3fs.S3FileSystem(key=response['accessKeyId'],\n secret=response['secretAccessKey'],\n token=response['sessionToken'], \n client_kwargs={'region_name':'us-west-2'})\n\ntry:\n fs = begin_s3_direct_access()\n # Load netCDF with 's3fs' and 'xarray' upon successful connection to S3:\n dd = xr.open_dataset(fs.open(f\"podaac-ops-cumulus-protected/{ShortName}/{target_file}\"))\nexcept:\n print(\"Failed to establish AWS in-region access. Downloading to local disk instead.\")\n request.urlretrieve(https_access, target_file)\n # Load netCDF with 'xarray' after download completes:\n \n dd = xr.open_dataset(target_file)\n\nprint(dd)\n\nFailed to establish AWS in-region access. Downloading to local disk instead.\n<xarray.Dataset>\nDimensions: (i: 192, i_g: 192, j: 349, j_g: 349, k: 84, k_l: 84, k_p1: 85, k_u: 84, nb: 2, time: 24)\nCoordinates:\n * j_g (j_g) float32 0.0 1.0 2.0 3.0 4.0 ... 345.0 346.0 347.0 348.0\n * i (i) float32 0.0 1.0 2.0 3.0 4.0 ... 187.0 188.0 189.0 190.0 191.0\n * i_g (i_g) float32 0.0 1.0 2.0 3.0 4.0 ... 188.0 189.0 190.0 191.0\n * j (j) float32 0.0 1.0 2.0 3.0 4.0 ... 344.0 345.0 346.0 347.0 348.0\n * k (k) int32 0 1 2 3 4 5 6 7 8 9 10 ... 74 75 76 77 78 79 80 81 82 83\n * k_u (k_u) int32 0 1 2 3 4 5 6 7 8 9 ... 74 75 76 77 78 79 80 81 82 83\n * k_l (k_l) int32 0 1 2 3 4 5 6 7 8 9 ... 74 75 76 77 78 79 80 81 82 83\n * k_p1 (k_p1) int32 0 1 2 3 4 5 6 7 8 9 ... 75 76 77 78 79 80 81 82 83 84\n * nb (nb) int32 0 1\n * time (time) datetime64[ns] 2011-12-21 ... 2011-12-21T23:00:00\nData variables:\n XC (j, i) float32 ...\n YC (j, i) float32 ...\n DXV (j, i) float32 ...\n DYU (j, i) float32 ...\n Depth (j, i) float32 ...\n AngleSN (j, i) float32 ...\n AngleCS (j, i) float32 ...\n DXC (j, i_g) float32 ...\n DYG (j, i_g) float32 ...\n DYC (j_g, i) float32 ...\n DXG (j_g, i) float32 ...\n XG (j_g, i_g) float32 ...\n YG (j_g, i_g) float32 ...\n RAZ (j_g, i_g) float32 ...\n XC_bnds (j, i, nb) float64 ...\n YC_bnds (j, i, nb) float64 ...\n Z (k) float32 ...\n Zp1 (k_p1) float32 ...\n Zu (k_u) float32 ...\n Zl (k_l) float32 ...\n Z_bnds (k, nb) float32 ...\n Eta (time, j, i) float64 ...\n KPPhbl (time, j, i) float64 ...\n PhiBot (time, j, i) float64 ...\n oceFWflx (time, j, i) float64 ...\n oceQnet (time, j, i) float64 ...\n oceQsw (time, j, i) float64 ...\n oceSflux (time, j, i) float64 ...\n oceTAUX (time, j, i_g) float64 ...\n oceTAUY (time, j_g, i) float64 ...\n Theta (time, k, j, i) float64 ...\n Salt (time, k, j, i) float64 ...\n U (time, k, j, i_g) float32 ...\n V (time, k, j_g, i) float64 ...\n W (time, k_l, j, i) float64 ...\nAttributes:\n acknowledgement: This research was carried out by the Jet...\n author: Dimitris Menemenlis et al.\n contributor: Chris Hill, Christopher E. Henze, Jinbo ...\n contributor_role: MITgcm developer, AMES supercomputer sup...\n cdm_data_type: Grid\n Conventions: CF-1.7, ACDD-1.3\n creator_email: menemenlis@jpl.nasa.gov\n creator_institution: NASA Jet Propulsion Laboratory (JPL)\n creator_name: Dimitris Menemelis et al.\n creator_type: group\n creator_url: https://science.jpl.nasa.gov/people/Mene...\n date_created: 2021-01-20T00:00:00\n date_issued: 2021-01-20T00:00:00\n date_metadata_modified: 2021-01-20T00:00:00\n geospatial_lat_max: -53.00567\n geospatial_lat_min: -56.989952\n geospatial_lat_units: degrees_north\n geospatial_lon_max: 154.28125\n geospatial_lon_min: 150.30208\n geospatial_lon_units: degrees_east\n geospatial_bounds_crs: EPSG:4326\n geospatial_vertical_max: 0\n geospatial_vertical_min: -6134.5\n geospatial_vertical_positive: up\n geospatial_vertical_resolution: variable\n geospatial_vertical_units: meter\n history: Inaugural release of LLC4320 regions to ...\n id: MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_...\n institution: NASA Jet Propulsion Laboratory (JPL)\n instrument_vocabulary: GCMD instrument keywords\n keywords: EARTH SCIENCE SERVICES > MODELS > EARTH ...\n keywords_vocabulary: NASA Global Change Master Directory (GCM...\n license: Public Domain\n metadata_link: http://podaac.jpl.nasa.gov/ws/metadata/d...\n naming_authority: gov.nasa.jpl\n platform_vocabulary: GCMD platform keywords\n processing_level: L4\n product_time_coverage_end: 2012-11-15T00:00:00\n product_time_coverage_start: 2011-09-13T00:00:00\n product_version: 1.0\n program: NASA Physical Oceanography\n project: Surface Water and Ocean Topography (SWOT...\n publisher_email: podaac@podaac.jpl.nasa.gov\n publisher_institution: PO.DAAC\n publisher_name: Physical Oceanography Distributed Active...\n publisher_type: institution\n publisher_url: https://podaac.jpl.nasa.gov\n source: MITgcm simulation\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadat...\n summary: This is a subset of a global ocean simul...\n time_coverage_end: 2011-12-21 23:00:00\n time_coverage_start: 2011-12-21 00:00:00\n title: LLC4320 regional Southern Ocean\n geospatial_lon_resolution: variable\n geospatial_lat_resolution: variable\n platform: MITgcm"
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+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Summary & Learning Objectives",
+ "text": "Summary & Learning Objectives\n\nNotebook showcasing how to work with OPERA DSWx data on a local machine\n\nUtilizing the earthaccess Python package. For more information visit: https://nsidc.github.io/earthaccess/\nOption to query the new dataset based on users choice; either by classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’), etc.\nVisualizing the dataset based on its classified layer values.\nMosaicking multiple layers into a single GeoTIFF file."
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- "title": "PRE-SWOT NUMERICAL SIMULATION VERSION 1 User Guide Demo",
- "section": "Plot eight 2D fields.",
- "text": "Plot eight 2D fields.\n\nfig,ax=plt.subplots(3,3,figsize=(16,16))\n\nvarn=['Eta','KPPhbl','PhiBot','oceFWflx','oceQnet','oceQsw','oceSflux','oceTAUY','oceTAUX']\n\nfor i in range(3):\n for j in range(3):\n dd[varn[i*3+j]][0,...].plot(ax=ax[j,i])\n ax[j,i].set_title(varn[i*3+j])\nplt.tight_layout()"
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+ "section": "Requirements",
+ "text": "Requirements\n\n1. Compute environment\nThis tutorial is written to run in the following environment: - Local compute environment e.g. laptop, server: this tutorial can be run on your local machine\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nImport libraries\n\n#from original notebook:\nimport requests\nimport json\nimport rasterio as rio\nfrom rasterio.plot import show\nfrom rasterio.merge import merge\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes \nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\nimport numpy as np\nfrom pathlib import Path\nimport os\nfrom urllib.request import urlretrieve\nfrom json import dumps\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store"
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- "title": "PRE-SWOT NUMERICAL SIMULATION VERSION 1 User Guide Demo",
- "section": "Plot a 3D field based (temperature)",
- "text": "Plot a 3D field based (temperature)\n\nfig,ax=plt.subplots(1,2,figsize=(20,10))\ntheta=dd['Theta'][:]\ntheta.coords['k']=dd['Z'].data\n\ntheta[0,0,...].plot(ax=ax[0])\nax[0].vlines(100,0,400,colors='w')\ntheta[0,:,:,100].plot(ax=ax[1])\n\n<matplotlib.collections.QuadMesh at 0x7f9110022550>"
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+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Authentication with earthaccess",
+ "text": "Authentication with earthaccess\nIn this notebook, we will be calling the authentication in the below cell.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)\n\n\nSearch using earthaccess for OPERA DSWx\nEach dataset has it’s own unique collection concept ID. For the OPERA_L3_DSWX-HLS_PROVISIONAL_V1 dataset, we can find the collection ID here.\nFor this tutorial, we are looking at the Lake Powell Reservoir.\nWe used bbox finder to get the exact coordinates for our area of interest.\nWe want to look at two different times for comparison: 04/11/2023 and 05/02/2023. To find these dates, let’s search for all the data granules between the two.\n\n#earthaccess data search\nresults = earthaccess.search_data(concept_id=\"C2617126679-POCLOUD\", bounding_box=(-111.144811,36.980121,-110.250799,37.915625), temporal=(\"2023-04-11\",\"2023-05-03\"))\n\nGranules found: 50\n\n\nFrom this search, we received 50 granules between 4/11/2023 and 05/02/2023.\n\n\nGet desired links\nOPERA has 10 different available layers within each granule. Each granule consists of 10 files, one for each layer. We will only need one of these files since we are only looking at one layer.\nLet’s get the download links for the desired files. We want to query the dataset based on a specific classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’) as well as for the two dates (04/11/2023 and 05/02/2023).\nWe will look at ‘B01_WTR’ which is the Water Classification (WTR) layer of the OPERA DSWx dataset. Details on each available layer and the data product can be found here.\n\ntype(results[0])\n\nearthaccess.results.DataGranule\n\n\nHere, we see that the results output is in the DataGranule format, allowing us to to use the data_links call\n\n#add the necessary data to a list, here we are looking for B01_WTR layer and two dates specified earlier\ndownloads_04112023 = []\ndownloads_05022023 = []\n\nfor g in results:\n for l in earthaccess.results.DataGranule.data_links(g):\n if 'B01_WTR' in l:\n if '20230411' in l:\n downloads_04112023.append(l)\n if '20230502' in l:\n downloads_05022023.append(l)\n\nprint(len(downloads_04112023))\nprint(len(downloads_05022023))\n\n4\n4\n\n\nFor the B01_WTR layer, each date has 4 files\n\n\nDownload the Data into a folder\nSince we are looking at two seperate times, we create two folder path names, one for each date, so we can mosaic all the files within one folder based on its respective time range later.\n\n#download data into folder on local machine\nearthaccess.download(downloads_04112023, \"./data_downloads/OPERA_041123\")\nearthaccess.download(downloads_05022023, \"./data_downloads/OPERA_050223\")\n\n\n\n\nFile OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVG_20230411T180222Z_20230414T030945Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVF_20230411T180222Z_20230414T030950Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SWF_20230411T180222Z_20230414T031011Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SWG_20230502T180919Z_20230504T155716Z_S2B_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVG_20230502T180919Z_20230504T094859Z_S2B_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVF_20230502T180919Z_20230504T094852Z_S2B_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SWF_20230502T180919Z_20230504T155656Z_S2B_30_v1.0_B01_WTR.tif already downloaded\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n['OPERA_L3_DSWx-HLS_T12SWG_20230502T180919Z_20230504T155716Z_S2B_30_v1.0_B01_WTR.tif',\n 'OPERA_L3_DSWx-HLS_T12SVG_20230502T180919Z_20230504T094859Z_S2B_30_v1.0_B01_WTR.tif',\n 'OPERA_L3_DSWx-HLS_T12SWF_20230502T180919Z_20230504T155656Z_S2B_30_v1.0_B01_WTR.tif',\n 'OPERA_L3_DSWx-HLS_T12SVF_20230502T180919Z_20230504T094852Z_S2B_30_v1.0_B01_WTR.tif']\n\n\nData should download into two folders seperated by date, each having four files.\n\n\nVisualizing the Dataset\nLet’s now visualize an individual layer for a single file that was downloaded using Rasterio to read the GeoTIFF image.\n\ndsw = rio.open('data_downloads/OPERA_041123/OPERA_L3_DSWx-HLS_T12SVG_20230411T180222Z_20230414T030945Z_L8_30_v1.0_B01_WTR.tif')\n\nOPERA is a single band image with specific classified rgb values.\nThis requires to read the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\nimage = dsw.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw2 = color_array[image]\n\n\nfig, ax = plt.subplots(figsize=(15,10))\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nax.legend(handles=patches,\n bbox_to_anchor=(1.28, 1),\n facecolor=\"gainsboro\")\n\nplt.imshow(dsw2)\nplt.show()\n\n\n\n\n\n\nMosaic Multiple OPERA Layers\nWhen creating a mosaic, make sure the temporal range is correct/matching. We define the output directory for the mosaic GeoTIFFs below.\nThe mosaic is being created because we have 4 results from the bounding box area provided. If you receive more than 1 result and would like to see a single raster image of all the results, mosaicking is the solution.\n\nPath('data_downloads/mosaic_outputs').mkdir(parents=True, exist_ok=True)\noutput_path = 'data_downloads/mosaic_outputs'\n\nWe define a function to convert files per timestamp to mosaicked geoTIFFs.\n\ndef raster2mosaic(data_folder, output_path, output_file_name):\n raster_files = list(data_folder.iterdir())\n raster_to_mosaic_list = [] #create empty list\n for p in raster_files:\n raster = rio.open(p)\n raster_to_mosaic_list.append(raster)\n mosaic, output = merge(raster_to_mosaic_list) #the merge function will mosaic the raster images\n #Then we update the raster's metadata to match the width and height of the mosaic\n output_meta = raster.meta.copy()\n output_meta.update(\n {\"driver\": \"GTiff\",\n \"height\": mosaic.shape[1],\n \"width\": mosaic.shape[2],\n \"transform\": output\n }\n )\n #Save the output in a new mosaicked raster image\n with rio.open(os.path.join(output_path, output_file_name), 'w', **output_meta) as m:\n m.write(mosaic)\n\n\n#set data to a list for each of the two data sets\nfolder1 = Path(\"data_downloads/OPERA_041123\")\nfolder2 = Path(\"data_downloads/OPERA_050223\")\n\nraster2mosaic(folder1, output_path, 'mosaic_041123.tif')\nraster2mosaic(folder2, output_path, 'mosaic_050223.tif')\n\n\n\nVisualizing the Mosaic\nOpen the new mosaicked raster images individually with its respective paths.\n\nmos1 = rio.open(os.path.join(output_path, 'mosaic_041123.tif'))\nmos2 = rio.open(os.path.join(output_path, 'mosaic_050223.tif')) \n\nTo visualize the mosaic, you must utilize the single layer colormap.\nThis will be the ‘dsw’ variable used earlier to visualize a single layer. Similarly reading the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\nimage2 = mos2.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw4 = color_array[image2]\n\n\nimage1 = mos1.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw3 = color_array[image1]\n\n\nfig = plt.figure(figsize=(20, 15))\n\nrows = 1\ncolumns = 2\n\n# Lake Powell 04/11/2023\nfig.add_subplot(rows, columns, 1)\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\nplt.imshow(dsw3)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\n# Lake Powell 05/02/2023\nfig.add_subplot(rows, columns, 2)\nplt.title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\nplt.imshow(dsw4)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nplt.show()\n\n\n\n\n\n\nTo take a closer look at a specific area of the image, we can create an inset map of a specified area.\n\nfig, ax = plt.subplots(1, 2, figsize=(20, 15))\n\nax[0].imshow(dsw3)\nax[0].set_title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\nax_ins1 = ax[0].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins1.imshow(dsw3)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins1.set_xlim(x1, x2)\nax_ins1.set_ylim(y1, y2)\nax_ins1.set_xticklabels('')\nax_ins1.set_yticklabels('')\n\nax[0].indicate_inset_zoom(ax_ins1, edgecolor='black')\n\nax[1].imshow(dsw4)\nax[1].set_title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nax_ins2 = ax[1].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins2.imshow(dsw4)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins2.set_xlim(x1, x2)\nax_ins2.set_ylim(y1, y2)\nax_ins2.set_xticklabels('')\nax_ins2.set_yticklabels('')\n\nax[1].indicate_inset_zoom(ax_ins2, edgecolor='black')\n\nplt.show()"
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@@ -553,270 +504,186 @@
"text": "Plot a time series at a point in the california current.\nPlot the time series of the SSS at (124W, 35N).\n\nplt.figure(figsize=(16,4))\ndata['sss'].interp(latitude=35,longitude=-124).plot()"
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- "objectID": "notebooks/datasets/OPERA_GIS_Notebook.html",
- "href": "notebooks/datasets/OPERA_GIS_Notebook.html",
- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "objectID": "notebooks/harmony_concatenation/Harmony_Concatenation.html",
+ "href": "notebooks/harmony_concatenation/Harmony_Concatenation.html",
+ "title": "Harmony EOSS Concise API Tutorial",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
- },
- {
- "objectID": "notebooks/datasets/OPERA_GIS_Notebook.html#local-machine-download-version",
- "href": "notebooks/datasets/OPERA_GIS_Notebook.html#local-machine-download-version",
- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Local Machine Download Version",
- "text": "Local Machine Download Version\n\nAuthor: Nicholas Tarpinian, PO.DAAC"
- },
- {
- "objectID": "notebooks/datasets/OPERA_GIS_Notebook.html#summary-learning-objectives",
- "href": "notebooks/datasets/OPERA_GIS_Notebook.html#summary-learning-objectives",
- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Summary & Learning Objectives",
- "text": "Summary & Learning Objectives\n\nNotebook showcasing how to work with OPERA DSWx data on a local machine\n\nUtilizing the earthaccess Python package. For more information visit: https://nsidc.github.io/earthaccess/\nOption to query the new dataset based on users choice; either by classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’), etc.\nVisualizing the dataset based on its classified layer values.\nMosaicking multiple layers into a single GeoTIFF file."
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- "objectID": "notebooks/datasets/OPERA_GIS_Notebook.html#requirements",
- "href": "notebooks/datasets/OPERA_GIS_Notebook.html#requirements",
- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Requirements",
- "text": "Requirements\n\n1. Compute environment\nThis tutorial is written to run in the following environment: - Local compute environment e.g. laptop, server: this tutorial can be run on your local machine\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nImport libraries\n\n#from original notebook:\nimport requests\nimport json\nimport rasterio as rio\nfrom rasterio.plot import show\nfrom rasterio.merge import merge\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes \nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\nimport numpy as np\nfrom pathlib import Path\nimport os\nfrom urllib.request import urlretrieve\nfrom json import dumps\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store"
- },
- {
- "objectID": "notebooks/datasets/OPERA_GIS_Notebook.html#authentication-with-earthaccess",
- "href": "notebooks/datasets/OPERA_GIS_Notebook.html#authentication-with-earthaccess",
- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Authentication with earthaccess",
- "text": "Authentication with earthaccess\nIn this notebook, we will be calling the authentication in the below cell.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)\n\n\nSearch using earthaccess for OPERA DSWx\nEach dataset has it’s own unique collection concept ID. For the OPERA_L3_DSWX-HLS_PROVISIONAL_V1 dataset, we can find the collection ID here.\nFor this tutorial, we are looking at the Lake Powell Reservoir.\nWe used bbox finder to get the exact coordinates for our area of interest.\nWe want to look at two different times for comparison: 04/11/2023 and 05/02/2023. To find these dates, let’s search for all the data granules between the two.\n\n#earthaccess data search\nresults = earthaccess.search_data(concept_id=\"C2617126679-POCLOUD\", bounding_box=(-111.144811,36.980121,-110.250799,37.915625), temporal=(\"2023-04-11\",\"2023-05-03\"))\n\nGranules found: 50\n\n\nFrom this search, we received 50 granules between 4/11/2023 and 05/02/2023.\n\n\nGet desired links\nOPERA has 10 different available layers within each granule. Each granule consists of 10 files, one for each layer. We will only need one of these files since we are only looking at one layer.\nLet’s get the download links for the desired files. We want to query the dataset based on a specific classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’) as well as for the two dates (04/11/2023 and 05/02/2023).\nWe will look at ‘B01_WTR’ which is the Water Classification (WTR) layer of the OPERA DSWx dataset. Details on each available layer and the data product can be found here.\n\ntype(results[0])\n\nearthaccess.results.DataGranule\n\n\nHere, we see that the results output is in the DataGranule format, allowing us to to use the data_links call\n\n#add the necessary data to a list, here we are looking for B01_WTR layer and two dates specified earlier\ndownloads_04112023 = []\ndownloads_05022023 = []\n\nfor g in results:\n for l in earthaccess.results.DataGranule.data_links(g):\n if 'B01_WTR' in l:\n if '20230411' in l:\n downloads_04112023.append(l)\n if '20230502' in l:\n downloads_05022023.append(l)\n\nprint(len(downloads_04112023))\nprint(len(downloads_05022023))\n\n4\n4\n\n\nFor the B01_WTR layer, each date has 4 files\n\n\nDownload the Data into a folder\nSince we are looking at two seperate times, we create two folder path names, one for each date, so we can mosaic all the files within one folder based on its respective time range later.\n\n#download data into folder on local machine\nearthaccess.download(downloads_04112023, \"./data_downloads/OPERA_041123\")\nearthaccess.download(downloads_05022023, \"./data_downloads/OPERA_050223\")\n\n\n\n\nFile OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVG_20230411T180222Z_20230414T030945Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVF_20230411T180222Z_20230414T030950Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SWF_20230411T180222Z_20230414T031011Z_L8_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SWG_20230502T180919Z_20230504T155716Z_S2B_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVG_20230502T180919Z_20230504T094859Z_S2B_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SVF_20230502T180919Z_20230504T094852Z_S2B_30_v1.0_B01_WTR.tif already downloaded\nFile OPERA_L3_DSWx-HLS_T12SWF_20230502T180919Z_20230504T155656Z_S2B_30_v1.0_B01_WTR.tif already downloaded\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n['OPERA_L3_DSWx-HLS_T12SWG_20230502T180919Z_20230504T155716Z_S2B_30_v1.0_B01_WTR.tif',\n 'OPERA_L3_DSWx-HLS_T12SVG_20230502T180919Z_20230504T094859Z_S2B_30_v1.0_B01_WTR.tif',\n 'OPERA_L3_DSWx-HLS_T12SWF_20230502T180919Z_20230504T155656Z_S2B_30_v1.0_B01_WTR.tif',\n 'OPERA_L3_DSWx-HLS_T12SVF_20230502T180919Z_20230504T094852Z_S2B_30_v1.0_B01_WTR.tif']\n\n\nData should download into two folders seperated by date, each having four files.\n\n\nVisualizing the Dataset\nLet’s now visualize an individual layer for a single file that was downloaded using Rasterio to read the GeoTIFF image.\n\ndsw = rio.open('data_downloads/OPERA_041123/OPERA_L3_DSWx-HLS_T12SVG_20230411T180222Z_20230414T030945Z_L8_30_v1.0_B01_WTR.tif')\n\nOPERA is a single band image with specific classified rgb values.\nThis requires to read the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\nimage = dsw.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw2 = color_array[image]\n\n\nfig, ax = plt.subplots(figsize=(15,10))\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nax.legend(handles=patches,\n bbox_to_anchor=(1.28, 1),\n facecolor=\"gainsboro\")\n\nplt.imshow(dsw2)\nplt.show()\n\n\n\n\n\n\nMosaic Multiple OPERA Layers\nWhen creating a mosaic, make sure the temporal range is correct/matching. We define the output directory for the mosaic GeoTIFFs below.\nThe mosaic is being created because we have 4 results from the bounding box area provided. If you receive more than 1 result and would like to see a single raster image of all the results, mosaicking is the solution.\n\nPath('data_downloads/mosaic_outputs').mkdir(parents=True, exist_ok=True)\noutput_path = 'data_downloads/mosaic_outputs'\n\nWe define a function to convert files per timestamp to mosaicked geoTIFFs.\n\ndef raster2mosaic(data_folder, output_path, output_file_name):\n raster_files = list(data_folder.iterdir())\n raster_to_mosaic_list = [] #create empty list\n for p in raster_files:\n raster = rio.open(p)\n raster_to_mosaic_list.append(raster)\n mosaic, output = merge(raster_to_mosaic_list) #the merge function will mosaic the raster images\n #Then we update the raster's metadata to match the width and height of the mosaic\n output_meta = raster.meta.copy()\n output_meta.update(\n {\"driver\": \"GTiff\",\n \"height\": mosaic.shape[1],\n \"width\": mosaic.shape[2],\n \"transform\": output\n }\n )\n #Save the output in a new mosaicked raster image\n with rio.open(os.path.join(output_path, output_file_name), 'w', **output_meta) as m:\n m.write(mosaic)\n\n\n#set data to a list for each of the two data sets\nfolder1 = Path(\"data_downloads/OPERA_041123\")\nfolder2 = Path(\"data_downloads/OPERA_050223\")\n\nraster2mosaic(folder1, output_path, 'mosaic_041123.tif')\nraster2mosaic(folder2, output_path, 'mosaic_050223.tif')\n\n\n\nVisualizing the Mosaic\nOpen the new mosaicked raster images individually with its respective paths.\n\nmos1 = rio.open(os.path.join(output_path, 'mosaic_041123.tif'))\nmos2 = rio.open(os.path.join(output_path, 'mosaic_050223.tif')) \n\nTo visualize the mosaic, you must utilize the single layer colormap.\nThis will be the ‘dsw’ variable used earlier to visualize a single layer. Similarly reading the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\nimage2 = mos2.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw4 = color_array[image2]\n\n\nimage1 = mos1.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw3 = color_array[image1]\n\n\nfig = plt.figure(figsize=(20, 15))\n\nrows = 1\ncolumns = 2\n\n# Lake Powell 04/11/2023\nfig.add_subplot(rows, columns, 1)\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\nplt.imshow(dsw3)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\n# Lake Powell 05/02/2023\nfig.add_subplot(rows, columns, 2)\nplt.title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\nplt.imshow(dsw4)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nplt.show()\n\n\n\n\n\n\nTo take a closer look at a specific area of the image, we can create an inset map of a specified area.\n\nfig, ax = plt.subplots(1, 2, figsize=(20, 15))\n\nax[0].imshow(dsw3)\nax[0].set_title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\nax_ins1 = ax[0].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins1.imshow(dsw3)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins1.set_xlim(x1, x2)\nax_ins1.set_ylim(y1, y2)\nax_ins1.set_xticklabels('')\nax_ins1.set_yticklabels('')\n\nax[0].indicate_inset_zoom(ax_ins1, edgecolor='black')\n\nax[1].imshow(dsw4)\nax[1].set_title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nax_ins2 = ax[1].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins2.imshow(dsw4)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins2.set_xlim(x1, x2)\nax_ins2.set_ylim(y1, y2)\nax_ins2.set_xticklabels('')\nax_ins2.set_yticklabels('')\n\nax[1].indicate_inset_zoom(ax_ins2, edgecolor='black')\n\nplt.show()"
+ "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
},
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- "objectID": "notebooks/opendap/MUR-OPeNDAP.html",
- "href": "notebooks/opendap/MUR-OPeNDAP.html",
- "title": "OPeNDAP Access",
+ "objectID": "notebooks/harmony_concatenation/Harmony_Concatenation.html#what-is-concise",
+ "href": "notebooks/harmony_concatenation/Harmony_Concatenation.html#what-is-concise",
+ "title": "Harmony EOSS Concise API Tutorial",
"section": "",
- "text": "Search the common metadata repository (CMR) for the MUR dataset\nObtain OPeNDAP links from CMR search\nDownload data from OPeNDAP links and open via xarray to visualize data\n\n\n#https://ghrc.nsstc.nasa.gov/opendap/globalir/data/2020/0525/globir.20146.0000\nfrom netCDF4 import Dataset\nimport xarray as xr\nimport dask\nimport os\nimport requests\n\n#Allows us to visualize the dask progress for parallel operations\nfrom dask.diagnostics import ProgressBar\nProgressBar().register()"
+ "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
},
{
- "objectID": "notebooks/opendap/MUR-OPeNDAP.html#before-you-start",
- "href": "notebooks/opendap/MUR-OPeNDAP.html#before-you-start",
- "title": "OPeNDAP Access",
+ "objectID": "notebooks/harmony_concatenation/Harmony_Concatenation.html#before-you-start",
+ "href": "notebooks/harmony_concatenation/Harmony_Concatenation.html#before-you-start",
+ "title": "Harmony EOSS Concise API Tutorial",
"section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata\nIn this notebook, we will be calling the authentication in the below cell, a work around if you do not yet have a netrc file.\n\nimport urllib\nfrom urllib import request, parse\nfrom http.cookiejar import CookieJar\nimport json\nimport getpass\nimport netrc\n\ndef setup_earthdata_login_auth(endpoint):\n \"\"\"\n Set up the request library so that it authenticates against the given Earthdata Login\n endpoint and is able to track cookies between requests. This looks in the .netrc file\n first and if no credentials are found, it prompts for them.\n Valid endpoints include:\n urs.earthdata.nasa.gov - Earthdata Login production\n \"\"\"\n try:\n username, _, password = netrc.netrc().authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n # FileNotFound = There's no .netrc file\n # TypeError = The endpoint isn't in the netrc file, causing the above to try unpacking None\n print('Please provide your Earthdata Login credentials to allow data access')\n print('Your credentials will only be passed to %s and will not be exposed in Jupyter' % (endpoint))\n username = input('Username:')\n password = getpass.getpass()\n\n \n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n\nedl=\"urs.earthdata.nasa.gov\"\n\nsetup_earthdata_login_auth(edl)\n\nPlease provide your Earthdata Login credentials to allow data access\nYour credentials will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter\n\n\nUsername: nickles\n ···········\n\n\n\n#CMR Link to use\n#https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=C1625128926-GHRC_CLOUD&temporal=2019-01-01T10:00:00Z,2019-12-31T23:59:59Z\nr = requests.get('https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=C1996881146-POCLOUD&temporal=2019-01-01T10:00:00Z,2019-02-01T00:00:00Z&pageSize=365')\nresponse_body = r.json()\n\n\nod_files = []\nfor itm in response_body['items']:\n for urls in itm['umm']['RelatedUrls']:\n if 'OPeNDAP' in urls['Description']:\n od_files.append(urls['URL'])\n\nod_files\n \n\n['https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190102090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190103090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190104090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190105090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190106090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190107090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190108090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190109090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190110090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190111090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190112090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190113090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190114090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190115090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190116090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190117090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190118090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190119090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190120090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190121090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190122090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190123090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190124090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190125090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190126090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190127090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190128090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190129090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190130090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190131090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190201090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1']\n\n\n\nlen(od_files)\n\n32\n\n\n\nfor f in od_files:\n print (\" opening \" + f)\n data_url = f'{f}.dap.nc4'\n \n \n # The notation below is [start index, step, end index]\n # lat[ /lat= 0..17998] start index. = -90\n # lon[ /lon= 0..35999] start index. = -180\n # time[ /time= 0..0] \n required_variables = {'analysed_sst[0:1:0][000:1:9000][000:1:9000]',\n 'analysis_error[0:1:0][000:1:9000][000:1:9000]',\n 'lat[000:1:9000]',\n 'lon[000:1:9000]',\n 'time[0:1:0]'}\n\n #upper latitude, left longitude, lower latitude, right longitude\n\n basename = os.path.basename(data_url)\n request_params = {'dap4.ce': ';'.join(required_variables)}\n #identity encoding to work around an issue with server side response compression (??)\n response = requests.get(data_url, params=request_params, headers={'Accept-Encoding': 'identity'})\n\n if response.ok:\n with open(basename, 'wb') as file_handler:\n file_handler.write(response.content)\n else:\n print(f'Request failed: {response.text}')\n\n\n opening 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https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190129090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190130090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190131090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n opening https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190201090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n\n\n\nimport xarray as xr\ncloud_data = xr.open_mfdataset('*.dap.nc4', engine='h5netcdf')\n\n\ncloud_data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 32, lat: 9001, lon: 9001)\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 -89.96 ... -0.01 0.0 0.01\n * lon (lon) float32 -180.0 -180.0 -180.0 ... -90.01 -90.0 -89.99\n * time (time) datetime64[ns] 2019-01-01T09:00:00 ... 2019-02-01T...\nData variables:\n analysed_sst (time, lat, lon) float32 dask.array<chunksize=(1, 9001, 9001), meta=np.ndarray>\n analysis_error (time, lat, lon) float32 dask.array<chunksize=(1, 9001, 9001), meta=np.ndarray>\nAttributes: (12/48)\n Conventions: CF-1.5\n title: Daily MUR SST, Final product\n summary: A merged, multi-sensor L4 Foundation SST anal...\n references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n institution: Jet Propulsion Laboratory\n history: created at nominal 4-day latency; replaced nr...\n ... ...\n publisher_name: GHRSST Project Office\n publisher_url: http://www.ghrsst.org\n publisher_email: ghrsst-po@nceo.ac.uk\n processing_level: L4\n cdm_data_type: grid\n history_json: [{\"$schema\":\"https:\\/\\/harmony.earthdata.nasa...xarray.DatasetDimensions:time: 32lat: 9001lon: 9001Coordinates: (3)lat(lat)float32-89.99 -89.98 -89.97 ... 0.0 0.01long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :[-90.]valid_max :[90.]comment :noneorigname :latfullnamepath :/latarray([-8.999e+01, -8.998e+01, -8.997e+01, ..., -1.000e-02, 0.000e+00,\n 1.000e-02], dtype=float32)lon(lon)float32-180.0 -180.0 ... -90.0 -89.99long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :[-180.]valid_max :[180.]comment :noneorigname :lonfullnamepath :/lonarray([-179.99, -179.98, -179.97, ..., -90.01, -90. , -89.99],\n dtype=float32)time(time)datetime64[ns]2019-01-01T09:00:00 ... 2019-02-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsorigname :timefullnamepath :/timearray(['2019-01-01T09:00:00.000000000', '2019-01-02T09:00:00.000000000',\n '2019-01-03T09:00:00.000000000', '2019-01-04T09:00:00.000000000',\n '2019-01-05T09:00:00.000000000', '2019-01-06T09:00:00.000000000',\n '2019-01-07T09:00:00.000000000', '2019-01-08T09:00:00.000000000',\n '2019-01-09T09:00:00.000000000', '2019-01-10T09:00:00.000000000',\n '2019-01-11T09:00:00.000000000', '2019-01-12T09:00:00.000000000',\n '2019-01-13T09:00:00.000000000', '2019-01-14T09:00:00.000000000',\n '2019-01-15T09:00:00.000000000', '2019-01-16T09:00:00.000000000',\n '2019-01-17T09:00:00.000000000', '2019-01-18T09:00:00.000000000',\n '2019-01-19T09:00:00.000000000', '2019-01-20T09:00:00.000000000',\n '2019-01-21T09:00:00.000000000', '2019-01-22T09:00:00.000000000',\n '2019-01-23T09:00:00.000000000', '2019-01-24T09:00:00.000000000',\n '2019-01-25T09:00:00.000000000', '2019-01-26T09:00:00.000000000',\n '2019-01-27T09:00:00.000000000', '2019-01-28T09:00:00.000000000',\n '2019-01-29T09:00:00.000000000', '2019-01-30T09:00:00.000000000',\n '2019-01-31T09:00:00.000000000', '2019-02-01T09:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (2)analysed_sst(time, lat, lon)float32dask.array<chunksize=(1, 9001, 9001), meta=np.ndarray>long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :[-32767]valid_max :[32767]comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRR19_G-NAVO, AVHRRMTA_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAForigname :analysed_sstfullnamepath :/analysed_sst\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n10.37 GB\n324.07 MB\n\n\nShape\n(32, 9001, 9001)\n(1, 9001, 9001)\n\n\nCount\n96 Tasks\n32 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanalysis_error\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 9001, 9001), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated error standard deviation of analysed_sst\n\nunits :\n\nkelvin\n\nvalid_min :\n\n[0]\n\nvalid_max :\n\n[32767]\n\ncomment :\n\nnone\n\norigname :\n\nanalysis_error\n\nfullnamepath :\n\n/analysis_error\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n10.37 GB\n324.07 MB\n\n\nShape\n(32, 9001, 9001)\n(1, 9001, 9001)\n\n\nCount\n96 Tasks\n32 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (48)Conventions :CF-1.5title :Daily MUR SST, Final productsummary :A merged, multi-sensor L4 Foundation SST analysis product from JPL.references :http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SSTinstitution :Jet Propulsion Laboratoryhistory :created at nominal 4-day latency; replaced nrt (1-day latency) version.\n2022-11-07 18:15:24 GMT hyrax-1.16.8-94 https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)/granules/20190101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.dap.nc4?dap4.ce=lat%5B000%3A1%3A9000%5D%3Banalysed_sst%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Banalysis_error%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Btime%5B0%3A1%3A0%5D%3Blon%5B000%3A1%3A9000%5D\ncomment :MUR = \\\"Multi-scale Ultra-high Reolution\\\"license :These data are available free of charge under data policy of JPL PO.DAAC.id :MUR-JPL-L4-GLOB-v04.1naming_authority :org.ghrsstproduct_version :04.1uuid :27665bc0-d5fc-11e1-9b23-0800200c9a66gds_version_id :2.0netcdf_version_id :4.1date_created :20190110T004403Zstart_time :20190101T090000Zstop_time :20190101T090000Ztime_coverage_start :20181231T210000Ztime_coverage_end :20190101T210000Zfile_quality_level :3source :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRR19_G-NAVO, AVHRRMTA_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAFplatform :Terra, Aqua, GCOM-W, NOAA-19, MetOp-A, Buoys/Shipssensor :MODIS, AMSR2, AVHRR, in-situMetadata_Conventions :Unidata Observation Dataset v1.0metadata_link :http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1keywords :Oceans > Ocean Temperature > Sea Surface Temperaturekeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsouthernmost_latitude :[-90.]northernmost_latitude :[90.]westernmost_longitude :[-180.]easternmost_longitude :[180.]spatial_resolution :0.01 degreesgeospatial_lat_units :degrees northgeospatial_lat_resolution :0.01 degreesgeospatial_lon_units :degrees eastgeospatial_lon_resolution :0.01 degreesacknowledgment :Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.creator_name :JPL MUR SST projectcreator_email :ghrsst@podaac.jpl.nasa.govcreator_url :http://mur.jpl.nasa.govproject :NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Programpublisher_name :GHRSST Project Officepublisher_url :http://www.ghrsst.orgpublisher_email :ghrsst-po@nceo.ac.ukprocessing_level :L4cdm_data_type :gridhistory_json :[{\"$schema\":\"https:\\/\\/harmony.earthdata.nasa.gov\\/schemas\\/history\\/0.1.0\\/history-0.1.0.json\",\"date_time\":\"2022-11-07T18:15:24.699+0000\",\"program\":\"hyrax\",\"version\":\"1.16.8-94\",\"parameters\":[{\"request_url\":\"https:\\/\\/opendap.earthdata.nasa.gov\\/providers\\/POCLOUD\\/collections\\/GHRSST%20Level%204%20MUR%20Global%20Foundation%20Sea%20Surface%20Temperature%20Analysis%20(v4.1)\\/granules\\/20190101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.dap.nc4?dap4.ce=lat%5B000%3A1%3A9000%5D%3Banalysed_sst%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Banalysis_error%5B0%3A1%3A0%5D%5B000%3A1%3A9000%5D%5B000%3A1%3A9000%5D%3Btime%5B0%3A1%3A0%5D%3Blon%5B000%3A1%3A9000%5D\"},{\"decoded_constraint\":\"dap4.ce=lat[000:1:9000];analysed_sst[0:1:0][000:1:9000][000:1:9000];analysis_error[0:1:0][000:1:9000][000:1:9000];time[0:1:0];lon[000:1:9000]\"}]}]\n\n\n\n#Histogram\ncloud_data['analysed_sst'].plot()\n\n[########################################] | 100% Completed | 29.9s\n\n\n(array([3.66360932e+08, 1.41839843e+08, 1.33124088e+08, 1.42820817e+08,\n 1.34985851e+08, 1.21022644e+08, 1.70274605e+08, 3.11394382e+08,\n 4.32103972e+08, 2.16985858e+08]),\n array([271.35 , 274.6785 , 278.007 , 281.3355 , 284.664 , 287.9925 ,\n 291.32098, 294.64948, 297.978 , 301.3065 , 304.63498],\n dtype=float32),\n <BarContainer object of 10 artists>)\n\n\n\n\n\n\n# Choose one time segment, plot the data\ncloud_data['analysed_sst'].isel(time=4).plot()\n\n[########################################] | 100% Completed | 1.8s\n\n\n<matplotlib.collections.QuadMesh at 0x1ede51eca30>\n\n\n\n\n\n\n#Plot a single point over time\ncloud_data['analysed_sst'].isel(lat=7000, lon=7000).plot()\n\n[########################################] | 100% Completed | 0.3s"
+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\nSet up libraries needed to run demo\n\nimport os\nfrom harmony import BBox, Client, Collection, Request, Environment\nimport xarray as xr\nimport netCDF4 as nc\nimport matplotlib.pyplot as plt\n\nSet up collection to run concise and how many granules to concatenate\n\ncollection_id = 'C1940473819-POCLOUD'\nmax_results = 5\n\nSetup harmony client to make our harmony request.\nCreate our request with the collection we want to concatenate, set concatenate to true, how many granules we want to concatenate, set skip preview to true so job doesn’t pause, and the format output we want.\nCheck to make sure our harmony request is valid.\n\nharmony_client = Client(env=Environment.PROD)\n\ncollection = Collection(id=collection_id)\n\nrequest = Request(\n collection = collection,\n concatenate = True,\n max_results = max_results,\n skip_preview = True,\n format=\"application/x-netcdf4\",\n)\n\nrequest.is_valid()\n\nTrue\n\n\nNow that we have a valid request we simply need to call the submit function using the client we created earlier and pass in the request as a parameter.\nTip: if you want to see the request before submitting it, use the request_as_curl function on the client to get an equivalent curl command for the request that will be submitted.\n\nprint(harmony_client.request_as_curl(request))\n\njob1_id = harmony_client.submit(request)\n\ncurl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=d2e082a6b1cc5b8ebff64aba5ebfd18e' -H 'User-Agent: Darwin/20.6.0 python-requests/2.26.0 harmony-py/0.4.2 CPython/3.8.10' 'https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&format=application%2Fx-netcdf4&maxResults=5&concatenate=true&skipPreview=true'\n\n\nAfter submitting the request it is possible to retrieve the current processing status by using the job ID returned from the submission.\nIf the request is still running, we can wait until the Harmony request has finished processing. This cell will wait until the request has finised.\n\nprint(f'\\n{job1_id}')\n\nprint(harmony_client.status(job1_id))\n\nprint('\\nWaiting for the job to finish')\nresults = harmony_client.result_json(job1_id, show_progress=True)\n\n\n848c36db-0fe4-472e-b0e4-51abfba08101\n{'status': 'running', 'message': 'CMR query identified 2406552 granules, but the request has been limited to process only the first 5 granules because you requested 5 maxResults.', 'progress': 0, 'created_at': datetime.datetime(2022, 7, 27, 21, 37, 40, 347000, tzinfo=tzutc()), 'updated_at': datetime.datetime(2022, 7, 27, 21, 37, 40, 577000, tzinfo=tzutc()), 'created_at_local': '2022-07-27T14:37:40-07:00', 'updated_at_local': '2022-07-27T14:37:40-07:00', 'data_expiration': datetime.datetime(2022, 8, 26, 21, 37, 40, 347000, tzinfo=tzutc()), 'data_expiration_local': '2022-08-26T14:37:40-07:00', 'request': 'https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&format=application%2Fx-netcdf4&maxResults=5&concatenate=true&skipPreview=true', 'num_input_granules': 5}\n\nWaiting for the job to finish\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\nAfter the harmony job is finished we download the resulting concatenated granule file.\n\nprint('\\nDownloading results:')\nfilename = None\nfutures = harmony_client.download_all(job1_id, overwrite=True)\nfor f in futures:\n print(f)\n print(f.result()) # f.result() is a filename, in this case\n filename = f.result()\nprint('\\nDone downloading.')\n\n\nDownloading results:\n<Future at 0x108e8ac70 state=running>\nC1940473819-POCLOUD_merged.nc4\n\nDone downloading.\n\n\nWith the output file downloaded, now we can open concatenated granule using xarray to inspect some of the metadata.\nNote: In some of the collections the time variable has a time dimension and when we concatenate files we add a subset_index into the time dimension which causes the time variable have two dimension. Xarray doesn’t allow the time variable have two dimensions so when using xarray to open concatenated files the time variable might need to be dropped. The file can be open with netcdf library\n\n#some collections time variabe has a time dimension which can cause an exception when we concatenate and makes time two dimension\ntry:\n ds = xr.open_dataset(filename, decode_times=False)\nexcept xr.core.variable.MissingDimensionsError:\n ds = xr.open_dataset(filename, decode_times=False, drop_variables=['time'])\n\nprint(list(ds.variables))\n \nassert len(ds.coords['subset_index']) == max_results\n\n['subset_files', 'lat', 'lon', 'sea_surface_temperature', 'sst_dtime', 'quality_level', 'sses_bias', 'sses_standard_deviation', 'l2p_flags', 'sea_surface_temperature_4um', 'quality_level_4um', 'sses_bias_4um', 'sses_standard_deviation_4um', 'wind_speed', 'dt_analysis']\n\n\nAfter opening the file we can use matplotlib to create a plot for each subindex where each subindex represents the data for the granule file. We will plot sea_surface_temperature for each granule using subset_index dimension.\n\nvariable = None\nfor v in list(ds.variables):\n if v not in ['subset_files', 'lat', 'lon']:\n variable = v\n break;\n\nfor index in range(0, max_results):\n \n ds.isel(subset_index=index).plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=variable,\n s=1,\n levels=9,\n cmap=\"jet\",\n aspect=2.5,\n size=9\n )\n \n plt.xlim( 0., 360.)\n plt.ylim(-90., 90.)\n plt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWe can also plot out the entire granule file which would plot all the data of the concatenated files.\n\nds.plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=variable,\n s=1,\n levels=9,\n cmap=\"jet\",\n aspect=2.5,\n size=9\n)\n\nplt.xlim( 0., 360.)\nplt.ylim(-90., 90.)\nplt.show()"
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"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.*"
+ "text": "This tutorial uses multiple satellite data products to explore the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region over several years. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use on-premises and cloud datasets and services. The notebook can be executed locally, or in AWS (in us-west-2 region where the cloud data is located)."
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- "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/River_Heights_in_the_Cloud.html#working-with-in-situ-measurements-and-satellite-hydrology-data-in-the-cloud",
- "title": "Mississippi River Heights Exploration:",
- "section": "Working with In Situ Measurements and Satellite Hydrology Data in the Cloud",
- "text": "Working with In Situ Measurements and Satellite Hydrology Data in the Cloud\n\nLearning Objectives\n\nAccess data from the cloud (Pre-SWOT MEaSUREs river heights) and utilize in tandem with locally hosted dataset (USGS gauges)\nSearch for products using Earthdata Search GUI\nAccess datasets using xarray and visualize\n\nThis tutorial explores the relationships between satellite and in situ river heights in the Mississippi River using the data sets listed below. The notebook is designed to be executed in Amazon Web Services (AWS) (in us-west-2 region where the cloud data is located)."
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+ "title": "This Notebook is no longer up to date, a newer version exists here.",
+ "section": "",
+ "text": "This tutorial uses multiple satellite data products to explore the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region over several years. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use on-premises and cloud datasets and services. The notebook can be executed locally, or in AWS (in us-west-2 region where the cloud data is located)."
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"section": "Datasets",
- "text": "Datasets\nThe tutorial itself will use two different datasets:\n1. PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\n\nDOI: https://doi.org/10.5067/PSGRA-DA2V2\n\nThe NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual river height gauges from various altimeter satellites. \n2. USGS Water Data for the Nations River Gauges\n\nURL: https://dashboard.waterdata.usgs.gov/app/nwd/?region=lower48&aoi=default\n\nRiver heights are obtained from the United States Geologic Survey (USGS) National Water Dashboard."
+ "text": "Datasets\nThe tutorial itself will use five different datasets, which represent a combination of cloud^^- and on premise- archived datasets:\n\nTELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\n\nDOI: [https://doi.org/10.5067/TEMSC-3JC62](https://doi.org/10.5067/TEMSC-3JC62) \n\nThe Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) satellite land water equivalent (LWE) thicknesses will be used to observe seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, thus highlighting a wet season. \n\nPRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\n\nDOI: [https://doi.org/10.5067/PSGRA-DA2V2](https://doi.org/10.5067/PSGRA-DA2V2)\n\nThe NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual gauges will be used as a proxy for Surface Water and Ocean Topography (SWOT) discharge until SWOT products are available. MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute.\n\nSMAP_JPL_L3_SSS_CAP_MONTHLY_V5\n\nDOI: [https://doi.org/10.5067/SMP50-3TMCS](https://doi.org/10.5067/SMP50-3TMCS) \n\nSea surface salinity is obtained from the Soil Moisture Active Passive (SMAP) satellite (2015-2019).\n\nAQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5\n\nDOI: [https://doi.org/10.5067/AQR50-3Y1CE](https://doi.org/10.5067/AQR50-3Y1CE)\n\nSea surface salinity is obtained from the Aquarius/SAC-D satellite (2011-2015).\n\nMODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0\n\nDOI: [https://doi.org/10.5067/MODAM-MO9N9](https://doi.org/10.5067/MODAM-MO9N9)\n\nSea surface temperature is obtained from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the Aqua satellite. \nThe tutorial will show how each of these datasets are accessed and subset for our specific location, the Amazon River estuary. Graphs and images of river height (river discharge when SWOT data is available), LWE thickness, salinity, and sea surface temperature will be created and shown side by side to enable the exploration of relationships between the data.\n^^During 2021 PO.DAAC is in the process of migrating its data and services to the Earthdata Cloud in Amazon Web Services (AWS). As such some data will be available for early access from or within the Earthdata Cloud, while also being available from the on-premise archive. One such Cloud Pathfinder dataset is from the GRACE and GRACE-FO missions. In this example we access GRACE/FO data from the Earthdata Cloud. As a user, during the migration period (in 2021), you will need early access to be able to access cloud-based Pathfinder datasets such as GRACE. To gain that early access, please contact podaac@podaac.jpl.nasa.gov with your request for Early Access to cloud data. Please include your Earthdata Login username. Once you’ve been added to the early access list, you can then see the available collections after logging into the PO.DAAC Cloud Earthdata Search Portal or run this notebook. For more information on the PO.DAAC transition to the cloud, please visit: https://podaac.jpl.nasa.gov/cloud-datasets/about"
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"section": "Needed Packages",
- "text": "Needed Packages\n\nimport os\nimport glob\nimport s3fs\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport hvplot.xarray\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy\nimport csv\nfrom datetime import datetime\nfrom os.path import isfile, basename, abspath"
+ "text": "Needed Packages\n\nimport time\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport cartopy.crs as ccrs\nimport cartopy\nfrom json import dumps, loads\nimport json\nfrom IPython.display import HTML\nfrom os.path import isfile, basename, abspath"
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- "title": "Mississippi River Heights Exploration:",
- "section": "Get Temporary AWS Credentials for Access",
- "text": "Get Temporary AWS Credentials for Access\nS3 is an ‘object store’ hosted in AWS for cloud processing. Direct S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. Note, these temporary credentials are valid for only 1 hour. A netrc file is required to aquire these credentials. Use the NASA Earthdata Authentication to create a netrc file in your home directory. (Note: A NASA Earthdata Login is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.)\nThe following crediential is for PODAAC, but other credentials are needed to access data from other NASA DAACs.\n\ns3_cred_endpoint = 'https://archive.podaac.earthdata.nasa.gov/s3credentials'\n\nCreate a function to make a request to an endpoint for temporary credentials.\n\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\n\n\ntemp_creds_req = get_temp_creds()\n#temp_creds_req # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.\n\n\nSet up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint.\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})"
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+ "title": "This Notebook is no longer up to date, a newer version exists here.",
+ "section": "Set the CMR, URS, and Harmony endpoints",
+ "text": "Set the CMR, URS, and Harmony endpoints\nCMR, or the Earthdata Common Metadata Repository, is a high-performance, high-quality, continuously evolving metadata system that catalogs Earth Science data and associated service metadata records. URS is the Earthdata login system, that allows (free) download access to Earthdata data. Harmony API allows you to seamlessly analyze Earth observation data from different NASA data centers.\n\ncmr = \"cmr.earthdata.nasa.gov\"\nurs = \"urs.earthdata.nasa.gov\"\nharmony = \"harmony.earthdata.nasa.gov\"\n\ncmr, urs, harmony\n\n('cmr.earthdata.nasa.gov',\n 'urs.earthdata.nasa.gov',\n 'harmony.earthdata.nasa.gov')"
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- "section": "Pre-SWOT MEaSUREs River Heights",
- "text": "Pre-SWOT MEaSUREs River Heights\nThe shortname for MEaSUREs is ‘PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2’ with the concept ID: C2036882359-POCLOUD. The guidebook explains the details of the Pre-SWOT MEaSUREs data.\nOur desired MEaSUREs variable is river height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Mississippi River. Time is between April 8, 1993 and April 20, 2019.\nFor this dataset, we found the cloud-enabled data directly using the Earthdata Search (see the Earthdata Search for downloading data tutorial) by searching directly for the concept ID, and locating the granule needed, G2105959746-POCLOUD, that will show us the Mississippi river.\n\n\n\nimage.png\n\n\nThe s3 link for this granule can be found in it’s meta data by viewing the details of the granule from the button with three vertical dots in the corner. The s3 link is under ‘relatedURLs’, or it can be found by going through the download process and instead of downloading, clicking the tab entitled “AWS S3 Access.”\n\n\n\nimage.png\n\n\nLet’s access the netCDF file from an s3 bucket and look at the data structure.\n\ns3_MEaSUREs_url = 's3://podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/North_America_Mississippi1kmdaily.nc'\n\n\ns3_file_obj = fs_s3.open(s3_MEaSUREs_url, mode='rb')\n\n\nds_MEaSUREs = xr.open_dataset(s3_file_obj, engine='h5netcdf')\nds_MEaSUREs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (X: 2766, Y: 2766, distance: 2766, time: 9440,\n charlength: 26)\nCoordinates:\n * time (time) datetime64[ns] 1993-04-14T17:00:38.973026816 ...\nDimensions without coordinates: X, Y, distance, charlength\nData variables:\n lon (X) float64 -89.35 -89.35 -89.36 ... -92.42 -92.43\n lat (Y) float64 29.27 29.28 29.29 ... 44.56 44.56 44.57\n FD (distance) float64 10.01 1.01e+03 ... 2.765e+06\n height (distance, time) float64 ...\n sat (charlength, time) |S1 ...\n storage (distance, time) float64 ...\n IceFlag (time) float64 nan nan nan nan nan ... nan nan nan nan\n LakeFlag (distance) float64 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0\n Storage_uncertainty (distance, time) float64 ...\nAttributes: (12/40)\n title: GRRATS (Global River Radar Altimetry Time ...\n Conventions: CF-1.6, ACDD-1.3\n institution: Ohio State University, School of Earth Sci...\n source: MEaSUREs OSU Storage toolbox 2018\n keywords: EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n keywords_vocabulary: Global Change Master Directory (GCMD)\n ... ...\n geospatial_lat_max: 44.56663081735837\n geospatial_lat_units: degree_north\n geospatial_vertical_max: 201.5947042200621\n geospatial_vertical_min: -2.2912740783007286\n geospatial_vertical_units: m\n geospatial_vertical_positive: upxarray.DatasetDimensions:X: 2766Y: 2766distance: 2766time: 9440charlength: 26Coordinates: (1)time(time)datetime64[ns]1993-04-14T17:00:38.973026816 .....long_name :timestandard_name :timeaxis :Tarray(['1993-04-14T17:00:38.973026816', '1993-04-15T17:00:38.973026816',\n '1993-04-16T17:00:38.973026816', ..., '2019-04-16T15:38:57.639261696',\n '2019-04-17T15:38:57.639261696', '2019-04-18T15:38:57.639261696'],\n dtype='datetime64[ns]')Data variables: (9)lon(X)float64...units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xarray([-89.345393, -89.351435, -89.356849, ..., -92.404921, -92.416217,\n -92.42713 ])lat(Y)float64...units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yarray([29.273779, 29.281522, 29.288734, ..., 44.558685, 44.562254, 44.566631])FD(distance)float64...long_name :distance_from_river_mouthunits :kmcomment :This is the distance along the river centerline from the river mouth to this VS.array([1.000915e+01, 1.010009e+03, 2.010009e+03, ..., 2.763010e+06,\n 2.764010e+06, 2.765010e+06])height(distance, time)float64...units :mpositive :uplong_name :interpolated_heightsstandard_name :heightvalid_min :-2.2912740783007286valid_max :201.5947042200621comment :A time flow distance grid of river water heights with respect to the EGM08 Geoid).-9999 fill values are for missing data, while -9995 fill values are for lakes and reservoirs.[26111040 values with dtype=float64]sat(charlength, time)|S1...long_name :satellitecomment :The satellite the measurement is derived from.[245440 values with dtype=|S1]storage(distance, time)float64...units :km3positive :uplong_name :river_channel_storagecomment :A time flow distance grid of river channel storage values.[26111040 values with dtype=float64]IceFlag(time)float64...long_name :Ice_Flagvalid_range :0, 1flag_masks :1flag_meaning :Time of ice covercomment : This is a flag for masking out times of Ice Cover.array([nan, nan, nan, ..., nan, nan, nan])LakeFlag(distance)float64...long_name :Lake_Flagvalid_range :0, 1flag_masks :1flag_meaning :River sections that are lakes or reservoirscomment : This is a flag for masking out setions that are lakes or reservoirs.array([0., 0., 0., ..., 1., 1., 1.])Storage_uncertainty(distance, time)float64...units :km3positive :uplong_name :river_channel_storage_uncertaintycomment :A time flow distance grid of river channel storage uncertainty values.[26111040 values with dtype=float64]Attributes: (40)title :GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Mississippi RiverConventions :CF-1.6, ACDD-1.3institution :Ohio State University, School of Earth Sciencessource :MEaSUREs OSU Storage toolbox 2018keywords :EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHTkeywords_vocabulary :Global Change Master Directory (GCMD)cdm_data_type :stationcreator_name :Coss,Stevecreator_email :Coss.31@osu.eduproject :MEaSUREs OSUprogram :NASA Earth Science Data Systems (ESDS)publisher_name :PO.DAAC (Physical Oceanography Distributed Active Archive Center)publisher_email :podaac@podaac.jpl.nasa.govpublisher_url :podaac.jpl.nasa.govpublisher_type :Institutionpublisher_institution :PO.DAACprocessing_level :L2doi :10.5067/PSGRA-DA2V2history :This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements. platform :ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)platform_vocabulary :NASA/GCMD Platform Keywords. Version 8.6instrument :RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)instrument_vocabulary :NASA/GCMD Platform Keywords. Version 8.6references :in review :doi.org/10.5194/essd-2019-84id :GRRATS(Global River Radar Altimeter Time Series) 1km/dailysummary :The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.time_coverage_resolution :1 daydate_created :2021-06-30T10:30:44time_coverage_start :1992-04-14T17:00:38time_coverage_end :2018-04-18T15:38:57geospatial_lon_min :-92.42712987654255geospatial_lon_max :-89.09954782976848geospatial_lon_units :degree_eastgeospatial_lat_min :29.27377910202398geospatial_lat_max :44.56663081735837geospatial_lat_units :degree_northgeospatial_vertical_max :201.5947042200621geospatial_vertical_min :-2.2912740783007286geospatial_vertical_units :mgeospatial_vertical_positive :up\n\n\n\nPlot a subset of the data\nPlotting the river distances and associated heights on the map at time t=9069 (March 16, 2018) using plt.\n\nfig = plt.figure(figsize=[11,7]) \nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent([-100, -70, 25, 45])\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()"
+ "objectID": "notebooks/AmazonRiver_Estuary_Exploration.html#nasa-earthdata-login-setup",
+ "href": "notebooks/AmazonRiver_Estuary_Exploration.html#nasa-earthdata-login-setup",
+ "title": "This Notebook is no longer up to date, a newer version exists here.",
+ "section": "NASA Earthdata Login Setup",
+ "text": "NASA Earthdata Login Setup\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\nThe setup_earthdata_login_auth function will allow Python scripts to log into any Earthdata Login application programmatically. To avoid being prompted for credentials every time you run and also allow clients such as curl to log in, you can add the following to a .netrc (_netrc on Windows) file in your home directory:\n machine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nMake sure that this file is only readable by the current user or you will receive an error stating “netrc access too permissive.”\n$ chmod 0600 ~/.netrc\nYou will be prompted for your username and password if you dont have a netrc file. Note: these imports are all in the Python 3.6+ standard library.\n\nfrom platform import system\nfrom netrc import netrc\nfrom getpass import getpass\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nfrom os.path import join, expanduser\n\nTOKEN_DATA = (\"<token>\"\n \"<username>%s</username>\"\n \"<password>%s</password>\"\n \"<client_id>PODAAC CMR Client</client_id>\"\n \"<user_ip_address>%s</user_ip_address>\"\n \"</token>\")\n\n\ndef setup_earthdata_login_auth(urs: str='urs.earthdata.nasa.gov', cmr: str='cmr.earthdata.nasa.gov'):\n\n # GET URS LOGIN INFO FROM NETRC OR USER PROMPTS:\n netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n try:\n username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(urs)\n print(\"# Your URS credentials were securely retrieved from your .netrc file.\")\n except (FileNotFoundError, TypeError):\n print('# Please provide your Earthdata Login credentials for access.')\n print('# Your info will only be passed to %s and will not be exposed in Jupyter.' % (urs))\n username = input('Username: ')\n password = getpass('Password: ')\n\n # SET UP URS AUTHENTICATION FOR HTTP DOWNLOADS:\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, urs, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n\n # GET TOKEN TO ACCESS RESTRICTED CMR METADATA:\n ip = requests.get(\"https://ipinfo.io/ip\").text.strip()\n r = requests.post(\n url=\"https://%s/legacy-services/rest/tokens\" % cmr,\n data=TOKEN_DATA % (str(username), str(password), ip),\n headers={'Content-Type': 'application/xml', 'Accept': 'application/json'}\n )\n return r.json()['token']['id']\n\n\n# Provide URS credentials for HTTP download auth & CMR token retrieval:\n_token = setup_earthdata_login_auth(urs=urs, cmr=cmr)\n\n# Your URS credentials were securely retrieved from your .netrc file.\n\n\n\nCloud data: GRACE Liquid Water Equivalent (LWE)\n\nSearch for GRACE LWE Thickness data\nGRACE/GRACE-FO data can be obtained from the Earthdata Cloud, as described in the introduction section of this notebook.\n\nHow to find a collection ID from the dataset landing page\nSuppose we are interested in LWE data from the dataset (DOI:10.5067/TEMSC-3JC62) described on this PO.DAAC dataset landing page: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\nFrom the landing page, we see the dataset Short Name under the Information tab. Copy that to paste and search later. Log in to your Earthdata account at: https://search.earthdata.nasa.gov. Enter the Short Name and search. Clicking on one of the search results brings us to a list of granules. Within that URL, we can grab the concept-id, a string starting with “C” and ending with “-POCLOUD”. For this dataset, it is “C1938032626-POCLOUD”.\n\n\n\nCollection search in CMR\nHere we use the requests library to search in collections by either short name or concept-id, which returns exactly one dataset, or one “hit”, in a JSON format.\n\ngrace_ShortName = \"TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\"\nr = requests.get(url=f\"https://{cmr}/search/collections.umm_json\",\n params = \n {\n 'provider': \"POCLOUD\",\n 'token': _token,\n 'concept-id': \"C1938032626-POCLOUD\"\n #'ShortName': grace_ShortName,\n }\n \n )\ngrace_coll = r.json()\ngrace_coll['hits']\n\n1\n\n\n\n\nSee collection metadata\n\ngrace_coll_meta = grace_coll['items'][0]['meta']\ngrace_coll_meta\n\n{'revision-id': 4,\n 'deleted': False,\n 'format': 'application/vnd.nasa.cmr.umm+json',\n 'provider-id': 'POCLOUD',\n 'user-id': 'chen5510',\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'native-id': 'JPL+GRACE+and+GRACE-FO+Mascon+Ocean,+Ice,+and+Hydrology+Equivalent+Water+Height+Coastal+Resolution+Improvement+(CRI)+Filtered+Release+06+Version+02',\n 'has-transforms': False,\n 'has-variables': False,\n 'concept-id': 'C1938032626-POCLOUD',\n 'revision-date': '2021-05-21T15:29:56.854Z',\n 'granule-count': 0,\n 'has-temporal-subsetting': False,\n 'concept-type': 'collection'}\n\n\n\n\nGranule search\nHere we use the requests library to search for granules in the collection. It returns 7 “hits”, or 7 granules.\n\nr = requests.get(url=f\"https://{cmr}/search/granules.umm_json\", \n params={'provider': \"POCLOUD\", \n 'ShortName': grace_ShortName, \n 'token': _token})\n\ngrace_gran = r.json()\ngrace_gran['hits']\n\n8\n\n\n\n\nFor GRACE, when there are multiple granules, take the latest monthly granule (automate finding of most recent month?)\n\nlatest_granule = grace_gran['hits']-1 # If not true, then sort grace_gran by 'native-id'\ngrace_gran['items'][latest_granule]['meta']\n\n{'concept-type': 'granule',\n 'concept-id': 'G2050191643-POCLOUD',\n 'revision-id': 1,\n 'native-id': 'GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI',\n 'provider-id': 'POCLOUD',\n 'format': 'application/vnd.nasa.cmr.umm+json',\n 'revision-date': '2021-05-11T03:30:34.686Z'}\n\n\n\n\nGet download link\nThe download link to the .nc file is one of the RelatedURLs.\n\ngrace_gran['items'][latest_granule]['umm']['RelatedUrls']\n\n[{'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc.md5',\n 'Type': 'EXTENDED METADATA',\n 'Description': 'File to download'},\n {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.cmr.json',\n 'Type': 'EXTENDED METADATA',\n 'Description': 'File to download'},\n {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc',\n 'Type': 'GET DATA',\n 'Description': 'File to download'}]\n\n\n\n# From above, select the link to the .nc file (links are not always listed in the same order)\nlink_num = 2\ngrace_url = grace_gran['items'][latest_granule]['umm']['RelatedUrls'][link_num]['URL']\ngrace_url\n\n'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc'\n\n\n\n\nDownload the .nc file from the Earthdata cloud\nAnd display overview of contents (metadata) using ncdump. This will download the GRACE data to your local machine, or whereever you are running this notebook from.\n\nr = requests.get(grace_url)\nwith open('iosos_demo_GRACEFO.nc', 'wb') as f:\n f.write(r.content)\n\n!ncdump -h iosos_demo_GRACEFO.nc\n\nnetcdf iosos_demo_GRACEFO {\ndimensions:\n lon = 720 ;\n lat = 360 ;\n time = 195 ;\n bounds = 2 ;\nvariables:\n double lon(lon) ;\n lon:units = \"degrees_east\" ;\n lon:long_name = \"longitude\" ;\n lon:standard_name = \"longitude\" ;\n lon:axis = \"X\" ;\n lon:valid_min = 0.25 ;\n lon:valid_max = 359.75 ;\n lon:bounds = \"lon_bounds\" ;\n double lat(lat) ;\n lat:units = \"degrees_north\" ;\n lat:long_name = \"latitude\" ;\n lat:standard_name = \"latitude\" ;\n lat:axis = \"Y\" ;\n lat:valid_min = -89.75 ;\n lat:valid_max = 89.75 ;\n lat:bounds = \"lat_bounds\" ;\n double time(time) ;\n time:units = \"days since 2002-01-01T00:00:00Z\" ;\n time:long_name = \"time\" ;\n time:standard_name = \"time\" ;\n time:axis = \"T\" ;\n time:calendar = \"gregorian\" ;\n time:bounds = \"time_bounds\" ;\n double lwe_thickness(time, lat, lon) ;\n lwe_thickness:units = \"cm\" ;\n lwe_thickness:long_name = \"Liquid_Water_Equivalent_Thickness\" ;\n lwe_thickness:standard_name = \"Liquid_Water_Equivalent_Thickness\" ;\n lwe_thickness:coordinates = \"time lat lon\" ;\n lwe_thickness:grid_mapping = \"WGS84\" ;\n lwe_thickness:_FillValue = -99999. ;\n lwe_thickness:valid_min = -1772.14897730884 ;\n lwe_thickness:valid_max = 767.736827711678 ;\n lwe_thickness:comment = \"Coastline Resolution Improvement (CRI) filter is applied\" ;\n double uncertainty(time, lat, lon) ;\n uncertainty:units = \"cm\" ;\n uncertainty:long_name = \"uncertainty\" ;\n uncertainty:standard_name = \"uncertainty\" ;\n uncertainty:coordinates = \"time lat lon\" ;\n uncertainty:grid_mapping = \"WGS84\" ;\n uncertainty:_FillValue = -99999. ;\n uncertainty:valid_min = 0.158623647924877 ;\n uncertainty:valid_max = 53.3446959856009 ;\n uncertainty:comment = \"1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate\" ;\n double lat_bounds(lat, bounds) ;\n lat_bounds:long_name = \"latitude boundaries\" ;\n lat_bounds:units = \"degrees_north\" ;\n lat_bounds:comment = \"latitude values at the north and south bounds of each pixel\" ;\n double lon_bounds(lon, bounds) ;\n lon_bounds:long_name = \"longitude boundaries\" ;\n lon_bounds:units = \"degrees_east\" ;\n lon_bounds:comment = \"longitude values at the west and east bounds of each pixel\" ;\n double time_bounds(time, bounds) ;\n time_bounds:long_name = \"time boundaries\" ;\n time_bounds:units = \"days since 2002-01-01T00:00:00Z\" ;\n time_bounds:comment = \"time bounds for each time value, i.e. the first day and last day included in the monthly solution\" ;\n\n// global attributes:\n :Conventions = \"CF-1.6, ACDD-1.3, ISO 8601\" ;\n :Metadata_Conventions = \"Unidata Dataset Discovery v1.0\" ;\n :standard_name_vocabulary = \"NetCDF Climate and Forecast (CF) Metadata Convention-1.6\" ;\n :title = \"JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI\" ;\n :summary = \"Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06Mv2 mascon solution - with CRI filter applied\" ;\n :keywords = \"Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thickness\" ;\n :keywords_vocabulary = \"NASA Global Change Master Directory (GCMD) Science Keywords\" ;\n :platform = \"GRACE and GRACE-FO\" ;\n :institution = \"NASA/JPL\" ;\n :creator_name = \"David Wiese\" ;\n :creator_email = \"grace@podaac.jpl.nasa.gov\" ;\n :creator_url = \"https://grace.jpl.nasa.gov\" ;\n :creator_type = \"group\" ;\n :creator_institution = \"NASA/JPL\" ;\n :publisher_name = \"Physical Oceanography Distributed Active Archive Center\" ;\n :publisher_email = \"podaac@jpl.nasa.gov\" ;\n :publisher_url = \"https://podaac.jpl.nasa.gov\" ;\n :publisher_type = \"group\" ;\n :publisher_institution = \"NASA/JPL\" ;\n :project = \"NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)\" ;\n :program = \"NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Program\" ;\n :id = \"10.5067/TEMSC-3JC62\" ;\n :naming_authority = \"org.doi.dx\" ;\n :source = \"GRACE and GRACE-FO JPL RL06Mv2-CRI\" ;\n :processing_level = \"2 and 3\" ;\n :acknowledgement = \"GRACE is a joint mission of NASA (USA) and DLR (Germany). GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAC\" ;\n :license = \"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy\" ;\n :product_version = \"v2.0\" ;\n :time_epoch = \"2002-01-01T00:00:00Z\" ;\n :time_coverage_start = \"2002-04-16T00:00:00Z\" ;\n :time_coverage_end = \"2021-03-16T23:59:59Z\" ;\n :geospatial_lat_min = -89.75 ;\n :geospatial_lat_max = 89.75 ;\n :geospatial_lat_units = \"degrees_north\" ;\n :geospatial_lat_resolution = \"0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons\" ;\n :geospatial_lon_min = 0.25 ;\n :geospatial_lon_max = 359.75 ;\n :geospatial_lon_units = \"degrees_east\" ;\n :geospatial_lon_resolution = \"0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons\" ;\n :time_mean_removed = \"2004.000 to 2009.999\" ;\n :months_missing = \"2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09\" ;\n :postprocess_1 = \" OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLY\" ;\n :postprocess_2 = \"Water density used to convert to equivalent water height: 1000 kg/m^3\" ;\n :postprocess_3 = \"Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlines\" ;\n :GIA_removed = \"ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.\" ;\n :geocenter_correction = \"We use a version of TN-13 based on the JPL mascons\" ;\n :C_20_substitution = \"TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929\" ;\n :C_30_substitution = \"TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929. This substitution is made for all months after August 2016.\" ;\n :user_note_1 = \"The accelerometer on the GRACE-B spacecraft was turned off after August 2016. After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure. This has led to a subsequent degradation in the quality of the gravity fields derived. The uncertainties in this file have been scaled to accomodate this degradation.\" ;\n :user_note_2 = \"The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018. After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure. The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.\" ;\n :journal_reference = \"Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth\\'s time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. \" ;\n :CRI_filter_journal_reference = \"Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. \" ;\n :date_created = \"2021-04-27T21:04:06Z\" ;\n}\n\n\n\n\nOpen file using xarray.\n\nds_GRACE = xr.open_dataset('iosos_demo_GRACEFO.nc')\nds_GRACE\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (bounds: 2, lat: 360, lon: 720, time: 195)\nCoordinates:\n * lon (lon) float64 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8\n * lat (lat) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * time (time) datetime64[ns] 2002-04-17T12:00:00 ... 2021-03-16T1...\nDimensions without coordinates: bounds\nData variables:\n lwe_thickness (time, lat, lon) float64 ...\n uncertainty (time, lat, lon) float64 ...\n lat_bounds (lat, bounds) float64 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0\n lon_bounds (lon, bounds) float64 0.0 0.5 0.5 1.0 ... 359.5 359.5 360.0\n time_bounds (time, bounds) datetime64[ns] 2002-04-04 ... 2021-03-31T23...\nAttributes:\n Conventions: CF-1.6, ACDD-1.3, ISO 8601\n Metadata_Conventions: Unidata Dataset Discovery v1.0\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata ...\n title: JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI\n summary: Monthly gravity solutions from GRACE and G...\n keywords: Solid Earth, Geodetics/Gravity, Gravity, l...\n keywords_vocabulary: NASA Global Change Master Directory (GCMD)...\n platform: GRACE and GRACE-FO\n institution: NASA/JPL\n creator_name: David Wiese\n creator_email: grace@podaac.jpl.nasa.gov\n creator_url: https://grace.jpl.nasa.gov\n creator_type: group\n creator_institution: NASA/JPL\n publisher_name: Physical Oceanography Distributed Active A...\n publisher_email: podaac@jpl.nasa.gov\n publisher_url: https://podaac.jpl.nasa.gov\n publisher_type: group\n publisher_institution: NASA/JPL\n project: NASA Gravity Recovery and Climate Experime...\n program: NASA Earth Science System Pathfinder and N...\n id: 10.5067/TEMSC-3JC62\n naming_authority: org.doi.dx\n source: GRACE and GRACE-FO JPL RL06Mv2-CRI\n processing_level: 2 and 3\n acknowledgement: GRACE is a joint mission of NASA (USA) and...\n license: https://science.nasa.gov/earth-science/ear...\n product_version: v2.0\n time_epoch: 2002-01-01T00:00:00Z\n time_coverage_start: 2002-04-16T00:00:00Z\n time_coverage_end: 2021-03-16T23:59:59Z\n geospatial_lat_min: -89.75\n geospatial_lat_max: 89.75\n geospatial_lat_units: degrees_north\n geospatial_lat_resolution: 0.5 degree grid; however the native resolu...\n geospatial_lon_min: 0.25\n geospatial_lon_max: 359.75\n geospatial_lon_units: degrees_east\n geospatial_lon_resolution: 0.5 degree grid; however the native resolu...\n time_mean_removed: 2004.000 to 2009.999\n months_missing: 2002-06;2002-07;2003-06;2011-01;2011-06;20...\n postprocess_1: OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MON...\n postprocess_2: Water density used to convert to equivalen...\n postprocess_3: Coastline Resolution Improvement (CRI) fil...\n GIA_removed: ICE6G-D; Peltier, W. R., D. F. Argus, and ...\n geocenter_correction: We use a version of TN-13 based on the JPL...\n C_20_substitution: TN-14; Loomis et al., 2019, Geophys. Res. ...\n C_30_substitution: TN-14; Loomis et al., 2019, Geophys. Res. ...\n user_note_1: The accelerometer on the GRACE-B spacecraf...\n user_note_2: The accelerometer on the GRACE-D spacecraf...\n journal_reference: Watkins, M. M., D. N. Wiese, D.-N. Yuan, C...\n CRI_filter_journal_reference: Wiese, D. N., F. W. Landerer, and M. M. Wa...\n date_created: 2021-04-27T21:04:06Zxarray.DatasetDimensions:bounds: 2lat: 360lon: 720time: 195Coordinates: (3)lon(lon)float640.25 0.75 1.25 ... 359.2 359.8units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xvalid_min :0.25valid_max :359.75bounds :lon_boundsarray([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,\n 3.5975e+02])lat(lat)float64-89.75 -89.25 ... 89.25 89.75units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yvalid_min :-89.75valid_max :89.75bounds :lat_boundsarray([-89.75, -89.25, -88.75, ..., 88.75, 89.25, 89.75])time(time)datetime64[ns]2002-04-17T12:00:00 ... 2021-03-...long_name :timestandard_name :timeaxis :Tbounds :time_boundsarray(['2002-04-17T12:00:00.000000000', '2002-05-10T12:00:00.000000000',\n '2002-08-16T12:00:00.000000000', '2002-09-16T00:00:00.000000000',\n '2002-10-16T12:00:00.000000000', '2002-11-16T00:00:00.000000000',\n '2002-12-16T12:00:00.000000000', '2003-01-16T12:00:00.000000000',\n '2003-02-15T00:00:00.000000000', '2003-03-16T12:00:00.000000000',\n '2003-04-16T00:00:00.000000000', '2003-05-11T12:00:00.000000000',\n '2003-07-16T12:00:00.000000000', '2003-08-16T12:00:00.000000000',\n '2003-09-16T00:00:00.000000000', '2003-10-16T00:00:00.000000000',\n '2003-11-16T00:00:00.000000000', '2003-12-16T12:00:00.000000000',\n '2004-01-07T12:00:00.000000000', '2004-02-17T00:00:00.000000000',\n '2004-03-16T12:00:00.000000000', '2004-04-16T00:00:00.000000000',\n '2004-05-16T12:00:00.000000000', '2004-06-16T00:00:00.000000000',\n '2004-07-16T12:00:00.000000000', '2004-08-16T12:00:00.000000000',\n '2004-09-16T00:00:00.000000000', '2004-10-16T12:00:00.000000000',\n '2004-11-16T00:00:00.000000000', '2004-12-16T12:00:00.000000000',\n '2005-01-16T12:00:00.000000000', '2005-02-15T00:00:00.000000000',\n '2005-03-16T12:00:00.000000000', '2005-04-16T00:00:00.000000000',\n '2005-05-16T12:00:00.000000000', '2005-06-16T00:00:00.000000000',\n '2005-07-16T12:00:00.000000000', '2005-08-16T12:00:00.000000000',\n '2005-09-16T00:00:00.000000000', '2005-10-16T12:00:00.000000000',\n '2005-11-16T00:00:00.000000000', '2005-12-16T12:00:00.000000000',\n '2006-01-16T12:00:00.000000000', '2006-02-15T00:00:00.000000000',\n '2006-03-16T12:00:00.000000000', '2006-04-16T00:00:00.000000000',\n '2006-05-16T12:00:00.000000000', '2006-06-16T00:00:00.000000000',\n '2006-07-16T12:00:00.000000000', '2006-08-16T12:00:00.000000000',\n '2006-09-16T00:00:00.000000000', '2006-10-16T12:00:00.000000000',\n '2006-11-16T00:00:00.000000000', '2006-12-16T12:00:00.000000000',\n '2007-01-16T12:00:00.000000000', '2007-02-15T00:00:00.000000000',\n '2007-03-16T12:00:00.000000000', '2007-04-16T00:00:00.000000000',\n '2007-05-16T12:00:00.000000000', '2007-06-16T00:00:00.000000000',\n '2007-07-16T12:00:00.000000000', '2007-08-16T12:00:00.000000000',\n '2007-09-16T00:00:00.000000000', '2007-10-16T12:00:00.000000000',\n '2007-11-16T00:00:00.000000000', '2007-12-16T12:00:00.000000000',\n '2008-01-16T12:00:00.000000000', '2008-02-15T12:00:00.000000000',\n '2008-03-16T12:00:00.000000000', '2008-04-16T00:00:00.000000000',\n '2008-05-16T12:00:00.000000000', '2008-06-16T00:00:00.000000000',\n '2008-07-16T12:00:00.000000000', '2008-08-16T12:00:00.000000000',\n '2008-09-16T00:00:00.000000000', '2008-10-16T12:00:00.000000000',\n '2008-11-16T00:00:00.000000000', '2008-12-16T12:00:00.000000000',\n '2009-01-16T12:00:00.000000000', '2009-02-15T00:00:00.000000000',\n '2009-03-16T12:00:00.000000000', '2009-04-16T00:00:00.000000000',\n '2009-05-16T12:00:00.000000000', '2009-06-16T00:00:00.000000000',\n '2009-07-16T12:00:00.000000000', '2009-08-16T12:00:00.000000000',\n '2009-09-16T00:00:00.000000000', '2009-10-16T12:00:00.000000000',\n '2009-11-16T00:00:00.000000000', '2009-12-16T12:00:00.000000000',\n '2010-01-16T12:00:00.000000000', '2010-02-15T00:00:00.000000000',\n '2010-03-16T12:00:00.000000000', '2010-04-16T00:00:00.000000000',\n '2010-05-16T12:00:00.000000000', '2010-06-16T00:00:00.000000000',\n '2010-07-16T12:00:00.000000000', '2010-08-16T12:00:00.000000000',\n '2010-09-16T00:00:00.000000000', '2010-10-16T12:00:00.000000000',\n '2010-11-16T00:00:00.000000000', '2010-12-14T12:00:00.000000000',\n '2011-02-18T12:00:00.000000000', '2011-03-16T12:00:00.000000000',\n '2011-04-16T00:00:00.000000000', '2011-05-16T12:00:00.000000000',\n '2011-07-18T12:00:00.000000000', '2011-08-16T12:00:00.000000000',\n '2011-09-16T00:00:00.000000000', '2011-10-16T12:00:00.000000000',\n '2011-11-01T12:00:00.000000000', '2012-01-01T00:00:00.000000000',\n '2012-01-16T12:00:00.000000000', '2012-02-15T12:00:00.000000000',\n '2012-03-16T12:00:00.000000000', '2012-04-04T12:00:00.000000000',\n '2012-06-16T00:00:00.000000000', '2012-07-16T12:00:00.000000000',\n '2012-08-16T12:00:00.000000000', '2012-09-13T00:00:00.000000000',\n '2012-11-18T12:00:00.000000000', '2012-12-16T12:00:00.000000000',\n '2013-01-16T12:00:00.000000000', '2013-02-14T00:00:00.000000000',\n '2013-04-21T00:00:00.000000000', '2013-05-16T12:00:00.000000000',\n '2013-06-16T00:00:00.000000000', '2013-07-16T12:00:00.000000000',\n '2013-10-16T12:00:00.000000000', '2013-11-16T00:00:00.000000000',\n '2013-12-16T12:00:00.000000000', '2014-01-09T12:00:00.000000000',\n '2014-03-16T12:00:00.000000000', '2014-04-16T00:00:00.000000000',\n '2014-05-16T12:00:00.000000000', '2014-06-13T00:00:00.000000000',\n '2014-08-16T12:00:00.000000000', '2014-09-16T00:00:00.000000000',\n '2014-10-16T12:00:00.000000000', '2014-11-17T00:00:00.000000000',\n '2015-01-22T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n '2015-03-16T12:00:00.000000000', '2015-04-16T00:00:00.000000000',\n '2015-04-27T00:00:00.000000000', '2015-07-15T12:00:00.000000000',\n '2015-08-16T12:00:00.000000000', '2015-09-14T12:00:00.000000000',\n '2015-12-23T12:00:00.000000000', '2016-01-16T12:00:00.000000000',\n '2016-02-14T00:00:00.000000000', '2016-03-16T12:00:00.000000000',\n '2016-05-20T00:00:00.000000000', '2016-06-16T00:00:00.000000000',\n '2016-07-15T12:00:00.000000000', '2016-08-21T12:00:00.000000000',\n '2016-11-27T12:00:00.000000000', '2016-12-24T12:00:00.000000000',\n '2017-01-21T00:00:00.000000000', '2017-03-31T12:00:00.000000000',\n '2017-04-24T12:00:00.000000000', '2017-05-12T12:00:00.000000000',\n '2017-06-11T00:00:00.000000000', '2018-06-16T00:00:00.000000000',\n '2018-07-10T00:00:00.000000000', '2018-10-31T12:00:00.000000000',\n '2018-11-16T00:00:00.000000000', '2018-12-16T12:00:00.000000000',\n '2019-01-16T12:00:00.000000000', '2019-02-14T00:00:00.000000000',\n '2019-03-16T12:00:00.000000000', '2019-04-16T00:00:00.000000000',\n '2019-05-16T12:00:00.000000000', '2019-06-16T00:00:00.000000000',\n '2019-07-16T12:00:00.000000000', '2019-08-16T12:00:00.000000000',\n '2019-09-16T00:00:00.000000000', '2019-10-16T12:00:00.000000000',\n '2019-11-16T00:00:00.000000000', '2019-12-16T12:00:00.000000000',\n '2020-01-16T12:00:00.000000000', '2020-02-15T12:00:00.000000000',\n '2020-03-16T12:00:00.000000000', '2020-04-16T00:00:00.000000000',\n '2020-05-16T12:00:00.000000000', '2020-06-16T00:00:00.000000000',\n '2020-07-16T12:00:00.000000000', '2020-08-16T12:00:00.000000000',\n '2020-09-16T00:00:00.000000000', '2020-10-16T12:00:00.000000000',\n '2020-11-16T00:00:00.000000000', '2020-12-16T12:00:00.000000000',\n '2021-01-16T12:00:00.000000000', '2021-02-15T00:00:00.000000000',\n '2021-03-16T12:00:00.000000000'], dtype='datetime64[ns]')Data variables: (5)lwe_thickness(time, lat, lon)float64...units :cmlong_name :Liquid_Water_Equivalent_Thicknessstandard_name :Liquid_Water_Equivalent_Thicknessgrid_mapping :WGS84valid_min :-1772.1489773088445valid_max :767.7368277116782comment :Coastline Resolution Improvement (CRI) filter is applied[50544000 values with dtype=float64]uncertainty(time, lat, lon)float64...units :cmlong_name :uncertaintystandard_name :uncertaintygrid_mapping :WGS84valid_min :0.1586236479248768valid_max :53.34469598560085comment :1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate[50544000 values with dtype=float64]lat_bounds(lat, bounds)float64...long_name :latitude boundariesunits :degrees_northcomment :latitude values at the north and south bounds of each pixelarray([[-90. , -89.5],\n [-89.5, -89. ],\n [-89. , -88.5],\n ...,\n [ 88.5, 89. ],\n [ 89. , 89.5],\n [ 89.5, 90. ]])lon_bounds(lon, bounds)float64...long_name :longitude boundariesunits :degrees_eastcomment :longitude values at the west and east bounds of each pixelarray([[ 0. , 0.5],\n [ 0.5, 1. ],\n [ 1. , 1.5],\n ...,\n [358.5, 359. ],\n [359. , 359.5],\n [359.5, 360. ]])time_bounds(time, bounds)datetime64[ns]...long_name :time boundariescomment :time bounds for each time value, i.e. the first day and last day included in the monthly solutionarray([['2002-04-04T00:00:00.000000000', '2002-04-30T23:59:59.913600000'],\n ['2002-05-02T00:00:00.000000000', '2002-05-18T23:59:59.913600000'],\n ['2002-08-01T00:00:00.000000000', '2002-08-31T23:59:59.913600000'],\n ...,\n ['2021-01-01T00:00:00.000000000', '2021-01-31T23:59:59.913600000'],\n ['2021-02-01T00:00:00.000000000', '2021-02-28T23:59:59.913600000'],\n ['2021-03-01T00:00:00.000000000', '2021-03-31T23:59:59.913600000']],\n dtype='datetime64[ns]')Attributes: (53)Conventions :CF-1.6, ACDD-1.3, ISO 8601Metadata_Conventions :Unidata Dataset Discovery v1.0standard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Convention-1.6title :JPL GRACE and GRACE-FO MASCON RL06Mv2 CRIsummary :Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06Mv2 mascon solution - with CRI filter appliedkeywords :Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thicknesskeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsplatform :GRACE and GRACE-FOinstitution :NASA/JPLcreator_name :David Wiesecreator_email :grace@podaac.jpl.nasa.govcreator_url :https://grace.jpl.nasa.govcreator_type :groupcreator_institution :NASA/JPLpublisher_name :Physical Oceanography Distributed Active Archive Centerpublisher_email :podaac@jpl.nasa.govpublisher_url :https://podaac.jpl.nasa.govpublisher_type :grouppublisher_institution :NASA/JPLproject :NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)program :NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Programid :10.5067/TEMSC-3JC62naming_authority :org.doi.dxsource :GRACE and GRACE-FO JPL RL06Mv2-CRIprocessing_level :2 and 3acknowledgement :GRACE is a joint mission of NASA (USA) and DLR (Germany). GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAClicense :https://science.nasa.gov/earth-science/earth-science-data/data-information-policyproduct_version :v2.0time_epoch :2002-01-01T00:00:00Ztime_coverage_start :2002-04-16T00:00:00Ztime_coverage_end :2021-03-16T23:59:59Zgeospatial_lat_min :-89.75geospatial_lat_max :89.75geospatial_lat_units :degrees_northgeospatial_lat_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconsgeospatial_lon_min :0.25geospatial_lon_max :359.75geospatial_lon_units :degrees_eastgeospatial_lon_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconstime_mean_removed :2004.000 to 2009.999months_missing :2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09postprocess_1 : OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLYpostprocess_2 :Water density used to convert to equivalent water height: 1000 kg/m^3postprocess_3 :Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlinesGIA_removed :ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.geocenter_correction :We use a version of TN-13 based on the JPL masconsC_20_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929C_30_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929. This substitution is made for all months after August 2016.user_note_1 :The accelerometer on the GRACE-B spacecraft was turned off after August 2016. After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure. This has led to a subsequent degradation in the quality of the gravity fields derived. The uncertainties in this file have been scaled to accomodate this degradation.user_note_2 :The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018. After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure. The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.journal_reference :Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. CRI_filter_journal_reference :Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. date_created :2021-04-27T21:04:06Z\n\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data to plot.\n\nlat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\nds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_GRACE_subset\n\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.add_feature(cartopy.feature.RIVERS)\nds_GRACE_subset.lwe_thickness[193,:,:].plot(cmap = 'bwr_r') #106: July 2011\nplt.show()\n\n\n\n\n\n\n\nOn-premise data via OPeNDAP: River heights\nUse xarray and OPeNDAP link to see an overview of the dataset.\nCurrently, Pre-SWOT MEaSUREs data do not have the tools required to access them on the cloud, but methods are in the works! Here, we will obtain this dataset using OPeNDAP. OPeNDAP provides an API on the host server to access data without downloading it. OPeNDAP will also have a cloud component in the future, so this method of access can be used for both on-premise and cloud-based data in the near future and moving forward.\nTo find the OPeNDAP links needed to open the data (using the python package xarray), go to the specific satellite’s page on PO.DAAC (ex. Pre-SWOT MEaSUREs’s site).\nClick on the dataset you want (e.g. https://podaac.jpl.nasa.gov/dataset/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2) and click the “Data Access” tab. This will give you a link to where you can find the data in OPeNDAP (ex. https://podaac-opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/).\nFrom there, navigate to the desired NetCDF file and copy its link (e.g. for MEaSUREs, we want the Amazon estuary, so we choose the South America Amazon file: https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc).\nThe guidebook explains the details of the Pre-SWOT MEaSUREs data.\nOur desired variable is height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Amazon estuary. Time is between April 8, 1993 and April 20, 2019.\nLet’s look at this example file to see how the data is organized:\n\nds_MEaSUREs = xr.open_dataset('https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc')\nds_MEaSUREs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (X: 3311, Y: 3311, charlength: 26, distance: 3311, time: 9469)\nCoordinates:\n * time (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...\nDimensions without coordinates: X, Y, charlength, distance\nData variables:\n lon (X) float64 -51.04 -51.05 -51.06 ... -73.35 -73.35\n lat (Y) float64 -0.6559 -0.6553 -0.6551 ... -4.179 -4.187\n FD (distance) float64 104.9 1.105e+03 ... 3.31e+06\n height (distance, time) float64 ...\n sat (charlength) |S64 b'--------------------------------...\n storage (distance, time) float64 ...\n LakeFlag (distance) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n Storage_uncertainty (distance, time) float64 ...\n IceFlag (time) float64 nan nan nan nan nan ... nan nan nan nan\nAttributes:\n title: GRRATS (Global River Radar Altimetry Time ...\n Conventions: CF-1.6, ACDD-1.3\n institution: Ohio State University, School of Earth Sci...\n source: MEaSUREs OSU Storage toolbox 2018\n keywords: EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n keywords_vocabulary: Global Change Master Directory (GCMD)\n cdm_data_type: station\n creator_name: Coss,Steve\n creator_email: Coss.31@osu.edu\n project: MEaSUREs OSU\n program: NASA Earth Science Data Systems (ESDS)\n publisher_name: PO.DAAC (Physical Oceanography Distributed...\n publisher_email: podaac@podaac.jpl.nasa.gov\n publisher_url: podaac.jpl.nasa.gov\n publisher_type: Institution\n publisher_institution: PO.DAAC\n processing_level: L2\n doi: 10.5067/PSGRA-DA2V2\n history: This GRRATS product adds data river surfac...\n platform: ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Ja...\n platform_vocabulary: NASA/GCMD Platform Keywords. Version 8.6\n instrument: RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(...\n instrument_vocabulary: NASA/GCMD Platform Keywords. Version 8.6\n references: in review :doi.org/10.5194/essd-2019-84\n id: GRRATS(Global River Radar Altimeter Time S...\n summary: The Global River Radar Altimeter Time Seri...\n time_coverage_resolution: 1 day\n date_created: 2021-05-17T21:26:51\n time_coverage_start: 1992-04-08T15:20:40\n time_coverage_end: 2018-04-20T03:39:13\n geospatial_lon_min: -73.35433106652545\n geospatial_lon_max: -51.0426448887506\n geospatial_lon_units: degree_east\n geospatial_lat_min: -4.380427586763687\n geospatial_lat_max: -0.6550700975069503\n geospatial_lat_units: degree_north\n geospatial_vertical_max: 92.7681246287056\n geospatial_vertical_min: -3.563409518163376\n geospatial_vertical_units: m\n geospatial_vertical_positive: upxarray.DatasetDimensions:X: 3311Y: 3311charlength: 26distance: 3311time: 9469Coordinates: (1)time(time)datetime64[ns]1993-04-08T15:20:40.665117184 .....long_name :timestandard_name :timeaxis :Tarray(['1993-04-08T15:20:40.665117184', '1993-04-09T15:20:40.665117184',\n '1993-04-10T15:20:40.665117184', ..., '2019-04-18T03:39:13.243964928',\n '2019-04-19T03:39:13.243964928', '2019-04-20T03:39:13.243964928'],\n dtype='datetime64[ns]')Data variables: (9)lon(X)float64...units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xarray([-51.042645, -51.051273, -51.06017 , ..., -73.354331, -73.351882,\n -73.348082])lat(Y)float64...units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yarray([-0.655885, -0.655342, -0.65507 , ..., -4.170956, -4.179361, -4.187492])FD(distance)float64...long_name :distance_from_river_mouthunits :kmcomment :This is the distance along the river centerline from the river mouth to this VS.array([1.049362e+02, 1.104936e+03, 2.104936e+03, ..., 3.308105e+06,\n 3.309105e+06, 3.310105e+06])height(distance, time)float64...units :mpositive :uplong_name :interpolated_heightsstandard_name :heightvalid_min :-3.563409518163376valid_max :92.7681246287056comment :A time flow distance grid of river water heights with respect to the EGM08 Geoid).-9999 fill values are for missing data, while -9995 fill values are for lakes and reservoirs.[31351859 values with dtype=float64]sat(charlength)|S64...long_name :satellitecomment :The satellite the measurement is derived from.string_length :9469array([b'----------------------------------------------------------------',\n b'EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE',\n b'RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR',\n b'SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS',\n b'1111111111111111111111111111111111111111111111111111111111111111',\n b'cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' '],\n dtype='|S64')storage(distance, time)float64...units :km3positive :uplong_name :river_channel_storagecomment :A time flow distance grid of river channel storage values.[31351859 values with dtype=float64]LakeFlag(distance)float64...long_name :Lake_Flagvalid_range :0, 1flag_masks :1flag_meaning :River sections that are lakes or reservoirscomment : This is a flag for masking out setions that are lakes or reservoirs.array([0., 0., 0., ..., 0., 0., 0.])Storage_uncertainty(distance, time)float64...units :km3positive :uplong_name :river_channel_storage_uncertaintycomment :A time flow distance grid of river channel storage uncertainty values.[31351859 values with dtype=float64]IceFlag(time)float64...long_name :Ice_Flagvalid_range :0, 1flag_masks :1flag_meaning :Time of ice covercomment : This is a flag for masking out times of Ice Cover.array([nan, nan, nan, ..., nan, nan, nan])Attributes: (40)title :GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Amazon RiverConventions :CF-1.6, ACDD-1.3institution :Ohio State University, School of Earth Sciencessource :MEaSUREs OSU Storage toolbox 2018keywords :EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHTkeywords_vocabulary :Global Change Master Directory (GCMD)cdm_data_type :stationcreator_name :Coss,Stevecreator_email :Coss.31@osu.eduproject :MEaSUREs OSUprogram :NASA Earth Science Data Systems (ESDS)publisher_name :PO.DAAC (Physical Oceanography Distributed Active Archive Center)publisher_email :podaac@podaac.jpl.nasa.govpublisher_url :podaac.jpl.nasa.govpublisher_type :Institutionpublisher_institution :PO.DAACprocessing_level :L2doi :10.5067/PSGRA-DA2V2history :This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements. platform :ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)platform_vocabulary :NASA/GCMD Platform Keywords. Version 8.6instrument :RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)instrument_vocabulary :NASA/GCMD Platform Keywords. Version 8.6references :in review :doi.org/10.5194/essd-2019-84id :GRRATS(Global River Radar Altimeter Time Series) 1km/dailysummary :The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.time_coverage_resolution :1 daydate_created :2021-05-17T21:26:51time_coverage_start :1992-04-08T15:20:40time_coverage_end :2018-04-20T03:39:13geospatial_lon_min :-73.35433106652545geospatial_lon_max :-51.0426448887506geospatial_lon_units :degree_eastgeospatial_lat_min :-4.380427586763687geospatial_lat_max :-0.6550700975069503geospatial_lat_units :degree_northgeospatial_vertical_max :92.7681246287056geospatial_vertical_min :-3.563409518163376geospatial_vertical_units :mgeospatial_vertical_positive :up\n\n\n\n\nOn-premise data via OPeNDAP: sea surface salinity (SMAP and Aquarius) and temperature (MODIS)\nPre-SWOT MEaSUREs data resides in one NetCDF file for the whole time period, but the same cannot be said for SMAP, Aquarius and MODIS data. They have one file per month for their monthly datasets. SMAP has salinity data from April 2015 - present, Aquarius has salinity data from August 2011 - June 2015, and MODIS has SST data for the entire 2011-2019 time period.\nFirst, we create strings of OPeNDAP links in lists for each satellite product so we can obtain them and merge them into one file. The links change depending on the date, so the pattern of how the links change needs to be observed and then looped over and appended to the links file list. For example, SMAP has month and year numbers in its links, while Aquarius has start day and end day of the year in its links, so writing their strings takes different logic.\n\n# Initialize SMAP file list\nfile_list_SMAP = []\n#create an array of the months of the year and an array for the years\ncounter_month = np.arange(1,13)\ncounter_year = np.arange(2015, 2020)\n\n# Make list of file paths\nfor j in counter_year:\n for i in counter_month:\n if j == 2015: #data starts in april for 2015\n if i > 3:\n if i < 10: #for single digit months, only one number needs to be changed\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d0%d_MONTHLY_V5.0.nc' % (j, j, i))\n else: #for double digit months, 2 numbers in the string need to change\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d%d_MONTHLY_V5.0.nc' % (j, j, i))\n else:\n if i < 10: #for single digit months, only one number needs to be changed\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d0%d_MONTHLY_V5.0.nc' % (j, j, i))\n else: #for double digit months, 2 numbers in the string need to change\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d%d_MONTHLY_V5.0.nc' % (j, j, i))\n\n# Initialize Aquarius file list\nfile_list_Aq = []\n# Create an array of the months of the year and an array for the years\ncounter_month = np.arange(0,12)\ncounter_year = np.arange(2011, 2016)\n\n# Make list of file paths\nfor j in counter_year:\n for i in counter_month:\n if j == 2012: #leap-year\n d1 = ['001', '032', '061', '092', '122', '153', '183', '214', '245', '275', '306', '336']\n d2 = ['031', '060', '091', '121', '152', '182', '213', '244', '274', '305', '335', '366']\n else: #all other years\n d1 = ['001', '032', '060', '091', '121', '152', '182', '213', '244', '274', '305', '335']\n d2 = ['031', '059', '090', '120', '151', '181', '212', '243', '273', '304', '334', '365']\n \n if j == 2011: #data starts in the 8th month (index 7)\n if i > 6:\n file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n elif j == 2015: #data ends in the 6th month (index 5)\n if i < 6:\n file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n else:\n file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n\n \n# Initialize MODIS file list for sea surface temperature\nfile_list_MODIS = []\n# Create an array of the months of the year and an array for the years\ncounter_month = np.arange(0,12)\ncounter_year = np.arange(2011, 2020)\n\n# Make list of file paths\nfor j in counter_year:\n for i in counter_month:\n if j == 2012 or j == 2016: #leap-year\n d1 = ['0101', '0201', '0301', '0401', '0501', '0601', '0701', '0801', '0901', '1001', '1101', '1201']\n d2 = ['0131', '0229', '0331', '0430', '0531', '0630', '0731', '0831', '0930', '1031', '1130', '1231']\n else: #all other years\n d1 = ['0101', '0201', '0301', '0401', '0501', '0601', '0701', '0801', '0901', '1001', '1101', '1201']\n d2 = ['0131', '0228', '0331', '0430', '0531', '0630', '0731', '0831', '0930', '1031', '1130', '1231']\n \n if j == 2011: #data starts in the 8th month (index 7)\n if i > 6:\n file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n elif j == 2019: #data ends in the 11th month (index 10)\n if i < 11:\n file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n else:\n file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n\n# Un comment to see links lists\n# file_list_SMAP\n# file_list_Aq\nfile_list_MODIS\n\n['https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20110801_20110831.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20110901_20110930.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20111001_20111031.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20111101_20111130.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20111201_20111231.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120101_20120131.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120201_20120229.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120301_20120331.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120401_20120430.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120501_20120531.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120601_20120630.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120701_20120731.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120801_20120831.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20120901_20120930.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2012/AQUA_MODIS.20121001_20121031.L3m.MO.SST4.sst4.9km.nc',\n 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'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20191101_20191130.L3m.MO.SST4.sst4.9km.nc']\n\n\n\n# For OPeNDAP, we need to set the number of files that can be opened at once to 10, \n# So that xa.open_mfdataset() actually reads all links (see https://github.com/pydata/xarray/issues/4082)\nxr.set_options(file_cache_maxsize=10)\n\n# To use xa.open_mfdataset which combines netCDF files\nds_SMAP = xr.open_mfdataset(file_list_SMAP, combine='by_coords')\nds_SMAP\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (latitude: 720, longitude: 1440, time: 57)\nCoordinates:\n * longitude (longitude) float32 -179.875 -179.625 ... 179.875\n * latitude (latitude) float32 89.875 89.625 ... -89.625 -89.875\n * time (time) datetime64[ns] 2015-04-16 ... 2019-12-16T12:...\nData variables:\n smap_sss (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n anc_sss (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n anc_sst (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n smap_spd (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n smap_high_spd (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n weight (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n land_fraction (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n ice_fraction (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n smap_sss_uncertainty (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\nAttributes:\n title: SMAP 0.25x0.25 deg grid averaged monthly SSS...\n institution: Jet Propulsion Laboratory\n source: SMAP L2B SSS\n history: DATA_SOURCE_VERSION V5.0 L2B SMAP SSS/WSPD\n comment: Gaussian-weighted map gridding of SMAP L2B S...\n Gaussian_window_radius: 45.0\n Gaussian_window_half_power: 30.0\n revs_used: [ 870 871 872 873 874 875 876 877 87...\n revs_missing: [ 881 882 908 909 910 911 933 934 95...\n l2b_files: SMAP_L2B_SSS_00870_20150401T004402_R17000_V5...\n TB_CRID: R17000\n Year: 2015\n Month: 4\n Conventions: CF-1.6, ACDD-1.3\n processing_level: 3\n cdm_data_type: Grid\n date_issued: 2020-293T18:28:59.223\n date_created: 2020-293T18:28:59.223\n time_coverage_start: 2015-091T00:00:00.000\n time_coverage_end: 2015-121T00:00:00.000\n geospatial_lat_max: 90.0\n geospatial_lat_min: -90.0\n geospatial_lon_max: 180.0\n geospatial_lon_min: -180.0\n geospatial_lat_units: degrees_north\n geospatial_lon_units: degrees_east\n platform: SMAP\n sensor: SMAP\n project: SMAP\n product_version: V5.0\n keywords_vocabulary: http://gcmd.gsfc.nasa.gov/Resources/valids/g...\n keywords: SEA SURFACE SALINITY, SALINITY, SMAP, Jet Pr...\n creator_name: JPL\n creator_email: fore@jpl.nasa.gov\n publisher_name: Alexander G. Fore\n publisher_email: fore@jpl.nasa.gov\n contributor_name: Alexander Fore, Simon Yueh, Wenqing Tang, Ak...\n references: 10.1109/TGRS.2016.2601486, 10.1109/TGRS.2016...xarray.DatasetDimensions:latitude: 720longitude: 1440time: 57Coordinates: (3)longitude(longitude)float32-179.875 -179.625 ... 179.875units :degrees_eaststandard_name :longitudelong_name :longitude of grid cellaxis :Xcoverage_content_type :coordinatearray([-179.875, -179.625, -179.375, ..., 179.375, 179.625, 179.875],\n dtype=float32)latitude(latitude)float3289.875 89.625 ... -89.625 -89.875units :degrees_northstandard_name :latitudelong_name :latitude of grid cellaxis :Ycoverage_content_type :coordinatearray([ 89.875, 89.625, 89.375, ..., -89.375, -89.625, -89.875],\n dtype=float32)time(time)datetime64[ns]2015-04-16 ... 2019-12-16T12:00:00long_name :Reference time of sss fieldstandard_name :timecomment :Midpoint of time interval of analyzed fieldscoverage_content_type :coordinatearray(['2015-04-16T00:00:00.000000000', '2015-05-16T12:00:00.000000000',\n '2015-06-16T00:00:00.000000000', '2015-07-16T12:00:00.000000000',\n '2015-08-16T12:00:00.000000000', '2015-09-16T00:00:00.000000000',\n '2015-10-16T12:00:00.000000000', '2015-11-16T00:00:00.000000000',\n '2015-12-16T12:00:00.000000000', '2016-01-16T12:00:00.000000000',\n '2016-02-15T12:00:00.000000000', '2016-03-16T12:00:00.000000000',\n '2016-04-16T00:00:00.000000000', '2016-05-16T12:00:00.000000000',\n '2016-06-16T00:00:00.000000000', '2016-07-16T12:00:00.000000000',\n '2016-08-16T12:00:00.000000000', '2016-09-16T00:00:00.000000000',\n '2016-10-16T12:00:00.000000000', '2016-11-16T00:00:00.000000000',\n '2016-12-16T12:00:00.000000000', '2017-01-16T12:00:00.000000000',\n '2017-02-15T00:00:00.000000000', '2017-03-16T12:00:00.000000000',\n '2017-04-16T00:00:00.000000000', '2017-05-16T12:00:00.000000000',\n '2017-06-16T00:00:00.000000000', '2017-07-16T12:00:00.000000000',\n '2017-08-16T12:00:00.000000000', '2017-09-16T00:00:00.000000000',\n '2017-10-16T12:00:00.000000000', '2017-11-16T00:00:00.000000000',\n '2017-12-16T12:00:00.000000000', '2018-01-16T12:00:00.000000000',\n '2018-02-15T00:00:00.000000000', '2018-03-16T12:00:00.000000000',\n '2018-04-16T00:00:00.000000000', '2018-05-16T12:00:00.000000000',\n '2018-06-16T00:00:00.000000000', '2018-07-16T12:00:00.000000000',\n '2018-08-16T12:00:00.000000000', '2018-09-16T00:00:00.000000000',\n '2018-10-16T12:00:00.000000000', '2018-11-16T00:00:00.000000000',\n '2018-12-16T12:00:00.000000000', '2019-01-16T12:00:00.000000000',\n '2019-02-15T00:00:00.000000000', '2019-03-16T12:00:00.000000000',\n '2019-04-16T00:00:00.000000000', '2019-05-16T12:00:00.000000000',\n '2019-06-16T00:00:00.000000000', '2019-07-16T12:00:00.000000000',\n '2019-08-16T12:00:00.000000000', '2019-09-16T00:00:00.000000000',\n '2019-10-16T12:00:00.000000000', '2019-11-16T00:00:00.000000000',\n '2019-12-16T12:00:00.000000000'], dtype='datetime64[ns]')Data variables: (9)smap_sss(time, latitude, longitude)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>long_name :SMAP sea surface salinityunits :1e-3standard_name :sea_surface_salinityvalid_min :0valid_max :45coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanc_sss\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nHYCOM sea surface salinity\n\nunits :\n\n1e-3\n\nstandard_name :\n\nsea_surface_salinity\n\nvalid_min :\n\n0\n\nvalid_max :\n\n45\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nanc_sst\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSea surface temperature\n\nunits :\n\nK\n\nstandard_name :\n\nsea_surface_temperature\n\nvalid_min :\n\n0\n\nvalid_max :\n\n340\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsmap_spd\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSMAP 10 m wind speed\n\nunits :\n\nm s-1\n\nstandard_name :\n\nwind_speed\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsmap_high_spd\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSMAP 10 m wind speed (using ancillary SSS)\n\nunits :\n\nm s-1\n\nstandard_name :\n\nwind_speed\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nweight\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nvalid_min :\n\n0\n\nlong_name :\n\nSum of Gaussian weighting factors\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nland_fraction\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nvalid_min :\n\n0\n\nvalid_max :\n\n1\n\nlong_name :\n\nAverage land fraction\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nice_fraction\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nvalid_min :\n\n0\n\nvalid_max :\n\n1\n\nlong_name :\n\nAverage ice fraction\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsmap_sss_uncertainty\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0\n\nvalid_max :\n\n50\n\nlong_name :\n\nSMAP SSS uncertainty\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (38)title :SMAP 0.25x0.25 deg grid averaged monthly SSS/WSPDinstitution :Jet Propulsion Laboratorysource :SMAP L2B SSShistory :DATA_SOURCE_VERSION V5.0 L2B SMAP SSS/WSPDcomment :Gaussian-weighted map gridding of SMAP L2B SSS ProductGaussian_window_radius :45.0Gaussian_window_half_power :30.0revs_used :[ 870 871 872 873 874 875 876 877 878 879 880 883 884 885\n 886 887 888 889 890 891 892 893 894 895 896 897 898 899\n 900 901 902 903 904 905 906 907 912 913 914 915 916 917\n 918 919 920 921 922 923 924 925 926 927 928 929 930 931\n 932 935 936 937 938 939 940 941 942 943 944 945 946 947\n 948 949 950 951 952 953 954 955 956 959 960 961 962 963\n 964 965 966 967 968 969 970 971 972 973 974 975 976 977\n 978 979 980 981 982 983 984 985 986 987 988 989 990 991\n 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005\n 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019\n 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1032 1033 1034\n 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048\n 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062\n 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076\n 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1090 1091\n 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105\n 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119\n 1120 1121 1122 1123 1124 1125 1127 1128 1129 1130 1131 1132 1133 1134\n 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148\n 1149 1150 1151 1152 1153 1154 1155 1157 1158 1159 1160 1161 1162 1163\n 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1178 1179\n 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193\n 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207\n 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221\n 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235\n 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249\n 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263\n 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277\n 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291\n 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305\n 1306 1307 1308]revs_missing :[ 881 882 908 909 910 911 933 934 957 958 1031 1089 1126 1156\n 1176 1177]l2b_files :SMAP_L2B_SSS_00870_20150401T004402_R17000_V5.0.h5\nSMAP_L2B_SSS_00871_20150401T022229_R17000_V5.0.h5\nSMAP_L2B_SSS_00872_20150401T040057_R17000_V5.0.h5\nSMAP_L2B_SSS_00873_20150401T053924_R17000_V5.0.h5\nSMAP_L2B_SSS_00874_20150401T071752_R17000_V5.0.h5\nSMAP_L2B_SSS_00875_20150401T085619_R17000_V5.0.h5\nSMAP_L2B_SSS_00876_20150401T103447_R17000_V5.0.h5\nSMAP_L2B_SSS_00877_20150401T121314_R17000_V5.0.h5\nSMAP_L2B_SSS_00878_20150401T135142_R17000_V5.0.h5\nSMAP_L2B_SSS_00879_20150401T153009_R17000_V5.0.h5\nSMAP_L2B_SSS_00880_20150401T170837_R17000_V5.0.h5\nSMAP_L2B_SSS_00883_20150401T220359_R17000_V5.0.h5\nSMAP_L2B_SSS_00884_20150401T234226_R17000_V5.0.h5\nSMAP_L2B_SSS_00885_20150402T012054_R17000_V5.0.h5\nSMAP_L2B_SSS_00886_20150402T025922_R17000_V5.0.h5\nSMAP_L2B_SSS_00887_20150402T043749_R17000_V5.0.h5\nSMAP_L2B_SSS_00888_20150402T061616_R17000_V5.0.h5\nSMAP_L2B_SSS_00889_20150402T075444_R17000_V5.0.h5\nSMAP_L2B_SSS_00890_20150402T093311_R17000_V5.0.h5\nSMAP_L2B_SSS_00891_20150402T111139_R17000_V5.0.h5\nSMAP_L2B_SSS_00892_20150402T125006_R17000_V5.0.h5\nSMAP_L2B_SSS_00893_20150402T142834_R17000_V5.0.h5\nSMAP_L2B_SSS_00894_20150402T160701_R17000_V5.0.h5\nSMAP_L2B_SSS_00895_20150402T174529_R17000_V5.0.h5\nSMAP_L2B_SSS_00896_20150402T192356_R17000_V5.0.h5\nSMAP_L2B_SSS_00897_20150402T210224_R17000_V5.0.h5\nSMAP_L2B_SSS_00898_20150402T224051_R17000_V5.0.h5\nSMAP_L2B_SSS_00899_20150403T001919_R17000_V5.0.h5\nSMAP_L2B_SSS_00900_20150403T015746_R17000_V5.0.h5\nSMAP_L2B_SSS_00901_20150403T033614_R17000_V5.0.h5\nSMAP_L2B_SSS_00902_20150403T051441_R17000_V5.0.h5\nSMAP_L2B_SSS_00903_20150403T065309_R17000_V5.0.h5\nSMAP_L2B_SSS_00904_20150403T083136_R17000_V5.0.h5\nSMAP_L2B_SSS_00905_20150403T101003_R17000_V5.0.h5\nSMAP_L2B_SSS_00906_20150403T114831_R17000_V5.0.h5\nSMAP_L2B_SSS_00907_20150403T132658_R17000_V5.0.h5\nSMAP_L2B_SSS_00912_20150403T213916_R17000_V5.0.h5\nSMAP_L2B_SSS_00913_20150403T231743_R17000_V5.0.h5\nSMAP_L2B_SSS_00914_20150404T005611_R17000_V5.0.h5\nSMAP_L2B_SSS_00915_20150404T023438_R17000_V5.0.h5\nSMAP_L2B_SSS_00916_20150404T041306_R17000_V5.0.h5\nSMAP_L2B_SSS_00917_20150404T055133_R17000_V5.0.h5\nSMAP_L2B_SSS_00918_20150404T073000_R17000_V5.0.h5\nSMAP_L2B_SSS_00919_20150404T090828_R17000_V5.0.h5\nSMAP_L2B_SSS_00920_20150404T104655_R17000_V5.0.h5\nSMAP_L2B_SSS_00921_20150404T122523_R17000_V5.0.h5\nSMAP_L2B_SSS_00922_20150404T140350_R17000_V5.0.h5\nSMAP_L2B_SSS_00923_20150404T154218_R17000_V5.0.h5\nSMAP_L2B_SSS_00924_20150404T172045_R17000_V5.0.h5\nSMAP_L2B_SSS_00925_20150404T185913_R17000_V5.0.h5\nSMAP_L2B_SSS_00926_20150404T203740_R17000_V5.0.h5\nSMAP_L2B_SSS_00927_20150404T221607_R17000_V5.0.h5\nSMAP_L2B_SSS_00928_20150404T235435_R17000_V5.0.h5\nSMAP_L2B_SSS_00929_20150405T013303_R17000_V5.0.h5\nSMAP_L2B_SSS_00930_20150405T031130_R17000_V5.0.h5\nSMAP_L2B_SSS_00931_20150405T044958_R17000_V5.0.h5\nSMAP_L2B_SSS_00932_20150405T062825_R17000_V5.0.h5\nSMAP_L2B_SSS_00935_20150405T112347_R17000_V5.0.h5\nSMAP_L2B_SSS_00936_20150405T130215_R17000_V5.0.h5\nSMAP_L2B_SSS_00937_20150405T144042_R17000_V5.0.h5\nSMAP_L2B_SSS_00938_20150405T161910_R17000_V5.0.h5\nSMAP_L2B_SSS_00939_20150405T175737_R17000_V5.0.h5\nSMAP_L2B_SSS_00940_20150405T193604_R17000_V5.0.h5\nSMAP_L2B_SSS_00941_20150405T211432_R17000_V5.0.h5\nSMAP_L2B_SSS_00942_20150405T225259_R17000_V5.0.h5\nSMAP_L2B_SSS_00943_20150406T003127_R17000_V5.0.h5\nSMAP_L2B_SSS_00944_20150406T020954_R17000_V5.0.h5\nSMAP_L2B_SSS_00945_20150406T034822_R17000_V5.0.h5\nSMAP_L2B_SSS_00946_20150406T052649_R17000_V5.0.h5\nSMAP_L2B_SSS_00947_20150406T070517_R17000_V5.0.h5\nSMAP_L2B_SSS_00948_20150406T084344_R17000_V5.0.h5\nSMAP_L2B_SSS_00949_20150406T102211_R17000_V5.0.h5\nSMAP_L2B_SSS_00950_20150406T120039_R17000_V5.0.h5\nSMAP_L2B_SSS_00951_20150406T133907_R17000_V5.0.h5\nSMAP_L2B_SSS_00952_20150406T151734_R17000_V5.0.h5\nSMAP_L2B_SSS_00953_20150406T165601_R17000_V5.0.h5\nSMAP_L2B_SSS_00954_20150406T183429_R17000_V5.0.h5\nSMAP_L2B_SSS_00955_20150406T201256_R17000_V5.0.h5\nSMAP_L2B_SSS_00956_20150406T215124_R17000_V5.0.h5\nSMAP_L2B_SSS_00959_20150407T024646_R17000_V5.0.h5\nSMAP_L2B_SSS_00960_20150407T042514_R17000_V5.0.h5\nSMAP_L2B_SSS_00961_20150407T060341_R17000_V5.0.h5\nSMAP_L2B_SSS_00962_20150407T074208_R17000_V5.0.h5\nSMAP_L2B_SSS_00963_20150407T092036_R17000_V5.0.h5\nSMAP_L2B_SSS_00964_20150407T105903_R17000_V5.0.h5\nSMAP_L2B_SSS_00965_20150407T123731_R17000_V5.0.h5\nSMAP_L2B_SSS_00966_20150407T141558_R17000_V5.0.h5\nSMAP_L2B_SSS_00967_20150407T155426_R17000_V5.0.h5\nSMAP_L2B_SSS_00968_20150407T173253_R17000_V5.0.h5\nSMAP_L2B_SSS_00969_20150407T191120_R17000_V5.0.h5\nSMAP_L2B_SSS_00970_20150407T204948_R17000_V5.0.h5\nSMAP_L2B_SSS_00971_20150407T222815_R17000_V5.0.h5\nSMAP_L2B_SSS_00972_20150408T000643_R17000_V5.0.h5\nSMAP_L2B_SSS_00973_20150408T014510_R17000_V5.0.h5\nSMAP_L2B_SSS_00974_20150408T032338_R17000_V5.0.h5\nSMAP_L2B_SSS_00975_20150408T050205_R17000_V5.0.h5\nSMAP_L2B_SSS_00976_20150408T064033_R17000_V5.0.h5\nSMAP_L2B_SSS_00977_20150408T081900_R17000_V5.0.h5\nSMAP_L2B_SSS_00978_20150408T095727_R17000_V5.0.h5\nSMAP_L2B_SSS_00979_20150408T113555_R17000_V5.0.h5\nSMAP_L2B_SSS_00980_20150408T131422_R17000_V5.0.h5\nSMAP_L2B_SSS_00981_20150408T145250_R17000_V5.0.h5\nSMAP_L2B_SSS_00982_20150408T163117_R17000_V5.0.h5\nSMAP_L2B_SSS_00983_20150408T180945_R17000_V5.0.h5\nSMAP_L2B_SSS_00984_20150408T194812_R17000_V5.0.h5\nSMAP_L2B_SSS_00985_20150408T212639_R17000_V5.0.h5\nSMAP_L2B_SSS_00986_20150408T230507_R17000_V5.0.h5\nSMAP_L2B_SSS_00987_20150409T004334_R17000_V5.0.h5\nSMAP_L2B_SSS_00988_20150409T022202_R17000_V5.0.h5\nSMAP_L2B_SSS_00989_20150409T040029_R17000_V5.0.h5\nSMAP_L2B_SSS_00990_20150409T053857_R17000_V5.0.h5\nSMAP_L2B_SSS_00991_20150409T071724_R17000_V5.0.h5\nSMAP_L2B_SSS_00992_20150409T085551_R17000_V5.0.h5\nSMAP_L2B_SSS_00993_20150409T103419_R17000_V5.0.h5\nSMAP_L2B_SSS_00994_20150409T121246_R17000_V5.0.h5\nSMAP_L2B_SSS_00995_20150409T135114_R17000_V5.0.h5\nSMAP_L2B_SSS_00996_20150409T152941_R17000_V5.0.h5\nSMAP_L2B_SSS_00997_20150409T170809_R17000_V5.0.h5\nSMAP_L2B_SSS_00998_20150409T184636_R17000_V5.0.h5\nSMAP_L2B_SSS_00999_20150409T202503_R17000_V5.0.h5\nSMAP_L2B_SSS_01000_20150409T220331_R17000_V5.0.h5\nSMAP_L2B_SSS_01001_20150409T234158_R17000_V5.0.h5\nSMAP_L2B_SSS_01002_20150410T012026_R17000_V5.0.h5\nSMAP_L2B_SSS_01003_20150410T025853_R17000_V5.0.h5\nSMAP_L2B_SSS_01004_20150410T043721_R17000_V5.0.h5\nSMAP_L2B_SSS_01005_20150410T061548_R17000_V5.0.h5\nSMAP_L2B_SSS_01006_20150410T075416_R17000_V5.0.h5\nSMAP_L2B_SSS_01007_20150410T093243_R17000_V5.0.h5\nSMAP_L2B_SSS_01008_20150410T111110_R17000_V5.0.h5\nSMAP_L2B_SSS_01009_20150410T124938_R17000_V5.0.h5\nSMAP_L2B_SSS_01010_20150410T142806_R17000_V5.0.h5\nSMAP_L2B_SSS_01011_20150410T160633_R17000_V5.0.h5\nSMAP_L2B_SSS_01012_20150410T174500_R17000_V5.0.h5\nSMAP_L2B_SSS_01013_20150410T192328_R17000_V5.0.h5\nSMAP_L2B_SSS_01014_20150410T210155_R17000_V5.0.h5\nSMAP_L2B_SSS_01015_20150410T224022_R17000_V5.0.h5\nSMAP_L2B_SSS_01016_20150411T001850_R17000_V5.0.h5\nSMAP_L2B_SSS_01017_20150411T015718_R17000_V5.0.h5\nSMAP_L2B_SSS_01018_20150411T033545_R17000_V5.0.h5\nSMAP_L2B_SSS_01019_20150411T051412_R17000_V5.0.h5\nSMAP_L2B_SSS_01020_20150411T065240_R17000_V5.0.h5\nSMAP_L2B_SSS_01021_20150411T083107_R17000_V5.0.h5\nSMAP_L2B_SSS_01022_20150411T100934_R17000_V5.0.h5\nSMAP_L2B_SSS_01023_20150411T114802_R17000_V5.0.h5\nSMAP_L2B_SSS_01024_20150411T132630_R17000_V5.0.h5\nSMAP_L2B_SSS_01025_20150411T150457_R17000_V5.0.h5\nSMAP_L2B_SSS_01026_20150411T164324_R17000_V5.0.h5\nSMAP_L2B_SSS_01027_20150411T182152_R17000_V5.0.h5\nSMAP_L2B_SSS_01028_20150411T200019_R17000_V5.0.h5\nSMAP_L2B_SSS_01029_20150411T213846_R17000_V5.0.h5\nSMAP_L2B_SSS_01030_20150411T231714_R17000_V5.0.h5\nSMAP_L2B_SSS_01032_20150412T023409_R17000_V5.0.h5\nSMAP_L2B_SSS_01033_20150412T041236_R17000_V5.0.h5\nSMAP_L2B_SSS_01034_20150412T055104_R17000_V5.0.h5\nSMAP_L2B_SSS_01035_20150412T072931_R17000_V5.0.h5\nSMAP_L2B_SSS_01036_20150412T090758_R17000_V5.0.h5\nSMAP_L2B_SSS_01037_20150412T104626_R17000_V5.0.h5\nSMAP_L2B_SSS_01038_20150412T122453_R17000_V5.0.h5\nSMAP_L2B_SSS_01039_20150412T140321_R17000_V5.0.h5\nSMAP_L2B_SSS_01040_20150412T154148_R17000_V5.0.h5\nSMAP_L2B_SSS_01041_20150412T172016_R17000_V5.0.h5\nSMAP_L2B_SSS_01042_20150412T185843_R17000_V5.0.h5\nSMAP_L2B_SSS_01043_20150412T203710_R17000_V5.0.h5\nSMAP_L2B_SSS_01044_20150412T221538_R17000_V5.0.h5\nSMAP_L2B_SSS_01045_20150412T235405_R17000_V5.0.h5\nSMAP_L2B_SSS_01046_20150413T013233_R17000_V5.0.h5\nSMAP_L2B_SSS_01047_20150413T031100_R17000_V5.0.h5\nSMAP_L2B_SSS_01048_20150413T044928_R17000_V5.0.h5\nSMAP_L2B_SSS_01049_20150413T062755_R17000_V5.0.h5\nSMAP_L2B_SSS_01050_20150413T080622_R17000_V5.0.h5\nSMAP_L2B_SSS_01051_20150413T094450_R17000_V5.0.h5\nSMAP_L2B_SSS_01052_20150413T112317_R17000_V5.0.h5\nSMAP_L2B_SSS_01053_20150413T130145_R17000_V5.0.h5\nSMAP_L2B_SSS_01054_20150413T144012_R17000_V5.0.h5\nSMAP_L2B_SSS_01055_20150413T161840_R17000_V5.0.h5\nSMAP_L2B_SSS_01056_20150413T175707_R17000_V5.0.h5\nSMAP_L2B_SSS_01057_20150413T193534_R17000_V5.0.h5\nSMAP_L2B_SSS_01058_20150413T211402_R17000_V5.0.h5\nSMAP_L2B_SSS_01059_20150413T225229_R17000_V5.0.h5\nSMAP_L2B_SSS_01060_20150414T003056_R17000_V5.0.h5\nSMAP_L2B_SSS_01061_20150414T020924_R17000_V5.0.h5\nSMAP_L2B_SSS_01062_20150414T034751_R17000_V5.0.h5\nSMAP_L2B_SSS_01063_20150414T052619_R17000_V5.0.h5\nSMAP_L2B_SSS_01064_20150414T070446_R17000_V5.0.h5\nSMAP_L2B_SSS_01065_20150414T084313_R17000_V5.0.h5\nSMAP_L2B_SSS_01066_20150414T102141_R17000_V5.0.h5\nSMAP_L2B_SSS_01067_20150414T120008_R17000_V5.0.h5\nSMAP_L2B_SSS_01068_20150414T133836_R17000_V5.0.h5\nSMAP_L2B_SSS_01069_20150414T151703_R17000_V5.0.h5\nSMAP_L2B_SSS_01070_20150414T165531_R17000_V5.0.h5\nSMAP_L2B_SSS_01071_20150414T183358_R17000_V5.0.h5\nSMAP_L2B_SSS_01072_20150414T201225_R17000_V5.0.h5\nSMAP_L2B_SSS_01073_20150414T215053_R17000_V5.0.h5\nSMAP_L2B_SSS_01074_20150414T232920_R17000_V5.0.h5\nSMAP_L2B_SSS_01075_20150415T010748_R17000_V5.0.h5\nSMAP_L2B_SSS_01076_20150415T024615_R17000_V5.0.h5\nSMAP_L2B_SSS_01077_20150415T042443_R17000_V5.0.h5\nSMAP_L2B_SSS_01078_20150415T060310_R17000_V5.0.h5\nSMAP_L2B_SSS_01079_20150415T074137_R17000_V5.0.h5\nSMAP_L2B_SSS_01080_20150415T092005_R17000_V5.0.h5\nSMAP_L2B_SSS_01081_20150415T105832_R17000_V5.0.h5\nSMAP_L2B_SSS_01082_20150415T123659_R17000_V5.0.h5\nSMAP_L2B_SSS_01083_20150415T141527_R17000_V5.0.h5\nSMAP_L2B_SSS_01084_20150415T155354_R17000_V5.0.h5\nSMAP_L2B_SSS_01085_20150415T173222_R17000_V5.0.h5\nSMAP_L2B_SSS_01086_20150415T191049_R17000_V5.0.h5\nSMAP_L2B_SSS_01087_20150415T204916_R17000_V5.0.h5\nSMAP_L2B_SSS_01088_20150415T222744_R17000_V5.0.h5\nSMAP_L2B_SSS_01090_20150416T014439_R17000_V5.0.h5\nSMAP_L2B_SSS_01091_20150416T032306_R17000_V5.0.h5\nSMAP_L2B_SSS_01092_20150416T050134_R17000_V5.0.h5\nSMAP_L2B_SSS_01093_20150416T064001_R17000_V5.0.h5\nSMAP_L2B_SSS_01094_20150416T081828_R17000_V5.0.h5\nSMAP_L2B_SSS_01095_20150416T095656_R17000_V5.0.h5\nSMAP_L2B_SSS_01096_20150416T113523_R17000_V5.0.h5\nSMAP_L2B_SSS_01097_20150416T131351_R17000_V5.0.h5\nSMAP_L2B_SSS_01098_20150416T145218_R17000_V5.0.h5\nSMAP_L2B_SSS_01099_20150416T163045_R17000_V5.0.h5\nSMAP_L2B_SSS_01100_20150416T180913_R17000_V5.0.h5\nSMAP_L2B_SSS_01101_20150416T194741_R17000_V5.0.h5\nSMAP_L2B_SSS_01102_20150416T212609_R17000_V5.0.h5\nSMAP_L2B_SSS_01103_20150416T230437_R17000_V5.0.h5\nSMAP_L2B_SSS_01104_20150417T004305_R17000_V5.0.h5\nSMAP_L2B_SSS_01105_20150417T022132_R17000_V5.0.h5\nSMAP_L2B_SSS_01106_20150417T040000_R17000_V5.0.h5\nSMAP_L2B_SSS_01107_20150417T053828_R17000_V5.0.h5\nSMAP_L2B_SSS_01108_20150417T071656_R17000_V5.0.h5\nSMAP_L2B_SSS_01109_20150417T085524_R17000_V5.0.h5\nSMAP_L2B_SSS_01110_20150417T103352_R17000_V5.0.h5\nSMAP_L2B_SSS_01111_20150417T121220_R17000_V5.0.h5\nSMAP_L2B_SSS_01112_20150417T135048_R17000_V5.0.h5\nSMAP_L2B_SSS_01113_20150417T152915_R17000_V5.0.h5\nSMAP_L2B_SSS_01114_20150417T170743_R17000_V5.0.h5\nSMAP_L2B_SSS_01115_20150417T184611_R17000_V5.0.h5\nSMAP_L2B_SSS_01116_20150417T202439_R17000_V5.0.h5\nSMAP_L2B_SSS_01117_20150417T220307_R17000_V5.0.h5\nSMAP_L2B_SSS_01118_20150417T234135_R17000_V5.0.h5\nSMAP_L2B_SSS_01119_20150418T012003_R17000_V5.0.h5\nSMAP_L2B_SSS_01120_20150418T025831_R17000_V5.0.h5\nSMAP_L2B_SSS_01121_20150418T043658_R17000_V5.0.h5\nSMAP_L2B_SSS_01122_20150418T061526_R17000_V5.0.h5\nSMAP_L2B_SSS_01123_20150418T075354_R17000_V5.0.h5\nSMAP_L2B_SSS_01124_20150418T093222_R17000_V5.0.h5\nSMAP_L2B_SSS_01125_20150418T111050_R17000_V5.0.h5\nSMAP_L2B_SSS_01127_20150418T142746_R17000_V5.0.h5\nSMAP_L2B_SSS_01128_20150418T160614_R17000_V5.0.h5\nSMAP_L2B_SSS_01129_20150418T174441_R17000_V5.0.h5\nSMAP_L2B_SSS_01130_20150418T192309_R17000_V5.0.h5\nSMAP_L2B_SSS_01131_20150418T210137_R17000_V5.0.h5\nSMAP_L2B_SSS_01132_20150418T224005_R17000_V5.0.h5\nSMAP_L2B_SSS_01133_20150419T001833_R17000_V5.0.h5\nSMAP_L2B_SSS_01134_20150419T015701_R17000_V5.0.h5\nSMAP_L2B_SSS_01135_20150419T033529_R17000_V5.0.h5\nSMAP_L2B_SSS_01136_20150419T051357_R17000_V5.0.h5\nSMAP_L2B_SSS_01137_20150419T065224_R17000_V5.0.h5\nSMAP_L2B_SSS_01138_20150419T083052_R17000_V5.0.h5\nSMAP_L2B_SSS_01139_20150419T100920_R17000_V5.0.h5\nSMAP_L2B_SSS_01140_20150419T114748_R17000_V5.0.h5\nSMAP_L2B_SSS_01141_20150419T132616_R17000_V5.0.h5\nSMAP_L2B_SSS_01142_20150419T150444_R17000_V5.0.h5\nSMAP_L2B_SSS_01143_20150419T164312_R17000_V5.0.h5\nSMAP_L2B_SSS_01144_20150419T182140_R17000_V5.0.h5\nSMAP_L2B_SSS_01145_20150419T200008_R17000_V5.0.h5\nSMAP_L2B_SSS_01146_20150419T213835_R17000_V5.0.h5\nSMAP_L2B_SSS_01147_20150419T231703_R17000_V5.0.h5\nSMAP_L2B_SSS_01148_20150420T005531_R17000_V5.0.h5\nSMAP_L2B_SSS_01149_20150420T023359_R17000_V5.0.h5\nSMAP_L2B_SSS_01150_20150420T041227_R17000_V5.0.h5\nSMAP_L2B_SSS_01151_20150420T055055_R17000_V5.0.h5\nSMAP_L2B_SSS_01152_20150420T072923_R17000_V5.0.h5\nSMAP_L2B_SSS_01153_20150420T090750_R17000_V5.0.h5\nSMAP_L2B_SSS_01154_20150420T104618_R17000_V5.0.h5\nSMAP_L2B_SSS_01155_20150420T122446_R17000_V5.0.h5\nSMAP_L2B_SSS_01157_20150420T154142_R17000_V5.0.h5\nSMAP_L2B_SSS_01158_20150420T172010_R17000_V5.0.h5\nSMAP_L2B_SSS_01159_20150420T185838_R17000_V5.0.h5\nSMAP_L2B_SSS_01160_20150420T203706_R17000_V5.0.h5\nSMAP_L2B_SSS_01161_20150420T221533_R17000_V5.0.h5\nSMAP_L2B_SSS_01162_20150420T235401_R17000_V5.0.h5\nSMAP_L2B_SSS_01163_20150421T013229_R17000_V5.0.h5\nSMAP_L2B_SSS_01164_20150421T031057_R17000_V5.0.h5\nSMAP_L2B_SSS_01165_20150421T044925_R17000_V5.0.h5\nSMAP_L2B_SSS_01166_20150421T062753_R17000_V5.0.h5\nSMAP_L2B_SSS_01167_20150421T080621_R17000_V5.0.h5\nSMAP_L2B_SSS_01168_20150421T094449_R17000_V5.0.h5\nSMAP_L2B_SSS_01169_20150421T112316_R17000_V5.0.h5\nSMAP_L2B_SSS_01170_20150421T130144_R17000_V5.0.h5\nSMAP_L2B_SSS_01171_20150421T144012_R17000_V5.0.h5\nSMAP_L2B_SSS_01172_20150421T161840_R17000_V5.0.h5\nSMAP_L2B_SSS_01173_20150421T175708_R17000_V5.0.h5\nSMAP_L2B_SSS_01174_20150421T193536_R17000_V5.0.h5\nSMAP_L2B_SSS_01175_20150421T211404_R17000_V5.0.h5\nSMAP_L2B_SSS_01178_20150422T020927_R17000_V5.0.h5\nSMAP_L2B_SSS_01179_20150422T034755_R17000_V5.0.h5\nSMAP_L2B_SSS_01180_20150422T052623_R17000_V5.0.h5\nSMAP_L2B_SSS_01181_20150422T070451_R17000_V5.0.h5\nSMAP_L2B_SSS_01182_20150422T084319_R17000_V5.0.h5\nSMAP_L2B_SSS_01183_20150422T102146_R17000_V5.0.h5\nSMAP_L2B_SSS_01184_20150422T120014_R17000_V5.0.h5\nSMAP_L2B_SSS_01185_20150422T133842_R17000_V5.0.h5\nSMAP_L2B_SSS_01186_20150422T151710_R17000_V5.0.h5\nSMAP_L2B_SSS_01187_20150422T165538_R17000_V5.0.h5\nSMAP_L2B_SSS_01188_20150422T183406_R17000_V5.0.h5\nSMAP_L2B_SSS_01189_20150422T201234_R17000_V5.0.h5\nSMAP_L2B_SSS_01190_20150422T215101_R17000_V5.0.h5\nSMAP_L2B_SSS_01191_20150422T232929_R17000_V5.0.h5\nSMAP_L2B_SSS_01192_20150423T010757_R17000_V5.0.h5\nSMAP_L2B_SSS_01193_20150423T024625_R17000_V5.0.h5\nSMAP_L2B_SSS_01194_20150423T042453_R17000_V5.0.h5\nSMAP_L2B_SSS_01195_20150423T060321_R17000_V5.0.h5\nSMAP_L2B_SSS_01196_20150423T074149_R17000_V5.0.h5\nSMAP_L2B_SSS_01197_20150423T092016_R17000_V5.0.h5\nSMAP_L2B_SSS_01198_20150423T105844_R17000_V5.0.h5\nSMAP_L2B_SSS_01199_20150423T123712_R17000_V5.0.h5\nSMAP_L2B_SSS_01200_20150423T141540_R17000_V5.0.h5\nSMAP_L2B_SSS_01201_20150423T155408_R17000_V5.0.h5\nSMAP_L2B_SSS_01202_20150423T173236_R17000_V5.0.h5\nSMAP_L2B_SSS_01203_20150423T191103_R17000_V5.0.h5\nSMAP_L2B_SSS_01204_20150423T204931_R17000_V5.0.h5\nSMAP_L2B_SSS_01205_20150423T222759_R17000_V5.0.h5\nSMAP_L2B_SSS_01206_20150424T000627_R17000_V5.0.h5\nSMAP_L2B_SSS_01207_20150424T014455_R17000_V5.0.h5\nSMAP_L2B_SSS_01208_20150424T032323_R17000_V5.0.h5\nSMAP_L2B_SSS_01209_20150424T050151_R17000_V5.0.h5\nSMAP_L2B_SSS_01210_20150424T064018_R17000_V5.0.h5\nSMAP_L2B_SSS_01211_20150424T081846_R17000_V5.0.h5\nSMAP_L2B_SSS_01212_20150424T095714_R17000_V5.0.h5\nSMAP_L2B_SSS_01213_20150424T113542_R17000_V5.0.h5\nSMAP_L2B_SSS_01214_20150424T131410_R17000_V5.0.h5\nSMAP_L2B_SSS_01215_20150424T145238_R17000_V5.0.h5\nSMAP_L2B_SSS_01216_20150424T163105_R17000_V5.0.h5\nSMAP_L2B_SSS_01217_20150424T180933_R17000_V5.0.h5\nSMAP_L2B_SSS_01218_20150424T194801_R17000_V5.0.h5\nSMAP_L2B_SSS_01219_20150424T212629_R17000_V5.0.h5\nSMAP_L2B_SSS_01220_20150424T230457_R17000_V5.0.h5\nSMAP_L2B_SSS_01221_20150425T004325_R17000_V5.0.h5\nSMAP_L2B_SSS_01222_20150425T022152_R17000_V5.0.h5\nSMAP_L2B_SSS_01223_20150425T040020_R17000_V5.0.h5\nSMAP_L2B_SSS_01224_20150425T053848_R17000_V5.0.h5\nSMAP_L2B_SSS_01225_20150425T071716_R17000_V5.0.h5\nSMAP_L2B_SSS_01226_20150425T085544_R17000_V5.0.h5\nSMAP_L2B_SSS_01227_20150425T103411_R17000_V5.0.h5\nSMAP_L2B_SSS_01228_20150425T121239_R17000_V5.0.h5\nSMAP_L2B_SSS_01229_20150425T135107_R17000_V5.0.h5\nSMAP_L2B_SSS_01230_20150425T152935_R17000_V5.0.h5\nSMAP_L2B_SSS_01231_20150425T170803_R17000_V5.0.h5\nSMAP_L2B_SSS_01232_20150425T184631_R17000_V5.0.h5\nSMAP_L2B_SSS_01233_20150425T202458_R17000_V5.0.h5\nSMAP_L2B_SSS_01234_20150425T220326_R17000_V5.0.h5\nSMAP_L2B_SSS_01235_20150425T234154_R17000_V5.0.h5\nSMAP_L2B_SSS_01236_20150426T012022_R17000_V5.0.h5\nSMAP_L2B_SSS_01237_20150426T025850_R17000_V5.0.h5\nSMAP_L2B_SSS_01238_20150426T043718_R17000_V5.0.h5\nSMAP_L2B_SSS_01239_20150426T061546_R17000_V5.0.h5\nSMAP_L2B_SSS_01240_20150426T075413_R17000_V5.0.h5\nSMAP_L2B_SSS_01241_20150426T093241_R17000_V5.0.h5\nSMAP_L2B_SSS_01242_20150426T111109_R17000_V5.0.h5\nSMAP_L2B_SSS_01243_20150426T124937_R17000_V5.0.h5\nSMAP_L2B_SSS_01244_20150426T142805_R17000_V5.0.h5\nSMAP_L2B_SSS_01245_20150426T160633_R17000_V5.0.h5\nSMAP_L2B_SSS_01246_20150426T174500_R17000_V5.0.h5\nSMAP_L2B_SSS_01247_20150426T192329_R17000_V5.0.h5\nSMAP_L2B_SSS_01248_20150426T210156_R17000_V5.0.h5\nSMAP_L2B_SSS_01249_20150426T224024_R17000_V5.0.h5\nSMAP_L2B_SSS_01250_20150427T001851_R17000_V5.0.h5\nSMAP_L2B_SSS_01251_20150427T015719_R17000_V5.0.h5\nSMAP_L2B_SSS_01252_20150427T033547_R17000_V5.0.h5\nSMAP_L2B_SSS_01253_20150427T051415_R17000_V5.0.h5\nSMAP_L2B_SSS_01254_20150427T065243_R17000_V5.0.h5\nSMAP_L2B_SSS_01255_20150427T083111_R17000_V5.0.h5\nSMAP_L2B_SSS_01256_20150427T100938_R17000_V5.0.h5\nSMAP_L2B_SSS_01257_20150427T114806_R17000_V5.0.h5\nSMAP_L2B_SSS_01258_20150427T132634_R17000_V5.0.h5\nSMAP_L2B_SSS_01259_20150427T150502_R17000_V5.0.h5\nSMAP_L2B_SSS_01260_20150427T164330_R17000_V5.0.h5\nSMAP_L2B_SSS_01261_20150427T182158_R17000_V5.0.h5\nSMAP_L2B_SSS_01262_20150427T200025_R17000_V5.0.h5\nSMAP_L2B_SSS_01263_20150427T213853_R17000_V5.0.h5\nSMAP_L2B_SSS_01264_20150427T231721_R17000_V5.0.h5\nSMAP_L2B_SSS_01265_20150428T005549_R17000_V5.0.h5\nSMAP_L2B_SSS_01266_20150428T023417_R17000_V5.0.h5\nSMAP_L2B_SSS_01267_20150428T041245_R17000_V5.0.h5\nSMAP_L2B_SSS_01268_20150428T055112_R17000_V5.0.h5\nSMAP_L2B_SSS_01269_20150428T072940_R17000_V5.0.h5\nSMAP_L2B_SSS_01270_20150428T090808_R17000_V5.0.h5\nSMAP_L2B_SSS_01271_20150428T104636_R17000_V5.0.h5\nSMAP_L2B_SSS_01272_20150428T122503_R17000_V5.0.h5\nSMAP_L2B_SSS_01273_20150428T140331_R17000_V5.0.h5\nSMAP_L2B_SSS_01274_20150428T154159_R17000_V5.0.h5\nSMAP_L2B_SSS_01275_20150428T172027_R17000_V5.0.h5\nSMAP_L2B_SSS_01276_20150428T185855_R17000_V5.0.h5\nSMAP_L2B_SSS_01277_20150428T203722_R17000_V5.0.h5\nSMAP_L2B_SSS_01278_20150428T221550_R17000_V5.0.h5\nSMAP_L2B_SSS_01279_20150428T235418_R17000_V5.0.h5\nSMAP_L2B_SSS_01280_20150429T013246_R17000_V5.0.h5\nSMAP_L2B_SSS_01281_20150429T031114_R17000_V5.0.h5\nSMAP_L2B_SSS_01282_20150429T044942_R17000_V5.0.h5\nSMAP_L2B_SSS_01283_20150429T062810_R17000_V5.0.h5\nSMAP_L2B_SSS_01284_20150429T080637_R17000_V5.0.h5\nSMAP_L2B_SSS_01285_20150429T094505_R17000_V5.0.h5\nSMAP_L2B_SSS_01286_20150429T112333_R17000_V5.0.h5\nSMAP_L2B_SSS_01287_20150429T130201_R17000_V5.0.h5\nSMAP_L2B_SSS_01288_20150429T144029_R17000_V5.0.h5\nSMAP_L2B_SSS_01289_20150429T161856_R17000_V5.0.h5\nSMAP_L2B_SSS_01290_20150429T175724_R17000_V5.0.h5\nSMAP_L2B_SSS_01291_20150429T193552_R17000_V5.0.h5\nSMAP_L2B_SSS_01292_20150429T211420_R17000_V5.0.h5\nSMAP_L2B_SSS_01293_20150429T225248_R17000_V5.0.h5\nSMAP_L2B_SSS_01294_20150430T003115_R17000_V5.0.h5\nSMAP_L2B_SSS_01295_20150430T020943_R17000_V5.0.h5\nSMAP_L2B_SSS_01296_20150430T034811_R17000_V5.0.h5\nSMAP_L2B_SSS_01297_20150430T052639_R17000_V5.0.h5\nSMAP_L2B_SSS_01298_20150430T070507_R17000_V5.0.h5\nSMAP_L2B_SSS_01299_20150430T084334_R17000_V5.0.h5\nSMAP_L2B_SSS_01300_20150430T102202_R17000_V5.0.h5\nSMAP_L2B_SSS_01301_20150430T120030_R17000_V5.0.h5\nSMAP_L2B_SSS_01302_20150430T133858_R17000_V5.0.h5\nSMAP_L2B_SSS_01303_20150430T151726_R17000_V5.0.h5\nSMAP_L2B_SSS_01304_20150430T165555_R17000_V5.0.h5\nSMAP_L2B_SSS_01305_20150430T183423_R17000_V5.0.h5\nSMAP_L2B_SSS_01306_20150430T201251_R17000_V5.0.h5\nSMAP_L2B_SSS_01307_20150430T215118_R17000_V5.0.h5\nSMAP_L2B_SSS_01308_20150430T232945_R17000_V5.0.h5TB_CRID :R17000Year :2015Month :4Conventions :CF-1.6, ACDD-1.3processing_level :3cdm_data_type :Griddate_issued :2020-293T18:28:59.223date_created :2020-293T18:28:59.223time_coverage_start :2015-091T00:00:00.000time_coverage_end :2015-121T00:00:00.000geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lon_max :180.0geospatial_lon_min :-180.0geospatial_lat_units :degrees_northgeospatial_lon_units :degrees_eastplatform :SMAPsensor :SMAPproject :SMAPproduct_version :V5.0keywords_vocabulary :http://gcmd.gsfc.nasa.gov/Resources/valids/gcmd_parameters.htmlkeywords :SEA SURFACE SALINITY, SALINITY, SMAP, Jet Propulsion Laboratory, NASA, https://smap.jpl.nasa.gov/, SMAP Radiometercreator_name :JPLcreator_email :fore@jpl.nasa.govpublisher_name :Alexander G. Forepublisher_email :fore@jpl.nasa.govcontributor_name :Alexander Fore, Simon Yueh, Wenqing Tang, Akiko Hayashi, Bryan Stilesreferences :10.1109/TGRS.2016.2601486, 10.1109/TGRS.2016.2600239, 10.1109/TGRS.2013.2266915, 10.1016/j.rse.2017.08.021\n\n\n\n#for OPeNDAP, we need to set the number of files that can be opened at once to 10, \n#so that xr.open_mfdataset() actually reads all links (see https://github.com/pydata/xarray/issues/4082)\nxr.set_options(file_cache_maxsize=10)\n\n#to use xr.open_mfdataset which combines netCDF files\nds_MODIS = xr.open_mfdataset(file_list_MODIS, combine='nested', concat_dim='time')\nds_MODIS\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (eightbitcolor: 256, lat: 2160, lon: 4320, rgb: 3, time: 100)\nCoordinates:\n * lat (lat) float32 89.958336 89.875 89.79167 ... -89.87501 -89.958336\n * lon (lon) float32 -179.95833 -179.875 ... 179.87502 179.95836\nDimensions without coordinates: eightbitcolor, rgb, time\nData variables:\n palette (time, rgb, eightbitcolor) int8 dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n qual_sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\nAttributes:\n product_name: AQUA_MODIS.20110801_20110831.L3m.MO.SST...\n instrument: MODIS\n title: MODISA Level-3 Standard Mapped Image\n project: Ocean Biology Processing Group (NASA/GS...\n platform: Aqua\n temporal_range: month\n processing_version: R2019.0\n date_created: 2019-12-17T18:29:09.000Z\n history: l3mapgen par=AQUA_MODIS.20110801_201108...\n l2_flag_names: LAND,~HISOLZEN\n time_coverage_start: 2011-07-31T13:00:01.000Z\n time_coverage_end: 2011-08-31T14:34:59.000Z\n start_orbit_number: 49153\n end_orbit_number: 49605\n map_projection: Equidistant Cylindrical\n latitude_units: degrees_north\n longitude_units: degrees_east\n northernmost_latitude: 90.0\n southernmost_latitude: -90.0\n westernmost_longitude: -180.0\n easternmost_longitude: 180.0\n geospatial_lat_max: 90.0\n geospatial_lat_min: -90.0\n geospatial_lon_max: 180.0\n geospatial_lon_min: -180.0\n latitude_step: 0.083333336\n longitude_step: 0.083333336\n sw_point_latitude: -89.958336\n sw_point_longitude: -179.95833\n spatialResolution: 9.28 km\n geospatial_lon_resolution: 0.083333336\n geospatial_lat_resolution: 0.083333336\n geospatial_lat_units: degrees_north\n geospatial_lon_units: degrees_east\n number_of_lines: 2160\n number_of_columns: 4320\n measure: Mean\n suggested_image_scaling_minimum: -2.0\n suggested_image_scaling_maximum: 45.0\n suggested_image_scaling_type: LINEAR\n suggested_image_scaling_applied: No\n _lastModified: 2019-12-17T18:29:09.000Z\n Conventions: CF-1.6 ACDD-1.3\n institution: NASA Goddard Space Flight Center, Ocean...\n standard_name_vocabulary: CF Standard Name Table v36\n naming_authority: gov.nasa.gsfc.sci.oceandata\n id: AQUA_MODIS.20110801_20110831.L3b.MO.SST...\n license: https://science.nasa.gov/earth-science/...\n creator_name: NASA/GSFC/OBPG\n publisher_name: NASA/GSFC/OBPG\n creator_email: data@oceancolor.gsfc.nasa.gov\n publisher_email: data@oceancolor.gsfc.nasa.gov\n creator_url: https://oceandata.sci.gsfc.nasa.gov\n publisher_url: https://oceandata.sci.gsfc.nasa.gov\n processing_level: L3 Mapped\n cdm_data_type: grid\n data_bins: Attribute edlided: Unsupported attribut...\n data_minimum: -1.665\n data_maximum: 35.065xarray.DatasetDimensions:eightbitcolor: 256lat: 2160lon: 4320rgb: 3time: 100Coordinates: (2)lat(lat)float3289.958336 89.875 ... -89.958336long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0array([ 89.958336, 89.875 , 89.79167 , ..., -89.791664, -89.87501 ,\n -89.958336], dtype=float32)lon(lon)float32-179.95833 -179.875 ... 179.95836long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0array([-179.95833, -179.875 , -179.79166, ..., 179.79167, 179.87502,\n 179.95836], dtype=float32)Data variables: (3)palette(time, rgb, eightbitcolor)int8dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n76.80 kB\n768 B\n\n\nShape\n(100, 3, 256)\n(1, 3, 256)\n\n\nCount\n400 Tasks\n100 Chunks\n\n\nType\nint8\nnumpy.ndarray\n\n\n\n\n\n\n\n\nsst4\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n\n\n\n\nlong_name :\n\n4um Sea Surface Temperature\n\nunits :\n\ndegree_C\n\nstandard_name :\n\nsea_surface_temperature\n\nvalid_min :\n\n-1000\n\nvalid_max :\n\n10000\n\ndisplay_scale :\n\nlinear\n\ndisplay_min :\n\n-2.0\n\ndisplay_max :\n\n45.0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.73 GB\n37.32 MB\n\n\nShape\n(100, 2160, 4320)\n(1, 2160, 4320)\n\n\nCount\n400 Tasks\n100 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nqual_sst4\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nQuality Levels, Sea Surface Temperature\n\nvalid_min :\n\n0\n\nvalid_max :\n\n5\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.73 GB\n37.32 MB\n\n\nShape\n(100, 2160, 4320)\n(1, 2160, 4320)\n\n\nCount\n400 Tasks\n100 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nAttributes: (59)product_name :AQUA_MODIS.20110801_20110831.L3m.MO.SST4.sst4.9km.ncinstrument :MODIStitle :MODISA Level-3 Standard Mapped Imageproject :Ocean Biology Processing Group (NASA/GSFC/OBPG)platform :Aquatemporal_range :monthprocessing_version :R2019.0date_created :2019-12-17T18:29:09.000Zhistory :l3mapgen par=AQUA_MODIS.20110801_20110831.L3m.MO.SST4.sst4.9km.nc.param l2_flag_names :LAND,~HISOLZENtime_coverage_start :2011-07-31T13:00:01.000Ztime_coverage_end :2011-08-31T14:34:59.000Zstart_orbit_number :49153end_orbit_number :49605map_projection :Equidistant Cylindricallatitude_units :degrees_northlongitude_units :degrees_eastnorthernmost_latitude :90.0southernmost_latitude :-90.0westernmost_longitude :-180.0easternmost_longitude :180.0geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lon_max :180.0geospatial_lon_min :-180.0latitude_step :0.083333336longitude_step :0.083333336sw_point_latitude :-89.958336sw_point_longitude :-179.95833spatialResolution :9.28 kmgeospatial_lon_resolution :0.083333336geospatial_lat_resolution :0.083333336geospatial_lat_units :degrees_northgeospatial_lon_units :degrees_eastnumber_of_lines :2160number_of_columns :4320measure :Meansuggested_image_scaling_minimum :-2.0suggested_image_scaling_maximum :45.0suggested_image_scaling_type :LINEARsuggested_image_scaling_applied :No_lastModified :2019-12-17T18:29:09.000ZConventions :CF-1.6 ACDD-1.3institution :NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Groupstandard_name_vocabulary :CF Standard Name Table v36naming_authority :gov.nasa.gsfc.sci.oceandataid :AQUA_MODIS.20110801_20110831.L3b.MO.SST4.nc/L3/AQUA_MODIS.20110801_20110831.L3b.MO.SST4.nclicense :https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/creator_name :NASA/GSFC/OBPGpublisher_name :NASA/GSFC/OBPGcreator_email :data@oceancolor.gsfc.nasa.govpublisher_email :data@oceancolor.gsfc.nasa.govcreator_url :https://oceandata.sci.gsfc.nasa.govpublisher_url :https://oceandata.sci.gsfc.nasa.govprocessing_level :L3 Mappedcdm_data_type :griddata_bins :Attribute edlided: Unsupported attribute type (NC_INT64)data_minimum :-1.665data_maximum :35.065\n\n\n\n#open and combine Aquarius files into a single .nc file\nxr.set_options(file_cache_maxsize=10)\n#to use xa.open_mfdataset which combines netCDF files\nds_Aq = xr.open_mfdataset(file_list_Aq, combine='nested', concat_dim='time')\nds_Aq\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (SSS_0: 180, SSS_1: 360, SSS_ran_unc_0: 180, SSS_ran_unc_1: 360, SSS_sys_unc_0: 180, SSS_sys_unc_1: 360, palette_0: 3, palette_1: 256, time: 47)\nDimensions without coordinates: SSS_0, SSS_1, SSS_ran_unc_0, SSS_ran_unc_1, SSS_sys_unc_0, SSS_sys_unc_1, palette_0, palette_1, time\nData variables:\n SSS (time, SSS_0, SSS_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n SSS_ran_unc (time, SSS_ran_unc_0, SSS_ran_unc_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n SSS_sys_unc (time, SSS_sys_unc_0, SSS_sys_unc_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n palette (time, palette_0, palette_1) int8 dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\nAttributes:\n product_name: Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg\n instrument: Aquarius\n title: Aquarius Level-3 Standard Mapped Image\n project: Ocean Biology Processing Group (NASA/G...\n platform: SAC-D Aquarius\n temporal_range: MO\n processing_version: V5.0\n date_created: 2017-10-21T21:22:21.000Z\n history: smigen par=Q20112132011243.L3m_MO_SCI_...\n l2_flag_names: POINTING,NAV,LANDRED,ICERED,REFL_1STOK...\n time_coverage_start: 2011-08-25T01:45:23.698Z\n time_coverage_end: 2011-09-01T01:30:15.207Z\n start_orbit_number: 1111\n end_orbit_number: 1213\n map_projection: Equidistant Cylindrical\n latitude_units: degrees_north\n longitude_units: degrees_east\n northernmost_latitude: 90.0\n southernmost_latitude: -90.0\n westernmost_longitude: -180.0\n easternmost_longitude: 180.0\n geospatial_lat_max: 90.0\n geospatial_lat_min: -90.0\n geospatial_lon_max: 180.0\n geospatial_lon_min: -180.0\n grid_mapping_name: latitude_longitude\n latitude_step: 1.0\n longitude_step: 1.0\n sw_point_latitude: -89.5\n sw_point_longitude: -179.5\n geospatial_lon_resolution: 1.0\n geospatial_lat_resolution: 1.0\n geospatial_lat_units: deg\n geospatial_lon_units: deg\n spatialResolution: 1.00 deg\n data_bins: 32001\n number_of_lines: 180\n number_of_columns: 360\n measure: Mean\n data_minimum: 3.82171\n data_maximum: 38.76831\n suggested_image_scaling_minimum: 0.0\n suggested_image_scaling_maximum: 70.0\n suggested_image_scaling_type: ATAN\n suggested_image_scaling_applied: No\n _lastModified: 2017-10-21T21:22:21.000Z\n Conventions: CF-1.6\n institution: NASA Goddard Space Flight Center, Ocea...\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metad...\n Metadata_Conventions: Unidata Dataset Discovery v1.0\n naming_authority: gov.nasa.gsfc.sci.oceandata\n id: Q20112132011243.L3b_MO_SCI_V5.0.main/L...\n license: http://science.nasa.gov/earth-science/...\n creator_name: NASA/GSFC/OBPG\n publisher_name: NASA/GSFC/OBPG\n creator_email: data@oceancolor.gsfc.nasa.gov\n publisher_email: data@oceancolor.gsfc.nasa.gov\n creator_url: http://oceandata.sci.gsfc.nasa.gov\n publisher_url: http://oceandata.sci.gsfc.nasa.gov\n processing_level: L3 Mapped\n cdm_data_type: grid\n identifier_product_doi_authority: http://dx.doi.org\n identifier_product_doi: http://dx.doi.org\n keywords: SURFACE SALINITY, SALINITY, AQUARIUS, ...\n keywords_vocabulary: NASA Global Change Master Directory (G...\n software_name: smigen\n software_version: 5.20\n source: Q20112132011243.L3b_MO_SCI_V5.0.mainxarray.DatasetDimensions:SSS_0: 180SSS_1: 360SSS_ran_unc_0: 180SSS_ran_unc_1: 360SSS_sys_unc_0: 180SSS_sys_unc_1: 360palette_0: 3palette_1: 256time: 47Coordinates: (0)Data variables: (4)SSS(time, SSS_0, SSS_1)float32dask.array<chunksize=(1, 180, 360), meta=np.ndarray>long_name :Sea Surface Salinityorigname :SSSfullnamepath :/SSS\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n12.18 MB\n259.20 kB\n\n\nShape\n(47, 180, 360)\n(1, 180, 360)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nSSS_ran_unc\n\n\n(time, SSS_ran_unc_0, SSS_ran_unc_1)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSea Surface Salinity Uncertainty (random)\n\norigname :\n\nSSS_ran_unc\n\nfullnamepath :\n\n/SSS_ran_unc\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n12.18 MB\n259.20 kB\n\n\nShape\n(47, 180, 360)\n(1, 180, 360)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nSSS_sys_unc\n\n\n(time, SSS_sys_unc_0, SSS_sys_unc_1)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSea Surface Salinity Uncertainty (systematic)\n\norigname :\n\nSSS_sys_unc\n\nfullnamepath :\n\n/SSS_sys_unc\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n12.18 MB\n259.20 kB\n\n\nShape\n(47, 180, 360)\n(1, 180, 360)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\npalette\n\n\n(time, palette_0, palette_1)\n\n\nint8\n\n\ndask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n\n\n\n\norigname :\n\npalette\n\nfullnamepath :\n\n/palette\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n36.10 kB\n768 B\n\n\nShape\n(47, 3, 256)\n(1, 3, 256)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nint8\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (68)product_name :Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deginstrument :Aquariustitle :Aquarius Level-3 Standard Mapped Imageproject :Ocean Biology Processing Group (NASA/GSFC/OBPG)platform :SAC-D Aquariustemporal_range :MOprocessing_version :V5.0date_created :2017-10-21T21:22:21.000Zhistory :smigen par=Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg.paraml2_flag_names :POINTING,NAV,LANDRED,ICERED,REFL_1STOKESMOONRED,REFL_1STOKESGAL,TFTADIFFRED,RFI_REGION,SAOVERFLOW,COLDWATERRED,WINDRED,TBCONStime_coverage_start :2011-08-25T01:45:23.698Ztime_coverage_end :2011-09-01T01:30:15.207Zstart_orbit_number :1111end_orbit_number :1213map_projection :Equidistant Cylindricallatitude_units :degrees_northlongitude_units :degrees_eastnorthernmost_latitude :90.0southernmost_latitude :-90.0westernmost_longitude :-180.0easternmost_longitude :180.0geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lon_max :180.0geospatial_lon_min :-180.0grid_mapping_name :latitude_longitudelatitude_step :1.0longitude_step :1.0sw_point_latitude :-89.5sw_point_longitude :-179.5geospatial_lon_resolution :1.0geospatial_lat_resolution :1.0geospatial_lat_units :deggeospatial_lon_units :degspatialResolution :1.00 degdata_bins :32001number_of_lines :180number_of_columns :360measure :Meandata_minimum :3.82171data_maximum :38.76831suggested_image_scaling_minimum :0.0suggested_image_scaling_maximum :70.0suggested_image_scaling_type :ATANsuggested_image_scaling_applied :No_lastModified :2017-10-21T21:22:21.000ZConventions :CF-1.6institution :NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Groupstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata ConventionMetadata_Conventions :Unidata Dataset Discovery v1.0naming_authority :gov.nasa.gsfc.sci.oceandataid :Q20112132011243.L3b_MO_SCI_V5.0.main/L3/Q20112132011243.L3b_MO_SCI_V5.0.mainlicense :http://science.nasa.gov/earth-science/earth-science-data/data-information-policy/creator_name :NASA/GSFC/OBPGpublisher_name :NASA/GSFC/OBPGcreator_email :data@oceancolor.gsfc.nasa.govpublisher_email :data@oceancolor.gsfc.nasa.govcreator_url :http://oceandata.sci.gsfc.nasa.govpublisher_url :http://oceandata.sci.gsfc.nasa.govprocessing_level :L3 Mappedcdm_data_type :grididentifier_product_doi_authority :http://dx.doi.orgidentifier_product_doi :http://dx.doi.orgkeywords :SURFACE SALINITY, SALINITY, AQUARIUS, Jet Propulsion Laboratory, NASA, http://aquarius.nasa.gov/, AQUARIUS SAC-D, Aquarius Scatterometer, Aquarius Radiometerkeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordssoftware_name :smigensoftware_version :5.20source :Q20112132011243.L3b_MO_SCI_V5.0.main\n\n\n\nPreview the SMAP, Aquarius, and MODIS data over region of interest\n\n#SMAP\nlat_bnds, lon_bnds = [6, -2], [-52, -43] #switched lat directions from GRACE, and longitude has positives and negatives\nds_SMAP_subset = ds_SMAP.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))\n#ds_SMAP_subset\n\n#plot SMAP subset\nds_SMAP_subset.smap_sss[2,:,:].plot() #at time '2', indicating June 2015\n\nplt.show()\n\n\n\n\n\n#Aquarius\nlat_bnds, lon_bnds = [84, 92], [128, 137] #See how commented out plot is distorted, only positive numbers\nds_Aq_subset = ds_Aq.sel(SSS_0=slice(*lat_bnds), SSS_1=slice(*lon_bnds))\n#ds_Aq_subset\n\n#plot Aquarius subset\n#this map is inverted compared to SMAP, but still capturing the same area\nds_Aq_subset.SSS[10,:,:].plot() #at time '10' indicating June 2012\n#ds_Aq.SSS[10,:,:].plot() #whole world map view to see the inversion of the data\n\nplt.show()\n\n\n\n\n\n#MODIS SST\nlat_bnds, lon_bnds = [6, -2], [-52, -43] #like SMAP\nds_MODIS_subset = ds_MODIS.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\n#ds_MODIS_subset\n\n#plot MODIS subset\nds_MODIS_subset.sst4[2,:,:].plot() #at time '2', indicating Oct 2011\n\nplt.show()\n\n\n\n\n\n\nPlotting the river distances and associated heights on the map at time t=9069 (March 16, 2018)\n\n#Change the extent to capture the data of the netCDF file\nextent = [-85, -30, -20, 20]\n\n#Add basemap\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent(extent)\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()\n\n\n\n\n\n#GRACE-FO (different bounds than others because GRACE is over land)\nlat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\nds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_GRACE_subset\n\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.add_feature(cartopy.feature.RIVERS)\nds_GRACE_subset.lwe_thickness[171,:,:].plot(cmap = 'bwr_r') # 171 for 2019-04\nplt.show()"
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- "title": "Mississippi River Heights Exploration:",
- "section": "USGS Gauge River Heights",
- "text": "USGS Gauge River Heights\nIn situ measurements on the Mississippi River can be obtained from the United States Geologic Survey (USGS) National Water Dashboard.\nHere, we zoom into one of the streamgauges toward the outlet of the Mississippi River, Monitoring location 07374525: Mississippi River at Belle Chasse, LA highlighted in green. \nIf the point is selected, data can be obtained for the particular location. This gauge is located at lon, lat: (-89.977847, 29.85715084) and we can obtain gauge height for the time period October 2008 - 2021.\nOnce the text file is downloaded for the gauge heights, and the headers removed, it can be uploaded to the cloud as a dataframe to work alongside the MEaSUREs data. Click the upload files button in the top left corner to do so.\n\ndf_gauge_data = pd.read_csv('Mississippi_outlet_gauge.txt', delimiter = \"\\t\")\n#clean data and convert units to meters\ndf_gauge_data.columns = [\"agency\", \"site_no\", \"datetime\", \"river_height\", \"qual_code\"]\ndf_gauge_data['datetime'] = pd.to_datetime(df_gauge_data['datetime']) \ndf_gauge_data['river_height'] = df_gauge_data['river_height']*0.3048\ndf_gauge_data\n\n\n\n\n\n\n\n\nagency\nsite_no\ndatetime\nriver_height\nqual_code\n\n\n\n\n0\nUSGS\n7374525\n2008-10-29\n2.322576\nA\n\n\n1\nUSGS\n7374525\n2008-10-30\n2.337816\nA\n\n\n2\nUSGS\n7374525\n2008-10-31\n2.368296\nA\n\n\n3\nUSGS\n7374525\n2008-11-01\n2.356104\nA\n\n\n4\nUSGS\n7374525\n2008-11-02\n2.459736\nA\n\n\n...\n...\n...\n...\n...\n...\n\n\n4807\nUSGS\n7374525\n2021-12-27\n2.819400\nA\n\n\n4808\nUSGS\n7374525\n2021-12-28\n2.901696\nA\n\n\n4809\nUSGS\n7374525\n2021-12-29\n2.919984\nA\n\n\n4810\nUSGS\n7374525\n2021-12-30\n2.874264\nA\n\n\n4811\nUSGS\n7374525\n2021-12-31\n2.819400\nA\n\n\n\n\n4812 rows × 5 columns\n\n\n\n\nPlot the data\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(df_gauge_data.datetime, df_gauge_data.river_height, color = 'darkorange')\n\nplt.xlabel('Date')\nplt.ylabel('USGS River Height (m)')\n\nplt.title('Mississippi River Gauge 07374525, 2008-2021')\nplt.grid()\nplt.show()\n\n\n\n\n\n\nFind the same location in the MEaSUREs Dataset using lat/lon\nThe closest location in the MEaSUREs dataset to the gauge (-89.977847, 29.85715084) is at index 106 where lon, lat is (-89.976628, 29.855369). We’ll use this for comparison.\n\nds_MEaSUREs.lat[106]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'lat' ()>\narray(29.855369)\nAttributes:\n units: degrees_north\n long_name: latitude\n standard_name: latitude\n axis: Yxarray.DataArray'lat'29.86array(29.855369)Coordinates: (0)Attributes: (4)units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Y\n\n\n\nds_MEaSUREs.lon[106]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'lon' ()>\narray(-89.976628)\nAttributes:\n units: degrees_east\n long_name: longitude\n standard_name: longitude\n axis: Xxarray.DataArray'lon'-89.98array(-89.976628)Coordinates: (0)Attributes: (4)units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :X\n\n\n\nfig = plt.figure(figsize=[14,7]) \nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent([-90.5, -89.5, 29.3, 30])\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon[106], ds_MEaSUREs.lat[106], lw=1)\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()\n\n\n\n\n\n\nCombined timeseries plot of river heights from each source\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(df_gauge_data.datetime[0:3823], df_gauge_data.river_height[0:3823], color = 'darkorange')\nds_MEaSUREs.height[106,5657:9439].plot(color='darkblue')\n\nplt.xlabel('Date')\nplt.ylabel('River Height (m)')\nplt.legend(['USGS', 'MEaSUREs'], loc='lower right')\n\nplt.grid()\nplt.show()\n\n\n\n\n\n\nLooks like the datums need fixing!\nThe USGS gauge datum is 6.58 feet below NAVD88 GEOID12B EPOCH 2010, while the MEaSUREs datum is height above the WGS84 Earth Gravitational Model (EGM 08) geoid, causing this discrepancy."
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+ "title": "This Notebook is no longer up to date, a newer version exists here.",
+ "section": "Time Series Comparison",
+ "text": "Time Series Comparison\nPlot each dataset for the time period 2011-2019.\nFirst, we need to average all pixels in the subset lat/lon per time for sea surface salinity across both satellites, and sea surface temperature to set up for the graphs. This could take a few minutes each.\n\n#SMAP\ntime_smap = np.arange('2015-04', '2020-01', dtype='datetime64[M]')\nsss_smap_mean = []\nfor t in np.arange(len(ds_SMAP_subset.time)):\n sss_smap_mean.append(np.nanmean(ds_SMAP_subset.smap_sss[t,:,:].values))\n \n#sss_smap_mean\n\n\n#Aquarius\ntime_Aq = np.arange('2011-08', '2015-07', dtype='datetime64[M]')\nsss_Aq_mean =[]\nfor t in np.arange(len(ds_Aq_subset.time)):\n sss_Aq_mean.append(np.nanmean(ds_Aq_subset.SSS[t,:,:].values))\n\n#sss_Aq_mean \n\n\n#MODIS\ntime_MODIS = np.arange('2011-08', '2019-12', dtype='datetime64[M]')\nsst_MODIS_mean = []\nfor t in np.arange(len(ds_MODIS_subset.time)):\n sst_MODIS_mean.append(np.nanmean(ds_MODIS_subset.sst4[t,:,:].values))\n \n#sst_MODIS_mean\n\n\nCombined timeseries plot of river height and LWE thickness\n\n#plot river height and land water equivalent thickness\nfig, ax1 = plt.subplots(figsize=[12,7])\n\n#plot river height\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\n\n#plot LWE thickness on secondary axis\nax2 = ax1.twinx()\nax2.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax1.set_ylabel('River Height (m)', color='darkblue')\nax2.legend(['GRACE-FO'], loc='upper right')\nax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')\n\nplt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')\nax1.grid()\nplt.show()\n\n\n\n\nLWE thickness captures the seasonality of Pre-SWOT MEaSUREs river heights well, and so LWE thickness can be compared to all other variables as a representative of the seasonality of both measurements for the purpose of this notebook.\n\n\nCombined timeseries plots of salinity and LWE thickness, followed by temperature\n\n#Combined Subplots\nfig = plt.figure(figsize=(10,10))\n\nax1 = fig.add_subplot(211)\nplt.title('Amazon Estuary, 2011-2019')\nax2 = ax1.twinx()\nax3 = plt.subplot(212)\n\n#lwe thickness\nax1.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\nax1.set_ylabel('LWE Thickness (cm)', color='darkorange')\nax1.grid()\n\n#sea surface salinity\nax2.plot(time_Aq, sss_Aq_mean, 'g-')\nax2.plot(time_smap, sss_smap_mean, 'g--')\nax2.set_ylabel('SSS (psu)', color='g')\nax2.legend(['Aquarius', 'SMAP'], loc='upper right')\n\n#sea surface temperature\nax3.plot(time_MODIS, sst_MODIS_mean, 'darkred')\nax3.set_ylabel('SST (deg C)', color='darkred')\nax3.grid()\nax3.legend(['MODIS'], loc='upper right')\n\n<matplotlib.legend.Legend at 0x7ff593314d50>\n\n\n\n\n\n\n\nA close-up view of salinity and LWE thickness in 2019\n\n#plot SSS and LWE thickness\n\nfig, ax1 = plt.subplots(figsize=[10,6])\n#plot LWE thickness\nax1.plot(ds_GRACE_subset.time[167:179], ds_GRACE_subset.lwe_thickness[167:179,34,69], color = 'darkorange')\n\n#plot SSS on secondary axis\nax2 = ax1.twinx()\nax2.plot(time_smap[45:], sss_smap_mean[45:], 'g-') # 45:\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Sea Surface Salinity (psu)', color='g')\nax1.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax2.legend(['SMAP'], loc='upper left')\nax1.legend(['GRACE-FO'], loc='lower left')\nplt.title('Measurements Near the Amazon Estuary for 2019')\nax1.grid()\nplt.show()\n\n\n\n\nFor the 2019 year, measurements of LWE thickness and SSS follow expected patterns. When lwe thickness is at its peak, indicating a large amount of water in the river from the wet season between March and June, SSS is at its lowest. The high volume of water from the river output into the estuary decreases the salinity. Points on the graph do not line up exactly month by month because GRACE-FO has specific dates for their monthly dataset whereas SMAP’s monthly dataset is calculated via averaging multiple measurements over the course of the month, so it does not have a specific day, but only a specific month."
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- "title": "Mississippi River Heights Exploration:",
- "section": "Use Case: Validation",
- "text": "Use Case: Validation\nTo validate the MEaSUREs dataset, the authors of the dataset actually compare relative heights between gauges, as opposed to absolute heights, in order to avoid the influence of datum errors and the lack of correspondence between satellite ground tracks and gauge locations. They calculate relative heights by removing the long-term mean of difference between the sample pairs of virtual station heights and the stage measured by the stream gauges. We’ll repeat this method below for completeness and calculate the Nash-Sutcliffe Efficiency (NSE) value.\n\n#create dataframes of the two dataset river heights so values can be subtracted easier (the datasets have different numbers of observations)\ng_height_df = pd.DataFrame()\nm_height_df = pd.DataFrame()\ng_height_df['time'] = df_gauge_data.datetime[0:3823].dt.date\ng_height_df['gauge_height'] = df_gauge_data.river_height[0:3823]\nm_height_df['time'] = ds_MEaSUREs.time[5657:9439].dt.date\nm_height_df['MEaSUREs_height'] = ds_MEaSUREs.height[106,5657:9439]\n#merge into one by time\nheight_df = pd.merge(g_height_df, m_height_df, on='time', how='left')\nheight_df\n\n\n\n\n\n\n\n\ntime\ngauge_height\nMEaSUREs_height\n\n\n\n\n0\n2008-10-29\n2.322576\n-0.238960\n\n\n1\n2008-10-30\n2.337816\n-0.209417\n\n\n2\n2008-10-31\n2.368296\n-0.180987\n\n\n3\n2008-11-01\n2.356104\n-0.178015\n\n\n4\n2008-11-02\n2.459736\n-0.177945\n\n\n...\n...\n...\n...\n\n\n3819\n2019-04-13\n5.312664\n1.132784\n\n\n3820\n2019-04-14\n5.236464\n1.152391\n\n\n3821\n2019-04-15\n5.230368\n1.172064\n\n\n3822\n2019-04-16\n5.221224\n1.192314\n\n\n3823\n2019-04-17\n5.202936\n1.208963\n\n\n\n\n3824 rows × 3 columns\n\n\n\n\ndiff = height_df.gauge_height - height_df.MEaSUREs_height\nmean_diff = diff.mean()\nmean_diff\n\n3.451153237576882\n\n\n\nheight_df['relative_gauge_height'] = height_df.gauge_height - mean_diff\n\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(height_df.time, height_df.relative_gauge_height, color = 'darkorange')\nplt.plot(height_df.time, height_df.MEaSUREs_height, color = 'darkblue')\n\nplt.xlabel('Date')\nplt.ylabel('River Height (m)')\nplt.legend(['USGS', 'MEaSUREs'], loc='lower right')\n\nplt.grid()\nplt.show()\n\n\n\n\n\nNash Sutcliffe Efficiency\n\nNSE = 1-(np.sum((height_df.MEaSUREs_height-height_df.relative_gauge_height)**2)/np.sum((height_df.relative_gauge_height-np.mean(height_df.relative_gauge_height))**2))\nNSE\n\n-0.2062487355865772\n\n\nNSE for Oct 2013 - Sept 2014 water year:\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(height_df.time[1799:2163], height_df.relative_gauge_height[1799:2163], color = 'darkorange')\nplt.plot(height_df.time[1799:2163], height_df.MEaSUREs_height[1799:2163], color = 'darkblue')\n\nplt.xlabel('Date')\nplt.ylabel('River Height (m)')\nplt.legend(['USGS', 'MEaSUREs'], loc='lower right')\n\nplt.grid()\nplt.show()\n\n\n\n\n\nNSE_2014 = 1-(np.sum((height_df.MEaSUREs_height[1799:2163]-height_df.relative_gauge_height[1799:2163])**2)/np.sum((height_df.relative_gauge_height[1799:2163]-np.mean(height_df.relative_gauge_height[1799:2163]))**2))\nNSE_2014\n\n0.18294061649986848\n\n\n\n\nPossible Explanations for discrepancies\n\nMultiple satellites, different return periods\nData interpolation\nSatellite tracks instead of swaths like SWOT will have, spatial interpolation\nRadar altimeter performance varies, was not designed to measure rivers\n\nMEaSUREs is comprised of the Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolation river heights from ERS1, ERS2, TOPEX/Poseidon, OSTM/Jason-2, Envisat, and Jason-3 that are interpolated and processed to create continuous heights for the study over the temporal range of the altimeters used. Each satellite has differing return periods (ie. Jason has a 10-day revist, Envisat 35 days) so to fill the data gaps, perhaps much needed to be interpolated and caused misalignment. In addition, the satellite tracks of these altimeter satellites do not capture entire river reaches with wide swath tracks like the Surface Water and Ocean Topography (SWOT) mission will do in the future. Thus locations observed among satellites may be different and data interpolated spatially, increasing errors. Also, radar altimeter performance varies dramatically across rivers and across Virtual Stations, as the creators of the dataset mention in the guidebook.\nIn addition, the authors note that the Mississippi NSE values ranged from -0.22 to 0.96 with an average of 0.43 when evaluating the dataset, so it looks like we unintentionally honed into one of the stations with the worst statistics on the Mississippi River."
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+ "title": "This Notebook is no longer up to date, a newer version exists here.",
+ "section": "More Visualizing Data on the Map",
+ "text": "More Visualizing Data on the Map\n\nTimelapses\nTo visualize GRACE and SMAP data, timelapses have been created for the year 2019:\n\n########################### Defining needed functions ###########################\n\n#create map for specific timestep function\ndef setup_map(ax, map, ds_subset, x, y, var, t, cmap, levels, title, extent):\n map.set_title(title, fontsize=14)\n map.coastlines()\n map.set_extent(extent)\n map.add_feature(cartopy.feature.RIVERS)\n variable_desired = var[t,:,:]\n title = str(pd.to_datetime(ds_subset.time[t].values))\n cont = map.contourf(x, y, variable_desired, cmap=cmap, levels=levels, zorder=1)\n return cont\n\n#create animation function for all timesteps, outlines what needs to change\ndef animate_ts(framenumber, ax, map, ds_subset, x, y, var, t, cmap, levels, title, extent):\n ax.clear()\n # change to next timestep\n t = t + framenumber\n title = str(pd.to_datetime(ds_subset.time[t].values))\n cont = setup_map(ax, map, ds_subset, x, y, var, t, cmap, levels, title, extent) \n return cont\n\n##################################################################################\n\n\nfig = plt.figure(figsize=[13,9]) \nax = fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)\nmap = plt.axes(projection=ccrs.PlateCarree())\n\n#Necessary Variables for functions\nextent = [-85, -30, -16, 11] #lat/lon extents of map\nx,y = np.meshgrid(ds_GRACE_subset.lon, ds_GRACE_subset.lat) #x, y lat/lon values for functions \nlevels = np.linspace(-100., 100., 14) #number of levels for color differentiation\ncmap='bwr_r' #blue white red color scheme\nt=168 #time to start with\nvar = ds_GRACE_subset.lwe_thickness #variable we will be subsetting from the GRACE-FO data\ntitle = str(pd.to_datetime(ds_GRACE_subset.time[t].values)) #Time of specific time step\n\n#Set up first time step\ncont = setup_map(ax, map, ds_GRACE_subset, x, y, var, t, cmap, levels, title, extent) \n\n#Make a color bar\nfig.colorbar(cont, cmap=cmap, boundaries=levels, ticks=levels, \n orientation='horizontal', label='Land Water Equivalent Thickness (cm)')\n\n#Create animation for 2019\nani = animation.FuncAnimation(fig, animate_ts, frames=range(0,12),\n fargs=(ax, map, ds_GRACE_subset, x, y, var, t, cmap, levels, title, extent), interval=500)\n\n#Will need to install 'ffmpeg' in the cmd prompt to save the .mpg (ie. conda install -c conda-forge ffmpeg)\nani.save(\"GRACE-FO_animation.mp4\", writer=animation.FFMpegWriter())\n\nHTML(ani.to_html5_video())\n\n\n \n Your browser does not support the video tag.\n\n\n\n\n\n\n\n\nSMAP Timelapse\n\n#A new figure window\nfig = plt.figure(figsize=[10,8]) \nax = fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)\nmap = plt.axes(projection=ccrs.PlateCarree())\n\n#Necessary Variables for functions\nextent = [-52, -43, -2, 6] #lat/lon extents of map\nx,y = np.meshgrid(ds_SMAP_subset.longitude, ds_SMAP_subset.latitude) #x, y lat/lon values for functions \nlevels = np.linspace(0., 45., 10) #number of levels for color differentiation\ncmap='viridis' #color scheme\nt=0 #time to start with\nvar = ds_SMAP_subset.smap_sss #variable we will be subsetting from the GRACE-FO data\ntitle = str(pd.to_datetime(ds_SMAP_subset.time[t].values)) #Time of specific time step\n\n#Set up first time step\ncont = setup_map(ax, map, ds_SMAP_subset, x, y, var, t, cmap, levels, title, extent) \n\n#Make a color bar\nfig.colorbar(cont, cmap=cmap, boundaries=levels, ticks=levels, \n orientation='horizontal', label='Sea Surface Salinity (psu)')\n\n#Create animation for the 2019 year (change the frame range for different time periods)\nani = animation.FuncAnimation(fig, animate_ts, frames=range(45,57),\n fargs=(ax, map, ds_SMAP_subset, x, y, var, t, cmap, levels, title, extent), interval=400)\n\n#Will need to install 'ffmpeg' in the cmd prompt to save the .mpg (ie. conda install -c conda-forge ffmpeg)\nani.save(\"SMAP_animation.mp4\", writer=animation.FFMpegWriter())\n\nHTML(ani.to_html5_video())\n\n\n \n Your browser does not support the video tag."
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- "title": "Mississippi River Heights Exploration:",
- "section": "Conclusions",
- "text": "Conclusions\n\nRegardless, the workflow works!\nData from the cloud (Pre-SWOT MEaSUREs river heights) is used in tandem with in situ measurements (USGS gauges)\nTime and download space saved"
+ "objectID": "notebooks/AmazonRiver_Estuary_Exploration.html#future-modifications",
+ "href": "notebooks/AmazonRiver_Estuary_Exploration.html#future-modifications",
+ "title": "This Notebook is no longer up to date, a newer version exists here.",
+ "section": "Future Modifications",
+ "text": "Future Modifications\nThis is not a static notebook and can be altered as more cloud data and services become available.\nIn the future, when the upcoming Surface Water and Ocean Topography (SWOT) satellite has been launched, data products for discharge can be added to analyze the impact discharge specifically has on the coastal environment.\nAll these datasets will be able to be accessed through the cloud in the future; OPeNDAP will have a cloud interface. Check back on PO.DAAC’s Cloud Data page for updates."
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
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+ "href": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html",
+ "title": "Access Sentinel-6 Data by Cycle and Pass Number",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nfrom pystac_client import Client \nfrom collections import defaultdict \nimport json\nimport geopandas\nimport geoviews as gv\nfrom cartopy import crs\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport os\nimport requests\nimport boto3\nimport numpy as np\nimport xarray as xr\nimport rasterio as rio\nfrom rasterio.session import AWSSession\nfrom rasterio.plot import show\nimport rioxarray\nimport geoviews as gv\nimport hvplot.xarray\nimport holoviews as hv\nfrom tqdm import tqdm\nfrom pprint import pprint\ngv.extension('bokeh', 'matplotlib')"
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Initiate Data Search",
- "text": "Initiate Data Search\n\nSTAC_URL = 'https://cmr.earthdata.nasa.gov/stac'\nprovider_cat = Client.open(STAC_URL)\ncatalog = Client.open(f'{STAC_URL}/LPCLOUD/')\n#collections = ['HLSL30.v2.0', 'HLSS30.v2.0']\ncollections = ['HLSL30.v2.0']"
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Define Date Range and Region of Interest",
- "text": "Define Date Range and Region of Interest\n\ndate_range = \"2021-01/2022-01\"\nroi = {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.60835266113281,\n 39.49874248613119\n ],\n [\n -121.26983642578124,\n 39.49874248613119\n ],\n [\n -121.26983642578124,\n 39.756824261131406\n ],\n [\n -121.60835266113281,\n 39.756824261131406\n ],\n [\n -121.60835266113281,\n 39.49874248613119\n ]\n ]\n ]\n }\n }['geometry']\nbase = gv.tile_sources.EsriImagery.opts(width=650, height=500)\nReservoir = gv.Polygons(roi['coordinates']).opts(line_color='yellow', line_width=10, color=None)\nReservoir * base"
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Search for HLS imagery matching search criteria",
- "text": "Search for HLS imagery matching search criteria\n\nsearch = catalog.search(\n collections=collections,\n intersects=roi,\n datetime=date_range,\n limit=100\n)\n\nitem_collection = search.get_all_items()\nsearch.matched()\n\n50"
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- "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#filter-imagery-for-low-cloud-images-and-identify-image-bands-needed-for-water-classification",
- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Filter imagery for low cloud images and identify image bands needed for water classification",
- "text": "Filter imagery for low cloud images and identify image bands needed for water classification\n\ns30_bands = ['B8A', 'B03'] # S30 bands for NDWI calculation and quality filtering -> NIR, GREEN, Quality \nl30_bands = ['B05', 'B03'] # L30 bands for NDWI calculation and quality filtering -> NIR, GREEN, Quality \ncloudcover = 10\n\n\nndwi_band_links = []\n\nfor i in item_collection:\n if i.properties['eo:cloud_cover'] <= cloudcover:\n if i.collection_id == 'HLSS30.v2.0':\n #print(i.properties['eo:cloud_cover'])\n ndwi_bands = s30_bands\n elif i.collection_id == 'HLSL30.v2.0':\n #print(i.properties['eo:cloud_cover'])\n ndwi_bands = l30_bands\n\n for a in i.assets:\n if any(b==a for b in ndwi_bands):\n ndwi_band_links.append(i.assets[a].href)\n\n\nndwi_band_links[:10]\n\n['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021016T184526.v2.0/HLS.L30.T10TFK.2021016T184526.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021016T184526.v2.0/HLS.L30.T10TFK.2021016T184526.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021048T184520.v2.0/HLS.L30.T10TFK.2021048T184520.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021048T184520.v2.0/HLS.L30.T10TFK.2021048T184520.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021048T184520.v2.0/HLS.L30.T10SFJ.2021048T184520.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021048T184520.v2.0/HLS.L30.T10SFJ.2021048T184520.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021064T184513.v2.0/HLS.L30.T10SFJ.2021064T184513.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021064T184513.v2.0/HLS.L30.T10SFJ.2021064T184513.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021064T184513.v2.0/HLS.L30.T10TFK.2021064T184513.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021064T184513.v2.0/HLS.L30.T10TFK.2021064T184513.v2.0.B05.tif']\n\n\n\ntile_dicts = defaultdict(list) \n\n\nfor l in ndwi_band_links:\n tile = l.split('.')[-6]\n tile_dicts[tile].append(l)\n\n\ntile_dicts.keys()\n\ndict_keys(['T10TFK', 'T10SFJ'])\n\n\n\ntile_links = tile_dicts['T10SFJ']\n\n\nbands_dicts = defaultdict(list)\nfor b in tile_links:\n band = b.split('.')[-2]\n bands_dicts[band].append(b)\nfor i in bands_dicts:\n print(i)\n\nB03\nB05"
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Locate Images in Amazon S3 Storage",
- "text": "Locate Images in Amazon S3 Storage\n\npath_dicts = defaultdict(list)\nfor l in bands_dicts['B05']:\n s3l = l.replace('https://data.lpdaac.earthdatacloud.nasa.gov/', 's3://')\n path_dicts['B05'].append(s3l)\n \ns3paths_LB3 = []\nfor l in bands_dicts['B03']:\n s3l = l.replace('https://data.lpdaac.earthdatacloud.nasa.gov/', 's3://')\n if s3l[38:39] == 'L':\n path_dicts['B03'].append(s3l)\n\n\ns3_cred_endpoint = 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\ntemp_creds_req = get_temp_creds()\nsession = boto3.Session(aws_access_key_id=temp_creds_req['accessKeyId'], \n aws_secret_access_key=temp_creds_req['secretAccessKey'],\n aws_session_token=temp_creds_req['sessionToken'],\n region_name='us-west-2')\n\n\nrio_env = rio.Env(AWSSession(session),\n GDAL_DISABLE_READDIR_ON_OPEN='EMPTY_DIR',\n GDAL_HTTP_COOKIEFILE=os.path.expanduser('~/cookies.txt'),\n GDAL_HTTP_COOKIEJAR=os.path.expanduser('~/cookies.txt'))\nrio_env.__enter__()\n\n<rasterio.env.Env at 0x7fd7e12fc580>"
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis notebook shows a simple way to search for Sentinel-6 data granules for a specific cycle and pass using the CMR Search API and download them to a local directory."
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Load images and visualize",
- "text": "Load images and visualize\n\ndef time_index_from_filenames(file_links):\n return [datetime.strptime(f.split('.')[-5], '%Y%jT%H%M%S') for f in file_links]\n\n\ntime = xr.Variable('time', time_index_from_filenames(path_dicts['B03']))\nchunks=dict(band=1, x=512, y=512)\nhls_ts_da_LB3 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B03']], dim=time)\nhls_ts_da_LB5 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B05']], dim=time)\nhls_ts_da_LB3 = hls_ts_da_LB3.rio.reproject(\"epsg:4326\")\nhls_ts_da_LB5 = hls_ts_da_LB5.rio.reproject(\"epsg:4326\")\n\n\nhls_ts_da_data_LB3 = hls_ts_da_LB3.load()\nhls_ts_da_data_LB5 = hls_ts_da_LB5.load()\nhls_ts_da_data_LB3 = hls_ts_da_data_LB3.rio.clip([roi])\nhls_ts_da_data_LB5 = hls_ts_da_data_LB5.rio.clip([roi])\n\n\nhls_ts_da_data_LB5.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='gray').opts(clim=(0,2000))\n\nNameError: name 'hls_ts_da_data_LB5' is not defined"
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+ "href": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html#before-you-start",
+ "title": "Access Sentinel-6 Data by Cycle and Pass Number",
+ "section": "Before you start",
+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an Earthdata account https://urs.earthdata.nasa.gov.\nAccounts are free to create and take just a moment to set up."
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- "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#caclulate-normalized-difference-water-index-ndwi-and-classify-innundated-areas",
- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Caclulate Normalized Difference Water Index (NDWI) and Classify Innundated Areas",
- "text": "Caclulate Normalized Difference Water Index (NDWI) and Classify Innundated Areas\n\nLB3 = hls_ts_da_data_LB3 \nLB5 = hls_ts_da_data_LB5\nNDWI = (LB3-LB5)/(LB3+LB5)\nNDWI.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='coolwarm').opts(clim=(-0.5,0.5))\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\nwater = NDWI>0\nwater.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='PuOr').opts(clim=(0,1))"
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+ "href": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html#authentication-setup",
+ "title": "Access Sentinel-6 Data by Cycle and Pass Number",
+ "section": "Authentication setup",
+ "text": "Authentication setup\nYou’ll probably need to use the netrc method when running from command line.\nWe need some boilerplate up front to log in to Earthdata Login. The function below will allow Python scripts to log into any Earthdata Login application programmatically. To avoid being prompted for credentials every time you run and also allow clients such as curl to log in, you can add the following to a .netrc (_netrc on Windows) file in your home directory:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nMake sure that this file is only readable by the current user or you will receive an error stating “netrc access too permissive.”\n$ chmod 0600 ~/.netrc\n\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nimport getpass\nimport netrc\n\ndef setup_earthdata_login_auth(endpoint):\n \"\"\"\n Set up the request library so that it authenticates against the given Earthdata Login\n endpoint and is able to track cookies between requests. This looks in the .netrc file \n first and if no credentials are found, it prompts for them.\n\n Valid endpoints include:\n urs.earthdata.nasa.gov - Earthdata Login production\n \"\"\"\n try:\n username, _, password = netrc.netrc().authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n # FileNotFound = There's no .netrc file\n # TypeError = The endpoint isn't in the netrc file, causing the above to try unpacking None\n print('Please provide your Earthdata Login credentials to allow data access')\n print('Your credentials will only be passed to %s and will not be exposed in Jupyter' % (endpoint))\n username = input('Username:')\n password = getpass.getpass()\n\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n\n\nsetup_earthdata_login_auth('urs.earthdata.nasa.gov')\n\nPlease provide your Earthdata Login credentials to allow data access\nYour credentials will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter\n\n\nUsername: nickles\n ···········\n\n\n\nimport requests\nfrom os import makedirs\nfrom os.path import isdir, basename\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen, urlretrieve\nfrom datetime import datetime, timedelta\nfrom json import dumps, loads"
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
- "section": "Caclulate surface area of reservoir and plot time series",
- "text": "Caclulate surface area of reservoir and plot time series\n\nif water.variable.max() == True:\n water_real = water*30*30\nwater_area = water_real.sum(axis=(1,2))\n\n%matplotlib inline\n\nfig, ax = plt.subplots()\n(water_area[:]/1000000).plot(ax=ax, linewidth=2, linestyle = '-', marker='o')\nax.set_title(\"Surface area of waterbody in km2\")\nax.set_ylabel('Area [km^2]')\n\nText(0, 0.5, 'Area [km^2]')"
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+ "href": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html#find-granules-by-cyclepass-number",
+ "title": "Access Sentinel-6 Data by Cycle and Pass Number",
+ "section": "Find granules by cycle/pass number",
+ "text": "Find granules by cycle/pass number\nThe CMR Search API provides for searching ingested granules by their cycle and pass numbers. A third parameter, the tile identifier, is provisioned for use during the upcoming SWOT mission but isn’t used by CMR Search at this time. Read more about these orbit identifiers here.\nPasses within a cycle are unique, there will be no repeats until the next cycle. Tile numbers are only unique within a pass, so if you’re looking only at tile numbers there will be over 300 per cycle, but only 1 per pass.\nInfo below may only apply to NRT use case:\n\nThis workflow/notebook can be run routinely to maintain a time series of NRT data, downloading new granules as they become available in CMR.\nThe notebook writes/overwrites a file .update to the target data directory with each successful run. The file tracks to date and time of the most recent update to the time series of NRT granules using a timestamp in the format yyyy-mm-ddThh:mm:ssZ.\nThe timestamp matches the value used for the created_at parameter in the last successful run. This parameter finds the granules created within a range of datetimes. This workflow leverages the created_at parameter to search backwards in time for new granules ingested between the time of our timestamp and now.\n\nThe variables in the cell below determine the workflow behavior on its initial run:\n\ntrackcycle and trackpass: Set the cycle and pass numbers to use for the CMR granule search.\ncmr: The domain of the target CMR instance, either cmr.earthdata.nasa.gov.\nccid: The unique CMR concept-id of the desired collection.\ndata: The path to a local directory in which to download/maintain a copy of the NRT granule time series.\n\n\ncmr = \"cmr.earthdata.nasa.gov\"\n\n# this function returns a concept id for a particular dataset\ndef get_collection(url: str=f\"https://{cmr}/search/collections.umm_json\", **params):\n return requests.get(url, params).json().get(\"items\")[0]\n#\n# This cell accepts parameters from command line with papermill: \n# https://papermill.readthedocs.io\n#\n# These variables should be set before the first run, then they \n# should be left alone. All subsequent runs expect the values \n# for cmr, ccid, data to be unchanged. The mins value has no \n# impact on subsequent runs.\n#\n\ntrackcycle = 25\ntrackpass = 1\n\nname = \"JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F\"\n\nccid = get_collection(ShortName=name).get(\"meta\").get(\"concept-id\")\n\ndata = \"resources/trackcycle\"\n\nThe variable data is pointed at a nearby folder resources/cyclepass by default. You should change data to a suitable download path on your file system. An unlucky sequence of git commands could disappear that folder and its downloads, if your not careful. Just change it.\nThe search retrieves granules ingested during the last n minutes. A file in your local data dir file that tracks updates to your data directory, if one file exists. The CMR Search falls back on the ten minute window if not.\n\n#timestamp = (datetime.utcnow()-timedelta(minutes=mins)).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n#timestamp\n\nThis cell will replace the timestamp above with the one read from the .update file in the data directory, if it exists.\n\nif not isdir(data):\n print(f\"NOTE: Making new data directory at '{data}'. (This is the first run.)\")\n makedirs(data)\n#else:\n# try:\n# with open(f\"{data}/.update\", \"r\") as f:\n# timestamp = f.read()\n# except FileNotFoundError:\n# print(\"WARN: No .update in the data directory. (Is this the first run?)\")\n# else:\n# print(f\"NOTE: .update found in the data directory. (The last run was at {timestamp}.)\")\n\nNOTE: Making new data directory at 'resources'. (This is the first run.)\n\n\nThere are several ways to query for CMR updates that occured during a given timeframe. Read on in the CMR Search documentation:\n\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-new-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-revised-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-production-date (Granules)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-created-at (Granules)\n\nThe created_at parameter works for our purposes. It’s a granule search parameter that returns the records ingested since the input timestamp.\n\nparams = {\n 'scroll': \"true\",\n 'page_size': 2000,\n 'sort_key': \"-start_date\",\n 'collection_concept_id': ccid, \n #'created_at': timestamp,\n # Limit results to granules matching cycle, pass numbers:\n 'cycle': trackcycle,\n 'passes[0][pass]': trackpass,\n}\n\nparams\n\n{'scroll': 'true',\n 'page_size': 2000,\n 'sort_key': '-start_date',\n 'collection_concept_id': 'C1968980576-POCLOUD',\n 'cycle': 25,\n 'passes[0][pass]': 1}\n\n\nGet the query parameters as a string and then the complete search url:\n\nquery = urlencode(params)\nurl = f\"https://{cmr}/search/granules.umm_json?{query}\"\nprint(url)\n\nhttps://cmr.earthdata.nasa.gov/search/granules.umm_json?scroll=true&page_size=2000&sort_key=-start_date&collection_concept_id=C1968980576-POCLOUD&cycle=25&passes%5B0%5D%5Bpass%5D=1\n\n\nDownload the granule records that match our search parameters.\n\nwith urlopen(url) as f:\n results = loads(f.read().decode())\n\nprint(f\"{results['hits']} granules results for '{ccid}' cycle '{trackcycle}' and pass '{trackpass}'.\")\n\n1 granules results for 'C1968980576-POCLOUD' cycle '25' and pass '1'.\n\n\nNeatly print the first granule’s data for reference (assuming at least one was returned).\n\nif len(results['items'])>0:\n #print(dumps(results['items'][0], indent=2)) #print whole record\n print(dumps(results['items'][0]['umm'][\"RelatedUrls\"], indent=2)) #print associated URLs\n \n # Also, replace timestamp with one corresponding to time of the search.\n #timestamp = datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n\n[\n {\n \"URL\": \"s3://podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\",\n \"Type\": \"GET DATA VIA DIRECT ACCESS\",\n \"Description\": \"This link provides direct download access via S3 to the granule.\"\n },\n {\n \"URL\": \"s3://podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.bufr.bin\",\n \"Type\": \"GET DATA VIA DIRECT ACCESS\",\n \"Description\": \"This link provides direct download access via S3 to the granule.\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\",\n \"Description\": \"Download S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\",\n \"Type\": \"GET DATA\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.xfdumanifest.xml\",\n \"Description\": \"Download S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.xfdumanifest.xml\",\n \"Type\": \"EXTENDED METADATA\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.bufr.bin\",\n \"Description\": \"Download S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.bufr.bin\",\n \"Type\": \"GET DATA\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/s3credentials\",\n \"Description\": \"api endpoint to retrieve temporary credentials valid for same-region direct s3 access\",\n \"Type\": \"VIEW RELATED INFORMATION\"\n },\n {\n \"URL\": \"https://opendap.earthdata.nasa.gov/collections/C1968980576-POCLOUD/granules/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02\",\n \"Type\": \"USE SERVICE API\",\n \"Subtype\": \"OPENDAP DATA\",\n \"Description\": \"OPeNDAP request URL\"\n }\n]\n\n\nThe link for http access denoted by \"Type\": \"GET DATA\" in the list of RelatedUrls.\nGrab the download URL, but do it in a way that’ll work for search results returning any number of granule records:\n\ndownloads = []\n\nfor l in results['items'][0]['umm'][\"RelatedUrls\"]:\n #if the link starts with the following, it is the download link we want\n if 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/' in l['URL']: \n #we want the .nc file\n if '.nc' in l['URL']:\n downloads.append(l['URL'])\ndownloads\n\n['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc']\n\n\nFinish by downloading the files to the data directory in a loop. Overwrite .update with a new timestamp on success.\n\nfor f in downloads:\n try:\n urlretrieve(f, f\"{data}/{basename(f)}\")\n except Exception as e:\n print(f\"[{datetime.now()}] FAILURE: {f}\\n\\n{e}\\n\")\n raise e\n else:\n print(f\"[{datetime.now()}] SUCCESS: {f}\")\n\n[2022-11-07 16:28:33.475579] SUCCESS: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\n\n\nIf there were updates to the local time series during this run and no exceptions were raised during the download loop, then overwrite the timestamp file that tracks updates to the data folder (resources/nrt/.update):\n\n#if len(results['items'])>0:\n# with open(f\"{data}/.update\", \"w\") as f:\n# f.write(timestamp)"
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- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
- },
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- "section": "Accessing and Visualizing SWOT Simulated Datasets",
- "text": "Accessing and Visualizing SWOT Simulated Datasets\n\nRequirement:\nThis tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\nLearning Objectives:\n\nAccess all 5 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine\nVisualize accessed data\n\n\n\nSWOT Simulated Level 2 North America Continent KaRIn High Rate Version 1 Datasets:\n\nRiver Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RSP1\n\n\nLake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2LSP1\n\n\nWater Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PIX1\n\n\nWater Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PXV1\n\n\nRaster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RAS1\n\nNotebook Author: Cassie Nickles, NASA PO.DAAC (Aug 2022)\n\n\nLibraries Needed\n\nimport glob\nimport os\nimport requests\nimport s3fs\nimport netCDF4 as nc\nimport h5netcdf\nimport xarray as xr\nimport pandas as pd\nimport geopandas as gpd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport hvplot.xarray\nimport shapefile as shp\nimport zipfile"
+ "text": "Author: Jinbo Wang Jinbo.Wang@jpl.nasa.gov, Jack McNelis jack.mcnelis@jpl.nasa.gov\nThis is a demonstration of accessing the ECCO-BASED PRE-SWOT NUMERICAL SIMULATION. The dataset can be found following https://search.earthdata.nasa.gov/search?q=pocloud%20pre-swot.\nimport s3fs\nimport requests\nimport xarray as xr\nimport pylab as plt\nfrom netrc import netrc\nfrom urllib import request\nfrom platform import system\nfrom getpass import getpass\nfrom http.cookiejar import CookieJar\nfrom os.path import expanduser, join\n\nShortName = \"MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0\"\ntarget_file = \"LLC4320_pre-SWOT_ACC_SMST_20111221.nc\"\nEarthdata Login\nAuthenticate with your Earthdata Login/URS credentials by configuring a .netrc file in your home directory.\nRun the next cell to authenticate. (You might be prompted for your Earthdata Login username and password.)\ndef setup_earthdata_login_auth(endpoint: str='urs.earthdata.nasa.gov'):\n netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n try:\n username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n print('Please provide your Earthdata Login credentials for access.')\n print('Your info will only be passed to %s and will not be exposed in Jupyter.' % (endpoint))\n username = input('Username: ')\n password = getpass('Password: ')\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n \nsetup_earthdata_login_auth()\n\nPlease provide your Earthdata Login credentials for access.\nYour info will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter.\n\n\nUsername: marscreature\nPassword: ·············\nYou should now be able to download the file at the following link:\nhttps_access = f\"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/{ShortName}/{target_file}\"\n\nprint(https_access)\n\nhttps://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0/LLC4320_pre-SWOT_ACC_SMST_2011122?.nc\nOpen the dataset\nRun the next cell to access/open the netCDF file with xarray:\ndef begin_s3_direct_access():\n \"\"\"Returns s3fs object for accessing datasets stored in S3.\"\"\"\n response = requests.get(\"https://archive.podaac.earthdata.nasa.gov/s3credentials\").json()\n return s3fs.S3FileSystem(key=response['accessKeyId'],\n secret=response['secretAccessKey'],\n token=response['sessionToken'], \n client_kwargs={'region_name':'us-west-2'})\n\ntry:\n fs = begin_s3_direct_access()\n # Load netCDF with 's3fs' and 'xarray' upon successful connection to S3:\n dd = xr.open_dataset(fs.open(f\"podaac-ops-cumulus-protected/{ShortName}/{target_file}\"))\nexcept:\n print(\"Failed to establish AWS in-region access. Downloading to local disk instead.\")\n request.urlretrieve(https_access, target_file)\n # Load netCDF with 'xarray' after download completes:\n \n dd = xr.open_dataset(target_file)\n\nprint(dd)\n\nFailed to establish AWS in-region access. Downloading to local disk instead.\n<xarray.Dataset>\nDimensions: (i: 192, i_g: 192, j: 349, j_g: 349, k: 84, k_l: 84, k_p1: 85, k_u: 84, nb: 2, time: 24)\nCoordinates:\n * j_g (j_g) float32 0.0 1.0 2.0 3.0 4.0 ... 345.0 346.0 347.0 348.0\n * i (i) float32 0.0 1.0 2.0 3.0 4.0 ... 187.0 188.0 189.0 190.0 191.0\n * i_g (i_g) float32 0.0 1.0 2.0 3.0 4.0 ... 188.0 189.0 190.0 191.0\n * j (j) float32 0.0 1.0 2.0 3.0 4.0 ... 344.0 345.0 346.0 347.0 348.0\n * k (k) int32 0 1 2 3 4 5 6 7 8 9 10 ... 74 75 76 77 78 79 80 81 82 83\n * k_u (k_u) int32 0 1 2 3 4 5 6 7 8 9 ... 74 75 76 77 78 79 80 81 82 83\n * k_l (k_l) int32 0 1 2 3 4 5 6 7 8 9 ... 74 75 76 77 78 79 80 81 82 83\n * k_p1 (k_p1) int32 0 1 2 3 4 5 6 7 8 9 ... 75 76 77 78 79 80 81 82 83 84\n * nb (nb) int32 0 1\n * time (time) datetime64[ns] 2011-12-21 ... 2011-12-21T23:00:00\nData variables:\n XC (j, i) float32 ...\n YC (j, i) float32 ...\n DXV (j, i) float32 ...\n DYU (j, i) float32 ...\n Depth (j, i) float32 ...\n AngleSN (j, i) float32 ...\n AngleCS (j, i) float32 ...\n DXC (j, i_g) float32 ...\n DYG (j, i_g) float32 ...\n DYC (j_g, i) float32 ...\n DXG (j_g, i) float32 ...\n XG (j_g, i_g) float32 ...\n YG (j_g, i_g) float32 ...\n RAZ (j_g, i_g) float32 ...\n XC_bnds (j, i, nb) float64 ...\n YC_bnds (j, i, nb) float64 ...\n Z (k) float32 ...\n Zp1 (k_p1) float32 ...\n Zu (k_u) float32 ...\n Zl (k_l) float32 ...\n Z_bnds (k, nb) float32 ...\n Eta (time, j, i) float64 ...\n KPPhbl (time, j, i) float64 ...\n PhiBot (time, j, i) float64 ...\n oceFWflx (time, j, i) float64 ...\n oceQnet (time, j, i) float64 ...\n oceQsw (time, j, i) float64 ...\n oceSflux (time, j, i) float64 ...\n oceTAUX (time, j, i_g) float64 ...\n oceTAUY (time, j_g, i) float64 ...\n Theta (time, k, j, i) float64 ...\n Salt (time, k, j, i) float64 ...\n U (time, k, j, i_g) float32 ...\n V (time, k, j_g, i) float64 ...\n W (time, k_l, j, i) float64 ...\nAttributes:\n acknowledgement: This research was carried out by the Jet...\n author: Dimitris Menemenlis et al.\n contributor: Chris Hill, Christopher E. Henze, Jinbo ...\n contributor_role: MITgcm developer, AMES supercomputer sup...\n cdm_data_type: Grid\n Conventions: CF-1.7, ACDD-1.3\n creator_email: menemenlis@jpl.nasa.gov\n creator_institution: NASA Jet Propulsion Laboratory (JPL)\n creator_name: Dimitris Menemelis et al.\n creator_type: group\n creator_url: https://science.jpl.nasa.gov/people/Mene...\n date_created: 2021-01-20T00:00:00\n date_issued: 2021-01-20T00:00:00\n date_metadata_modified: 2021-01-20T00:00:00\n geospatial_lat_max: -53.00567\n geospatial_lat_min: -56.989952\n geospatial_lat_units: degrees_north\n geospatial_lon_max: 154.28125\n geospatial_lon_min: 150.30208\n geospatial_lon_units: degrees_east\n geospatial_bounds_crs: EPSG:4326\n geospatial_vertical_max: 0\n geospatial_vertical_min: -6134.5\n geospatial_vertical_positive: up\n geospatial_vertical_resolution: variable\n geospatial_vertical_units: meter\n history: Inaugural release of LLC4320 regions to ...\n id: MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_...\n institution: NASA Jet Propulsion Laboratory (JPL)\n instrument_vocabulary: GCMD instrument keywords\n keywords: EARTH SCIENCE SERVICES > MODELS > EARTH ...\n keywords_vocabulary: NASA Global Change Master Directory (GCM...\n license: Public Domain\n metadata_link: http://podaac.jpl.nasa.gov/ws/metadata/d...\n naming_authority: gov.nasa.jpl\n platform_vocabulary: GCMD platform keywords\n processing_level: L4\n product_time_coverage_end: 2012-11-15T00:00:00\n product_time_coverage_start: 2011-09-13T00:00:00\n product_version: 1.0\n program: NASA Physical Oceanography\n project: Surface Water and Ocean Topography (SWOT...\n publisher_email: podaac@podaac.jpl.nasa.gov\n publisher_institution: PO.DAAC\n publisher_name: Physical Oceanography Distributed Active...\n publisher_type: institution\n publisher_url: https://podaac.jpl.nasa.gov\n source: MITgcm simulation\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadat...\n summary: This is a subset of a global ocean simul...\n time_coverage_end: 2011-12-21 23:00:00\n time_coverage_start: 2011-12-21 00:00:00\n title: LLC4320 regional Southern Ocean\n geospatial_lon_resolution: variable\n geospatial_lat_resolution: variable\n platform: MITgcm"
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- "text": "Get Temporary AWS Credentials for Access\nS3 is an ‘object store’ hosted in AWS for cloud processing. Direct S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. Note, these temporary credentials are valid for only 1 hour. A netrc file is required to aquire these credentials. Use the NASA Earthdata Authentication to create a netrc file in your home directory. (Note: A NASA Earthdata Login is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.)\nThe following crediential is for PODAAC, but other credentials are needed to access data from other NASA DAACs.\n\ns3_cred_endpoint = 'https://archive.podaac.earthdata.nasa.gov/s3credentials'\n\nCreate a function to make a request to an endpoint for temporary credentials.\n\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\n\n\ntemp_creds_req = get_temp_creds()\n#temp_creds_req # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.\n\n\nSet up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint.\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})"
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+ "text": "Plot eight 2D fields.\n\nfig,ax=plt.subplots(3,3,figsize=(16,16))\n\nvarn=['Eta','KPPhbl','PhiBot','oceFWflx','oceQnet','oceQsw','oceSflux','oceTAUY','oceTAUX']\n\nfor i in range(3):\n for j in range(3):\n dd[varn[i*3+j]][0,...].plot(ax=ax[j,i])\n ax[j,i].set_title(varn[i*3+j])\nplt.tight_layout()"
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- "objectID": "notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html#single-file-access",
- "href": "notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html#single-file-access",
- "title": "SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud",
- "section": "Single File Access",
- "text": "Single File Access\nThe s3 access link can be found using Earthdata Search (see tutorial) for a single file is as follows:\n1. River Vector Shapefiles\n\ns3_SWOT_HR_url1 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip'\n\n\ns3_file_obj1 = fs_s3.open(s3_SWOT_HR_url1, mode='rb')\n\nThe native format for this sample data is a .zip file, and we want the .shp file within the .zip file, so we need to download the contents of the zip file into the cloud environment. I created a folder called SWOT_HR_shp to write to. Change the path to where you would like your extracted files to be written.\n\nwith zipfile.ZipFile(s3_file_obj1, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\nNext, we’ll look at the attribute table of the .shp file we just extracted to the ‘SWOT_HR_shp’ folder.\n\nSWOT_HR_shp1 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shp') \nSWOT_HR_shp1\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n0\n71224300241\n7.145115e+08\n7.145114e+08\n2022-08-22T19:2441Z\n49.364818\n-94.879318\nno_data\n3.472248e+01\n-1.000000e+12\n1.511000e-02\n...\n7294.5\n3.265803e+06\n15\n390935.258\n3008.959150\n-1.000000e+12\n0\n15\n8\nLINESTRING (-94.86483 49.37485, -94.86515 49.3...\n\n\n1\n71224300253\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2446Z\n49.049486\n-94.899554\nno_data\n3.439994e+01\n-1.000000e+12\n7.600000e-03\n...\n394.5\n7.876447e+06\n42\n444613.943\n8411.845753\n-1.000000e+12\n0\n8\n1\nLINESTRING (-94.92557 49.08401, -94.92556 49.0...\n\n\n2\n71224300263\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2448Z\n48.977915\n-94.869598\nno_data\n3.434701e+01\n-1.000000e+12\n9.620000e-03\n...\n6365.5\n2.935181e+06\n42\n453020.631\n8406.687501\n-1.000000e+12\n0\n3\n1\nLINESTRING (-94.88015 49.01512, -94.88006 49.0...\n\n\n3\n71224300273\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2449Z\n48.902998\n-94.854720\nno_data\n3.416786e+01\n-1.000000e+12\n1.372000e-02\n...\n4650.0\n3.770782e+06\n43\n461636.940\n8616.309267\n-1.000000e+12\n0\n1\n1\nLINESTRING (-94.86229 48.94092, -94.86228 48.9...\n\n\n4\n71224300283\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2450Z\n48.883377\n-94.783621\nno_data\n3.426341e+01\n-1.000000e+12\n5.050000e-03\n...\n10439.0\n2.952077e+07\n46\n470821.047\n9184.106587\n-1.000000e+12\n0\n5\n1\nLINESTRING (-94.73002 48.90430, -94.72952 48.9...\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n666\n74291700141\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2729Z\n39.811344\n-92.684233\nno_data\n3.869180e+01\n-1.000000e+12\n3.840000e-01\n...\n42.0\n1.060619e+02\n48\n2463828.961\n9553.659853\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68784 39.77391, -92.68804 39.7...\n\n\n667\n74291700151\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2727Z\n39.888856\n-92.683047\nno_data\n3.687848e+01\n-1.000000e+12\n1.897500e-01\n...\n42.0\n1.208373e+02\n48\n2473386.489\n9557.528221\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68497 39.84931, -92.68497 39.8...\n\n\n668\n74291700161\n-1.000000e+12\n-1.000000e+12\nno_data\n39.962507\n-92.671510\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n42.0\n9.634731e+01\n48\n2482916.982\n9530.492810\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.66461 39.92629, -92.66425 39.9...\n\n\n669\n74291700171\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2725Z\n40.045903\n-92.690485\nno_data\n3.647532e+01\n-1.000000e+12\n1.394000e-01\n...\n54.0\n6.557368e+02\n59\n2494780.714\n11863.732260\n-1.000000e+12\n0\n2\n1\nLINESTRING (-92.68041 40.00013, -92.68006 40.0...\n\n\n670\n74291700181\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2723Z\n40.133798\n-92.687305\nno_data\n3.384407e+01\n-1.000000e+12\n1.019290e+00\n...\n42.0\n1.250056e+02\n58\n2506424.880\n11644.165636\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68547 40.09150, -92.68519 40.0...\n\n\n\n\n671 rows × 111 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(11,7))\nSWOT_HR_shp1.plot(ax=ax, color='black')\n\n<AxesSubplot:>\n\n\n\n\n\n2. Lake Vector Shapefiles\nThe lake vector shapefiles can be accessed in the same way as the river shapefiles above.\n\ns3_SWOT_HR_url2 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.zip'\n\n\ns3_file_obj2 = fs_s3.open(s3_SWOT_HR_url2, mode='rb')\n\n\nwith zipfile.ZipFile(s3_file_obj2, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\n\nSWOT_HR_shp2 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.shp') \nSWOT_HR_shp2\n\n\n\n\n\n\n\n\nobs_id\nlake_id\noverlap\ntime\ntime_tai\ntime_str\nwse\nwse_u\nwse_r_u\nwse_std\n...\niono_c\nxovr_cal_c\np_name\np_grand_id\np_max_wse\np_max_area\np_ref_date\np_ref_ds\np_storage\ngeometry\n\n\n\n\n0\n742081R000002\n7420470702\n93\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.934\n0.051\n0.051\n0.159\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.35\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.75926 42.04142, -92.75977 42.041...\n\n\n1\n742081R000003\n7420472462\n75\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n37.037\n0.080\n0.080\n0.143\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.62\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.91651 42.01167, -92.91681 42.011...\n\n\n2\n742081R000008\n7420473212\n58\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.578\n0.181\n0.181\n0.058\n...\n0.0\n0.0\nHENDRICKSON MARSH LAKE\n-99999999\n-1.000000e+12\n45.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-93.24060 41.93319, -93.24066 41.933...\n\n\n3\n742081R000009\n7420470712\n73\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.910\n0.110\n0.110\n0.136\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n4.50\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.72557 42.03424, -92.72560 42.034...\n\n\n4\n742081R000011\n7420470582\n76\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.904\n0.109\n0.109\n0.628\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.89\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-93.39929 41.90871, -93.39945 41.908...\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n17240\n742070R000729\n7420857422\n92\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.186\n0.098\n0.098\n0.056\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n14.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.07581 47.57853, -95.07587 47.578...\n\n\n17241\n742070R000730\n7420848152\n96\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.071\n0.038\n0.038\n0.174\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n3.06\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-94.88532 47.61669, -94.88577 47.616...\n\n\n17242\n712070R000731\n7120812272\n74\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.104\n0.077\n0.077\n0.085\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n4.41\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.08313 47.57179, -95.08341 47.571...\n\n\n17243\n712070R000732\n7120816202\n67\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n32.713\n0.106\n0.106\n0.198\n...\n0.0\n0.0\nMUD LAKE\n-99999999\n-1.000000e+12\n6.30\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.39968 47.51004, -95.39975 47.510...\n\n\n17244\n742070R000733\n7420857422\n95\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n32.725\n0.093\n0.093\n0.119\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n14.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.07228 47.57473, -95.07257 47.574...\n\n\n\n\n17245 rows × 43 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(7,12))\nSWOT_HR_shp2.plot(ax=ax, color='black')\n\n<AxesSubplot:>\n\n\n\n\n\n3. Water Mask Pixel Cloud NetCDF\nAccessing the remaining files is different than the shp files above. We do not need to unzip the files because they are stored in native netCDF files in the cloud. For the rest of the products, we will open via xarray.\n\ns3_SWOT_HR_url3 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1/SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc'\n\n\ns3_file_obj3 = fs_s3.open(s3_SWOT_HR_url3, mode='rb')\n\nThe pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.\n\nds_PIXC = xr.open_dataset(s3_file_obj3, group = 'pixel_cloud', engine='h5netcdf')\nds_PIXC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (points: 769290, complex_depth: 2)\nCoordinates:\n latitude (points) float64 ...\n longitude (points) float64 ...\nDimensions without coordinates: points, complex_depth\nData variables: (12/49)\n azimuth_index (points) float64 ...\n range_index (points) float64 ...\n interferogram (points, complex_depth) float32 ...\n power_plus_y (points) float32 ...\n power_minus_y (points) float32 ...\n coherent_power (points) float32 ...\n ... ...\n solid_earth_tide (points) float32 ...\n load_tide_fes (points) float32 ...\n load_tide_got (points) float32 ...\n pole_tide (points) float32 ...\n ancillary_surface_classification_flag (points) float32 ...\n pixc_qual (points) float32 ...\nAttributes:\n description: cloud of geolocated interferogram pixels\n interferogram_size_azimuth: 2924\n interferogram_size_range: 4575\n looks_to_efflooks: 1.75xarray.DatasetDimensions:points: 769290complex_depth: 2Coordinates: (2)latitude(points)float64...long_name :latitude (positive N, negative S)standard_name :latitudeunits :degrees_northvalid_min :-80.0valid_max :80.0comment :Geodetic latitude [-80,80] (degrees north of equator) of the pixel.[769290 values with dtype=float64]longitude(points)float64...long_name :longitude (degrees East)standard_name :longitudeunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :Longitude [-180,180) (east of the Greenwich meridian) of the pixel.[769290 values with dtype=float64]Data variables: (49)azimuth_index(points)float64...long_name :rare interferogram azimuth indexunits :1valid_min :0valid_max :999999comment :Rare interferogram azimuth index (indexed from 0).[769290 values with dtype=float64]range_index(points)float64...long_name :rare interferogram range indexunits :1valid_min :0valid_max :999999comment :Rare interferogram range index (indexed from 0).[769290 values with dtype=float64]interferogram(points, complex_depth)float32...long_name :rare interferogramunits :1valid_min :-999999.0valid_max :999999.0comment :Complex unflattened rare interferogram.[1538580 values with dtype=float32]power_plus_y(points)float32...long_name :power for plus_y channelunits :1valid_min :0.0valid_max :999999.0comment :Power for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).[769290 values with dtype=float32]power_minus_y(points)float32...long_name :power for minus_y channelunits :1valid_min :0.0valid_max :999999.0comment :Power for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).[769290 values with dtype=float32]coherent_power(points)float32...long_name :coherent power combination of minus_y and plus_y channelsunits :1valid_min :0.0valid_max :999999.0comment :Power computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).[769290 values with dtype=float32]x_factor_plus_y(points)float32...long_name :X factor for plus_y channel powerunits :1valid_min :0.0valid_max :999999.0comment :X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).[769290 values with dtype=float32]x_factor_minus_y(points)float32...long_name :X factor for minus_y channel powerunits :1valid_min :0.0valid_max :999999.0comment :X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).[769290 values with dtype=float32]water_frac(points)float32...long_name :water fractionunits :1valid_min :-1000.0valid_max :10000.0comment :Noisy estimate of the fraction of the pixel that is water.[769290 values with dtype=float32]water_frac_uncert(points)float32...long_name :water fraction uncertaintyunits :1valid_min :0.0valid_max :999999.0comment :Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).[769290 values with dtype=float32]classification(points)float32...long_name :classificationflag_meanings :land land_near_water water_near_land open_water land_near_dark_water dark_water_edge dark_waterflag_values :[ 1 2 3 4 22 23 24]valid_min :1valid_max :24comment :Flags indicating water detection results.[769290 values with dtype=float32]false_detection_rate(points)float32...long_name :false detection rateunits :1valid_min :0.0valid_max :1.0comment :Probability of falsely detecting water when there is none.[769290 values with dtype=float32]missed_detection_rate(points)float32...long_name :missed detection rateunits :1valid_min :0.0valid_max :1.0comment :Probability of falsely detecting no water when there is water.[769290 values with dtype=float32]prior_water_prob(points)float32...long_name :prior water probabilityunits :1valid_min :0.0valid_max :1.0comment :Prior probability of water occurring.[769290 values with dtype=float32]bright_land_flag(points)float32...long_name :bright land flagstandard_name :status_flagflag_meanings :not_bright_land bright_land bright_land_or_waterflag_values :[0 1 2]valid_min :0valid_max :2comment :Flag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice). Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.[769290 values with dtype=float32]layover_impact(points)float32...long_name :layover impactunits :mvalid_min :-999999.0valid_max :999999.0comment :Estimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.[769290 values with dtype=float32]eff_num_rare_looks(points)float32...long_name :effective number of rare looksunits :1valid_min :0.0valid_max :999999.0comment :Effective number of independent looks taken to form the rare interferogram.[769290 values with dtype=float32]height(points)float32...long_name :height above reference ellipsoidunits :mvalid_min :-1500.0valid_max :15000.0comment :Height of the pixel above the reference ellipsoid.[769290 values with dtype=float32]cross_track(points)float32...long_name :approximate cross-track locationunits :mvalid_min :-75000.0valid_max :75000.0comment :Approximate cross-track location of the pixel.[769290 values with dtype=float32]pixel_area(points)float32...long_name :pixel areaunits :m^2valid_min :0.0valid_max :999999.0comment :Pixel area.[769290 values with dtype=float32]inc(points)float32...long_name :incidence angleunits :degreesvalid_min :0.0valid_max :999999.0comment :Incidence angle.[769290 values with dtype=float32]phase_noise_std(points)float32...long_name :phase noise standard deviationunits :radiansvalid_min :-999999.0valid_max :999999.0comment :Estimate of the phase noise standard deviation.[769290 values with dtype=float32]dlatitude_dphase(points)float32...long_name :sensitivity of latitude estimate to interferogram phaseunits :degrees/radianvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the latitude estimate to the interferogram phase.[769290 values with dtype=float32]dlongitude_dphase(points)float32...long_name :sensitivity of longitude estimate to interferogram phaseunits :degrees/radianvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the longitude estimate to the interferogram phase.[769290 values with dtype=float32]dheight_dphase(points)float32...long_name :sensitivity of height estimate to interferogram phaseunits :m/radianvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the interferogram phase.[769290 values with dtype=float32]dheight_droll(points)float32...long_name :sensitivity of height estimate to spacecraft rollunits :m/degreesvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the spacecraft roll.[769290 values with dtype=float32]dheight_dbaseline(points)float32...long_name :sensitivity of height estimate to interferometric baselineunits :m/mvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the interferometric baseline.[769290 values with dtype=float32]dheight_drange(points)float32...long_name :sensitivity of height estimate to range (delay)units :m/mvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the range (delay).[769290 values with dtype=float32]darea_dheight(points)float32...long_name :sensitivity of pixel area to reference heightunits :m^2/mvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the pixel area to the reference height.[769290 values with dtype=float32]illumination_time(points)datetime64[ns]...long_name :time of illumination of each pixel (UTC)standard_name :timetai_utc_difference :[Value of TAI-UTC at time of first record]leap_second :YYYY-MM-DD hh:mm:sscomment :Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.[769290 values with dtype=datetime64[ns]]illumination_time_tai(points)datetime64[ns]...long_name :time of illumination of each pixel (TAI)standard_name :timecomment :Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].[769290 values with dtype=datetime64[ns]]eff_num_medium_looks(points)float32...long_name :effective number of medium looksunits :1valid_min :0.0valid_max :999999.0comment :Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).[769290 values with dtype=float32]sig0(points)float32...long_name :sigma0units :1valid_min :-999999.0valid_max :999999.0comment :Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.[769290 values with dtype=float32]phase_unwrapping_region(points)float64...long_name :phase unwrapping region indexunits :1valid_min :-1valid_max :99999999comment :Phase unwrapping region index.[769290 values with dtype=float64]instrument_range_cor(points)float32...long_name :instrument range correctionunits :mvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to range before geolocation.[769290 values with dtype=float32]instrument_phase_cor(points)float32...long_name :instrument phase correctionunits :radiansvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to phase before geolocation.[769290 values with dtype=float32]instrument_baseline_cor(points)float32...long_name :instrument baseline correctionunits :mvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to baseline before geolocation.[769290 values with dtype=float32]instrument_attitude_cor(points)float32...long_name :instrument attitude correctionunits :degreesvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to attitude before geolocation.[769290 values with dtype=float32]model_dry_tropo_cor(points)float32...long_name :dry troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFunits :mvalid_min :-3.0valid_max :-1.5comment :Equivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.[769290 values with dtype=float32]model_wet_tropo_cor(points)float32...long_name :wet troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFunits :mvalid_min :-1.0valid_max :0.0comment :Equivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.[769290 values with dtype=float32]iono_cor_gim_ka(points)float32...long_name :ionosphere vertical correctionsource :Global Ionosphere Mapsinstitution :JPLunits :mvalid_min :-0.5valid_max :0.0comment :Equivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.[769290 values with dtype=float32]height_cor_xover(points)float32...long_name :height correction from KaRIn crossoversunits :mvalid_min :-10.0valid_max :10.0comment :Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.[769290 values with dtype=float32]geoid(points)float32...long_name :geoid heightstandard_name :geoid_height_above_reference_ellipsoidsource :EGM2008 (Pavlis et al., 2012)units :mvalid_min :-150.0valid_max :150.0comment :Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).[769290 values with dtype=float32]solid_earth_tide(points)float32...long_name :solid Earth tide heightsource :Cartwright and Taylor (1971) and Cartwright and Edden (1973)units :mvalid_min :-1.0valid_max :1.0comment :Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.[769290 values with dtype=float32]load_tide_fes(points)float32...long_name :geocentric load tide height (FES)source :FES2014b (Carrere et al., 2016)institution :LEGOS/CNESunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.[769290 values with dtype=float32]load_tide_got(points)float32...long_name :geocentric load tide height (GOT)source :GOT4.10c (Ray, 2013)institution :GSFCunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.[769290 values with dtype=float32]pole_tide(points)float32...long_name :geocentric pole tide heightsource :Wahr (1985) and Desai et al. (2015)units :mvalid_min :-0.2valid_max :0.2comment :Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust).[769290 values with dtype=float32]ancillary_surface_classification_flag(points)float32...long_name :surface classificationstandard_name :status_flagsource :MODIS/GlobCoverinstitution :European Space Agencyflag_meanings :open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basinflag_values :[0 1 2 3 4 5 6]valid_min :0valid_max :6comment :7-state surface type classification computed from a mask built with MODIS and GlobCover data.[769290 values with dtype=float32]pixc_qual(points)float32...standard_name :status_flagflag_meanings :good badflag_values :[0 1]valid_min :0valid_max :1comment :Quality flag for pixel cloud data[769290 values with dtype=float32]Attributes: (4)description :cloud of geolocated interferogram pixelsinterferogram_size_azimuth :2924interferogram_size_range :4575looks_to_efflooks :1.75\n\n\n\nplt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)\nplt.colorbar().set_label('Height (m)')\n\n\n\n\n4. Water Mask Pixel Cloud Vector Attribute NetCDF\n\ns3_SWOT_HR_url4 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1/SWOT_L2_HR_PIXCVec_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc'\n\n\ns3_file_obj4 = fs_s3.open(s3_SWOT_HR_url4, mode='rb')\n\n\nds_PIXCVEC = xr.open_dataset(s3_file_obj4, decode_cf=False, engine='h5netcdf')\nds_PIXCVEC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (points: 769290, nchar_reach_id: 11,\n nchar_node_id: 14, nchar_lake_id: 10,\n nchar_obs_id: 13)\nDimensions without coordinates: points, nchar_reach_id, nchar_node_id,\n nchar_lake_id, nchar_obs_id\nData variables:\n azimuth_index (points) int32 ...\n range_index (points) int32 ...\n latitude_vectorproc (points) float64 ...\n longitude_vectorproc (points) float64 ...\n height_vectorproc (points) float32 ...\n reach_id (points, nchar_reach_id) |S1 ...\n node_id (points, nchar_node_id) |S1 ...\n lake_id (points, nchar_lake_id) |S1 ...\n obs_id (points, nchar_obs_id) |S1 ...\n ice_clim_f (points) int8 ...\n ice_dyn_f (points) int8 ...\nAttributes: (12/36)\n Conventions: CF-1.7\n title: Level 2 KaRIn high rate pixe...\n institution: CNES\n source: Simulation\n history: 2021-04-14 17:35:28Z: Creation\n platform: SWOT\n ... ...\n xref_input_l2_hr_pixc_vec_river_file: /work/ALT/swot/swotdev/desro...\n xref_static_river_db_file: \n xref_static_lake_db_file: /work/ALT/swot/swotpub/BD/BD...\n xref_l2_hr_lake_tile_config_parameter_file: /work/ALT/swot/swotdev/desro...\n ellipsoid_semi_major_axis: 6371008.771416667\n ellipsoid_flattening: 0.0xarray.DatasetDimensions:points: 769290nchar_reach_id: 11nchar_node_id: 14nchar_lake_id: 10nchar_obs_id: 13Coordinates: (0)Data variables: (11)azimuth_index(points)int32..._FillValue :2147483647long_name :rare interferogram azimuth indexunits :1valid_min :0valid_max :999999coordinates :longitude_vectorproc latitude_vectorproccomment :Rare interferogram azimuth index (indexed from 0).[769290 values with dtype=int32]range_index(points)int32..._FillValue :2147483647long_name :rare interferogram range indexunits :1valid_min :0valid_max :999999coordinates :longitude_vectorproc latitude_vectorproccomment :Rare interferogram range index (indexed from 0).[769290 values with dtype=int32]latitude_vectorproc(points)float64..._FillValue :9.969209968386869e+36long_name :height-constrained geolocation latitudestandard_name :latitudeunits :degrees_northvalid_min :-80.0valid_max :80.0comment :Height-constrained geodetic latitude of the pixel. Units are in degrees north of the equator.[769290 values with dtype=float64]longitude_vectorproc(points)float64..._FillValue :9.969209968386869e+36long_name :height-constrained geolocation longitudestandard_name :longitudeunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :Height-constrained geodetic longitude of the pixel. Positive=degrees east of the Greenwich meridian. Negative=degrees west of the Greenwich meridian.[769290 values with dtype=float64]height_vectorproc(points)float32..._FillValue :9.96921e+36long_name :height above reference ellipsoidunits :mvalid_min :-1500.0valid_max :15000.0coordinates :longitude_vectorproc latitude_vectorproccomment :Height-constrained height of the pixel above the reference ellipsoid.[769290 values with dtype=float32]reach_id(points, nchar_reach_id)|S1...long_name :identifier of the associated prior river reachcoordinates :longitude_vectorproc latitude_vectorproccomment :Unique reach identifier from the prior river database. The format of the identifier is CBBBBBRRRRT, where C=continent, B=basin, R=reach, T=type.[8462190 values with dtype=|S1]node_id(points, nchar_node_id)|S1...long_name :identifier of the associated prior river nodecoordinates :longitude_vectorproc latitude_vectorproccomment :Unique node identifier from the prior river database. The format of the identifier is CBBBBBRRRRNNNT, where C=continent, B=basin, R=reach, N=node, T=type of water body.[10770060 values with dtype=|S1]lake_id(points, nchar_lake_id)|S1...long_name :identifier of the associated prior lakecoordinates :longitude_vectorproc latitude_vectorproccomment :Identifier of the lake from the lake prior database) associated to the pixel. The format of the identifier is CBBNNNNNNT, where C=continent, B=basin, N=counter within the basin, T=type of water body.[7692900 values with dtype=|S1]obs_id(points, nchar_obs_id)|S1...long_name :identifier of the observed featurecoordinates :longitude_vectorproc latitude_vectorproccomment :Tile-specific identifier of the observed feature associated to the pixel. The format of the identifier is CBBTTTSNNNNNN, where C=continent, B=basin, T=tile number, S=swath side, N=lake counter within the PIXC tile.[10000770 values with dtype=|S1]ice_clim_f(points)int8..._FillValue :127long_name :climatological ice cover flagflag_meanings :no_ice_cover partial_ice_cover full_ice_coverflag_values :[0 1 2]institution :University of North Carolinacoordinates :longitude_vectorproc latitude_vectorproccomment :Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.[769290 values with dtype=int8]ice_dyn_f(points)int8..._FillValue :127long_name :dynamical ice cover flagflag_meanings :no_ice_cover partial_ice_cover full_ice_coverflag_values :[0 1 2]institution :University of North Carolinacoordinates :longitude_vectorproc latitude_vectorproccomment :Dynamic ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.[769290 values with dtype=int8]Attributes: (36)Conventions :CF-1.7title :Level 2 KaRIn high rate pixel cloud vector attribute productinstitution :CNESsource :Simulationhistory :2021-04-14 17:35:28Z: Creationplatform :SWOTreferences :0.2reference_document :SWOT-TN-CDM-0677-CNEScontact :test@cnes.frcycle_number :7pass_number :522tile_number :94swath_side :Rtile_name :522_094Rcontinent :NAtime_coverage_start :2022-08-22 19:29:00.955125Ztime_coverage_end :2022-08-22 19:29:10.946208Zgeospatial_lon_min :-91.20419918342002geospatial_lon_max :-90.32335511732916geospatial_lat_min :34.08946438801658geospatial_lat_max :34.78081317550924inner_first_longitude :-90.46892035209136inner_first_latitude :34.78081317550924inner_last_longitude :-90.32335511732916inner_last_latitude :34.21685259524421outer_first_longitude :-91.20419918342002outer_first_latitude :34.65198821586228outer_last_longitude :-91.05402175230344outer_last_latitude :34.08946438801658xref_input_l2_hr_pixc_file :/work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/simu/SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.ncxref_input_l2_hr_pixc_vec_river_file :/work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/river/SWOT_L2_HR_PIXCVecRiver_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.ncxref_static_river_db_file :xref_static_lake_db_file :/work/ALT/swot/swotpub/BD/BD_lakes/PLDxref_l2_hr_lake_tile_config_parameter_file :/work/ALT/swot/swotdev/desrochesd/swot-sds-16-10/swotCNES/PGE/lake_tile/lake_tile_param.cfgellipsoid_semi_major_axis :6371008.771416667ellipsoid_flattening :0.0\n\n\n\npixcvec_htvals = ds_PIXCVEC.height_vectorproc\npixcvec_latvals = ds_PIXCVEC.latitude_vectorproc\npixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc\n\n#Before plotting, we set all fill values to nan so that the graph shows up better spatially\npixcvec_htvals[pixcvec_htvals > 15000] = np.nan\npixcvec_latvals[pixcvec_latvals > 80] = np.nan\npixcvec_lonvals[pixcvec_lonvals > 180] = np.nan\n\n\nplt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)\nplt.colorbar().set_label('Height (m)')\n\n\n\n\n5. Raster NetCDF\n\ns3_SWOT_HR_url5 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1/SWOT_L2_HR_Raster_100m_UTM15S_N_x_x_x_007_522_047F_20220822T192850_20220822T192911_Dx0000_01.nc'\n\n\ns3_file_obj5 = fs_s3.open(s3_SWOT_HR_url5, mode='rb')\n\n\nds_raster = xr.open_dataset(s3_file_obj5, engine='h5netcdf')\nds_raster\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (x: 1543, y: 1540)\nCoordinates:\n * x (x) float64 6.567e+05 6.568e+05 ... 8.109e+05\n * y (y) float64 3.775e+06 3.775e+06 ... 3.929e+06\nData variables: (12/30)\n crs object b'1'\n longitude (y, x) float64 ...\n latitude (y, x) float64 ...\n wse (y, x) float32 ...\n wse_uncert (y, x) float32 ...\n water_area (y, x) float32 ...\n ... ...\n load_tide_fes (y, x) float32 ...\n load_tide_got (y, x) float32 ...\n pole_tide (y, x) float32 ...\n model_dry_tropo_cor (y, x) float32 ...\n model_wet_tropo_cor (y, x) float32 ...\n iono_cor_gim_ka (y, x) float32 ...\nAttributes: (12/45)\n Conventions: CF-1.7\n title: Level 2 KaRIn High Rate Raster Data Product\n institution: JPL\n source: Large scale simulator\n history: 2021-09-08T22:28:33Z : Creation\n mission_name: SWOT\n ... ...\n utm_zone_num: 15\n mgrs_latitude_band: S\n x_min: 656700.0\n x_max: 810900.0\n y_min: 3775000.0\n y_max: 3928900.0xarray.DatasetDimensions:x: 1543y: 1540Coordinates: (2)x(x)float646.567e+05 6.568e+05 ... 8.109e+05long_name :x coordinate of projectionstandard_name :projection_x_coordinateunits :mvalid_min :-10000000.0valid_max :10000000.0comment :UTM easting coordinate of the pixel.array([656700., 656800., 656900., ..., 810700., 810800., 810900.])y(y)float643.775e+06 3.775e+06 ... 3.929e+06long_name :y coordinate of projectionstandard_name :projection_y_coordinateunits :mvalid_min :-20000000.0valid_max :20000000.0comment :UTM northing coordinate of the pixel.array([3775000., 3775100., 3775200., ..., 3928700., 3928800., 3928900.])Data variables: (30)crs()object...long_name :CRS Definitiongrid_mapping_name :transverse_mercatorprojected_crs_name :WGS 84 / UTM zone 15Ngeographic_crs_name :WGS 84reference_ellipsoid_name :WGS 84horizontal_datum_name :WGS_1984prime_meridian_name :Greenwichfalse_easting :500000.0false_northing :0.0longitude_of_central_meridian :-93.0longitude_of_prime_meridian :0.0latitude_of_projection_origin :0.0scale_factor_at_central_meridian :0.9996semi_major_axis :6378137.0inverse_flattening :298.257223563crs_wkt :PROJCS[\"WGS 84 / UTM zone 15N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32615\"]]spatial_ref :PROJCS[\"WGS 84 / UTM zone 15N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32615\"]]comment :UTM zone coordinate reference system.array(b'1', dtype=object)longitude(y, x)float64...long_name :longitude (degrees East)standard_name :longitudegrid_mapping :crsunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :Longitude [-180,180) (east of the Greenwich meridian) of the pixel.[2376220 values with dtype=float64]latitude(y, x)float64...long_name :latitude (positive N, negative S)standard_name :latitudegrid_mapping :crsunits :degrees_northvalid_min :-80.0valid_max :80.0comment :Latitude [-80,80] (degrees north of equator) of the pixel.[2376220 values with dtype=float64]wse(y, x)float32...long_name :water surface elevation above geoidgrid_mapping :crsunits :mvalid_min :-1500.0valid_max :15000.0comment :Water surface elevation of the pixel above the geoid and after using models to subtract the effects of tides (solid_earth_tide, load_tide_fes, pole_tide).[2376220 values with dtype=float32]wse_uncert(y, x)float32...long_name :uncertainty in the water surface elevationgrid_mapping :crsunits :mvalid_min :0.0valid_max :999999.0comment :1-sigma uncertainty in the water surface elevation.[2376220 values with dtype=float32]water_area(y, x)float32...long_name :surface area of watergrid_mapping :crsunits :m^2valid_min :-2000000.0valid_max :2000000000.0comment :Surface area of the water pixels.[2376220 values with dtype=float32]water_area_uncert(y, x)float32...long_name :uncertainty in the water surface areagrid_mapping :crsunits :m^2valid_min :0.0valid_max :2000000000.0comment :1-sigma uncertainty in the water surface area[2376220 values with dtype=float32]water_frac(y, x)float32...long_name :water fractiongrid_mapping :crsunits :1valid_min :-1000.0valid_max :10000.0comment :Fraction of the pixel that is water.[2376220 values with dtype=float32]water_frac_uncert(y, x)float32...long_name :uncertainty in the water fractiongrid_mapping :crsunits :1valid_min :0.0valid_max :999999.0comment :1-sigma uncertainty in the water fraction.[2376220 values with dtype=float32]sig0(y, x)float32...long_name :sigma0grid_mapping :crsunits :1valid_min :-1000.0valid_max :10000000.0comment :Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.[2376220 values with dtype=float32]sig0_uncert(y, x)float32...long_name :uncertainty in sigma0grid_mapping :crsunits :1valid_min :0.0valid_max :1000.0comment :1-sigma uncertainty in sigma0. The value is provided in linear units. This value is a one-sigma additive (not multiplicative) uncertainty term, which can be added to or subtracted from sigma0.[2376220 values with dtype=float32]inc(y, x)float32...long_name :incidence anglegrid_mapping :crsunits :degreesvalid_min :0.0valid_max :90.0comment :Incidence angle.[2376220 values with dtype=float32]cross_track(y, x)float32...long_name :approximate cross-track locationgrid_mapping :crsunits :mvalid_min :-75000.0valid_max :75000.0comment :Approximate cross-track location of the pixel.[2376220 values with dtype=float32]illumination_time(y, x)datetime64[ns]...long_name :time of illumination of each pixel (UTC)standard_name :timetai_utc_difference :-32.0leap_second :YYYY-MM-DDThh:mm:ssZcomment :Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.[2376220 values with dtype=datetime64[ns]]illumination_time_tai(y, x)datetime64[ns]...long_name :time of illumination of each pixel (TAI)standard_name :timecomment :Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].[2376220 values with dtype=datetime64[ns]]raster_qual(y, x)float32...standard_name :status_flaggrid_mapping :crsflag_meanings :good badflag_values :[0 1]valid_min :0valid_max :1comment :Quality flag for raster data.[2376220 values with dtype=float32]n_wse_pix(y, x)float64...long_name :number of wse pixelsgrid_mapping :crsunits :lvalid_min :0valid_max :999999comment :Number of pixel cloud samples used in water surface elevation aggregation.[2376220 values with dtype=float64]n_area_pix(y, x)float64...long_name :number of area pixelsgrid_mapping :crsunits :lvalid_min :0valid_max :999999comment :Number of pixel cloud samples used in water area and water fraction aggregation.[2376220 values with dtype=float64]dark_frac(y, x)float32...long_name :fractional area of dark watergrid_mapping :crsunits :lvalid_min :-1000.0valid_max :10000.0comment :Fraction of pixel water area covered by dark water.[2376220 values with dtype=float32]ice_clim_flag(y, x)float32...long_name :climatological ice cover flagsource :UNCgrid_mapping :crsflag_meanings :no_ice_cover uncertain_ice_cover full_ice_coverflag_values :[0 1 2]valid_min :0valid_max :2comment :Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the pixel is likely not ice covered, may or may not be partially or fully ice covered, and likely fully ice covered, respectively.[2376220 values with dtype=float32]ice_dyn_flag(y, x)float32...long_name :dynamic ice cover flagsource :UNCgrid_mapping :crsflag_meanings :no_ice_cover partial_ice_cover full_ice_coverflag_values :[0 1 2]valid_min :0valid_max :2comment :Dynamic ice cover flag indicating whether the surface is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the pixel is not ice covered, partially ice covered, and fully ice covered, respectively.[2376220 values with dtype=float32]layover_impact(y, x)float32...long_name :layover impactgrid_mapping :crsunits :mvalid_min :-999999.0valid_max :999999.0comment :Estimate of the water surface elevation error caused by layover.[2376220 values with dtype=float32]geoid(y, x)float32...long_name :geoid heightstandard_name :geoid_height_above_reference_ellipsoidsource :EGM2008 (Pavlis et al., 2012)grid_mapping :crsunits :mvalid_min :-150.0valid_max :150.0comment :Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).[2376220 values with dtype=float32]solid_earth_tide(y, x)float32...long_name :solid Earth tide heightsource :Cartwright and Taylor (1971) and Cartwright and Edden (1973)grid_mapping :crsunits :mvalid_min :-1.0valid_max :1.0comment :Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.[2376220 values with dtype=float32]load_tide_fes(y, x)float32...long_name :geocentric load tide height (FES)source :FES2014b (Carrere et al., 2016)institution :LEGOS/CNESgrid_mapping :crsunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust.[2376220 values with dtype=float32]load_tide_got(y, x)float32...long_name :geocentric load tide height (GOT)source :GOT4.10c (Ray, 2013)institution :GSFCgrid_mapping :crsunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust. This value is reported for reference but is not applied to the reported height.[2376220 values with dtype=float32]pole_tide(y, x)float32...long_name :geocentric pole tide heightsource :Wahr (1985) and Desai et al. (2015)grid_mapping :crsunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth’s crust).[2376220 values with dtype=float32]model_dry_tropo_cor(y, x)float32...long_name :dry troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFgrid_mapping :crsunits :mvalid_min :-3.0valid_max :-1.5comment :Equivalent vertical correction due to dry troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.[2376220 values with dtype=float32]model_wet_tropo_cor(y, x)float32...long_name :wet troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFgrid_mapping :crsunits :mvalid_min :-1.0valid_max :0.0comment :Equivalent vertical correction due to wet troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.[2376220 values with dtype=float32]iono_cor_gim_ka(y, x)float32...long_name :ionosphere vertical correctionsource :Global Ionosphere Mapsinstitution :JPLgrid_mapping :crsunits :mvalid_min :-0.5valid_max :0.0comment :Equivalent vertical correction due to ionosphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.[2376220 values with dtype=float32]Attributes: (45)Conventions :CF-1.7title :Level 2 KaRIn High Rate Raster Data Productinstitution :JPLsource :Large scale simulatorhistory :2021-09-08T22:28:33Z : Creationmission_name :SWOTreferences :https://github.com/SWOTAlgorithms/Raster-Processorreference_document :JPL D-56416 - Revision A (DRAFT) - November 5, 2020contact :alexander.t.corben[at]jpl.nasa.govcycle_number :7pass_number :522scene_number :47tile_numbers :[92 93 94 95 92 93 94 95]tile_names :522_092L, 522_093L, 522_094L, 522_095L, 522_092R, 522_093R, 522_094R, 522_095Rtile_polarizations :V, V, V, V, V, V, V, Vcoordinate_reference_system :Universal Transverse Mercatorresolution :100.0short_name :L2_HR_Rasterdescriptor_string :100m_UTM15S_N_x_x_xcrid :Dx0000product_version :V0.1pge_name :adt_pge_standinpge_version :V0.1time_coverage_start :2022-08-22 19:28:50.964042Ztime_coverage_end :2022-08-22 19:29:10.946208Zgeospatial_lon_min :-91.27757002156555geospatial_lon_max :-89.62061588835118geospatial_lat_min :34.09943218249787geospatial_lat_max :35.464214684504334left_first_longitude :-89.89843338760357left_first_latitude :35.464214684504334left_last_longitude :-89.62061588835118left_last_latitude :34.33243031374548right_first_longitude :-91.27757002156555right_first_latitude :35.22613283570163right_last_longitude :-90.98228790375923right_last_latitude :34.09943218249787xref_input_l2_hr_pixc_files :SWOT_L2_HR_PIXC_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.ncxref_input_l2_hr_pixcvec_files :SWOT_L2_HR_PIXCVec_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.ncutm_zone_num :15mgrs_latitude_band :Sx_min :656700.0x_max :810900.0y_min :3775000.0y_max :3928900.0\n\n\nIt’s easy to analyze and plot the data with packages such as hvplot!\n\nds_raster.wse.hvplot.image(y='y', x='x')"
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+ "title": "PRE-SWOT NUMERICAL SIMULATION VERSION 1 User Guide Demo",
+ "section": "Plot a 3D field based (temperature)",
+ "text": "Plot a 3D field based (temperature)\n\nfig,ax=plt.subplots(1,2,figsize=(20,10))\ntheta=dd['Theta'][:]\ntheta.coords['k']=dd['Z'].data\n\ntheta[0,0,...].plot(ax=ax[0])\nax[0].vlines(100,0,400,colors='w')\ntheta[0,:,:,100].plot(ax=ax[1])\n\n<matplotlib.collections.QuadMesh at 0x7f9110022550>"
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- "title": "Amazon Estuary Exploration:",
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+ "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
- },
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- "title": "Amazon Estuary Exploration:",
- "section": "Cloud Direct Download Version",
- "text": "Cloud Direct Download Version\nThis tutorial is one of two jupyter notebook versions of the same use case exploring multiple satellite data products over the Amazon Estuary. In this version, we use data that has been downloaded onto our local machine from the cloud.\n\nLearning Objectives\n\nCompare cloud access methods (in tandem with notebook “Amazon Estuary Exploration: In Cloud AWS Version”)\nSearch for data products using earthaccess Python library\nAccess datasets using xarray and visualize using hvplot or plot tools\n\nThis tutorial explores the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region from multiple datasets listed below. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use cloud datasets and services. This notebook is meant to be executed locally."
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- "title": "Amazon Estuary Exploration:",
- "section": "Cloud Datasets",
- "text": "Cloud Datasets\nThe tutorial itself will use four different datasets:\n1. TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3\n\nDOI: https://doi.org/10.5067/TEMSC-3JC63\n\nThe Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) satellite land water equivalent (LWE) thicknesses will be used to observe seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, thus highlighting a wet season. \n2. PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\n\nDOI: https://doi.org/10.5067/PSGRA-DA2V2\n\nThe NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual gauges will be used as a proxy for Surface Water and Ocean Topography (SWOT) discharge until SWOT products are available. MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute.\n3. OISSS_L4_multimission_7day_v1\n\nDOI: https://doi.org/10.5067/SMP10-4U7CS\n\nOptimally Interpolated Sea surface salinity (OISSS) is a level 4 product that combines the records from Aquarius (Sept 2011-June 2015), the Soil Moisture Active Passive (SMAP) satellite (April 2015-present), and ESAs Soil Moisture Ocean Salinity (SMOS) data to fill in data gaps.\n4. MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0\n\nDOI: https://doi.org/10.5067/MODAM-MO9N9\n\nSea surface temperature is obtained from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the Aqua satellite. \nMore details on available collections are on the PO.DAAC Cloud Earthdata Search Portal. For more information on the PO.DAAC transition to the cloud, please visit: https://podaac.jpl.nasa.gov/cloud-datasets/about\n\nNote: NASA Earthdata Login Required\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below."
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\n#!rm *.nc*\nIn this tutorial you will learn how to access variable subsets from OPeNDAP in the Cloud."
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- "title": "Amazon Estuary Exploration:",
- "section": "Needed Packages",
- "text": "Needed Packages\n\nimport glob\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport hvplot.xarray\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy\nfrom datetime import datetime\nimport os\nfrom os.path import isfile, basename, abspath\nimport dask\ndask.config.set({\"array.slicing.split_large_chunks\": False})\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\n\n\n\n\n\n\n\n\n\n\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)"
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+ "href": "notebooks/meetings_workshops/workshop_osm_2022/S6_OPeNDAP_Access_Gridding.html#requirements",
+ "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
+ "section": "Requirements",
+ "text": "Requirements\nThis workflow was developed using Python 3.9 (and tested against versions 3.7, 3.8). The pyresample package is the only remaining dependency besides common packages like numpy and xarray. You may uncomment the first line of the following cell to install pyresample, if necessary. Then, import all the required Python packages.\n\n#!python -m pip install numpy pyresample xarray\nimport os\nimport tqdm\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport pyresample as pr\nimport matplotlib.pyplot as plt\nfrom concurrent.futures import ThreadPoolExecutor\nfrom pyresample.kd_tree import resample_gauss\nfrom io import StringIO\n\ncmr = \"cmr.earthdata.nasa.gov\"\n\ndef get_collection(url: str=f\"https://{cmr}/search/collections.umm_json\", **params):\n return requests.get(url, params).json().get(\"items\")[0]\n \ndef get_opendap(record: dict):\n for url in record.get(\"RelatedUrls\"):\n if 'opendap.earthdata.nasa.gov' in url.get(\"URL\"):\n return url.get(\"URL\")\n\ndef get_granules(ShortName: str, provider: str=\"POCLOUD\", page_size: int=200, **kwargs):\n url = f\"https://{cmr}/search/granules.umm_json\"\n params = dict(ShortName=ShortName, provider=\"POCLOUD\", page_size=page_size)\n granules = pd.DataFrame(requests.get(url, {**params,**kwargs}).json().get(\"items\"))\n granules['GranuleUR'] = granules.umm.apply(lambda x: x.get(\"GranuleUR\"))\n granules['OPeNDAP'] = granules.umm.apply(get_opendap)\n coverage = granules.umm.apply(lambda x: x.get(\"TemporalExtent\").get(\"RangeDateTime\").values()).apply(list)\n granules['Start'] = coverage.apply(sorted).apply(lambda x: x[0])\n granules['End'] = coverage.apply(sorted).apply(lambda x: x[1])\n return granules"
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- "section": "Liquid Water Equivalent (LWE) Thickness (GRACE & GRACE-FO)",
- "text": "Liquid Water Equivalent (LWE) Thickness (GRACE & GRACE-FO)\n\nSearch for GRACE LWE Thickness data\nSuppose we are interested in LWE data from the dataset (DOI:10.5067/TEMSC-3JC62) described on this PO.DAAC dataset landing page: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3\nFrom the landing page, we see the dataset Short Name under the Information tab. (For this dataset it is “TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3”) We will be using this to search for the necessary granules.\n\n#earthaccess search\ngrace_results = earthaccess.search_data(short_name=\"TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3\")\n\nGranules found: 1\n\n\n\n#download data into folder on local machine\nearthaccess.download(grace_results, \"./grace_data\")\n\n Getting 1 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n['GRCTellus.JPL.200204_202303.GLO.RL06.1M.MSCNv03CRI.nc']\n\n\n\n\nOpen file using xarray.\n\n#open dataset for visualization\nds_GRACE = xr.open_mfdataset(\"./grace_data/GRCTellus.JPL.200204_202303.GLO.RL06.1M.MSCNv03CRI.nc\", engine=\"h5netcdf\")\nds_GRACE\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lon: 720, lat: 360, time: 219, bounds: 2)\nCoordinates:\n * lon (lon) float64 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8\n * lat (lat) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * time (time) datetime64[ns] 2002-04-17T12:00:00 ... 2023-03-16T1...\nDimensions without coordinates: bounds\nData variables:\n lwe_thickness (time, lat, lon) float64 dask.array<chunksize=(219, 360, 720), meta=np.ndarray>\n uncertainty (time, lat, lon) float64 dask.array<chunksize=(219, 360, 720), meta=np.ndarray>\n lat_bounds (lat, bounds) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>\n lon_bounds (lon, bounds) float64 dask.array<chunksize=(720, 2), meta=np.ndarray>\n time_bounds (time, bounds) datetime64[ns] dask.array<chunksize=(219, 2), meta=np.ndarray>\n land_mask (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>\n scale_factor (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>\n mascon_ID (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>\nAttributes: (12/53)\n Conventions: CF-1.6, ACDD-1.3, ISO 8601\n Metadata_Conventions: Unidata Dataset Discovery v1.0\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata ...\n title: JPL GRACE and GRACE-FO MASCON RL06.1Mv03 CRI\n summary: Monthly gravity solutions from GRACE and G...\n keywords: Solid Earth, Geodetics/Gravity, Gravity, l...\n ... ...\n C_30_substitution: TN-14; Loomis et al., 2019, Geophys. Res. ...\n user_note_1: The accelerometer on the GRACE-B spacecraf...\n user_note_2: The accelerometer on the GRACE-D spacecraf...\n journal_reference: Watkins, M. M., D. N. Wiese, D.-N. Yuan, C...\n CRI_filter_journal_reference: Wiese, D. N., F. W. Landerer, and M. M. Wa...\n date_created: 2023-05-22T06:05:03Zxarray.DatasetDimensions:lon: 720lat: 360time: 219bounds: 2Coordinates: (3)lon(lon)float640.25 0.75 1.25 ... 359.2 359.8units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xvalid_min :0.25valid_max :359.75bounds :lon_boundsarray([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,\n 3.5975e+02])lat(lat)float64-89.75 -89.25 ... 89.25 89.75units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yvalid_min :-89.75valid_max :89.75bounds :lat_boundsarray([-89.75, -89.25, -88.75, ..., 88.75, 89.25, 89.75])time(time)datetime64[ns]2002-04-17T12:00:00 ... 2023-03-...long_name :timestandard_name :timeaxis :Tbounds :time_boundsarray(['2002-04-17T12:00:00.000000000', '2002-05-10T12:00:00.000000000',\n '2002-08-16T12:00:00.000000000', ..., '2023-01-16T12:00:00.000000000',\n '2023-02-15T00:00:00.000000000', '2023-03-16T12:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (8)lwe_thickness(time, lat, lon)float64dask.array<chunksize=(219, 360, 720), meta=np.ndarray>units :cmlong_name :Liquid_Water_Equivalent_Thicknessstandard_name :Liquid_Water_Equivalent_Thicknessgrid_mapping :WGS84valid_min :-1986.9763606523888valid_max :965.4782725418918comment :Coastline Resolution Improvement (CRI) filter is applied\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n433.08 MiB\n433.08 MiB\n\n\nShape\n(219, 360, 720)\n(219, 360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\nuncertainty\n\n\n(time, lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(219, 360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\ncm\n\nlong_name :\n\nuncertainty\n\nstandard_name :\n\nuncertainty\n\ngrid_mapping :\n\nWGS84\n\nvalid_min :\n\n0.15854006805783352\n\nvalid_max :\n\n53.34469598560085\n\ncomment :\n\n1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n433.08 MiB\n433.08 MiB\n\n\nShape\n(219, 360, 720)\n(219, 360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nlat_bounds\n\n\n(lat, bounds)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 2), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlatitude boundaries\n\nunits :\n\ndegrees_north\n\ncomment :\n\nlatitude values at the north and south bounds of each pixel\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n5.62 kiB\n5.62 kiB\n\n\nShape\n(360, 2)\n(360, 2)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nlon_bounds\n\n\n(lon, bounds)\n\n\nfloat64\n\n\ndask.array<chunksize=(720, 2), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlongitude boundaries\n\nunits :\n\ndegrees_east\n\ncomment :\n\nlongitude values at the west and east bounds of each pixel\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n11.25 kiB\n11.25 kiB\n\n\nShape\n(720, 2)\n(720, 2)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\ntime_bounds\n\n\n(time, bounds)\n\n\ndatetime64[ns]\n\n\ndask.array<chunksize=(219, 2), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime boundaries\n\ncomment :\n\ntime bounds for each time value, i.e. the first day and last day included in the monthly solution\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.42 kiB\n3.42 kiB\n\n\nShape\n(219, 2)\n(219, 2)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\ndatetime64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nland_mask\n\n\n(lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\nbinary\n\nlong_name :\n\nLand_Mask\n\nstandard_name :\n\nLand_Mask\n\ndescription :\n\nLand Mask that was used with the CRI filter\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.98 MiB\n1.98 MiB\n\n\nShape\n(360, 720)\n(360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nscale_factor\n\n\n(lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\ndimensionless\n\nlong_name :\n\nScale_Factor_CRI\n\nstandard_name :\n\nScale_Factor_CRI\n\nvalid_min :\n\n-99999.0\n\nvalid_max :\n\n24.133988467789724\n\ndescription :\n\nGridded scale factors to be used with mascon solution that has the CRI filter applied; based on CLM data from 2002-2009\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.98 MiB\n1.98 MiB\n\n\nShape\n(360, 720)\n(360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nmascon_ID\n\n\n(lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\ndimensionless\n\nlong_name :\n\nMascon_Identifier\n\nstandard_name :\n\nMascon_ID\n\nvalid_min :\n\n1\n\nvalid_max :\n\n4551\n\ndescription :\n\nMascon identifier mapped to the grid\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.98 MiB\n1.98 MiB\n\n\nShape\n(360, 720)\n(360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\nIndexes: (3)lonPandasIndexPandasIndex(Float64Index([ 0.25, 0.75, 1.25, 1.75, 2.25, 2.75, 3.25, 3.75,\n 4.25, 4.75,\n ...\n 355.25, 355.75, 356.25, 356.75, 357.25, 357.75, 358.25, 358.75,\n 359.25, 359.75],\n dtype='float64', name='lon', length=720))latPandasIndexPandasIndex(Float64Index([-89.75, -89.25, -88.75, -88.25, -87.75, -87.25, -86.75, -86.25,\n -85.75, -85.25,\n ...\n 85.25, 85.75, 86.25, 86.75, 87.25, 87.75, 88.25, 88.75,\n 89.25, 89.75],\n dtype='float64', name='lat', length=360))timePandasIndexPandasIndex(DatetimeIndex(['2002-04-17 12:00:00', '2002-05-10 12:00:00',\n '2002-08-16 12:00:00', '2002-09-16 00:00:00',\n '2002-10-16 12:00:00', '2002-11-16 00:00:00',\n '2002-12-16 12:00:00', '2003-01-16 12:00:00',\n '2003-02-15 00:00:00', '2003-03-16 12:00:00',\n ...\n '2022-06-16 00:00:00', '2022-07-16 12:00:00',\n '2022-08-16 12:00:00', '2022-09-16 00:00:00',\n '2022-10-16 12:00:00', '2022-11-16 00:00:00',\n '2022-12-16 12:00:00', '2023-01-16 12:00:00',\n '2023-02-15 00:00:00', '2023-03-16 12:00:00'],\n dtype='datetime64[ns]', name='time', length=219, freq=None))Attributes: (53)Conventions :CF-1.6, ACDD-1.3, ISO 8601Metadata_Conventions :Unidata Dataset Discovery v1.0standard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Convention-1.6title :JPL GRACE and GRACE-FO MASCON RL06.1Mv03 CRIsummary :Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06.1Mv03 mascon solution - with CRI filter appliedkeywords :Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thicknesskeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsplatform :GRACE and GRACE-FOinstitution :NASA/JPLcreator_name :David Wiesecreator_email :grace@podaac.jpl.nasa.govcreator_url :https://grace.jpl.nasa.govcreator_type :groupcreator_institution :NASA/JPLpublisher_name :Physical Oceanography Distributed Active Archive Centerpublisher_email :podaac@jpl.nasa.govpublisher_url :https://podaac.jpl.nasa.govpublisher_type :grouppublisher_institution :NASA/JPLproject :NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)program :NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Programid :10.5067/TEMSC-3JC62naming_authority :org.doi.dxsource :GRACE and GRACE-FO JPL RL06.1Mv03-CRIprocessing_level :2 and 3acknowledgement :GRACE is a joint mission of NASA (USA) and DLR (Germany). GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAClicense :https://science.nasa.gov/earth-science/earth-science-data/data-information-policyproduct_version :v3.0time_epoch :2002-01-01T00:00:00Ztime_coverage_start :2002-04-16T00:00:00Ztime_coverage_end :2023-03-16T23:59:59Zgeospatial_lat_min :-89.75geospatial_lat_max :89.75geospatial_lat_units :degrees_northgeospatial_lat_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconsgeospatial_lon_min :0.25geospatial_lon_max :359.75geospatial_lon_units :degrees_eastgeospatial_lon_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconstime_mean_removed :2004.000 to 2009.999months_missing :2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09postprocess_1 : OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLYpostprocess_2 :Water density used to convert to equivalent water height: 1000 kg/m^3postprocess_3 :Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlinesGIA_removed :ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.geocenter_correction :We use a version of TN-13 based on the JPL masconsC_20_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929C_30_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929. This substitution is made for all months after August 2016.user_note_1 :The accelerometer on the GRACE-B spacecraft was turned off after August 2016. After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure. This has led to a subsequent degradation in the quality of the gravity fields derived. The uncertainties in this file have been scaled to accomodate this degradation.user_note_2 :The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018. After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure. The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.journal_reference :Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. CRI_filter_journal_reference :Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. date_created :2023-05-22T06:05:03Z\n\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data to plot with hvplot.\n\nlat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\nds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_GRACE_subset\n\nds_GRACE_subset.lwe_thickness.hvplot.image(y='lat', x='lon', cmap='bwr_r',).opts(clim=(-80,80))"
+ "objectID": "notebooks/meetings_workshops/workshop_osm_2022/S6_OPeNDAP_Access_Gridding.html#dataset",
+ "href": "notebooks/meetings_workshops/workshop_osm_2022/S6_OPeNDAP_Access_Gridding.html#dataset",
+ "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
+ "section": "Dataset",
+ "text": "Dataset\n\nAbout the mission\nThis demo uses data acquired by the Sentinel-6A Michael Freilich (S6A) satellite altimetry mission, which provides precise measurements of ocean surface height. It is the latest iteration in a series of missions, which together provide an uninterupted sea surface height record going back more than 30 years.\nSatellite altimetry is a precise science carried out by Ocean Surface Topography researchers through the Jason-series radar altimetry missions. Instrument specifications, operational procedures, data calibration and analysis are sometimes referred to colloquially as “along-track altimetry” (a term that I find useful to understanding the data provided at level 2, like in the dataset we use here).\nLearn more through resources linked in the Appendix).\n\n\nAbout the data\nIn a nutshell:\n\nWhat? calibrated sea surface height measurements,\nWhere? from -66.0 to 66.0 degrees latitude,\nWhen? beginning in June 2021,\nHow? global coverage acquired every 10 days (1 cycle of 128 orbits)\n\n\nFigure: depicted data structure for level-2 along-track altimetry datasets from Sentinel-6A\nPO.DAAC typically refers to datasets by their ShortName: JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F. The ShortName contains the following info for all Sentinel-6 datasets at level 2:\n\nJASON_CS: referring to Jason Continuity of Service (Jason-CS), the mission series/instrument class;\nS6A: referring to Sentinel-6A (instead of Sentinel-6B, which is expected to launch in 2025);\nL2: Level 2, the data processing level;\nALT: Altimetry, the data product type and application;\nLR: Low Resolution, versus High Resolution (HR);\nRED: Reduced, the smaller of two datasets distributed at Level 2 (the other being Standard, which contains more variables)\nOST: Ocean Surface Topography, the science domain/team/community;\nNRT: Near Real Time, the data latency; i.e. accessible within 3 hours (vs. STC or NTC; lower latencies)"
},
{
- "objectID": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#river-heights-pre-swot-measures",
- "href": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#river-heights-pre-swot-measures",
- "title": "Amazon Estuary Exploration:",
- "section": "River heights (Pre-SWOT MEaSUREs)",
- "text": "River heights (Pre-SWOT MEaSUREs)\nThe shortname for MEaSUREs is ‘PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2’.\nOur desired variable is height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Amazon estuary. Time is between April 8, 1993 and April 20, 2019.\nTo get the data for the exact area we need, we have set the boundaries of (-74.67188,-4.51279,-51.04688,0.19622) as reflected in our earthaccess data search.\n\nMEaSUREs_results = earthaccess.search_data(short_name=\"PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\", temporal = (\"1993-04-08\", \"2019-04-20\"), bounding_box=(-74.67188,-4.51279,-51.04688,0.19622))\n\nGranules found: 1\n\n\n\nearthaccess.download(MEaSUREs_results, \"./MEaSUREs_data\")\n\n Getting 1 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n['South_America_Amazon1kmdaily.nc']\n\n\n\nds_MEaSUREs = xr.open_dataset(\"./MEaSUREs_data/South_America_Amazon1kmdaily.nc\", engine=\"h5netcdf\")\nds_MEaSUREs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (X: 3311, Y: 3311, distance: 3311, time: 9469,\n charlength: 26)\nCoordinates:\n * time (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...\nDimensions without coordinates: X, Y, distance, charlength\nData variables:\n lon (X) float64 ...\n lat (Y) float64 ...\n FD (distance) float64 ...\n height (distance, time) float64 ...\n sat (charlength, time) |S1 ...\n storage (distance, time) float64 ...\n IceFlag (time) float64 ...\n LakeFlag (distance) float64 ...\n Storage_uncertainty (distance, time) float64 ...\nAttributes: (12/40)\n title: GRRATS (Global River Radar Altimetry Time ...\n Conventions: CF-1.6, ACDD-1.3\n institution: Ohio State University, School of Earth Sci...\n source: MEaSUREs OSU Storage toolbox 2018\n keywords: EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n keywords_vocabulary: Global Change Master Directory (GCMD)\n ... ...\n geospatial_lat_max: -0.6550700975069503\n geospatial_lat_units: degree_north\n geospatial_vertical_max: 92.7681246287056\n geospatial_vertical_min: -3.5634095181633763\n geospatial_vertical_units: m\n geospatial_vertical_positive: upxarray.DatasetDimensions:X: 3311Y: 3311distance: 3311time: 9469charlength: 26Coordinates: (1)time(time)datetime64[ns]1993-04-08T15:20:40.665117184 .....long_name :timestandard_name :timeaxis :Tarray(['1993-04-08T15:20:40.665117184', '1993-04-09T15:20:40.665117184',\n '1993-04-10T15:20:40.665117184', ..., '2019-04-18T03:39:13.243964928',\n '2019-04-19T03:39:13.243964928', '2019-04-20T03:39:13.243964928'],\n dtype='datetime64[ns]')Data variables: (9)lon(X)float64...units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :X[3311 values with dtype=float64]lat(Y)float64...units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Y[3311 values with dtype=float64]FD(distance)float64...long_name :distance_from_river_mouthunits :kmcomment :This is the distance along the river centerline from the river mouth to this VS.[3311 values with dtype=float64]height(distance, time)float64...units :mpositive :uplong_name :interpolated_heightsstandard_name :heightvalid_min :-3.5634095181633763valid_max :92.7681246287056comment :A time flow distance grid of river water heights with respect to the EGM08 Geoid).-9999 fill values are for missing data, while -9995 fill values are for lakes and reservoirs.[31351859 values with dtype=float64]sat(charlength, time)|S1...long_name :satellitecomment :The satellite the measurement is derived from.[246194 values with dtype=|S1]storage(distance, time)float64...units :km3positive :uplong_name :river_channel_storagecomment :A time flow distance grid of river channel storage values.[31351859 values with dtype=float64]IceFlag(time)float64...long_name :Ice_Flagvalid_range :0, 1flag_masks :1flag_meaning :Time of ice covercomment : This is a flag for masking out times of Ice Cover.[9469 values with dtype=float64]LakeFlag(distance)float64...long_name :Lake_Flagvalid_range :0, 1flag_masks :1flag_meaning :River sections that are lakes or reservoirscomment : This is a flag for masking out setions that are lakes or reservoirs.[3311 values with dtype=float64]Storage_uncertainty(distance, time)float64...units :km3positive :uplong_name :river_channel_storage_uncertaintycomment :A time flow distance grid of river channel storage uncertainty values.[31351859 values with dtype=float64]Indexes: (1)timePandasIndexPandasIndex(DatetimeIndex(['1993-04-08 15:20:40.665117184',\n '1993-04-09 15:20:40.665117184',\n '1993-04-10 15:20:40.665117184',\n '1993-04-11 15:20:40.665117184',\n '1993-04-12 15:20:40.665117184',\n '1993-04-13 15:20:40.665117184',\n '1993-04-14 15:20:40.665117184',\n '1993-04-15 15:20:40.665117184',\n '1993-04-16 15:20:40.665117184',\n '1993-04-17 15:20:40.665117184',\n ...\n '2019-04-11 03:39:13.243964928',\n '2019-04-12 03:39:13.243964928',\n '2019-04-13 03:39:13.243964928',\n '2019-04-14 03:39:13.243964928',\n '2019-04-15 03:39:13.243964928',\n '2019-04-16 03:39:13.243964928',\n '2019-04-17 03:39:13.243964928',\n '2019-04-18 03:39:13.243964928',\n '2019-04-19 03:39:13.243964928',\n '2019-04-20 03:39:13.243964928'],\n dtype='datetime64[ns]', name='time', length=9469, freq=None))Attributes: (40)title :GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Amazon RiverConventions :CF-1.6, ACDD-1.3institution :Ohio State University, School of Earth Sciencessource :MEaSUREs OSU Storage toolbox 2018keywords :EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHTkeywords_vocabulary :Global Change Master Directory (GCMD)cdm_data_type :stationcreator_name :Coss,Stevecreator_email :Coss.31@osu.eduproject :MEaSUREs OSUprogram :NASA Earth Science Data Systems (ESDS)publisher_name :PO.DAAC (Physical Oceanography Distributed Active Archive Center)publisher_email :podaac@podaac.jpl.nasa.govpublisher_url :podaac.jpl.nasa.govpublisher_type :Institutionpublisher_institution :PO.DAACprocessing_level :L2doi :10.5067/PSGRA-DA2V2history :This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements. platform :ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)platform_vocabulary :NASA/GCMD Platform Keywords. Version 8.6instrument :RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)instrument_vocabulary :NASA/GCMD Platform Keywords. Version 8.6references :in review :doi.org/10.5194/essd-2019-84id :GRRATS(Global River Radar Altimeter Time Series) 1km/dailysummary :The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.time_coverage_resolution :1 daydate_created :2021-06-30T08:03:41time_coverage_start :1992-04-08T15:20:40time_coverage_end :2018-04-20T03:39:13geospatial_lon_min :-73.35433106652545geospatial_lon_max :-51.0426448887506geospatial_lon_units :degree_eastgeospatial_lat_min :-4.3804275867636875geospatial_lat_max :-0.6550700975069503geospatial_lat_units :degree_northgeospatial_vertical_max :92.7681246287056geospatial_vertical_min :-3.5634095181633763geospatial_vertical_units :mgeospatial_vertical_positive :up\n\n\n\nPlot a subset of the data\nPlotting the river distances and associated heights on the map at time t=9069 (March 16, 2018) using plt.\n\nfig = plt.figure(figsize=[11,7]) \nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent([-85, -30, -20, 20])\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()"
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+ "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
+ "section": "Discovery",
+ "text": "Discovery\nThe unique ‘concept-id’ assigned to each PO.DAAC dataset, or ‘collection’, within the Earthdata system is functionally the same as the ShortName in the context of PO.DAAC’s collections in the cloud (because we also assign unique ShortNames). This cell is downloading metadata to retrieve that identifier from an external source, then download metadata about the series of files that make up the time series for the cycle specified by the variable ‘cycle’, and merging to table.\n\nSearch for files/granules\nPick any cycle after cycle 25, which was around the time of the first release of data from S6A. (This cell calls three functions defined in the cell above.)\n\ncycle = 25\n\nname = \"JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F\"\n\nconcept_id = get_collection(ShortName=name).get(\"meta\").get(\"concept-id\")\n\ngranules = get_granules(name, cycle=cycle) \n\ngranules[['GranuleUR', 'Start', 'End']].set_index(\"GranuleUR\")\n\n\n\n\n\n\n\n\nStart\nEnd\n\n\nGranuleUR\n\n\n\n\n\n\nS6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02\n2021-07-13T16:26:44.514Z\n2021-07-13T18:22:34.471Z\n\n\nS6A_P4_2__LR_RED__NR_025_003_20210713T182234_20210713T201839_F02\n2021-07-13T18:22:34.522Z\n2021-07-13T20:18:39.482Z\n\n\nS6A_P4_2__LR_RED__NR_025_006_20210713T201839_20210713T215450_F02\n2021-07-13T20:18:39.532Z\n2021-07-13T21:54:50.473Z\n\n\nS6A_P4_2__LR_RED__NR_025_007_20210713T215450_20210713T234732_F02\n2021-07-13T21:54:50.523Z\n2021-07-13T23:47:32.482Z\n\n\nS6A_P4_2__LR_RED__NR_025_009_20210713T234732_20210714T014224_F02\n2021-07-13T23:47:32.533Z\n2021-07-14T01:42:24.454Z\n\n\n...\n...\n...\n\n\nS6A_P4_2__LR_RED__NR_025_245_20210723T050533_20210723T064603_F02\n2021-07-23T05:05:33.543Z\n2021-07-23T06:46:03.471Z\n\n\nS6A_P4_2__LR_RED__NR_025_247_20210723T064603_20210723T083817_F02\n2021-07-23T06:46:03.521Z\n2021-07-23T08:38:17.483Z\n\n\nS6A_P4_2__LR_RED__NR_025_249_20210723T083817_20210723T103256_F02\n2021-07-23T08:38:17.533Z\n2021-07-23T10:32:56.490Z\n\n\nS6A_P4_2__LR_RED__NR_025_251_20210723T103256_20210723T122904_F02\n2021-07-23T10:32:56.540Z\n2021-07-23T12:29:04.459Z\n\n\nS6A_P4_2__LR_RED__NR_025_253_20210723T122904_20210723T142514_F02\n2021-07-23T12:29:04.509Z\n2021-07-23T14:25:14.452Z\n\n\n\n\n125 rows × 2 columns"
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- "title": "Amazon Estuary Exploration:",
- "section": "Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)",
- "text": "Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)\nThe shortname for this dataset is ‘OISSS_L4_multimission_7day_v1’. This dataset contains hundreds of granules, by using earthaccess search, we access 998 granules.\nSince this dataset has more than 1 granule that we want to open for visualization, we have to establish the full file path in a different way. For the previous datasets, we could list the exact file, but that would be difficult to do with hundreds of granules. Therefore, the extra step to recurse through the directory to access all files.\n\n#earthaccess search\nsss_results = earthaccess.search_data(short_name=\"OISSS_L4_multimission_7day_v1\")\n\nGranules found: 998\n\n\n\n#earthaccess download\nsss_files = earthaccess.download(sss_results, \"./sss_data\")\n\n Getting 998 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\n#ensures that all files are included in the path\nsss_path = [os.path.join(\"./sss_data\", f) \n for pth, dirs, files in os.walk(\"./sss_data\") for f in files]\n\n\nds_sss = xr.open_mfdataset(sss_path,\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks='auto',\n engine='h5netcdf')\nds_sss\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (longitude: 1440, latitude: 720, time: 998)\nCoordinates:\n * longitude (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n * latitude (latitude) float32 -89.88 -89.62 ... 89.62 89.88\n * time (time) datetime64[ns] 2011-08-28 ... 2022-08-02\nData variables:\n sss (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\n sss_empirical_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 962), meta=np.ndarray>\n sss_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\nAttributes: (12/42)\n Conventions: CF-1.8, ACDD-1.3\n standard_name_vocabulary: CF Standard Name Table v27\n Title: Multi-Mission Optimally Interpolated Sea S...\n Short_Name: OISSS_L4_multimission_7d_v1\n Version: V1.0\n Processing_Level: Level 4\n ... ...\n geospatial_lat_resolution: 0.25\n geospatial_lat_units: degrees_north\n geospatial_lon_min: -180.0\n geospatial_lon_max: 180.0\n geospatial_lon_resolution: 0.25\n geospatial_lon_units: degrees_eastxarray.DatasetDimensions:longitude: 1440latitude: 720time: 998Coordinates: (3)longitude(longitude)float32-179.9 -179.6 ... 179.6 179.9long_name :longitudestandard_name :longitudeunits :degrees_eastaxis :Xvalid_min :-180.0valid_max :180.0coverage_content_type :coordinatearray([-179.875, -179.625, -179.375, ..., 179.375, 179.625, 179.875],\n dtype=float32)latitude(latitude)float32-89.88 -89.62 ... 89.62 89.88long_name :latitudestandard_name :latitudeunits :degrees_northaxis :Yvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([-89.875, -89.625, -89.375, ..., 89.375, 89.625, 89.875],\n dtype=float32)time(time)datetime64[ns]2011-08-28 ... 2022-08-02long_name :center day of a time period over which satellite Level 2 SSS data have been collected for OISSS analysisstandard_name :timeaxis :Tcoverage_content_type :coordinatearray(['2011-08-28T00:00:00.000000000', '2011-09-01T00:00:00.000000000',\n '2011-09-05T00:00:00.000000000', ..., '2022-07-25T00:00:00.000000000',\n '2022-07-29T00:00:00.000000000', '2022-08-02T00:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (3)sss(latitude, longitude, time)float32dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>long_name :sea surface salinitystandard_name :sea_surface_salinityunits :1e-3valid_min :0.0valid_max :45.0add_factor :0.0coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n13461 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nsss_empirical_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array<chunksize=(720, 1440, 962), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n3.72 GiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 962)\n\n\nCount\n338 Tasks\n37 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoordinates :\n\ntime longitude latudude\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n8654 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (3)longitudePandasIndexPandasIndex(Float64Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625,\n -178.375, -178.125, -177.875, -177.625,\n ...\n 177.625, 177.875, 178.125, 178.375, 178.625, 178.875,\n 179.125, 179.375, 179.625, 179.875],\n dtype='float64', name='longitude', length=1440))latitudePandasIndexPandasIndex(Float64Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375,\n -88.125, -87.875, -87.625,\n ...\n 87.625, 87.875, 88.125, 88.375, 88.625, 88.875, 89.125,\n 89.375, 89.625, 89.875],\n dtype='float64', name='latitude', length=720))timePandasIndexPandasIndex(DatetimeIndex(['2011-08-28', '2011-09-01', '2011-09-05', '2011-09-09',\n '2011-09-13', '2011-09-17', '2011-09-21', '2011-09-25',\n '2011-09-29', '2011-10-03',\n ...\n '2022-06-27', '2022-07-01', '2022-07-05', '2022-07-09',\n '2022-07-13', '2022-07-17', '2022-07-21', '2022-07-25',\n '2022-07-29', '2022-08-02'],\n dtype='datetime64[ns]', name='time', length=998, freq=None))Attributes: (42)Conventions :CF-1.8, ACDD-1.3standard_name_vocabulary :CF Standard Name Table v27Title :Multi-Mission Optimally Interpolated Sea Surface Salinity 7-Day Global Dataset V1.0Short_Name :OISSS_L4_multimission_7d_v1Version :V1.0Processing_Level :Level 4source :Aquarius V5.0 Level 2 SSS; SMAP RSS V4.0 Level 2 SSS_40km; SMOS Level 2 SSS L2OS version 662sourse_of_input_Aquarius_SSS :Aquarius Official Release Level 2 Sea Surface Salinity & Wind Speed Cal Data V5.0. Distributed by PO.DAAC at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L2_SSS_CAL_V5sourse_of_input_SMAP_SSS :Meissner, T., F. Wentz, A. Manaster, R. Lindsley, 2019. Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 4.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA, Available online at www.remss.com/missions/smap.sourse_of_input_SMOS_SSS :ESA SMOS online dissemination service at https://smos-diss.eo.esa.int/oads/accessplatform :Aquarius/SAC-D, SMAP, SMOSinstrument :Aquarius radiometer, SMAP radiometer, SMOS MIRASCreation_Date :2023-01-16T04:04:41ZCreator_Name :Oleg MelnichenkoCreator_Email :oleg@hawaii.eduCreator_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBProject :NASA Ocean SalinityKeywords :Sea Surface Salinity, SSS, Aquarius, SMAP, Optimum Interpolation, OISSSKeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science KeywordsInstitution :IPRC/SOEST, University of Hawaii, Honolulu, HI; Remote Sensing Systems (RSS), Santa Rosa, CAPublisher_Name :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzPublisher_Email :oleg@hawaii.edu.orgPublisher_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBDataset_Citation_Authors :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzDataset_Citation_Year :2021Dataset_Citation_Product :Aquarius/SMAP Sea Surface Salinity Optimum Interpolation AnalysisTechnical_Notes :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdfyear_of_observation :2022month_of_observation :3day_of_observation :11time_coverage_start :2022-03-07T12:00:00Ztime_coverage_end :2022-03-15T12:00:00Ztime_coverage_resolution :P7Dcdm_data_type :gridgeospatial_lat_min :-90.0geospatial_lat_max :90.0geospatial_lat_resolution :0.25geospatial_lat_units :degrees_northgeospatial_lon_min :-180.0geospatial_lon_max :180.0geospatial_lon_resolution :0.25geospatial_lon_units :degrees_east\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data at the outlet of the Amazon to plot at time t=0 (August 28, 2011) with hvplot.\n\nlat_bnds, lon_bnds = [-2, 6], [-52, -44] \nds_sss_subset = ds_sss.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))\nds_sss_subset\n\nds_sss_subset.sss[:,:,0].hvplot()"
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+ "href": "notebooks/meetings_workshops/workshop_osm_2022/S6_OPeNDAP_Access_Gridding.html#access",
+ "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
+ "section": "Access",
+ "text": "Access\nThese functions have nothing to do with Earthdata or PO.DAAC services, all Python 3 standard library except for tqdm and wget:\n\ndef download(source: str):\n target = os.path.basename(source.split(\"?\")[0])\n if not os.path.isfile(target):\n !wget --quiet --continue --output-document $target $source\n return target\n\ndef download_all(urls: list, max_workers: int=12):\n with ThreadPoolExecutor(max_workers=max_workers) as pool:\n workers = pool.map(download, urls)\n return list(tqdm.tqdm(workers, total=len(urls)))\n\n\nExplore dataset variables\nThe S6A level 2 altimetry datasets include variables for sea surface height anomaly (SSHA), significant wave height (SWH), wind speed, others.\n\nopendap_url = f\"https://opendap.earthdata.nasa.gov/collections/{concept_id}\"\n\nurls = granules.GranuleUR.apply(lambda f: f\"{opendap_url}/granules/{f}.nc4\")\n \nurls.iloc[0].replace(\".nc4\", \".html\")\n\n'https://opendap.earthdata.nasa.gov/collections/C1968980576-POCLOUD/granules/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.html'\n\n\n\n\nGet subsets from OPeNDAP\nPrepare the full request urls by adding a comma-delimited list of variables, after the question mark ?.\n\nvariables = ['data_01_time',\n 'data_01_longitude',\n 'data_01_latitude',\n 'data_01_ku_ssha']\n\nquery = \",\".join(variables)\n\nreqs = urls.apply(lambda x: f\"{x}?{query}\")\n\nprint(reqs.iloc[0])\n\nhttps://opendap.earthdata.nasa.gov/collections/C1968980576-POCLOUD/granules/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc4?data_01_time,data_01_longitude,data_01_latitude,data_01_ku_ssha\n\n\nThe function(s) below download granules from a remote source to a local target file, and reliably manage simultaneous streaming downloads divided between multiple threads.\n\nfiles = download_all(urls=reqs, max_workers=12)\n\n100%|██████████| 125/125 [00:53<00:00, 2.32it/s]\n\n\n\n!du -sh .\n\n16M .\n\n\nWhy this way?\nTo be explained…\nWill it scale?\nThe source netcdf files range from 2500KB to 3000KB per file. The OPeNDAP subsets that we just downloaded are around 100KB a pop. It took less than 10 minutes to download the same subsets for ~1700 files, that covers a period of roughly You can extrapolate to a reasonable estimate for time series of any length (even the whole mission).\nTotal size of the source data is ~4.25GB, based on:\n2500KB x 1700 = 4250000KB (4250 megabytes)\nversus, total size of the subset time series:\n100KB x 1700 = 17000KB (170 megabytes)\nPlot it to put this in context, because our goal is to produce one global grid for the entire cycle of data that we just downloaded.\n\n\nOpen, plot ssh time series\nSort the list of subset files to ensure they concatenate in proper order. Call open_mfdataset on the list to open all the subsets in memory as one dataset in xarray.\n\nds = xr.open_mfdataset(sorted(files), engine=\"netcdf4\")\n\nprint(ds)\n\n<xarray.Dataset>\nDimensions: (data_01_time: 827001)\nCoordinates:\n * data_01_time (data_01_time) datetime64[ns] 2021-07-13T16:26:45 ... ...\nData variables:\n data_01_longitude (data_01_time) float64 dask.array<chunksize=(6950,), meta=np.ndarray>\n data_01_latitude (data_01_time) float64 dask.array<chunksize=(6950,), meta=np.ndarray>\n data_01_ku_ssha (data_01_time) float64 dask.array<chunksize=(6950,), meta=np.ndarray>\nAttributes: (12/63)\n Convention: CF-1.7\n institution: EUMETSAT\n references: Sentinel-6_Jason-CS ALT Generic P...\n contact: ops@eumetsat.int\n radiometer_sensor_name: AMR-C\n doris_sensor_name: DORIS\n ... ...\n xref_solid_earth_tide: S6__P4_2__SETD_AX_20151008T000000...\n xref_surface_classification: S6__P4____SURF_AX_20151008T000000...\n xref_wind_speed_alt: S6A_P4_2__WNDL_AX_20151008T000000...\n product_name: S6A_P4_2__LR______20210713T162644...\n history: 2021-07-13 18:38:07 : Creation\\n2...\n history_json: [{\"$schema\":\"https:\\/\\/harmony.ea...\n\n\nTwo prerequisites to plot based on personal preference:\n\nrename all the variables to drop the group names (because I just think they’re too long as is)\nget a tuple with two timestamps for the start and end of the time series coverage for the cycle\n\nPlot the cycle as a series on a geographic plot, which should look just like the one at the top of this notebook:\n\nnew_variable_names = list(map(lambda x: x.split(\"_\")[-1], variables))\nmap_variable_names = dict(zip(variables, new_variable_names))\nds = ds.rename(map_variable_names).set_coords(['time','longitude','latitude']) # rename variables\n\ntimeframe = (str(ds.time.data[0]).split('T')[0],\n str(ds.time.data[-1]).split('T')[0]) # get timestamps tuple\n\nds.plot.scatter(y=\"latitude\", x=\"longitude\", hue=\"ssha\", \n vmin=-0.4, vmax=0.4, cmap=\"jet\", levels=9, \n aspect=2.5, size=6, s=1, )\nplt.ylim(-67., 67.)\nplt.xlim(0., 360.)\nplt.tight_layout()\nplt.title(f\"ssha for cycle {cycle} ({timeframe})\")\n\nText(0.5, 1.0, \"ssha for cycle 25 (('2021-07-13', '2021-07-23'))\")"
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- "title": "Amazon Estuary Exploration:",
- "section": "Sea Surface Temperature (MODIS)",
- "text": "Sea Surface Temperature (MODIS)\nMODIS has SST data with separate files on a monthly basis. By using earthaccess search with the shortname: “MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0”, and filtering by time to coincide when SSS started (2011), we access 143 granules.\nGet a list of files so we can open them all at once, creating an xarray dataset using the open_mfdataset() function to “read in” all of the netCDF4 files in one call. MODIS does not have a built-in time variable like SSS, but it is subset by latitude and longitude coordinates. We need to combine the files using the nested format with a created ‘time’ dimension.\n\nsss_results = earthaccess.search_data(short_name=\"MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0\", temporal = (\"2011-01-01\", \"2023-01-01\"))\n\nGranules found: 143\n\n\n\nsss_files = earthaccess.download(sss_results, \"./modis_data\")\n\n Getting 143 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\nMODIS did not come with a time variable, so it needs to be extracted from the file names and added in the file preprocessing so files can be successfully concatenated.\n\n#repeat this step since we are accessing multiple granules\nmodis_path = [os.path.join(\"./modis_data\", f) \n for pth, dirs, files in os.walk(\"./modis_data\") for f in files]\n\n\n#function for time dimension added to each netCDF file\ndef preprocessing(ds): \n file_name = ds.product_name \n file_date = basename(file_name).split(\"_\")[2][:6]\n file_date_c = datetime.strptime(file_date, \"%Y%m\")\n time_point = [file_date_c]\n ds.coords['time'] = ('time', time_point) #expand the dimensions to include time\n return ds\n\nds_MODIS = xr.open_mfdataset(modis_path, combine='by_coords', join='override', preprocess = preprocessing)\nds_MODIS\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 143, lat: 2160, lon: 4320, rgb: 3, eightbitcolor: 256)\nCoordinates:\n * lat (lat) float32 89.96 89.88 89.79 89.71 ... -89.79 -89.88 -89.96\n * lon (lon) float32 -180.0 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0\n * time (time) datetime64[ns] 2011-01-01 2011-02-01 ... 2023-01-01\nDimensions without coordinates: rgb, eightbitcolor\nData variables:\n sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n qual_sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n palette (time, lon, lat, rgb, eightbitcolor) uint8 dask.array<chunksize=(1, 4320, 2160, 3, 256), meta=np.ndarray>\nAttributes: (12/59)\n product_name: AQUA_MODIS.20110101_20110131.L3m.MO.SST...\n instrument: MODIS\n title: MODISA Level-3 Standard Mapped Image\n project: Ocean Biology Processing Group (NASA/GS...\n platform: Aqua\n temporal_range: month\n ... ...\n publisher_url: https://oceandata.sci.gsfc.nasa.gov\n processing_level: L3 Mapped\n cdm_data_type: grid\n data_bins: 4834400\n data_minimum: -1.635\n data_maximum: 32.06999xarray.DatasetDimensions:time: 143lat: 2160lon: 4320rgb: 3eightbitcolor: 256Coordinates: (3)lat(lat)float3289.96 89.88 89.79 ... -89.88 -89.96long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0array([ 89.958336, 89.875 , 89.79167 , ..., -89.791664, -89.87501 ,\n -89.958336], dtype=float32)lon(lon)float32-180.0 -179.9 ... 179.9 180.0long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0array([-179.95833, -179.875 , -179.79166, ..., 179.79167, 179.87502,\n 179.95836], dtype=float32)time(time)datetime64[ns]2011-01-01 ... 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'2020-08-01T00:00:00.000000000',\n '2020-09-01T00:00:00.000000000', '2020-10-01T00:00:00.000000000',\n '2020-11-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000',\n '2021-01-01T00:00:00.000000000', '2021-02-01T00:00:00.000000000',\n '2021-03-01T00:00:00.000000000', '2021-04-01T00:00:00.000000000',\n '2021-05-01T00:00:00.000000000', '2021-06-01T00:00:00.000000000',\n '2021-07-01T00:00:00.000000000', '2021-09-01T00:00:00.000000000',\n '2021-10-01T00:00:00.000000000', '2021-11-01T00:00:00.000000000',\n '2021-12-01T00:00:00.000000000', '2022-01-01T00:00:00.000000000',\n '2022-02-01T00:00:00.000000000', '2022-03-01T00:00:00.000000000',\n '2022-04-01T00:00:00.000000000', '2022-05-01T00:00:00.000000000',\n '2022-06-01T00:00:00.000000000', '2022-07-01T00:00:00.000000000',\n '2022-08-01T00:00:00.000000000', '2022-09-01T00:00:00.000000000',\n '2022-10-01T00:00:00.000000000', '2022-11-01T00:00:00.000000000',\n '2023-01-01T00:00:00.000000000'], dtype='datetime64[ns]')Data variables: (3)sst4(time, lat, lon)float32dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>long_name :4um Sea Surface Temperatureunits :degree_Cstandard_name :sea_surface_temperaturevalid_min :-1000valid_max :10000display_scale :lineardisplay_min :-2.0display_max :45.0\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n4.97 GiB\n35.60 MiB\n\n\nShape\n(143, 2160, 4320)\n(1, 2160, 4320)\n\n\nCount\n572 Tasks\n143 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nqual_sst4\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nQuality Levels, Sea Surface Temperature\n\nvalid_min :\n\n0\n\nvalid_max :\n\n5\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n4.97 GiB\n35.60 MiB\n\n\nShape\n(143, 2160, 4320)\n(1, 2160, 4320)\n\n\nCount\n572 Tasks\n143 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\npalette\n\n\n(time, lon, lat, rgb, eightbitcolor)\n\n\nuint8\n\n\ndask.array<chunksize=(1, 4320, 2160, 3, 256), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n0.93 TiB\n6.67 GiB\n\n\nShape\n(143, 4320, 2160, 3, 256)\n(1, 4320, 2160, 3, 256)\n\n\nCount\n858 Tasks\n143 Chunks\n\n\nType\nuint8\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (3)latPandasIndexPandasIndex(Float64Index([ 89.95833587646484, 89.875, 89.79167175292969,\n 89.70833587646484, 89.625, 89.54167175292969,\n 89.45833587646484, 89.375, 89.29167175292969,\n 89.20833587646484,\n ...\n -89.20833587646484, -89.29166412353516, -89.37500762939453,\n -89.45833587646484, -89.54166412353516, -89.62500762939453,\n -89.70833587646484, -89.79166412353516, -89.87500762939453,\n -89.95833587646484],\n dtype='float64', name='lat', length=2160))lonPandasIndexPandasIndex(Float64Index([ -179.9583282470703, -179.875, -179.79165649414062,\n -179.7083282470703, -179.625, -179.54165649414062,\n -179.4583282470703, -179.375, -179.29165649414062,\n -179.2083282470703,\n ...\n 179.20835876464844, 179.2916717529297, 179.37501525878906,\n 179.45835876464844, 179.5416717529297, 179.62501525878906,\n 179.70835876464844, 179.7916717529297, 179.87501525878906,\n 179.95835876464844],\n dtype='float64', name='lon', length=4320))timePandasIndexPandasIndex(DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',\n '2011-05-01', '2011-06-01', '2011-07-01', '2011-08-01',\n '2011-09-01', '2011-10-01',\n ...\n '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',\n '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',\n '2022-11-01', '2023-01-01'],\n dtype='datetime64[ns]', name='time', length=143, freq=None))Attributes: (59)product_name :AQUA_MODIS.20110101_20110131.L3m.MO.SST4.sst4.9km.ncinstrument :MODIStitle :MODISA Level-3 Standard Mapped Imageproject :Ocean Biology Processing Group (NASA/GSFC/OBPG)platform :Aquatemporal_range :monthprocessing_version :R2019.0date_created :2019-12-17T18:28:57.000Zhistory :l3mapgen par=AQUA_MODIS.20110101_20110131.L3m.MO.SST4.sst4.9km.nc.param l2_flag_names :LAND,~HISOLZENtime_coverage_start :2010-12-31T12:10:01.000Ztime_coverage_end :2011-01-31T14:29:59.000Zstart_orbit_number :46065end_orbit_number :46518map_projection :Equidistant Cylindricallatitude_units :degrees_northlongitude_units :degrees_eastnorthernmost_latitude :90.0southernmost_latitude :-90.0westernmost_longitude :-180.0easternmost_longitude :180.0geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lon_max :180.0geospatial_lon_min :-180.0latitude_step :0.083333336longitude_step :0.083333336sw_point_latitude :-89.958336sw_point_longitude :-179.95833spatialResolution :9.28 kmgeospatial_lon_resolution :0.083333336geospatial_lat_resolution :0.083333336geospatial_lat_units :degrees_northgeospatial_lon_units :degrees_eastnumber_of_lines :2160number_of_columns :4320measure :Meansuggested_image_scaling_minimum :-2.0suggested_image_scaling_maximum :45.0suggested_image_scaling_type :LINEARsuggested_image_scaling_applied :No_lastModified :2019-12-17T18:28:57.000ZConventions :CF-1.6 ACDD-1.3institution :NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Groupstandard_name_vocabulary :CF Standard Name Table v36naming_authority :gov.nasa.gsfc.sci.oceandataid :AQUA_MODIS.20110101_20110131.L3b.MO.SST4.nc/L3/AQUA_MODIS.20110101_20110131.L3b.MO.SST4.nclicense :https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/creator_name :NASA/GSFC/OBPGpublisher_name :NASA/GSFC/OBPGcreator_email :data@oceancolor.gsfc.nasa.govpublisher_email :data@oceancolor.gsfc.nasa.govcreator_url :https://oceandata.sci.gsfc.nasa.govpublisher_url :https://oceandata.sci.gsfc.nasa.govprocessing_level :L3 Mappedcdm_data_type :griddata_bins :4834400data_minimum :-1.635data_maximum :32.06999"
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+ "href": "notebooks/meetings_workshops/workshop_osm_2022/S6_OPeNDAP_Access_Gridding.html#process",
+ "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
+ "section": "Process",
+ "text": "Process\n\n0.5-degree grid from ECCO V4r4 (int)\n\nAcknowledgement: pyresample approach shared by Ian Fenty, NASA JPL/ECCO.\n\nECCO V4r4 products are distributed in two spatial formats. One set of collections provides the ocean state estimates on the native model grid (LLC0090) and the other provides them after interpolating to a regular grid defined in geographic coordinates with horizontal cell size of 0.5-degrees. The latitude/longitude grid is distributed as its own collection in one netcdf file: https://search.earthdata.nasa.gov/search/granules?p=C2013583732-POCLOUD\nDownload the ECCO grid geometry netcdf from its https download endpoint in NASA Earthdata Cloud. Open the file and print the header content for the maskC variable, which contains a boolean mask representing the wet/dry state of the area contained in each cell of a 3d grid with dimensions mapped to Z, latitude, and longitude.\n\necco_file = download(\"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/\"\n \"ECCO_L4_GEOMETRY_05DEG_V4R4/GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg.nc\")\n\necco_grid = xr.open_dataset(ecco_file).isel(Z=0) # select 0 on z-axis\n\necco_mask = ecco_grid.maskC\n\nprint(ecco_grid)\n\n<xarray.Dataset>\nDimensions: (latitude: 360, longitude: 720, nv: 2)\nCoordinates:\n Z float32 -5.0\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8\n latitude_bnds (latitude, nv) float32 ...\n longitude_bnds (longitude, nv) float32 ...\n Z_bnds (nv) float32 ...\nDimensions without coordinates: nv\nData variables:\n hFacC (latitude, longitude) float64 ...\n Depth (latitude, longitude) float64 ...\n area (latitude, longitude) float64 ...\n drF float32 ...\n maskC (latitude, longitude) bool ...\nAttributes: (12/57)\n acknowledgement: This research was carried out by the Jet...\n author: Ian Fenty and Ou Wang\n cdm_data_type: Grid\n comment: Fields provided on a regular lat-lon gri...\n Conventions: CF-1.8, ACDD-1.3\n coordinates_comment: Note: the global 'coordinates' attribute...\n ... ...\n references: ECCO Consortium, Fukumori, I., Wang, O.,...\n source: The ECCO V4r4 state estimate was produce...\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadat...\n summary: This dataset provides geometric paramete...\n title: ECCO Geometry Parameters for the 0.5 deg...\n uuid: b4795c62-86e5-11eb-9c5f-f8f21e2ee3e0\n\n\n\n\nGet ssha variable on 0.5-degree grid\nResample ssha data using kd-tree gaussian weighting neighbour approach. Define a function that implements the following steps:\n\nGenerate two 2d arrays of lats/lons using the permuted 1d coordinates from an input gridded dataset.\nDefine the target grid geometry using the 2d arrays of lats/lons.\nDefine the source grid geometry using the 1d arrays of lats/lons from an input dataset.\n\n\nimport warnings\n\ndef l2alt2grid(source: xr.DataArray, target: xr.DataArray, **options):\n nans = ~np.isnan(source.values)\n data = source.values[nans]\n\n lats = source.latitude.values[nans]\n lons = (source.longitude.values[nans] + 180) % 360 - 180\n src = pr.SwathDefinition(lons, lats)\n\n lons1d = target.longitude.values\n lats1d = target.latitude.values\n lons2d, lats2d = np.meshgrid(lons1d, lats1d)\n tgt = pr.SwathDefinition(lons2d, lats2d)\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=UserWarning)\n res, std, cnt = resample_gauss(src, data, tgt, **options)\n \n coords = {'latitude': lats1d, 'longitude': lons1d}\n return (xr.DataArray(res, coords=coords),\n xr.DataArray(std, coords=coords),\n xr.DataArray(cnt, coords=coords), )\n\nSet the gridding parameters in the python dictionary below; then pass it to the function as the last of three required positional arguments (the first two are the source dataset and the dataset that provides the target grid geometry).\n\ngridding_options = dict(\n radius_of_influence = 175000, \n sigmas = 25000,\n neighbours = 100,\n fill_value = np.nan,\n with_uncert = True\n)\n\nresult, stddev, counts = l2alt2grid(ds.ssha, ecco_mask, **gridding_options)\n\nresult.shape == (ecco_grid.latitude.size, \n ecco_grid.longitude.size)\n\nTrue\n\n\n\n\nPlot gridded ssha, gridding statistics\nPlot each array for the output ‘grid’ and the grid statistics ‘stddev’ and ‘counts’.\n\nresult.sel(latitude=slice(-66.,66.)).plot(cmap=\"jet\", vmin=-0.4, vmax=0.4, figsize=(15,6))\n\n<matplotlib.collections.QuadMesh at 0x7f47d0256520>\n\n\n\n\n\nLook at this plot and adjust gridding parameters as needed to refine ssha grid.\n\nstddev.sel(latitude=slice(-67.,67.)).plot(cmap=\"jet\", robust=True, figsize=(15,6))\n\n<matplotlib.collections.QuadMesh at 0x7f47e0302e50>\n\n\n\n\n\na guess: the yellow areas with lower counts correspond to pass positions at the start/end of the cycle.\n\ncounts.sel(latitude=slice(-67.,67.)).plot(cmap=\"jet\", robust=True, figsize=(15,6))\n\n<matplotlib.collections.QuadMesh at 0x7f47d0f560a0>\n\n\n\n\n\n\nresult.sel(latitude=slice(-66.,66.)).to_pandas().T.describe().T\n\n\n\n\n\n\n\n\ncount\nmean\nstd\nmin\n25%\n50%\n75%\nmax\n\n\nlatitude\n\n\n\n\n\n\n\n\n\n\n\n\n-65.75\n304.0\n0.209026\n0.830608\n-1.471174\n-0.069080\n0.039957\n0.259600\n6.104942\n\n\n-65.25\n316.0\n0.216497\n0.479879\n-0.793696\n0.002569\n0.080189\n0.301957\n3.173239\n\n\n-64.75\n326.0\n0.218253\n0.465126\n-0.593413\n0.008729\n0.085963\n0.296550\n2.885002\n\n\n-64.25\n332.0\n0.090540\n0.453919\n-0.909328\n-0.030323\n0.040090\n0.097437\n2.914544\n\n\n-63.75\n371.0\n0.029598\n0.379806\n-0.884937\n-0.064511\n0.001728\n0.062316\n2.879200\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n63.75\n310.0\n0.034539\n0.133022\n-0.580132\n-0.010876\n0.038027\n0.081060\n0.806144\n\n\n64.25\n314.0\n0.018076\n0.118825\n-0.690086\n-0.016610\n0.036694\n0.077421\n0.286705\n\n\n64.75\n311.0\n-0.085785\n1.264886\n-19.397700\n-0.022718\n0.041526\n0.080252\n1.397690\n\n\n65.25\n307.0\n4.177751\n42.431829\n-23.955439\n-0.043362\n0.043002\n0.090967\n505.812000\n\n\n65.75\n290.0\n3.392658\n34.873463\n-10.643720\n-0.075147\n0.058857\n0.105139\n430.164759\n\n\n\n\n264 rows × 8 columns"
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- "text": "Time Series Comparison\nPlot each dataset for the time period 2011-2019.\nFirst, we need to average all pixels in the subset lat/lon per time for sea surface salinity and sea surface temperature to set up for the graphs.\n\nsss_mean = []\nfor t in np.arange(len(ds_sss_subset.time)):\n sss_mean.append(np.nanmean(ds_sss_subset.sss[:,:,t].values))\n\n#sss_mean\n\n\n#MODIS\nsst_MODIS_mean = []\nfor t in np.arange(len(ds_MODIS_subset.time)):\n sst_MODIS_mean.append(np.nanmean(ds_MODIS_subset.sst4[t,:,:].values))\n \n#sst_MODIS_mean\n\n\nCombined timeseries plot of river height and LWE thickness\nBoth datasets are mapped for the outlet of the Amazon River into the estuary.\n\n#plot river height and land water equivalent thickness\nfig, ax1 = plt.subplots(figsize=[12,7])\n\n#plot river height\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\n\n#plot LWE thickness on secondary axis\nax2 = ax1.twinx()\nax2.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax1.set_ylabel('River Height (m)', color='darkblue')\nax2.legend(['GRACE-FO'], loc='upper right')\nax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')\n\nplt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')\nax1.grid()\nplt.show()\n\n\n\n\nLWE thickness captures the seasonality of Pre-SWOT MEaSUREs river heights well, and so LWE thickness can be compared to all other variables as a representative of the seasonality of both measurements for the purpose of this notebook.\n\n\nCombined timeseries plots of salinity and LWE thickness, followed by temperature\n\n#Combined Subplots\nfig = plt.figure(figsize=(10,10))\n\nax1 = fig.add_subplot(211)\nplt.title('Amazon Estuary, 2011-2019')\nax2 = ax1.twinx()\nax3 = plt.subplot(212)\nax4 = ax3.twinx()\n\n#lwe thickness\nax1.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\nax1.set_ylabel('LWE Thickness (cm)', color='darkorange')\nax1.grid()\n\n#sea surface salinity\nax2.plot(ds_sss_subset.time[0:750], sss_mean[0:750], 'g')\nax2.set_ylabel('SSS (psu)', color='g')\n\n#sea surface temperature\nax3.plot(ds_MODIS_subset.time[7:108], sst_MODIS_mean[7:108], 'darkred')\nax3.set_ylabel('SST (deg C)', color='darkred')\nax3.grid()\n\n#river height at outlet\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\nax4.set_ylabel('River Height (m)', color='darkblue')\n\nText(0, 0.5, 'River Height (m)')\n\n\n\n\n\nMeasurements of LWE thickness and SSS follow expected patterns. When lwe thickness is at its lowest, indicating less water is flowing through during the drought, salinity is at its highest. Without high volume of water pouring into the estuary, salinity increases. We can see that temperature is shifted a bit in time from river height as well at the outlet, a relationship that could be further explored."
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+ "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
+ "section": "Appendix",
+ "text": "Appendix\n\nSentinel-6A MF\nThe Sentinel-6A Michael Freilich radar altimeter mission, or Sentinel-6, produces high-precision measurements of global sea-level. You can learn about the mission and ocean altimetry applications and datasets through the following links:\n\nhttps://www.nasa.gov/sentinel-6\nhttps://sealevel.jpl.nasa.gov/missions/jasoncs/\nhttps://sentinel-6.cnes.fr/en/JASON-CS/index.htm\nhttps://podaac.jpl.nasa.gov/Sentinel-6\n\n\n\nOcean Surface Topography\nThe primary contribution of satellite altimetry to satellite oceanography has been to:\n\nImprove the knowledge of ocean tides and develop global tide models.\nMonitor the variation of global mean sea level and its relationship to changes in ocean mass and heat content.\nMap the general circulation variability of the ocean, including the ocean mesoscale, over decades and in near real-time using multi-satellite altimetric sampling.\n\n\n\n\naltimetry\n\n\nThe Surface Water Ocean Topography (SWOT) mission represents the next-generation of sea surface height observation. It will bring together oceanography and hydrology to focus on gaining a better understanding of the world’s oceans and its terrestrial surface waters. U.S. and French oceanographers and hydrologists have joined forces to develop this new space mission to make the first global survey of Earth’s surface water, observe the fine details of the ocean’s surface topography and measure how water bodies change over time. The payload on SWOT will include a Jason-class radar altimeter that will serve to extend the time series of sea surface height data into the future, beyond the lifespan of Sentinel-6 MF, which is introduced immediately below. Read more about SWOT at: https://podaac.jpl.nasa.gov/SWOT/\n\n\nEarthdata Cloud Services Overview\nThis workflow example downloads subsets of the netcdf datasets via OPeNDAP for massive efficiency gains (network/compute).\nAccess for direct download:\n\nBrowse and download granules through Earthdata Search – https://search.earthdata.nasa.gov/search/granules?p=C1968980576-POCLOUD\nBrowse and download granules from HTTPS endpoints – https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1968980576-POCLOUD\nBrowse and download granules from S3 endpoints (example forthcoming, assuming s3 direct access has been enabled for the collection)\n\nAccess through data services:\nData and metadata are also accessible in reduced forms through higher-level cloud data services, for example:\n\nhttps://harmony.earthdata.nasa.gov/ – Data reduction via on-demand subsetting and other high-level reformatting\n\nInterface to backend services such as data file format conversion, subsetting at L2+, regridding and reprojection at L3+, and more.\nCompatibility depends on the data processing level and data/file format, and so their expected behavior vary also.\nServices available through Harmony API reduce the technical burden on users by covering certain low-level data transformations that a user would normally have to apply themselves, even to simply subset a dataset from OPeNDAP.\n\nhttps://opendap.earthdata.nasa.gov/ – Data reduction via basic subsetting along coordinate dimensions and by variable\n\nRequires more familiarity with the contents of the target dataset, as well knowledge of how to select for data along the dimensions which correspond to space/time coordinates fitting the geographic and temporal coverage of interest.\nUser Guide: https://opendap.github.io/documentation/UserGuideComprehensive.html\n\n\n\n\nPython API References\n\nBash\n\nhttps://www.gnu.org/software/coreutils/manual/html_node/du-invocation.html\n\nPython\n\nhttps://docs.python.org/3/library/functions.html#map\n\nhttps://docs.python.org/3/library/functions.html#zip\nhttps://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor\n\nnumpy (https://numpy.org/doc/stable/reference)\n\nnumpy.ndarray.data\n\nnumpy.where\n\nnumpy.isnan\n\ndatetimes\n\nnumpy.sum\n\nnumpy.nansum\n\n\nxarray (https://xarray.pydata.org/en/stable)\n\nxarray.DataArray\n\nxarray.DataArray.values\n\nxarray.DataArray.mean\n\nxarray.DataArray.isel\nxarray.open_dataset\nxarray.DataArray.plot\nxarray.Dataset.rename\n\npyresample\n\npyresample.geometry.SwathDefinition\npyresample.kd_tree.resample_gauss"
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"text": "Time Series Comparison\nPlot each dataset for the time period 2011-2019.\nFirst, we need to average all pixels in the subset lat/lon per time for sea surface salinity and sea surface temperature to set up for the graphs.\n\nsss_mean = []\nfor t in np.arange(len(ds_sss_subset.time)):\n sss_mean.append(np.nanmean(ds_sss_subset.sss[:,:,t].values))\n\n#sss_mean\n\n\n#MODIS\nsst_MODIS_mean = []\nfor t in np.arange(len(ds_MODIS_subset.time)):\n sst_MODIS_mean.append(np.nanmean(ds_MODIS_subset.sst4[t,:,:].values))\n \n#sst_MODIS_mean\n\n\nCombined timeseries plot of river height and LWE thickness\nBoth datasets are mapped for the outlet of the Amazon River into the estuary.\n\n#plot river height and land water equivalent thickness\nfig, ax1 = plt.subplots(figsize=[12,7])\n\n#plot river height\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\n\n#plot LWE thickness on secondary axis\nax2 = ax1.twinx()\nax2.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax1.set_ylabel('River Height (m)', color='darkblue')\nax2.legend(['GRACE-FO'], loc='upper right')\nax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')\n\nplt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')\nax1.grid()\nplt.show()\n\n\n\n\nLWE thickness captures the seasonality of Pre-SWOT MEaSUREs river heights well, and so LWE thickness can be compared to all other variables as a representative of the seasonality of both measurements for the purpose of this notebook.\n\n\nCombined timeseries plots of salinity and LWE thickness, followed by temperature\n\n#Combined Subplots\nfig = plt.figure(figsize=(10,10))\n\nax1 = fig.add_subplot(211)\nplt.title('Amazon Estuary, 2011-2019')\nax2 = ax1.twinx()\nax3 = plt.subplot(212)\nax4 = ax3.twinx()\n\n#lwe thickness\nax1.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\nax1.set_ylabel('LWE Thickness (cm)', color='darkorange')\nax1.grid()\n\n#sea surface salinity\nax2.plot(ds_sss_subset.time[0:750], sss_mean[0:750], 'g')\nax2.set_ylabel('SSS (psu)', color='g')\n\n#sea surface temperature\nax3.plot(ds_MODIS_subset.time[7:108], sst_MODIS_mean[7:108], 'darkred')\nax3.set_ylabel('SST (deg C)', color='darkred')\nax3.grid()\n\n#river height at outlet\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\nax4.set_ylabel('River Height (m)', color='darkblue')\n\nText(0, 0.5, 'River Height (m)')\n\n\n\n\n\nMeasurements of LWE thickness and SSS follow expected patterns. When lwe thickness is at its lowest, indicating less water is flowing through during the drought, salinity is at its highest. Without high volume of water pouring into the estuary, salinity increases. We can see that temperature is shifted a bit in time from river height as well at the outlet, a relationship that could be further explored."
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+ "title": "SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud",
"section": "",
- "text": "Follow these instructions on the Earthdata Wiki to authenticate and store your URS cookies in a local file. You can batch download really efficiently this way, effectively “pre-authenticated” through your previous session.\n\n\n\nNow the only step that remains is to get a list of URLs to pass to wget or curl for downloading. There’s a lot of ways to do this but here we will rely on Earthdata Search.\n1. Find the collection/dataset of interest in Earthdata Search.\nStart by searching for your collection of interest. Here we are providing an ECCO example from this complete list of ECCO collections in Earthdata Search (79 in total), and refine the results until we see the dataset of interest. In this example we want monthly sea surface height grids provided at 0.5-degree cell resolution on an interpolated latitude/longitude grid.\n2. Pick your collection, then click the green Download All button on the next page.\nClick the big green button identified by the red arrow/box in the screenshot below.\n\nThat will add all the granules in the collection to your “shopping cart” and then redirect you straight there and present you with the available options for customizing the data prior to download. We will ignore those because they’re mostly in active development and because we want to download all data in the collection.\n\n\n\nThe screenshot above shows the download customization interface (i.e. “shopping cart”)\n\n\n3. Click Download Data to get your list of download urls (bottom-left, another green button)\nThe Download Data button takes you to one final page that provides the list of urls from which to download the files matching your search parameters and any customization options that you selected in the steps that followed. This page will be retained in your User History in case you need to return to it later.\n\nThere are several ways that you could get the list of urls into a text file that’s accessible from Jupyter or your local shell. Click the Save button to download the list of files to a text file. This will by default save it with a name like 5237392644-download.txt (numbers will be different for each download job).\n\nNote: Earthdata Search also provides a shell script for downloading this list of files, accessible from the “Download Script” tab.\n\n\n\n\nThe key wget option for this purpose is specified using the -i argument – it takes the path to the text file containing the download urls.\nAnother nice feature of wget is the capability to continue downloads started during a previous session if they were interrupted. Pass -c to enable.\nMake a data/ directory, then run wget and give its path to the -P argument to download the files into that directory:\nmkdir data\n\nwget --no-verbose \\\n --no-clobber \\\n --continue \\\n -i 5237392644-download.txt -P data/"
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
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- "text": "Follow these instructions on the Earthdata Wiki to authenticate and store your URS cookies in a local file. You can batch download really efficiently this way, effectively “pre-authenticated” through your previous session."
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+ "title": "SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud",
+ "section": "Accessing and Visualizing SWOT Simulated Datasets",
+ "text": "Accessing and Visualizing SWOT Simulated Datasets\n\nRequirement:\nThis tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\nLearning Objectives:\n\nAccess all 5 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine\nVisualize accessed data\n\n\n\nSWOT Simulated Level 2 North America Continent KaRIn High Rate Version 1 Datasets:\n\nRiver Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RSP1\n\n\nLake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2LSP1\n\n\nWater Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PIX1\n\n\nWater Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2PXV1\n\n\nRaster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1\n\n\nDOI: https://doi.org/10.5067/KARIN-2RAS1\n\nNotebook Author: Cassie Nickles, NASA PO.DAAC (Aug 2022)\n\n\nLibraries Needed\n\nimport glob\nimport os\nimport requests\nimport s3fs\nimport netCDF4 as nc\nimport h5netcdf\nimport xarray as xr\nimport pandas as pd\nimport geopandas as gpd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport hvplot.xarray\nimport shapefile as shp\nimport zipfile"
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- "title": "Instructions for HTTPS download from the PO.DAAC and NASA Earthdata",
- "section": "",
- "text": "Now the only step that remains is to get a list of URLs to pass to wget or curl for downloading. There’s a lot of ways to do this but here we will rely on Earthdata Search.\n1. Find the collection/dataset of interest in Earthdata Search.\nStart by searching for your collection of interest. Here we are providing an ECCO example from this complete list of ECCO collections in Earthdata Search (79 in total), and refine the results until we see the dataset of interest. In this example we want monthly sea surface height grids provided at 0.5-degree cell resolution on an interpolated latitude/longitude grid.\n2. Pick your collection, then click the green Download All button on the next page.\nClick the big green button identified by the red arrow/box in the screenshot below.\n\nThat will add all the granules in the collection to your “shopping cart” and then redirect you straight there and present you with the available options for customizing the data prior to download. We will ignore those because they’re mostly in active development and because we want to download all data in the collection.\n\n\n\nThe screenshot above shows the download customization interface (i.e. “shopping cart”)\n\n\n3. Click Download Data to get your list of download urls (bottom-left, another green button)\nThe Download Data button takes you to one final page that provides the list of urls from which to download the files matching your search parameters and any customization options that you selected in the steps that followed. This page will be retained in your User History in case you need to return to it later.\n\nThere are several ways that you could get the list of urls into a text file that’s accessible from Jupyter or your local shell. Click the Save button to download the list of files to a text file. This will by default save it with a name like 5237392644-download.txt (numbers will be different for each download job).\n\nNote: Earthdata Search also provides a shell script for downloading this list of files, accessible from the “Download Script” tab."
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+ "href": "notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html#get-temporary-aws-credentials-for-access",
+ "title": "SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud",
+ "section": "Get Temporary AWS Credentials for Access",
+ "text": "Get Temporary AWS Credentials for Access\nS3 is an ‘object store’ hosted in AWS for cloud processing. Direct S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. Note, these temporary credentials are valid for only 1 hour. A netrc file is required to aquire these credentials. Use the NASA Earthdata Authentication to create a netrc file in your home directory. (Note: A NASA Earthdata Login is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.)\nThe following crediential is for PODAAC, but other credentials are needed to access data from other NASA DAACs.\n\ns3_cred_endpoint = 'https://archive.podaac.earthdata.nasa.gov/s3credentials'\n\nCreate a function to make a request to an endpoint for temporary credentials.\n\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\n\n\ntemp_creds_req = get_temp_creds()\n#temp_creds_req # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.\n\n\nSet up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint.\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})"
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- "href": "notebooks/batch_download_podaac_data.html#download-files-in-a-batch-with-gnu-wget",
- "title": "Instructions for HTTPS download from the PO.DAAC and NASA Earthdata",
- "section": "",
- "text": "The key wget option for this purpose is specified using the -i argument – it takes the path to the text file containing the download urls.\nAnother nice feature of wget is the capability to continue downloads started during a previous session if they were interrupted. Pass -c to enable.\nMake a data/ directory, then run wget and give its path to the -P argument to download the files into that directory:\nmkdir data\n\nwget --no-verbose \\\n --no-clobber \\\n --continue \\\n -i 5237392644-download.txt -P data/"
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+ "href": "notebooks/meetings_workshops/swot_ea_workshop_sept2022/SWOTHR_s3Access.html#single-file-access",
+ "title": "SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud",
+ "section": "Single File Access",
+ "text": "Single File Access\nThe s3 access link can be found using Earthdata Search (see tutorial) for a single file is as follows:\n1. River Vector Shapefiles\n\ns3_SWOT_HR_url1 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip'\n\n\ns3_file_obj1 = fs_s3.open(s3_SWOT_HR_url1, mode='rb')\n\nThe native format for this sample data is a .zip file, and we want the .shp file within the .zip file, so we need to download the contents of the zip file into the cloud environment. I created a folder called SWOT_HR_shp to write to. Change the path to where you would like your extracted files to be written.\n\nwith zipfile.ZipFile(s3_file_obj1, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\nNext, we’ll look at the attribute table of the .shp file we just extracted to the ‘SWOT_HR_shp’ folder.\n\nSWOT_HR_shp1 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shp') \nSWOT_HR_shp1\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n0\n71224300241\n7.145115e+08\n7.145114e+08\n2022-08-22T19:2441Z\n49.364818\n-94.879318\nno_data\n3.472248e+01\n-1.000000e+12\n1.511000e-02\n...\n7294.5\n3.265803e+06\n15\n390935.258\n3008.959150\n-1.000000e+12\n0\n15\n8\nLINESTRING (-94.86483 49.37485, -94.86515 49.3...\n\n\n1\n71224300253\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2446Z\n49.049486\n-94.899554\nno_data\n3.439994e+01\n-1.000000e+12\n7.600000e-03\n...\n394.5\n7.876447e+06\n42\n444613.943\n8411.845753\n-1.000000e+12\n0\n8\n1\nLINESTRING (-94.92557 49.08401, -94.92556 49.0...\n\n\n2\n71224300263\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2448Z\n48.977915\n-94.869598\nno_data\n3.434701e+01\n-1.000000e+12\n9.620000e-03\n...\n6365.5\n2.935181e+06\n42\n453020.631\n8406.687501\n-1.000000e+12\n0\n3\n1\nLINESTRING (-94.88015 49.01512, -94.88006 49.0...\n\n\n3\n71224300273\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2449Z\n48.902998\n-94.854720\nno_data\n3.416786e+01\n-1.000000e+12\n1.372000e-02\n...\n4650.0\n3.770782e+06\n43\n461636.940\n8616.309267\n-1.000000e+12\n0\n1\n1\nLINESTRING (-94.86229 48.94092, -94.86228 48.9...\n\n\n4\n71224300283\n7.145115e+08\n7.145115e+08\n2022-08-22T19:2450Z\n48.883377\n-94.783621\nno_data\n3.426341e+01\n-1.000000e+12\n5.050000e-03\n...\n10439.0\n2.952077e+07\n46\n470821.047\n9184.106587\n-1.000000e+12\n0\n5\n1\nLINESTRING (-94.73002 48.90430, -94.72952 48.9...\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n666\n74291700141\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2729Z\n39.811344\n-92.684233\nno_data\n3.869180e+01\n-1.000000e+12\n3.840000e-01\n...\n42.0\n1.060619e+02\n48\n2463828.961\n9553.659853\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68784 39.77391, -92.68804 39.7...\n\n\n667\n74291700151\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2727Z\n39.888856\n-92.683047\nno_data\n3.687848e+01\n-1.000000e+12\n1.897500e-01\n...\n42.0\n1.208373e+02\n48\n2473386.489\n9557.528221\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68497 39.84931, -92.68497 39.8...\n\n\n668\n74291700161\n-1.000000e+12\n-1.000000e+12\nno_data\n39.962507\n-92.671510\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n42.0\n9.634731e+01\n48\n2482916.982\n9530.492810\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.66461 39.92629, -92.66425 39.9...\n\n\n669\n74291700171\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2725Z\n40.045903\n-92.690485\nno_data\n3.647532e+01\n-1.000000e+12\n1.394000e-01\n...\n54.0\n6.557368e+02\n59\n2494780.714\n11863.732260\n-1.000000e+12\n0\n2\n1\nLINESTRING (-92.68041 40.00013, -92.68006 40.0...\n\n\n670\n74291700181\n7.145116e+08\n7.145116e+08\n2022-08-22T19:2723Z\n40.133798\n-92.687305\nno_data\n3.384407e+01\n-1.000000e+12\n1.019290e+00\n...\n42.0\n1.250056e+02\n58\n2506424.880\n11644.165636\n-1.000000e+12\n0\n1\n1\nLINESTRING (-92.68547 40.09150, -92.68519 40.0...\n\n\n\n\n671 rows × 111 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(11,7))\nSWOT_HR_shp1.plot(ax=ax, color='black')\n\n<AxesSubplot:>\n\n\n\n\n\n2. Lake Vector Shapefiles\nThe lake vector shapefiles can be accessed in the same way as the river shapefiles above.\n\ns3_SWOT_HR_url2 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.zip'\n\n\ns3_file_obj2 = fs_s3.open(s3_SWOT_HR_url2, mode='rb')\n\n\nwith zipfile.ZipFile(s3_file_obj2, 'r') as zip_ref:\n zip_ref.extractall('SWOT_HR_shp')\n\n\nSWOT_HR_shp2 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.shp') \nSWOT_HR_shp2\n\n\n\n\n\n\n\n\nobs_id\nlake_id\noverlap\ntime\ntime_tai\ntime_str\nwse\nwse_u\nwse_r_u\nwse_std\n...\niono_c\nxovr_cal_c\np_name\np_grand_id\np_max_wse\np_max_area\np_ref_date\np_ref_ds\np_storage\ngeometry\n\n\n\n\n0\n742081R000002\n7420470702\n93\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.934\n0.051\n0.051\n0.159\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.35\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.75926 42.04142, -92.75977 42.041...\n\n\n1\n742081R000003\n7420472462\n75\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n37.037\n0.080\n0.080\n0.143\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.62\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.91651 42.01167, -92.91681 42.011...\n\n\n2\n742081R000008\n7420473212\n58\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.578\n0.181\n0.181\n0.058\n...\n0.0\n0.0\nHENDRICKSON MARSH LAKE\n-99999999\n-1.000000e+12\n45.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-93.24060 41.93319, -93.24066 41.933...\n\n\n3\n742081R000009\n7420470712\n73\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.910\n0.110\n0.110\n0.136\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n4.50\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-92.72557 42.03424, -92.72560 42.034...\n\n\n4\n742081R000011\n7420470582\n76\n7.145116e+08\n7.145116e+08\n2022-08-22T19:26:51\n36.904\n0.109\n0.109\n0.628\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n1.89\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-93.39929 41.90871, -93.39945 41.908...\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n17240\n742070R000729\n7420857422\n92\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.186\n0.098\n0.098\n0.056\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n14.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.07581 47.57853, -95.07587 47.578...\n\n\n17241\n742070R000730\n7420848152\n96\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.071\n0.038\n0.038\n0.174\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n3.06\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-94.88532 47.61669, -94.88577 47.616...\n\n\n17242\n712070R000731\n7120812272\n74\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n33.104\n0.077\n0.077\n0.085\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n4.41\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.08313 47.57179, -95.08341 47.571...\n\n\n17243\n712070R000732\n7120816202\n67\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n32.713\n0.106\n0.106\n0.198\n...\n0.0\n0.0\nMUD LAKE\n-99999999\n-1.000000e+12\n6.30\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.39968 47.51004, -95.39975 47.510...\n\n\n17244\n742070R000733\n7420857422\n95\n7.145115e+08\n7.145115e+08\n2022-08-22T19:25:10\n32.725\n0.093\n0.093\n0.119\n...\n0.0\n0.0\nno_data\n-99999999\n-1.000000e+12\n14.94\n-9999\n-9999.0\n-1.000000e+12\nPOLYGON ((-95.07228 47.57473, -95.07257 47.574...\n\n\n\n\n17245 rows × 43 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(7,12))\nSWOT_HR_shp2.plot(ax=ax, color='black')\n\n<AxesSubplot:>\n\n\n\n\n\n3. Water Mask Pixel Cloud NetCDF\nAccessing the remaining files is different than the shp files above. We do not need to unzip the files because they are stored in native netCDF files in the cloud. For the rest of the products, we will open via xarray.\n\ns3_SWOT_HR_url3 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1/SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc'\n\n\ns3_file_obj3 = fs_s3.open(s3_SWOT_HR_url3, mode='rb')\n\nThe pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.\n\nds_PIXC = xr.open_dataset(s3_file_obj3, group = 'pixel_cloud', engine='h5netcdf')\nds_PIXC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (points: 769290, complex_depth: 2)\nCoordinates:\n latitude (points) float64 ...\n longitude (points) float64 ...\nDimensions without coordinates: points, complex_depth\nData variables: (12/49)\n azimuth_index (points) float64 ...\n range_index (points) float64 ...\n interferogram (points, complex_depth) float32 ...\n power_plus_y (points) float32 ...\n power_minus_y (points) float32 ...\n coherent_power (points) float32 ...\n ... ...\n solid_earth_tide (points) float32 ...\n load_tide_fes (points) float32 ...\n load_tide_got (points) float32 ...\n pole_tide (points) float32 ...\n ancillary_surface_classification_flag (points) float32 ...\n pixc_qual (points) float32 ...\nAttributes:\n description: cloud of geolocated interferogram pixels\n interferogram_size_azimuth: 2924\n interferogram_size_range: 4575\n looks_to_efflooks: 1.75xarray.DatasetDimensions:points: 769290complex_depth: 2Coordinates: (2)latitude(points)float64...long_name :latitude (positive N, negative S)standard_name :latitudeunits :degrees_northvalid_min :-80.0valid_max :80.0comment :Geodetic latitude [-80,80] (degrees north of equator) of the pixel.[769290 values with dtype=float64]longitude(points)float64...long_name :longitude (degrees East)standard_name :longitudeunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :Longitude [-180,180) (east of the Greenwich meridian) of the pixel.[769290 values with dtype=float64]Data variables: (49)azimuth_index(points)float64...long_name :rare interferogram azimuth indexunits :1valid_min :0valid_max :999999comment :Rare interferogram azimuth index (indexed from 0).[769290 values with dtype=float64]range_index(points)float64...long_name :rare interferogram range indexunits :1valid_min :0valid_max :999999comment :Rare interferogram range index (indexed from 0).[769290 values with dtype=float64]interferogram(points, complex_depth)float32...long_name :rare interferogramunits :1valid_min :-999999.0valid_max :999999.0comment :Complex unflattened rare interferogram.[1538580 values with dtype=float32]power_plus_y(points)float32...long_name :power for plus_y channelunits :1valid_min :0.0valid_max :999999.0comment :Power for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).[769290 values with dtype=float32]power_minus_y(points)float32...long_name :power for minus_y channelunits :1valid_min :0.0valid_max :999999.0comment :Power for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).[769290 values with dtype=float32]coherent_power(points)float32...long_name :coherent power combination of minus_y and plus_y channelsunits :1valid_min :0.0valid_max :999999.0comment :Power computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).[769290 values with dtype=float32]x_factor_plus_y(points)float32...long_name :X factor for plus_y channel powerunits :1valid_min :0.0valid_max :999999.0comment :X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).[769290 values with dtype=float32]x_factor_minus_y(points)float32...long_name :X factor for minus_y channel powerunits :1valid_min :0.0valid_max :999999.0comment :X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).[769290 values with dtype=float32]water_frac(points)float32...long_name :water fractionunits :1valid_min :-1000.0valid_max :10000.0comment :Noisy estimate of the fraction of the pixel that is water.[769290 values with dtype=float32]water_frac_uncert(points)float32...long_name :water fraction uncertaintyunits :1valid_min :0.0valid_max :999999.0comment :Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).[769290 values with dtype=float32]classification(points)float32...long_name :classificationflag_meanings :land land_near_water water_near_land open_water land_near_dark_water dark_water_edge dark_waterflag_values :[ 1 2 3 4 22 23 24]valid_min :1valid_max :24comment :Flags indicating water detection results.[769290 values with dtype=float32]false_detection_rate(points)float32...long_name :false detection rateunits :1valid_min :0.0valid_max :1.0comment :Probability of falsely detecting water when there is none.[769290 values with dtype=float32]missed_detection_rate(points)float32...long_name :missed detection rateunits :1valid_min :0.0valid_max :1.0comment :Probability of falsely detecting no water when there is water.[769290 values with dtype=float32]prior_water_prob(points)float32...long_name :prior water probabilityunits :1valid_min :0.0valid_max :1.0comment :Prior probability of water occurring.[769290 values with dtype=float32]bright_land_flag(points)float32...long_name :bright land flagstandard_name :status_flagflag_meanings :not_bright_land bright_land bright_land_or_waterflag_values :[0 1 2]valid_min :0valid_max :2comment :Flag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice). Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.[769290 values with dtype=float32]layover_impact(points)float32...long_name :layover impactunits :mvalid_min :-999999.0valid_max :999999.0comment :Estimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.[769290 values with dtype=float32]eff_num_rare_looks(points)float32...long_name :effective number of rare looksunits :1valid_min :0.0valid_max :999999.0comment :Effective number of independent looks taken to form the rare interferogram.[769290 values with dtype=float32]height(points)float32...long_name :height above reference ellipsoidunits :mvalid_min :-1500.0valid_max :15000.0comment :Height of the pixel above the reference ellipsoid.[769290 values with dtype=float32]cross_track(points)float32...long_name :approximate cross-track locationunits :mvalid_min :-75000.0valid_max :75000.0comment :Approximate cross-track location of the pixel.[769290 values with dtype=float32]pixel_area(points)float32...long_name :pixel areaunits :m^2valid_min :0.0valid_max :999999.0comment :Pixel area.[769290 values with dtype=float32]inc(points)float32...long_name :incidence angleunits :degreesvalid_min :0.0valid_max :999999.0comment :Incidence angle.[769290 values with dtype=float32]phase_noise_std(points)float32...long_name :phase noise standard deviationunits :radiansvalid_min :-999999.0valid_max :999999.0comment :Estimate of the phase noise standard deviation.[769290 values with dtype=float32]dlatitude_dphase(points)float32...long_name :sensitivity of latitude estimate to interferogram phaseunits :degrees/radianvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the latitude estimate to the interferogram phase.[769290 values with dtype=float32]dlongitude_dphase(points)float32...long_name :sensitivity of longitude estimate to interferogram phaseunits :degrees/radianvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the longitude estimate to the interferogram phase.[769290 values with dtype=float32]dheight_dphase(points)float32...long_name :sensitivity of height estimate to interferogram phaseunits :m/radianvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the interferogram phase.[769290 values with dtype=float32]dheight_droll(points)float32...long_name :sensitivity of height estimate to spacecraft rollunits :m/degreesvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the spacecraft roll.[769290 values with dtype=float32]dheight_dbaseline(points)float32...long_name :sensitivity of height estimate to interferometric baselineunits :m/mvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the interferometric baseline.[769290 values with dtype=float32]dheight_drange(points)float32...long_name :sensitivity of height estimate to range (delay)units :m/mvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the height estimate to the range (delay).[769290 values with dtype=float32]darea_dheight(points)float32...long_name :sensitivity of pixel area to reference heightunits :m^2/mvalid_min :-999999.0valid_max :999999.0comment :Sensitivity of the pixel area to the reference height.[769290 values with dtype=float32]illumination_time(points)datetime64[ns]...long_name :time of illumination of each pixel (UTC)standard_name :timetai_utc_difference :[Value of TAI-UTC at time of first record]leap_second :YYYY-MM-DD hh:mm:sscomment :Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.[769290 values with dtype=datetime64[ns]]illumination_time_tai(points)datetime64[ns]...long_name :time of illumination of each pixel (TAI)standard_name :timecomment :Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].[769290 values with dtype=datetime64[ns]]eff_num_medium_looks(points)float32...long_name :effective number of medium looksunits :1valid_min :0.0valid_max :999999.0comment :Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).[769290 values with dtype=float32]sig0(points)float32...long_name :sigma0units :1valid_min :-999999.0valid_max :999999.0comment :Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.[769290 values with dtype=float32]phase_unwrapping_region(points)float64...long_name :phase unwrapping region indexunits :1valid_min :-1valid_max :99999999comment :Phase unwrapping region index.[769290 values with dtype=float64]instrument_range_cor(points)float32...long_name :instrument range correctionunits :mvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to range before geolocation.[769290 values with dtype=float32]instrument_phase_cor(points)float32...long_name :instrument phase correctionunits :radiansvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to phase before geolocation.[769290 values with dtype=float32]instrument_baseline_cor(points)float32...long_name :instrument baseline correctionunits :mvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to baseline before geolocation.[769290 values with dtype=float32]instrument_attitude_cor(points)float32...long_name :instrument attitude correctionunits :degreesvalid_min :-999999.0valid_max :999999.0comment :Term that incorporates all calibration corrections applied to attitude before geolocation.[769290 values with dtype=float32]model_dry_tropo_cor(points)float32...long_name :dry troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFunits :mvalid_min :-3.0valid_max :-1.5comment :Equivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.[769290 values with dtype=float32]model_wet_tropo_cor(points)float32...long_name :wet troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFunits :mvalid_min :-1.0valid_max :0.0comment :Equivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.[769290 values with dtype=float32]iono_cor_gim_ka(points)float32...long_name :ionosphere vertical correctionsource :Global Ionosphere Mapsinstitution :JPLunits :mvalid_min :-0.5valid_max :0.0comment :Equivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.[769290 values with dtype=float32]height_cor_xover(points)float32...long_name :height correction from KaRIn crossoversunits :mvalid_min :-10.0valid_max :10.0comment :Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.[769290 values with dtype=float32]geoid(points)float32...long_name :geoid heightstandard_name :geoid_height_above_reference_ellipsoidsource :EGM2008 (Pavlis et al., 2012)units :mvalid_min :-150.0valid_max :150.0comment :Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).[769290 values with dtype=float32]solid_earth_tide(points)float32...long_name :solid Earth tide heightsource :Cartwright and Taylor (1971) and Cartwright and Edden (1973)units :mvalid_min :-1.0valid_max :1.0comment :Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.[769290 values with dtype=float32]load_tide_fes(points)float32...long_name :geocentric load tide height (FES)source :FES2014b (Carrere et al., 2016)institution :LEGOS/CNESunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.[769290 values with dtype=float32]load_tide_got(points)float32...long_name :geocentric load tide height (GOT)source :GOT4.10c (Ray, 2013)institution :GSFCunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.[769290 values with dtype=float32]pole_tide(points)float32...long_name :geocentric pole tide heightsource :Wahr (1985) and Desai et al. (2015)units :mvalid_min :-0.2valid_max :0.2comment :Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust).[769290 values with dtype=float32]ancillary_surface_classification_flag(points)float32...long_name :surface classificationstandard_name :status_flagsource :MODIS/GlobCoverinstitution :European Space Agencyflag_meanings :open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basinflag_values :[0 1 2 3 4 5 6]valid_min :0valid_max :6comment :7-state surface type classification computed from a mask built with MODIS and GlobCover data.[769290 values with dtype=float32]pixc_qual(points)float32...standard_name :status_flagflag_meanings :good badflag_values :[0 1]valid_min :0valid_max :1comment :Quality flag for pixel cloud data[769290 values with dtype=float32]Attributes: (4)description :cloud of geolocated interferogram pixelsinterferogram_size_azimuth :2924interferogram_size_range :4575looks_to_efflooks :1.75\n\n\n\nplt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)\nplt.colorbar().set_label('Height (m)')\n\n\n\n\n4. Water Mask Pixel Cloud Vector Attribute NetCDF\n\ns3_SWOT_HR_url4 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1/SWOT_L2_HR_PIXCVec_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc'\n\n\ns3_file_obj4 = fs_s3.open(s3_SWOT_HR_url4, mode='rb')\n\n\nds_PIXCVEC = xr.open_dataset(s3_file_obj4, decode_cf=False, engine='h5netcdf')\nds_PIXCVEC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (points: 769290, nchar_reach_id: 11,\n nchar_node_id: 14, nchar_lake_id: 10,\n nchar_obs_id: 13)\nDimensions without coordinates: points, nchar_reach_id, nchar_node_id,\n nchar_lake_id, nchar_obs_id\nData variables:\n azimuth_index (points) int32 ...\n range_index (points) int32 ...\n latitude_vectorproc (points) float64 ...\n longitude_vectorproc (points) float64 ...\n height_vectorproc (points) float32 ...\n reach_id (points, nchar_reach_id) |S1 ...\n node_id (points, nchar_node_id) |S1 ...\n lake_id (points, nchar_lake_id) |S1 ...\n obs_id (points, nchar_obs_id) |S1 ...\n ice_clim_f (points) int8 ...\n ice_dyn_f (points) int8 ...\nAttributes: (12/36)\n Conventions: CF-1.7\n title: Level 2 KaRIn high rate pixe...\n institution: CNES\n source: Simulation\n history: 2021-04-14 17:35:28Z: Creation\n platform: SWOT\n ... ...\n xref_input_l2_hr_pixc_vec_river_file: /work/ALT/swot/swotdev/desro...\n xref_static_river_db_file: \n xref_static_lake_db_file: /work/ALT/swot/swotpub/BD/BD...\n xref_l2_hr_lake_tile_config_parameter_file: /work/ALT/swot/swotdev/desro...\n ellipsoid_semi_major_axis: 6371008.771416667\n ellipsoid_flattening: 0.0xarray.DatasetDimensions:points: 769290nchar_reach_id: 11nchar_node_id: 14nchar_lake_id: 10nchar_obs_id: 13Coordinates: (0)Data variables: (11)azimuth_index(points)int32..._FillValue :2147483647long_name :rare interferogram azimuth indexunits :1valid_min :0valid_max :999999coordinates :longitude_vectorproc latitude_vectorproccomment :Rare interferogram azimuth index (indexed from 0).[769290 values with dtype=int32]range_index(points)int32..._FillValue :2147483647long_name :rare interferogram range indexunits :1valid_min :0valid_max :999999coordinates :longitude_vectorproc latitude_vectorproccomment :Rare interferogram range index (indexed from 0).[769290 values with dtype=int32]latitude_vectorproc(points)float64..._FillValue :9.969209968386869e+36long_name :height-constrained geolocation latitudestandard_name :latitudeunits :degrees_northvalid_min :-80.0valid_max :80.0comment :Height-constrained geodetic latitude of the pixel. Units are in degrees north of the equator.[769290 values with dtype=float64]longitude_vectorproc(points)float64..._FillValue :9.969209968386869e+36long_name :height-constrained geolocation longitudestandard_name :longitudeunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :Height-constrained geodetic longitude of the pixel. Positive=degrees east of the Greenwich meridian. Negative=degrees west of the Greenwich meridian.[769290 values with dtype=float64]height_vectorproc(points)float32..._FillValue :9.96921e+36long_name :height above reference ellipsoidunits :mvalid_min :-1500.0valid_max :15000.0coordinates :longitude_vectorproc latitude_vectorproccomment :Height-constrained height of the pixel above the reference ellipsoid.[769290 values with dtype=float32]reach_id(points, nchar_reach_id)|S1...long_name :identifier of the associated prior river reachcoordinates :longitude_vectorproc latitude_vectorproccomment :Unique reach identifier from the prior river database. The format of the identifier is CBBBBBRRRRT, where C=continent, B=basin, R=reach, T=type.[8462190 values with dtype=|S1]node_id(points, nchar_node_id)|S1...long_name :identifier of the associated prior river nodecoordinates :longitude_vectorproc latitude_vectorproccomment :Unique node identifier from the prior river database. The format of the identifier is CBBBBBRRRRNNNT, where C=continent, B=basin, R=reach, N=node, T=type of water body.[10770060 values with dtype=|S1]lake_id(points, nchar_lake_id)|S1...long_name :identifier of the associated prior lakecoordinates :longitude_vectorproc latitude_vectorproccomment :Identifier of the lake from the lake prior database) associated to the pixel. The format of the identifier is CBBNNNNNNT, where C=continent, B=basin, N=counter within the basin, T=type of water body.[7692900 values with dtype=|S1]obs_id(points, nchar_obs_id)|S1...long_name :identifier of the observed featurecoordinates :longitude_vectorproc latitude_vectorproccomment :Tile-specific identifier of the observed feature associated to the pixel. The format of the identifier is CBBTTTSNNNNNN, where C=continent, B=basin, T=tile number, S=swath side, N=lake counter within the PIXC tile.[10000770 values with dtype=|S1]ice_clim_f(points)int8..._FillValue :127long_name :climatological ice cover flagflag_meanings :no_ice_cover partial_ice_cover full_ice_coverflag_values :[0 1 2]institution :University of North Carolinacoordinates :longitude_vectorproc latitude_vectorproccomment :Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.[769290 values with dtype=int8]ice_dyn_f(points)int8..._FillValue :127long_name :dynamical ice cover flagflag_meanings :no_ice_cover partial_ice_cover full_ice_coverflag_values :[0 1 2]institution :University of North Carolinacoordinates :longitude_vectorproc latitude_vectorproccomment :Dynamic ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.[769290 values with dtype=int8]Attributes: (36)Conventions :CF-1.7title :Level 2 KaRIn high rate pixel cloud vector attribute productinstitution :CNESsource :Simulationhistory :2021-04-14 17:35:28Z: Creationplatform :SWOTreferences :0.2reference_document :SWOT-TN-CDM-0677-CNEScontact :test@cnes.frcycle_number :7pass_number :522tile_number :94swath_side :Rtile_name :522_094Rcontinent :NAtime_coverage_start :2022-08-22 19:29:00.955125Ztime_coverage_end :2022-08-22 19:29:10.946208Zgeospatial_lon_min :-91.20419918342002geospatial_lon_max :-90.32335511732916geospatial_lat_min :34.08946438801658geospatial_lat_max :34.78081317550924inner_first_longitude :-90.46892035209136inner_first_latitude :34.78081317550924inner_last_longitude :-90.32335511732916inner_last_latitude :34.21685259524421outer_first_longitude :-91.20419918342002outer_first_latitude :34.65198821586228outer_last_longitude :-91.05402175230344outer_last_latitude :34.08946438801658xref_input_l2_hr_pixc_file :/work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/simu/SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.ncxref_input_l2_hr_pixc_vec_river_file :/work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/river/SWOT_L2_HR_PIXCVecRiver_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.ncxref_static_river_db_file :xref_static_lake_db_file :/work/ALT/swot/swotpub/BD/BD_lakes/PLDxref_l2_hr_lake_tile_config_parameter_file :/work/ALT/swot/swotdev/desrochesd/swot-sds-16-10/swotCNES/PGE/lake_tile/lake_tile_param.cfgellipsoid_semi_major_axis :6371008.771416667ellipsoid_flattening :0.0\n\n\n\npixcvec_htvals = ds_PIXCVEC.height_vectorproc\npixcvec_latvals = ds_PIXCVEC.latitude_vectorproc\npixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc\n\n#Before plotting, we set all fill values to nan so that the graph shows up better spatially\npixcvec_htvals[pixcvec_htvals > 15000] = np.nan\npixcvec_latvals[pixcvec_latvals > 80] = np.nan\npixcvec_lonvals[pixcvec_lonvals > 180] = np.nan\n\n\nplt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)\nplt.colorbar().set_label('Height (m)')\n\n\n\n\n5. Raster NetCDF\n\ns3_SWOT_HR_url5 = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1/SWOT_L2_HR_Raster_100m_UTM15S_N_x_x_x_007_522_047F_20220822T192850_20220822T192911_Dx0000_01.nc'\n\n\ns3_file_obj5 = fs_s3.open(s3_SWOT_HR_url5, mode='rb')\n\n\nds_raster = xr.open_dataset(s3_file_obj5, engine='h5netcdf')\nds_raster\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (x: 1543, y: 1540)\nCoordinates:\n * x (x) float64 6.567e+05 6.568e+05 ... 8.109e+05\n * y (y) float64 3.775e+06 3.775e+06 ... 3.929e+06\nData variables: (12/30)\n crs object b'1'\n longitude (y, x) float64 ...\n latitude (y, x) float64 ...\n wse (y, x) float32 ...\n wse_uncert (y, x) float32 ...\n water_area (y, x) float32 ...\n ... ...\n load_tide_fes (y, x) float32 ...\n load_tide_got (y, x) float32 ...\n pole_tide (y, x) float32 ...\n model_dry_tropo_cor (y, x) float32 ...\n model_wet_tropo_cor (y, x) float32 ...\n iono_cor_gim_ka (y, x) float32 ...\nAttributes: (12/45)\n Conventions: CF-1.7\n title: Level 2 KaRIn High Rate Raster Data Product\n institution: JPL\n source: Large scale simulator\n history: 2021-09-08T22:28:33Z : Creation\n mission_name: SWOT\n ... ...\n utm_zone_num: 15\n mgrs_latitude_band: S\n x_min: 656700.0\n x_max: 810900.0\n y_min: 3775000.0\n y_max: 3928900.0xarray.DatasetDimensions:x: 1543y: 1540Coordinates: (2)x(x)float646.567e+05 6.568e+05 ... 8.109e+05long_name :x coordinate of projectionstandard_name :projection_x_coordinateunits :mvalid_min :-10000000.0valid_max :10000000.0comment :UTM easting coordinate of the pixel.array([656700., 656800., 656900., ..., 810700., 810800., 810900.])y(y)float643.775e+06 3.775e+06 ... 3.929e+06long_name :y coordinate of projectionstandard_name :projection_y_coordinateunits :mvalid_min :-20000000.0valid_max :20000000.0comment :UTM northing coordinate of the pixel.array([3775000., 3775100., 3775200., ..., 3928700., 3928800., 3928900.])Data variables: (30)crs()object...long_name :CRS Definitiongrid_mapping_name :transverse_mercatorprojected_crs_name :WGS 84 / UTM zone 15Ngeographic_crs_name :WGS 84reference_ellipsoid_name :WGS 84horizontal_datum_name :WGS_1984prime_meridian_name :Greenwichfalse_easting :500000.0false_northing :0.0longitude_of_central_meridian :-93.0longitude_of_prime_meridian :0.0latitude_of_projection_origin :0.0scale_factor_at_central_meridian :0.9996semi_major_axis :6378137.0inverse_flattening :298.257223563crs_wkt :PROJCS[\"WGS 84 / UTM zone 15N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32615\"]]spatial_ref :PROJCS[\"WGS 84 / UTM zone 15N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-93],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32615\"]]comment :UTM zone coordinate reference system.array(b'1', dtype=object)longitude(y, x)float64...long_name :longitude (degrees East)standard_name :longitudegrid_mapping :crsunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :Longitude [-180,180) (east of the Greenwich meridian) of the pixel.[2376220 values with dtype=float64]latitude(y, x)float64...long_name :latitude (positive N, negative S)standard_name :latitudegrid_mapping :crsunits :degrees_northvalid_min :-80.0valid_max :80.0comment :Latitude [-80,80] (degrees north of equator) of the pixel.[2376220 values with dtype=float64]wse(y, x)float32...long_name :water surface elevation above geoidgrid_mapping :crsunits :mvalid_min :-1500.0valid_max :15000.0comment :Water surface elevation of the pixel above the geoid and after using models to subtract the effects of tides (solid_earth_tide, load_tide_fes, pole_tide).[2376220 values with dtype=float32]wse_uncert(y, x)float32...long_name :uncertainty in the water surface elevationgrid_mapping :crsunits :mvalid_min :0.0valid_max :999999.0comment :1-sigma uncertainty in the water surface elevation.[2376220 values with dtype=float32]water_area(y, x)float32...long_name :surface area of watergrid_mapping :crsunits :m^2valid_min :-2000000.0valid_max :2000000000.0comment :Surface area of the water pixels.[2376220 values with dtype=float32]water_area_uncert(y, x)float32...long_name :uncertainty in the water surface areagrid_mapping :crsunits :m^2valid_min :0.0valid_max :2000000000.0comment :1-sigma uncertainty in the water surface area[2376220 values with dtype=float32]water_frac(y, x)float32...long_name :water fractiongrid_mapping :crsunits :1valid_min :-1000.0valid_max :10000.0comment :Fraction of the pixel that is water.[2376220 values with dtype=float32]water_frac_uncert(y, x)float32...long_name :uncertainty in the water fractiongrid_mapping :crsunits :1valid_min :0.0valid_max :999999.0comment :1-sigma uncertainty in the water fraction.[2376220 values with dtype=float32]sig0(y, x)float32...long_name :sigma0grid_mapping :crsunits :1valid_min :-1000.0valid_max :10000000.0comment :Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.[2376220 values with dtype=float32]sig0_uncert(y, x)float32...long_name :uncertainty in sigma0grid_mapping :crsunits :1valid_min :0.0valid_max :1000.0comment :1-sigma uncertainty in sigma0. The value is provided in linear units. This value is a one-sigma additive (not multiplicative) uncertainty term, which can be added to or subtracted from sigma0.[2376220 values with dtype=float32]inc(y, x)float32...long_name :incidence anglegrid_mapping :crsunits :degreesvalid_min :0.0valid_max :90.0comment :Incidence angle.[2376220 values with dtype=float32]cross_track(y, x)float32...long_name :approximate cross-track locationgrid_mapping :crsunits :mvalid_min :-75000.0valid_max :75000.0comment :Approximate cross-track location of the pixel.[2376220 values with dtype=float32]illumination_time(y, x)datetime64[ns]...long_name :time of illumination of each pixel (UTC)standard_name :timetai_utc_difference :-32.0leap_second :YYYY-MM-DDThh:mm:ssZcomment :Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.[2376220 values with dtype=datetime64[ns]]illumination_time_tai(y, x)datetime64[ns]...long_name :time of illumination of each pixel (TAI)standard_name :timecomment :Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].[2376220 values with dtype=datetime64[ns]]raster_qual(y, x)float32...standard_name :status_flaggrid_mapping :crsflag_meanings :good badflag_values :[0 1]valid_min :0valid_max :1comment :Quality flag for raster data.[2376220 values with dtype=float32]n_wse_pix(y, x)float64...long_name :number of wse pixelsgrid_mapping :crsunits :lvalid_min :0valid_max :999999comment :Number of pixel cloud samples used in water surface elevation aggregation.[2376220 values with dtype=float64]n_area_pix(y, x)float64...long_name :number of area pixelsgrid_mapping :crsunits :lvalid_min :0valid_max :999999comment :Number of pixel cloud samples used in water area and water fraction aggregation.[2376220 values with dtype=float64]dark_frac(y, x)float32...long_name :fractional area of dark watergrid_mapping :crsunits :lvalid_min :-1000.0valid_max :10000.0comment :Fraction of pixel water area covered by dark water.[2376220 values with dtype=float32]ice_clim_flag(y, x)float32...long_name :climatological ice cover flagsource :UNCgrid_mapping :crsflag_meanings :no_ice_cover uncertain_ice_cover full_ice_coverflag_values :[0 1 2]valid_min :0valid_max :2comment :Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the pixel is likely not ice covered, may or may not be partially or fully ice covered, and likely fully ice covered, respectively.[2376220 values with dtype=float32]ice_dyn_flag(y, x)float32...long_name :dynamic ice cover flagsource :UNCgrid_mapping :crsflag_meanings :no_ice_cover partial_ice_cover full_ice_coverflag_values :[0 1 2]valid_min :0valid_max :2comment :Dynamic ice cover flag indicating whether the surface is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the pixel is not ice covered, partially ice covered, and fully ice covered, respectively.[2376220 values with dtype=float32]layover_impact(y, x)float32...long_name :layover impactgrid_mapping :crsunits :mvalid_min :-999999.0valid_max :999999.0comment :Estimate of the water surface elevation error caused by layover.[2376220 values with dtype=float32]geoid(y, x)float32...long_name :geoid heightstandard_name :geoid_height_above_reference_ellipsoidsource :EGM2008 (Pavlis et al., 2012)grid_mapping :crsunits :mvalid_min :-150.0valid_max :150.0comment :Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).[2376220 values with dtype=float32]solid_earth_tide(y, x)float32...long_name :solid Earth tide heightsource :Cartwright and Taylor (1971) and Cartwright and Edden (1973)grid_mapping :crsunits :mvalid_min :-1.0valid_max :1.0comment :Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.[2376220 values with dtype=float32]load_tide_fes(y, x)float32...long_name :geocentric load tide height (FES)source :FES2014b (Carrere et al., 2016)institution :LEGOS/CNESgrid_mapping :crsunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust.[2376220 values with dtype=float32]load_tide_got(y, x)float32...long_name :geocentric load tide height (GOT)source :GOT4.10c (Ray, 2013)institution :GSFCgrid_mapping :crsunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust. This value is reported for reference but is not applied to the reported height.[2376220 values with dtype=float32]pole_tide(y, x)float32...long_name :geocentric pole tide heightsource :Wahr (1985) and Desai et al. (2015)grid_mapping :crsunits :mvalid_min :-0.2valid_max :0.2comment :Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth’s crust).[2376220 values with dtype=float32]model_dry_tropo_cor(y, x)float32...long_name :dry troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFgrid_mapping :crsunits :mvalid_min :-3.0valid_max :-1.5comment :Equivalent vertical correction due to dry troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.[2376220 values with dtype=float32]model_wet_tropo_cor(y, x)float32...long_name :wet troposphere vertical correctionsource :European Centre for Medium-Range Weather Forecastsinstitution :ECMWFgrid_mapping :crsunits :mvalid_min :-1.0valid_max :0.0comment :Equivalent vertical correction due to wet troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.[2376220 values with dtype=float32]iono_cor_gim_ka(y, x)float32...long_name :ionosphere vertical correctionsource :Global Ionosphere Mapsinstitution :JPLgrid_mapping :crsunits :mvalid_min :-0.5valid_max :0.0comment :Equivalent vertical correction due to ionosphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.[2376220 values with dtype=float32]Attributes: (45)Conventions :CF-1.7title :Level 2 KaRIn High Rate Raster Data Productinstitution :JPLsource :Large scale simulatorhistory :2021-09-08T22:28:33Z : Creationmission_name :SWOTreferences :https://github.com/SWOTAlgorithms/Raster-Processorreference_document :JPL D-56416 - Revision A (DRAFT) - November 5, 2020contact :alexander.t.corben[at]jpl.nasa.govcycle_number :7pass_number :522scene_number :47tile_numbers :[92 93 94 95 92 93 94 95]tile_names :522_092L, 522_093L, 522_094L, 522_095L, 522_092R, 522_093R, 522_094R, 522_095Rtile_polarizations :V, V, V, V, V, V, V, Vcoordinate_reference_system :Universal Transverse Mercatorresolution :100.0short_name :L2_HR_Rasterdescriptor_string :100m_UTM15S_N_x_x_xcrid :Dx0000product_version :V0.1pge_name :adt_pge_standinpge_version :V0.1time_coverage_start :2022-08-22 19:28:50.964042Ztime_coverage_end :2022-08-22 19:29:10.946208Zgeospatial_lon_min :-91.27757002156555geospatial_lon_max :-89.62061588835118geospatial_lat_min :34.09943218249787geospatial_lat_max :35.464214684504334left_first_longitude :-89.89843338760357left_first_latitude :35.464214684504334left_last_longitude :-89.62061588835118left_last_latitude :34.33243031374548right_first_longitude :-91.27757002156555right_first_latitude :35.22613283570163right_last_longitude :-90.98228790375923right_last_latitude :34.09943218249787xref_input_l2_hr_pixc_files :SWOT_L2_HR_PIXC_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.ncxref_input_l2_hr_pixcvec_files :SWOT_L2_HR_PIXCVec_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.ncutm_zone_num :15mgrs_latitude_band :Sx_min :656700.0x_max :810900.0y_min :3775000.0y_max :3928900.0\n\n\nIt’s easy to analyze and plot the data with packages such as hvplot!\n\nds_raster.wse.hvplot.image(y='y', x='x')"
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- "title": "Access Sentinel-6 Data by Cycle and Pass Number",
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+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis notebook shows a simple way to search for Sentinel-6 data granules for a specific cycle and pass using the CMR Search API and download them to a local directory."
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nfrom pystac_client import Client \nfrom collections import defaultdict \nimport json\nimport geopandas\nimport geoviews as gv\nfrom cartopy import crs\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport os\nimport requests\nimport boto3\nimport numpy as np\nimport xarray as xr\nimport rasterio as rio\nfrom rasterio.session import AWSSession\nfrom rasterio.plot import show\nimport rioxarray\nimport geoviews as gv\nimport hvplot.xarray\nimport holoviews as hv\nfrom tqdm import tqdm\nfrom pprint import pprint\nimport time\ngv.extension('bokeh', 'matplotlib')"
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- "objectID": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html#before-you-start",
- "href": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html#before-you-start",
- "title": "Access Sentinel-6 Data by Cycle and Pass Number",
- "section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an Earthdata account https://urs.earthdata.nasa.gov.\nAccounts are free to create and take just a moment to set up."
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#initiate-data-search",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#initiate-data-search",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Initiate Data Search",
+ "text": "Initiate Data Search\n\nSTAC_URL = 'https://cmr.earthdata.nasa.gov/stac'\nprovider_cat = Client.open(STAC_URL)\ncatalog = Client.open(f'{STAC_URL}/LPCLOUD/')\n#collections = ['HLSL30.v2.0', 'HLSS30.v2.0']\ncollections = ['HLSL30.v2.0']"
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- "title": "Access Sentinel-6 Data by Cycle and Pass Number",
- "section": "Authentication setup",
- "text": "Authentication setup\nYou’ll probably need to use the netrc method when running from command line.\nWe need some boilerplate up front to log in to Earthdata Login. The function below will allow Python scripts to log into any Earthdata Login application programmatically. To avoid being prompted for credentials every time you run and also allow clients such as curl to log in, you can add the following to a .netrc (_netrc on Windows) file in your home directory:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nMake sure that this file is only readable by the current user or you will receive an error stating “netrc access too permissive.”\n$ chmod 0600 ~/.netrc\n\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nimport getpass\nimport netrc\n\ndef setup_earthdata_login_auth(endpoint):\n \"\"\"\n Set up the request library so that it authenticates against the given Earthdata Login\n endpoint and is able to track cookies between requests. This looks in the .netrc file \n first and if no credentials are found, it prompts for them.\n\n Valid endpoints include:\n urs.earthdata.nasa.gov - Earthdata Login production\n \"\"\"\n try:\n username, _, password = netrc.netrc().authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n # FileNotFound = There's no .netrc file\n # TypeError = The endpoint isn't in the netrc file, causing the above to try unpacking None\n print('Please provide your Earthdata Login credentials to allow data access')\n print('Your credentials will only be passed to %s and will not be exposed in Jupyter' % (endpoint))\n username = input('Username:')\n password = getpass.getpass()\n\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n\n\nsetup_earthdata_login_auth('urs.earthdata.nasa.gov')\n\nPlease provide your Earthdata Login credentials to allow data access\nYour credentials will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter\n\n\nUsername: nickles\n ···········\n\n\n\nimport requests\nfrom os import makedirs\nfrom os.path import isdir, basename\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen, urlretrieve\nfrom datetime import datetime, timedelta\nfrom json import dumps, loads"
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#define-date-range-and-region-of-interest",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#define-date-range-and-region-of-interest",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Define Date Range and Region of Interest",
+ "text": "Define Date Range and Region of Interest\n\ndate_range = \"2021-01/2022-01\"\nroi = {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.60835266113281,\n 39.49874248613119\n ],\n [\n -121.26983642578124,\n 39.49874248613119\n ],\n [\n -121.26983642578124,\n 39.756824261131406\n ],\n [\n -121.60835266113281,\n 39.756824261131406\n ],\n [\n -121.60835266113281,\n 39.49874248613119\n ]\n ]\n ]\n }\n }['geometry']\nbase = gv.tile_sources.EsriImagery.opts(width=650, height=500)\nReservoir = gv.Polygons(roi['coordinates']).opts(line_color='yellow', line_width=10, color=None)\nReservoir * base"
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- "objectID": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html#find-granules-by-cyclepass-number",
- "href": "notebooks/sentinel-6/Access_Sentinel6_By_CyclePass.html#find-granules-by-cyclepass-number",
- "title": "Access Sentinel-6 Data by Cycle and Pass Number",
- "section": "Find granules by cycle/pass number",
- "text": "Find granules by cycle/pass number\nThe CMR Search API provides for searching ingested granules by their cycle and pass numbers. A third parameter, the tile identifier, is provisioned for use during the upcoming SWOT mission but isn’t used by CMR Search at this time. Read more about these orbit identifiers here.\nPasses within a cycle are unique, there will be no repeats until the next cycle. Tile numbers are only unique within a pass, so if you’re looking only at tile numbers there will be over 300 per cycle, but only 1 per pass.\nInfo below may only apply to NRT use case:\n\nThis workflow/notebook can be run routinely to maintain a time series of NRT data, downloading new granules as they become available in CMR.\nThe notebook writes/overwrites a file .update to the target data directory with each successful run. The file tracks to date and time of the most recent update to the time series of NRT granules using a timestamp in the format yyyy-mm-ddThh:mm:ssZ.\nThe timestamp matches the value used for the created_at parameter in the last successful run. This parameter finds the granules created within a range of datetimes. This workflow leverages the created_at parameter to search backwards in time for new granules ingested between the time of our timestamp and now.\n\nThe variables in the cell below determine the workflow behavior on its initial run:\n\ntrackcycle and trackpass: Set the cycle and pass numbers to use for the CMR granule search.\ncmr: The domain of the target CMR instance, either cmr.earthdata.nasa.gov.\nccid: The unique CMR concept-id of the desired collection.\ndata: The path to a local directory in which to download/maintain a copy of the NRT granule time series.\n\n\ncmr = \"cmr.earthdata.nasa.gov\"\n\n# this function returns a concept id for a particular dataset\ndef get_collection(url: str=f\"https://{cmr}/search/collections.umm_json\", **params):\n return requests.get(url, params).json().get(\"items\")[0]\n#\n# This cell accepts parameters from command line with papermill: \n# https://papermill.readthedocs.io\n#\n# These variables should be set before the first run, then they \n# should be left alone. All subsequent runs expect the values \n# for cmr, ccid, data to be unchanged. The mins value has no \n# impact on subsequent runs.\n#\n\ntrackcycle = 25\ntrackpass = 1\n\nname = \"JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F\"\n\nccid = get_collection(ShortName=name).get(\"meta\").get(\"concept-id\")\n\ndata = \"resources/trackcycle\"\n\nThe variable data is pointed at a nearby folder resources/cyclepass by default. You should change data to a suitable download path on your file system. An unlucky sequence of git commands could disappear that folder and its downloads, if your not careful. Just change it.\nThe search retrieves granules ingested during the last n minutes. A file in your local data dir file that tracks updates to your data directory, if one file exists. The CMR Search falls back on the ten minute window if not.\n\n#timestamp = (datetime.utcnow()-timedelta(minutes=mins)).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n#timestamp\n\nThis cell will replace the timestamp above with the one read from the .update file in the data directory, if it exists.\n\nif not isdir(data):\n print(f\"NOTE: Making new data directory at '{data}'. (This is the first run.)\")\n makedirs(data)\n#else:\n# try:\n# with open(f\"{data}/.update\", \"r\") as f:\n# timestamp = f.read()\n# except FileNotFoundError:\n# print(\"WARN: No .update in the data directory. (Is this the first run?)\")\n# else:\n# print(f\"NOTE: .update found in the data directory. (The last run was at {timestamp}.)\")\n\nNOTE: Making new data directory at 'resources'. (This is the first run.)\n\n\nThere are several ways to query for CMR updates that occured during a given timeframe. Read on in the CMR Search documentation:\n\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-new-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-revised-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-production-date (Granules)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-created-at (Granules)\n\nThe created_at parameter works for our purposes. It’s a granule search parameter that returns the records ingested since the input timestamp.\n\nparams = {\n 'scroll': \"true\",\n 'page_size': 2000,\n 'sort_key': \"-start_date\",\n 'collection_concept_id': ccid, \n #'created_at': timestamp,\n # Limit results to granules matching cycle, pass numbers:\n 'cycle': trackcycle,\n 'passes[0][pass]': trackpass,\n}\n\nparams\n\n{'scroll': 'true',\n 'page_size': 2000,\n 'sort_key': '-start_date',\n 'collection_concept_id': 'C1968980576-POCLOUD',\n 'cycle': 25,\n 'passes[0][pass]': 1}\n\n\nGet the query parameters as a string and then the complete search url:\n\nquery = urlencode(params)\nurl = f\"https://{cmr}/search/granules.umm_json?{query}\"\nprint(url)\n\nhttps://cmr.earthdata.nasa.gov/search/granules.umm_json?scroll=true&page_size=2000&sort_key=-start_date&collection_concept_id=C1968980576-POCLOUD&cycle=25&passes%5B0%5D%5Bpass%5D=1\n\n\nDownload the granule records that match our search parameters.\n\nwith urlopen(url) as f:\n results = loads(f.read().decode())\n\nprint(f\"{results['hits']} granules results for '{ccid}' cycle '{trackcycle}' and pass '{trackpass}'.\")\n\n1 granules results for 'C1968980576-POCLOUD' cycle '25' and pass '1'.\n\n\nNeatly print the first granule’s data for reference (assuming at least one was returned).\n\nif len(results['items'])>0:\n #print(dumps(results['items'][0], indent=2)) #print whole record\n print(dumps(results['items'][0]['umm'][\"RelatedUrls\"], indent=2)) #print associated URLs\n \n # Also, replace timestamp with one corresponding to time of the search.\n #timestamp = datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n\n[\n {\n \"URL\": \"s3://podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\",\n \"Type\": \"GET DATA VIA DIRECT ACCESS\",\n \"Description\": \"This link provides direct download access via S3 to the granule.\"\n },\n {\n \"URL\": \"s3://podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.bufr.bin\",\n \"Type\": \"GET DATA VIA DIRECT ACCESS\",\n \"Description\": \"This link provides direct download access via S3 to the granule.\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\",\n \"Description\": \"Download S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\",\n \"Type\": \"GET DATA\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.xfdumanifest.xml\",\n \"Description\": \"Download S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.xfdumanifest.xml\",\n \"Type\": \"EXTENDED METADATA\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.bufr.bin\",\n \"Description\": \"Download S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.bufr.bin\",\n \"Type\": \"GET DATA\"\n },\n {\n \"URL\": \"https://archive.podaac.earthdata.nasa.gov/s3credentials\",\n \"Description\": \"api endpoint to retrieve temporary credentials valid for same-region direct s3 access\",\n \"Type\": \"VIEW RELATED INFORMATION\"\n },\n {\n \"URL\": \"https://opendap.earthdata.nasa.gov/collections/C1968980576-POCLOUD/granules/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02\",\n \"Type\": \"USE SERVICE API\",\n \"Subtype\": \"OPENDAP DATA\",\n \"Description\": \"OPeNDAP request URL\"\n }\n]\n\n\nThe link for http access denoted by \"Type\": \"GET DATA\" in the list of RelatedUrls.\nGrab the download URL, but do it in a way that’ll work for search results returning any number of granule records:\n\ndownloads = []\n\nfor l in results['items'][0]['umm'][\"RelatedUrls\"]:\n #if the link starts with the following, it is the download link we want\n if 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/' in l['URL']: \n #we want the .nc file\n if '.nc' in l['URL']:\n downloads.append(l['URL'])\ndownloads\n\n['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc']\n\n\nFinish by downloading the files to the data directory in a loop. Overwrite .update with a new timestamp on success.\n\nfor f in downloads:\n try:\n urlretrieve(f, f\"{data}/{basename(f)}\")\n except Exception as e:\n print(f\"[{datetime.now()}] FAILURE: {f}\\n\\n{e}\\n\")\n raise e\n else:\n print(f\"[{datetime.now()}] SUCCESS: {f}\")\n\n[2022-11-07 16:28:33.475579] SUCCESS: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc\n\n\nIf there were updates to the local time series during this run and no exceptions were raised during the download loop, then overwrite the timestamp file that tracks updates to the data folder (resources/nrt/.update):\n\n#if len(results['items'])>0:\n# with open(f\"{data}/.update\", \"w\") as f:\n# f.write(timestamp)"
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+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#search-for-hls-imagery-matching-search-criteria",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Search for HLS imagery matching search criteria",
+ "text": "Search for HLS imagery matching search criteria\n\nsearch = catalog.search(\n collections=collections,\n intersects=roi,\n datetime=date_range,\n limit=100\n)\n\nitem_collection = search.get_all_items()\nsearch.matched()\n\n50"
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- "title": "L2SS-Py Shapefile Subsetting",
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- "text": "This notebook demonstates subsetting multiple data files by an existing shapefile.\nImport Harmony Python library\n\nfrom harmony import BBox, Client, Collection, Request\nfrom harmony.config import Environment\n\nImport libraries used to visualize l2ss-py result\n\nimport datetime as dt\nimport xarray as xr\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\n\nimport cartopy.io.img_tiles as cimgt\nimport cartopy.crs as ccrs\nfrom cartopy.io.shapereader import Reader\nfrom cartopy.feature import ShapelyFeature\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nCreate Harmony client. In this case, point the Harmony client at the LOCAL Harmony environment.\n\nharmony_client = Client()\n\nThe example utilized in this demo uses a shapefile of the Gulf of Mexico. That shapefile ZIP file is in the local directory this notebook is running in.\n\nshapefile_path = 'gulf_shapefile.zip' \ncollection_id = 'C2075141605-POCLOUD' # ASCATB-L2-Coastal\n\nrequest = Request(\n collection=Collection(id=collection_id),\n shape=shapefile_path,\n granule_id=[\n 'G2244750740-POCLOUD',\n 'G2244133671-POCLOUD',\n 'G2243066020-POCLOUD'\n ]\n)\n\nrequest.is_valid()\n\nTrue\n\n\nWait for processing and then view the output\n\njob_id = harmony_client.submit(request)\nprint(f'jobID = {job_id}')\n\nprint('\\n Waiting for the job to finish. . .\\n')\nharmony_client.wait_for_processing(job_id, show_progress=True)\nprint(\"\\n. . .DONE!\")\n\njobID = 3af6c0ce-bbed-45b0-86ae-265a00182a6f\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\n\ndef display_wind_speed(nc_file_name):\n ds = xr.load_dataset(nc_file_name, engine='netcdf4')\n lats = ds.lat.values\n lons = ds.lon.values\n wind_speed_data = ds.wind_speed.values\n\n ax = plt.axes(projection=ccrs.PlateCarree())\n ax.set_global()\n ax.coastlines()\n # Zoom in to ~ north america\n ax.set_extent([-170, -20, 0, 40])\n ax.contourf(lons, lats, wind_speed_data)\n plt.show()\n\n\nfor filename in [f.result() for f in harmony_client.download_all(job_id)]:\n display_wind_speed(filename)\n print(filename)\n \n\n\n\n\nascat_20220328_023300_metopb_49418_eps_o_coa_3202_ovw.l2_subsetted.nc4\nascat_20220329_140000_metopb_49439_eps_o_coa_3202_ovw.l2_subsetted.nc4\nascat_20220330_152100_metopb_49454_eps_o_coa_3202_ovw.l2_subsetted.nc4"
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+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#filter-imagery-for-low-cloud-images-and-identify-image-bands-needed-for-water-classification",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Filter imagery for low cloud images and identify image bands needed for water classification",
+ "text": "Filter imagery for low cloud images and identify image bands needed for water classification\n\ns30_bands = ['B8A', 'B03'] # S30 bands for NDWI calculation and quality filtering -> NIR, GREEN, Quality \nl30_bands = ['B05', 'B03'] # L30 bands for NDWI calculation and quality filtering -> NIR, GREEN, Quality \ncloudcover = 10\n\n\nndwi_band_links = []\n\nfor i in item_collection:\n if i.properties['eo:cloud_cover'] <= cloudcover:\n if i.collection_id == 'HLSS30.v2.0':\n #print(i.properties['eo:cloud_cover'])\n ndwi_bands = s30_bands\n elif i.collection_id == 'HLSL30.v2.0':\n #print(i.properties['eo:cloud_cover'])\n ndwi_bands = l30_bands\n\n for a in i.assets:\n if any(b==a for b in ndwi_bands):\n ndwi_band_links.append(i.assets[a].href)\n\n\nndwi_band_links[:10]\n\n['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021016T184526.v2.0/HLS.L30.T10TFK.2021016T184526.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021016T184526.v2.0/HLS.L30.T10TFK.2021016T184526.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021048T184520.v2.0/HLS.L30.T10TFK.2021048T184520.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021048T184520.v2.0/HLS.L30.T10TFK.2021048T184520.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021048T184520.v2.0/HLS.L30.T10SFJ.2021048T184520.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021048T184520.v2.0/HLS.L30.T10SFJ.2021048T184520.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021064T184513.v2.0/HLS.L30.T10SFJ.2021064T184513.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021064T184513.v2.0/HLS.L30.T10SFJ.2021064T184513.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021064T184513.v2.0/HLS.L30.T10TFK.2021064T184513.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021064T184513.v2.0/HLS.L30.T10TFK.2021064T184513.v2.0.B05.tif']\n\n\n\ntile_dicts = defaultdict(list) \n\n\nfor l in ndwi_band_links:\n tile = l.split('.')[-6]\n tile_dicts[tile].append(l)\n\n\ntile_dicts.keys()\n\ndict_keys(['T10TFK', 'T10SFJ'])\n\n\n\ntile_links = tile_dicts['T10SFJ']\n\n\nbands_dicts = defaultdict(list)\nfor b in tile_links:\n band = b.split('.')[-2]\n bands_dicts[band].append(b)\nfor i in bands_dicts:\n print(i)\n\nB03\nB05"
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- "objectID": "notebooks/Harmony API.html#before-you-start",
- "href": "notebooks/Harmony API.html#before-you-start",
- "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
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+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#download-identified-images-to-local-computer",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Download identified images to local computer",
+ "text": "Download identified images to local computer\n\nos.makedirs(\"downloads\", exist_ok=True)\n\n\ndef download(url: str, fname: str):\n resp = requests.get(url, stream=True)\n total = int(resp.headers.get('content-length', 0))\n with open(fname, 'wb') as file, tqdm(\n desc=fname,\n ncols=110,\n total=total,\n unit='iB',\n unit_scale=True,\n unit_divisor=1024,\n ) as bar:\n for data in resp.iter_content(chunk_size=1024):\n size = file.write(data)\n bar.update(size)\n\n\npath_dicts = defaultdict(list)\nprint('Begin Downloading Imagery')\nstart_time = time.time()\nfor key in bands_dicts:\n url = bands_dicts[key]\n for u in url:\n filename = u.split('/')[-1]\n path = './downloads/' + filename\n download(u,path)\n path_dicts[key].append(path)\nprint('Download Complete')\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n\nBegin Downloading Imagery\nDownload Complete\n--- 195.1027250289917 seconds ---\n\n\n./downloads/HLS.L30.T10SFJ.2021048T184520.v2.0.B03.tif: 100%|███████████| 24.2M/24.2M [00:01<00:00, 13.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021064T184513.v2.0.B03.tif: 100%|███████████| 23.9M/23.9M [00:01<00:00, 13.7MiB/s]\n./downloads/HLS.L30.T10SFJ.2021080T184505.v2.0.B03.tif: 100%|███████████| 23.8M/23.8M [00:02<00:00, 10.7MiB/s]\n./downloads/HLS.L30.T10SFJ.2021096T184501.v2.0.B03.tif: 100%|███████████| 23.7M/23.7M [00:01<00:00, 13.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184454.v2.0.B03.tif: 100%|███████████| 23.4M/23.4M [00:01<00:00, 13.2MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184518.v2.0.B03.tif: 100%|███████████| 22.3M/22.3M [00:01<00:00, 12.5MiB/s]\n./downloads/HLS.L30.T10SFJ.2021128T184447.v2.0.B03.tif: 100%|███████████| 23.6M/23.6M [00:01<00:00, 14.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021176T184509.v2.0.B03.tif: 100%|███████████| 23.9M/23.9M [00:01<00:00, 13.7MiB/s]\n./downloads/HLS.L30.T10SFJ.2021192T184511.v2.0.B03.tif: 100%|███████████| 24.0M/24.0M [00:01<00:00, 14.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021224T184524.v2.0.B03.tif: 100%|███████████| 22.2M/22.2M [00:01<00:00, 13.2MiB/s]\n./downloads/HLS.L30.T10SFJ.2021240T184529.v2.0.B03.tif: 100%|███████████| 22.5M/22.5M [00:01<00:00, 13.6MiB/s]\n./downloads/HLS.L30.T10SFJ.2021256T184533.v2.0.B03.tif: 100%|███████████| 23.3M/23.3M [00:02<00:00, 12.2MiB/s]\n./downloads/HLS.L30.T10SFJ.2021272T184537.v2.0.B03.tif: 100%|███████████| 23.9M/23.9M [00:01<00:00, 13.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2021288T184542.v2.0.B03.tif: 100%|███████████| 24.0M/24.0M [00:01<00:00, 14.1MiB/s]\n./downloads/HLS.L30.T10SFJ.2021336T184538.v2.0.B03.tif: 100%|███████████| 23.5M/23.5M [00:01<00:00, 14.4MiB/s]\n./downloads/HLS.L30.T10SFJ.2022019T184528.v2.0.B03.tif: 100%|███████████| 24.4M/24.4M [00:01<00:00, 15.6MiB/s]\n./downloads/HLS.L30.T10SFJ.2021048T184520.v2.0.B05.tif: 100%|███████████| 26.9M/26.9M [00:02<00:00, 11.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2021064T184513.v2.0.B05.tif: 100%|███████████| 26.6M/26.6M [00:02<00:00, 13.8MiB/s]\n./downloads/HLS.L30.T10SFJ.2021080T184505.v2.0.B05.tif: 100%|███████████| 26.6M/26.6M [00:03<00:00, 7.98MiB/s]\n./downloads/HLS.L30.T10SFJ.2021096T184501.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:02<00:00, 9.77MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184454.v2.0.B05.tif: 100%|███████████| 26.0M/26.0M [00:01<00:00, 14.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184518.v2.0.B05.tif: 100%|███████████| 24.9M/24.9M [00:02<00:00, 9.89MiB/s]\n./downloads/HLS.L30.T10SFJ.2021128T184447.v2.0.B05.tif: 100%|███████████| 26.1M/26.1M [00:02<00:00, 9.87MiB/s]\n./downloads/HLS.L30.T10SFJ.2021176T184509.v2.0.B05.tif: 100%|███████████| 26.3M/26.3M [00:02<00:00, 13.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021192T184511.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:02<00:00, 10.1MiB/s]\n./downloads/HLS.L30.T10SFJ.2021224T184524.v2.0.B05.tif: 100%|███████████| 25.3M/25.3M [00:02<00:00, 12.5MiB/s]\n./downloads/HLS.L30.T10SFJ.2021240T184529.v2.0.B05.tif: 100%|███████████| 25.1M/25.1M [00:02<00:00, 12.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2021256T184533.v2.0.B05.tif: 100%|███████████| 26.2M/26.2M [00:01<00:00, 14.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021272T184537.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:02<00:00, 12.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021288T184542.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:01<00:00, 14.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021336T184538.v2.0.B05.tif: 100%|███████████| 26.8M/26.8M [00:02<00:00, 10.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2022019T184528.v2.0.B05.tif: 100%|███████████| 27.3M/27.3M [00:01<00:00, 14.9MiB/s]"
},
{
- "objectID": "notebooks/Harmony API.html#build-the-eoss-root-url",
- "href": "notebooks/Harmony API.html#build-the-eoss-root-url",
- "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "section": "Build the EOSS Root URL",
- "text": "Build the EOSS Root URL\nNext we will build a URL for the EOSS service for a given granule. To get data using the service, you need a CMR collection ID for a supported collection and the ID of a granule within that collection.\nBy convention, all Harmony services are accessed through <harmony_root>/<collection_id>/<service_name>\n\nharmony_root = 'https://harmony.earthdata.nasa.gov'\nconfig = {\n 'collection_id': 'C1233800302-EEDTEST',\n 'eoss_version': '0.1.0'\n}\neoss_collection_root = harmony_root+'/{collection_id}/eoss/{eoss_version}/items/'.format(**config)\nprint(eoss_collection_root)\n\nhttps://harmony.earthdata.nasa.gov/C1233800302-EEDTEST/eoss/0.1.0/items/"
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#load-images-and-visualize",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#load-images-and-visualize",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Load images and visualize",
+ "text": "Load images and visualize\n\ndef time_index_from_filenames(file_links):\n return [datetime.strptime(f.split('.')[-5], '%Y%jT%H%M%S') for f in file_links]\n\n\ntime = xr.Variable('time', time_index_from_filenames(path_dicts['B03']))\nchunks=dict(band=1, x=512, y=512)\nhls_ts_da_LB3 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B03']], dim=time)\nhls_ts_da_LB5 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B05']], dim=time)\nhls_ts_da_LB3 = hls_ts_da_LB3.rio.reproject(\"epsg:4326\")\nhls_ts_da_LB5 = hls_ts_da_LB5.rio.reproject(\"epsg:4326\")\n\n\nhls_ts_da_data_LB3 = hls_ts_da_LB3.load()\nhls_ts_da_data_LB5 = hls_ts_da_LB5.load()\nhls_ts_da_data_LB3 = hls_ts_da_data_LB3.rio.clip([roi])\nhls_ts_da_data_LB5 = hls_ts_da_data_LB5.rio.clip([roi])\n\n\nhls_ts_da_data_LB5.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='gray').opts(clim=(0,2000))"
},
{
- "objectID": "notebooks/Harmony API.html#variable-subset-of-a-granule",
- "href": "notebooks/Harmony API.html#variable-subset-of-a-granule",
- "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "section": "Variable Subset of a Granule",
- "text": "Variable Subset of a Granule\nWe can now build onto the root URL in order to actually perform a transformation. The first transformation is a variable subset of a selected granule. At this time, this requires discovering the granule id and variable id from CMR. That information can then be appended to the root URL and used to call Harmony with the help of the request library.\nHarmony stages transformed data in S3 to make it easy to do additional processing in the cloud. The response that Harmony returns is actually a redirect to the S3 location where your data is staged. Should you call Harmony in a tool that follows redirects, like your web browser, your file will be seamlessly downloaded locally for you. However, should you desire to do additional processing in AWS, you have that option as well by simply looking at the redirected URL. The code snippet below uses “geturl()” to show the URL of your staged data.\n\nvarSubsetConfig = {\n 'granule_id' : 'G2524192900-POCLOUD',\n 'variable_id' : 'red_var'\n}\neoss_var_subset_url = eoss_collection_root+'{granule_id}/?rangeSubset={variable_id}'.format(**varSubsetConfig)\n\nprint('Request URL', eoss_var_subset_url)\n\nwith request.urlopen(eoss_var_subset_url) as response:\n print('URL for data staged in S3:', response.geturl())"
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#caclulate-normalized-difference-water-index-ndwi-and-classify-innundated-areas",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#caclulate-normalized-difference-water-index-ndwi-and-classify-innundated-areas",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Caclulate Normalized Difference Water Index (NDWI) and Classify Innundated Areas",
+ "text": "Caclulate Normalized Difference Water Index (NDWI) and Classify Innundated Areas\n\nLB3 = hls_ts_da_data_LB3 \nLB5 = hls_ts_da_data_LB5\nNDWI = (LB3-LB5)/(LB3+LB5)\nNDWI.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='coolwarm').opts(clim=(-0.5,0.5))\n\n\nwater = NDWI>0\nwater.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='PuOr').opts(clim=(0,1))"
},
{
- "objectID": "notebooks/Harmony API.html#add-on-a-spatial-subset",
- "href": "notebooks/Harmony API.html#add-on-a-spatial-subset",
- "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "section": "Add on a spatial subset",
- "text": "Add on a spatial subset\nThe second transformation is a spatial subset of a selected granule. This can be combined with the request we already built above by simply specifying a bounding box.\n\nspatialSubsetConfig = {\n 'west' : '-128',\n 'south' : '23',\n 'east' : '-63',\n 'north' : '47'\n}\neoss_spatial_subset_url = eoss_var_subset_url+'&bbox={west},{south},{east},{north}'.format(**spatialSubsetConfig)\n\nprint('Request URL', eoss_spatial_subset_url)\n\nwith request.urlopen(eoss_spatial_subset_url) as response:\n print('URL for data staged in S3:', response.geturl())"
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#caclulate-surface-area-of-reservoir-and-plot-time-series",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#caclulate-surface-area-of-reservoir-and-plot-time-series",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "section": "Caclulate surface area of reservoir and plot time series",
+ "text": "Caclulate surface area of reservoir and plot time series\n\nif water.variable.max() == True:\n water_real = water*30*30\nwater_area = water_real.sum(axis=(1,2))\n\n%matplotlib inline\n\nfig, ax = plt.subplots()\n(water_area[:]/1000000).plot(ax=ax, linewidth=2, linestyle = '-', marker='o')\nax.set_title(\"Surface area of waterbody in km2\")\nax.set_ylabel('Area [km^2]')"
},
{
- "objectID": "notebooks/Harmony API.html#reprojection",
- "href": "notebooks/Harmony API.html#reprojection",
- "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "section": "Reprojection",
- "text": "Reprojection\nThe third transformation is a reprojection of the data. This can be combined with the requests we already built above by simply specifying a coordinate reference system. Coordinate reference systems are identified by a common name, EPSG code, or URI. Today, this is based on reference systems supported by gdal. Examples include: ‘CRS:84’, ‘EPSG:32611’.\n\nreprojectionConfig = {\n 'crs' : 'EPSG:32611'\n}\neoss_reprojection_url = eoss_spatial_subset_url+'&crs={crs}'.format(**reprojectionConfig)\n\nprint('Request URL', eoss_reprojection_url)\n\nwith request.urlopen(eoss_reprojection_url) as response:\n print('URL for data staged in S3:', response.geturl())"
+ "objectID": "notebooks/SWORD_River_Demo.html",
+ "href": "notebooks/SWORD_River_Demo.html",
+ "title": "SWORD River Demo",
+ "section": "",
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis Jupyter Notebook contains examples related to querying river reaches (segments) using the SWOT River Database (SWORD) and visualizing results and then querying related datasets for the identified spatial extent through NASA’s Common Metadata Repository (CMR) search.\nExample Use Case: In this example, we geospatially search a single river reach, multiple reaches, and river nodes within the database. We then use geospatial coordinates of the features (here river reaches/node along the Kasai River, a tributary of the Congo River in Africa) to query against a dataset in CMR, namely Pre SWOT Hydrology.\nNote: PO.DAAC is in the process of publishing SWOT sample data to the POCLOUD archive (expected June 2022). Once this is complete, the example below will be updated to work with this sample data as well. SWOT is expected to launch Nov 2022.\nResources - SWOT River Database (SWORD) data can be found here: https://zenodo.org/record/4917236#.YTKLPd9lCST - Other SWOT SWORD documentation can be found here: https://swot.jpl.nasa.gov/documents/4031/ - MEaSUREs - Pre-Surface Water and Ocean Topography (Pre-SWOT) Hydrology data can be found here: https://podaac.jpl.nasa.gov/MEaSUREs-Pre-SWOT?sections=data"
},
{
- "objectID": "notebooks/Harmony API.html#reformatting",
- "href": "notebooks/Harmony API.html#reformatting",
- "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "section": "Reformatting",
- "text": "Reformatting\nNext is a reformatting of the output file of the data. This can be combined with the requests we already built above by simply specifying a format. Examples include: image/tiff’, ‘image/png’\n\nreformattingConfig = {\n 'format' : 'image/png'\n}\neoss_reformatting_url = eoss_reprojection_url+'&format={format}'.format(**reformattingConfig)\n\nprint('Request URL', eoss_reformatting_url)\n\nwith request.urlopen(eoss_reformatting_url) as response:\n print('URL for data staged in S3:', response.geturl())"
+ "objectID": "notebooks/SWORD_River_Demo.html#query-cmr-by-coordinates",
+ "href": "notebooks/SWORD_River_Demo.html#query-cmr-by-coordinates",
+ "title": "SWORD River Demo",
+ "section": "Query CMR by Coordinates",
+ "text": "Query CMR by Coordinates\nWe can use results obtained from the FTS query to then directly and automatically query data using CMR. We will use the coordinate information of a single reach to search for granules (files) available through the Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2 data, which has the collection ID C2036882359-POCLOUD.\nWe query FTS using the previously used reach ID of 13227000061 over Kasai River, a tributary of the Congo River in Africa.\n\nresponse = requests.get(\"https://fts.podaac.earthdata.nasa.gov/rivers/reach/13227000061\")\nfeatureCollection = response_to_FeatureCollection(response)\n\npprint.pprint(response.json(), compact=True, width=60, depth=2)\n\n{'hits': 1,\n 'results': {'13227000061': {...}},\n 'search on': {'exact': False,\n 'page_number': 1,\n 'page_size': 100,\n 'parameter': 'reach'},\n 'status': '200 OK',\n 'time': '5.389 ms.'}\n\n\nThe next cell queries CMR using the coordinates of the reach. Note that coordinates should be listed in the format lon1, lat1, lon2, lat2, lon3, lat3, and so on. The CMR json response proivides a link to the data file (granule) from the Pre SWOT Hydroology GRRATS Daily River Heights data collection that overlaps the geospatial search from FTS-SWORD for the river reaches of interest, e.g. \"href\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc\"\n\nCOLLECTION_ID = \"C2036882359-POCLOUD\" # Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2\n\n# derive lon,lat bounds of nodes along the reach\nlats = [xy[1] for feature in featureCollection['features'] for xy in feature['coordinates']]\nlons = [xy[0] for feature in featureCollection['features'] for xy in feature['coordinates']]\n\n# find max and min of lat and lon\nmaxlat, maxlon, minlat, minlon = max(lats), max(lons), min(lats), min(lons)\n\n# create one single list of lon,lat coordinates that creates a bounding box of extent\ncoord_list = [maxlon, maxlat, maxlon, minlat, minlon, maxlat, minlon, minlat]\n\n# create a string of the list to input into CMR\ncoord_list_string = str(coord_list)[1:-1]\nlonlat_bbox = coord_list_string.replace(\" \", \"\")\n\n# query CMR\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?line={}&echo_collection_id={}&pretty=True\".format(lonlat_bbox, COLLECTION_ID))\n\n# Print out results\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2022-06-08T14:11:18.434Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?line=21.005094,-4.611049,21.005094,-4.633254,20.917191,-4.611049,20.917191,-4.633254&echo_collection_id=C2036882359-POCLOUD&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"boxes\": [\n \"-6.013 12.708 2.185 25.948\"\n ],\n \"time_start\": \"1992-05-01T20:48:54.000Z\",\n \"updated\": \"2022-03-11T19:11:49.948Z\",\n \"dataset_id\": \"Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2\",\n \"data_center\": \"POCLOUD\",\n \"title\": \"Africa_Congo1kmdaily\",\n \"coordinate_system\": \"CARTESIAN\",\n \"day_night_flag\": \"UNSPECIFIED\",\n \"time_end\": \"2018-04-19T22:01:11.000Z\",\n \"id\": \"G2105958909-POCLOUD\",\n \"original_format\": \"UMM_JSON\",\n \"granule_size\": \"5.435943603515625E-5\",\n \"browse_flag\": false,\n \"collection_concept_id\": \"C2036882359-POCLOUD\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"title\": \"Download Africa_Congo1kmdaily.nc\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/s3#\",\n \"title\": \"This link provides direct download access via S3 to the granule\",\n \"hreflang\": \"en-US\",\n \"href\": \"s3://podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"title\": \"Download Africa_Congo1kmdaily.nc.md5\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc.md5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"title\": \"This link provides direct download access via S3 to the granule\",\n \"hreflang\": \"en-US\",\n \"href\": \"s3://podaac-ops-cumulus-public/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc.md5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"title\": \"api endpoint to retrieve temporary credentials valid for same-region direct s3 access\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://archive.podaac.earthdata.nasa.gov/s3credentials\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/service#\",\n \"title\": \"OPeNDAP request URL\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://opendap.earthdata.nasa.gov/collections/C2036882359-POCLOUD/granules/Africa_Congo1kmdaily\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://podaac-tools.jpl.nasa.gov/drive/files/allData/preswot_hydrology/L2/rivers/docs/GRRATS_user_handbookV2.pdf\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://github.com/podaac/data-readers\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://podaac.jpl.nasa.gov/CitingPODAAC\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0MB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C2036882359-POCLOUD\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0MB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C2036882359-POCLOUD\"\n }\n ]\n }\n ]\n }\n}\n\n\nFrom all that was printed out above, we want to hone in specifically on the granule file from the Pre-SWOT MEaSUREs dataset that gives us the data over our desired region.\n\ngranule = cmr_response.json()['feed']['entry'][0]['id']\ngranule\n\n'G2105958909-POCLOUD'\n\n\nIf we want to direct download to our local machine, we want the link with the title Download Africa_Congo1kmdaily.nc. If we want to directly access this granule in the cloud, we want the link entitled, This link provides direct download access via S3 to the granule. Here, we access the netCDF file links and print them out respectively. From here, you’re ready to access the data either locally or via cloud direct access!\nIt should be noted that the links are not always in the same order across datasets (collections), and thus referencing other datasets with the ‘[0]’ and ‘[1]’ indexes may not work for the download and s3 links respectively. For direct download, the link should always start with “https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/”, and for direct cloud access to PO.DAAC data, the link should start with “s3://podaac-ops-cumulus-protected/” no matter the dataset.\n\ngranule_download_link = cmr_response.json()['feed']['entry'][0]['links'][0]['href']\ngranule_cloud_s3_link = cmr_response.json()['feed']['entry'][0]['links'][1]['href']\ngranule_download_link, granule_cloud_s3_link\n\n('https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc',\n 's3://podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc')\n\n\n(Note: the cell above just prints the links of interest for either downloading the file, or accessing from the cloud. It didn’t yet download or access the data. That would be your next step.)"
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- "href": "notebooks/Harmony API.html#continue-exploring",
- "title": "The practice dataset used for this tutorial is no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "section": "Continue Exploring",
- "text": "Continue Exploring\nHarmony’s specification is available online. Feel free to read more and continue exploring how to use Harmony. https://harmony.uat.earthdata.nasa.gov/docs/eoss/0.1.0/spec"
+ "objectID": "quarto_text/Workshops.html",
+ "href": "quarto_text/Workshops.html",
+ "title": "Workshops",
+ "section": "",
+ "text": "We develop tutorials for teaching events that each have their own e-book. We often do this in collaboration with NASA OpenScapes. Tutorials are developed to teach open science and Cloud workflows for specific audiences. They are a snapshot in time as workflows with NASA Earthdata Cloud emerge and evolve."
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- "title": "podaac-data-subscriber NetCDF to Geotiff Conversion",
- "section": "",
- "text": "Author: Jack McNelis, PO.DAAC\n\nSummary\nThe following workflow extracts a single variable of interest (water surface elevation (wse)) from the SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1 and writes the original raster netCDF it to a geotiff via the podaac-data-subscriber tool.\n\n\nRequirements\n\nThis tutorial can be run in any Linux environment. (The subscriber tool can be run in any environment, not just Linux, but the bash script used as the process will only work in a Linux environment.)\nEarthdata Login - An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\nnetrc File - You will need a .netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A .netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata tutorial.\n\n\n\nLearning Objectives\n\nUse the “–process” option in the podaac-data-subscriber\nConvert netCDF files to geotiff files using gdalmdimtranslate in a bash script\n\n\n\nCreate a Bash Script\nInside a bash script entitled, test.sh, set up the following command which calls gdalmdimtranslate to write the wse variable in the downloaded netcdf file to a new geotiff in the same directory:\n#!/bin/bash\ngdalmdimtranslate \\\n -of \"GTiff\" \\\n -co \"COMPRESS=LZW\" \\\n -array \"wse\" \\\n ${1} $(basename $1 | sed 's/.nc/.wse.tif/g')\n\n\nCall the Data Subscriber\nThen, after installing the podaac-data-subscriber package, write the following command in your terminal that uses the podaac-data-subscriber to run the bash script:\npodaac-data-downloader -c SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1 -d ~/test_podaac/ -sd 2022-08-22T19:30:00Z -ed 2022-08-22T19:35:00Z --process \"${PWD}/test.sh\"\nIf you get a permission denied error for test.sh, Make sure the permissions on the .sh file are set to read, write & executable. If you are unsure what your permissions are, in the command prompt you can execute “chmod 0755 test.sh” and permissions should be updated.\nListed Results from the above command should be as follows:\n$ ls\nSWOT_L2_HR_Raster_100m_UTM16R_N_x_x_x_007_522_050F_20220822T192950_20220822T193011_Dx0000_01.nc\nSWOT_L2_HR_Raster_100m_UTM16R_N_x_x_x_007_522_050F_20220822T192950_20220822T193011_Dx0000_01.wse.tif\nSWOT_L2_HR_Raster_100m_UTM16R_N_x_x_x_007_522_051F_20220822T193010_20220822T193031_Dx0000_01.nc\nSWOT_L2_HR_Raster_100m_UTM16R_N_x_x_x_007_522_051F_20220822T193010_20220822T193031_Dx0000_01.wse.tif\nSWOT_L2_HR_Raster_100m_UTM16R_N_x_x_x_007_522_052F_20220822T193030_20220822T193051_Dx0000_01.nc\nSWOT_L2_HR_Raster_100m_UTM16R_N_x_x_x_007_522_052F_20220822T193030_20220822T193051_Dx0000_01.wse.tif\nSWOT_L2_HR_Raster_250m_UTM16R_N_x_x_x_007_522_050F_20220822T192950_20220822T193011_Dx0000_01.nc\nSWOT_L2_HR_Raster_250m_UTM16R_N_x_x_x_007_522_050F_20220822T192950_20220822T193011_Dx0000_01.wse.tif\nSWOT_L2_HR_Raster_250m_UTM16R_N_x_x_x_007_522_051F_20220822T193010_20220822T193031_Dx0000_01.nc\nSWOT_L2_HR_Raster_250m_UTM16R_N_x_x_x_007_522_051F_20220822T193010_20220822T193031_Dx0000_01.wse.tif\nSWOT_L2_HR_Raster_250m_UTM16R_N_x_x_x_007_522_052F_20220822T193030_20220822T193051_Dx0000_01.nc\nSWOT_L2_HR_Raster_250m_UTM16R_N_x_x_x_007_522_052F_20220822T193030_20220822T193051_Dx0000_01.wse.tif\nSWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1.citation.txt\ntest.sh"
+ "objectID": "quarto_text/Workshops.html#s-mode-open-data-workshop",
+ "href": "quarto_text/Workshops.html#s-mode-open-data-workshop",
+ "title": "Workshops",
+ "section": "2022 S-MODE Open Data Workshop",
+ "text": "2022 S-MODE Open Data Workshop\nhttps://espo.nasa.gov/s-mode/content/S-MODE_2022_Open_Data_Workshop - Recordings and Presentations - Github tutorials\nThe Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) science team is hosted a virtual Open Data Workshop on 1 December 2022 from 11:00am – 1:00pm ET to share about the S-MODE mission, to learn about its instrumentation, and find out how to access and use its data products."
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- "href": "notebooks/GIS/SWOTsample_CSVconversion.html",
- "title": "SWOT Shapefile Data Conversion to CSV",
- "section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
+ "objectID": "quarto_text/Workshops.html#swot-ocean-cloud-workshop",
+ "href": "quarto_text/Workshops.html#swot-ocean-cloud-workshop",
+ "title": "Workshops",
+ "section": "2022 SWOT Ocean Cloud Workshop",
+ "text": "2022 SWOT Ocean Cloud Workshop\nhttps://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/\nThe goal of the workshop is to get ready for Surface Water and Ocean Topography (SWOT) and enable the (oceanography) science team to be ready for processing and handling the large volumes of SWOT SSH data in the cloud. Learning objectives focus on how to access the simulated SWOT L2 SSH data from Earthdata Cloud either by downloading or accessing the data on the cloud. PO.DAAC is the NASA archive for the SWOT mission, and once launched will be making data available via the NASA Earthdata Cloud, hosted in AWS."
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- "objectID": "notebooks/GIS/SWOTsample_CSVconversion.html#before-you-start",
- "href": "notebooks/GIS/SWOTsample_CSVconversion.html#before-you-start",
- "title": "SWOT Shapefile Data Conversion to CSV",
- "section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata\nIn this notebook, we will be calling the authentication in the below cell, a work around if you do not yet have a netrc file.\n\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nfrom getpass import getpass\nimport netrc\nfrom platform import system\nfrom os.path import join, isfile, basename, abspath, expanduser\n\ndef setup_earthdata_login_auth(endpoint: str='urs.earthdata.nasa.gov'):\n netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n try:\n username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n print('Please provide your Earthdata Login credentials for access.')\n print('Your info will only be passed to %s and will not be exposed in Jupyter.' % (endpoint))\n username = input('Username: ')\n password = getpass('Password: ')\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n \nsetup_earthdata_login_auth('urs.earthdata.nasa.gov')\n\nPlease provide your Earthdata Login credentials for access.\nYour info will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter.\n\n\nUsername: nickles\nPassword: ···········\n\n\n\nSearch Common Metadata Repository (CMR) for SWOT sample data links by Shapefile\nWe want to find the SWOT sample files that will cross over our region of interest. For this tutorial, we use a shapefile of the United States, finding 44 total granules. Each dataset has it’s own unique collection ID. For the SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1 dataset, we can find the collection ID here.\n\n# the URL of the CMR service\ncmr_url = 'https://cmr.earthdata.nasa.gov/search/granules.json'\n\n#The shapefile we want to use in our search\nshp_file = open('../resources/US_shapefile.zip', 'rb')\n\n#need to declare the file and the type we are uploading\nfiles = {'shapefile':('US_shapefile.zip',shp_file, 'application/shapefile+zip')}\n\n#used to define parameters such as the concept-id and things like temporal searches\nparameters = {'collection_concept_id':'C2263384307-POCLOUD',\n 'page_size': 2000}#, #default will only return 10 granules, so we set it to the max\n #'bounding_box':\"-124.848974,24.396308,-66.885444,49.384358\"} #could also use a bounding box\n\n#request the granules from this collection that align with the shapefile\nresponse = requests.post(cmr_url, params=parameters, files=files)\n\nif len(response.json()['feed']['entry'])>0:\n print(len(response.json()['feed']['entry'])) #print out number of files found\n #print(dumps(response.json()['feed']['entry'][0], indent=2)) #print out the first file information\n\n44\n\n\n\n\nGet Download links from CMR search results\n\ndownloads = []\nfor r in response.json()['feed']['entry']:\n for l in r['links']:\n #if the link starts with the following, it is the download link we want\n if 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/' in l['href']: \n #if the link has \"Reach\" instead of \"Node\" in the name, we want to download it for the swath use case\n if 'Reach' in l['href']:\n downloads.append(l['href'])\nprint(len(downloads)) #should end up with half the number of files above since we only need reach files, not node files\n\n22\n\n\n\n\nDownload the Data into a folder\n\n#Create folder to house downloaded data \nfolder = Path(\"SWOT_sample_files\")\n#newpath = r'SWOT_sample_files' \nif not os.path.exists(folder):\n os.makedirs(folder)\n\n\nfor f in downloads:\n urlretrieve(f, f\"{folder}/{os.path.basename(f)}\")\n\n\n\nUnzip shapefiles in existing folder\n\nfor item in os.listdir(folder): # loop through items in dir\n if item.endswith(\".zip\"): # check for \".zip\" extension\n zip_ref = zipfile.ZipFile(f\"{folder}/{item}\") # create zipfile object\n zip_ref.extractall(folder) # extract file to dir\n zip_ref.close() # close file\n\n\n\nMerging two seperate shapefiles into one\n\n# Read shapefiles\nSWOT_1 = gpd.read_file(folder / 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.shp')\nSWOT_2 = gpd.read_file(folder / 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.shp')\n \n# Merge/Combine multiple shapefiles into one\nSWOT_Merge = gpd.pd.concat([SWOT_1, SWOT_2])\n \n#Export merged geodataframe into shapefile\nSWOT_Merge.to_file(folder / 'SWOT_Merge.shp')\n\n\n\nMerging multiple shapefiles from within a folder\n\n# State filename extension to look for within folder, in this case .shp which is the shapefile\nshapefiles = folder.glob(\"*.shp\")\n\n# Merge/Combine multiple shapefiles in folder into one\ngdf = pd.concat([\n gpd.read_file(shp)\n for shp in shapefiles\n]).pipe(gpd.GeoDataFrame)\n\n# Export merged geodataframe into shapefile\ngdf.to_file(folder / 'SWOTReaches.shp')\n\n\n\nConverting to CSV\nConverting merged geodataframe into a csv file.\n\ngdf.to_csv(folder / 'csvmerge.csv')\n\n\n\nQuerying a Shapefile\nIf you want to search for a specific reach id or a specific length of river reach that is possible through a spatial query using Geopandas.\nUtilizing comparison operators (>, <, ==, >=, <=).\nYou can zoom into a particular river reach by specifying by it’s reach_id or looking for duplicate overlapping river reaches.\n\nreach = gdf.query(\"reach_id == '74292500301'\")\nreach\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n2\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING (-98.50490 40.06789, -98.50525 40.0...\n\n\n308\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING (-98.50490 40.06789, -98.50525 40.0...\n\n\n262\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING (-98.50490 40.06789, -98.50525 40.0...\n\n\n51\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING (-98.50490 40.06789, -98.50525 40.0...\n\n\n308\n74292500301\n-1.000000e+12\n-1.000000e+12\nno_data\n40.063235\n-98.551296\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n54.0\n387.837794\n47\n3200409.359\n9496.587434\n-1.000000e+12\n0\n2\n1\nLINESTRING (-98.50490 40.06789, -98.50525 40.0...\n\n\n\n\n5 rows × 111 columns\n\n\n\n\nWSE = gdf.query('wse > 75')\nWSE\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n263\n77158000011\n7.132750e+08\n7.132750e+08\n2022-08-08T11:5628Z\n25.297171\n-108.473158\nno_data\n123.71461\n-1.000000e+12\n0.00000\n...\n69.5\n1719.195048\n49\n9731.610\n9731.609922\n-1.000000e+12\n0\n2\n1\nLINESTRING (-108.49317 25.28405, -108.49287 25...\n\n\n119\n73282800021\n7.134418e+08\n7.134418e+08\n2022-08-10T10:1658Z\n33.634414\n-87.209808\nno_data\n88.18387\n-1.000000e+12\n4.26350\n...\n211.5\n3285.033201\n57\n687962.665\n11346.636403\n-1.000000e+12\n0\n2\n1\nLINESTRING (-87.23478 33.62552, -87.23452 33.6...\n\n\n630\n74267700121\n7.134419e+08\n7.134419e+08\n2022-08-10T10:1834Z\n38.778477\n-84.107260\nno_data\n134.81383\n-1.000000e+12\n2.68570\n...\n669.0\n2311.101872\n57\n2560861.191\n11466.933285\n-1.000000e+12\n0\n2\n1\nLINESTRING (-84.17021 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+ "text": "2021 Cloud AGU Workshop\nhttps://nasa-openscapes.github.io/2021-Cloud-Workshop-AGU\nThe 2021 Cloud Workshop at AGU: Enabling Analysis in the Cloud Using NASA Earth Science Data is a virtual half-day collaborative open science learning experience aimed at exploring, learning, and promoting effective cloud-based science and applications workflows using NASA Earthdata Cloud data, tools, and services (among others), in support of Earth science data processing and analysis in the era of big data."
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+ "text": "2021 Cloud Hackathon\nhttps://nasa-openscapes.github.io/2021-Cloud-Hackathon\nThe Cloud Hackathon: Transitioning Earthdata Workflows to the Cloud is a virtual 5-day (4 hours per day) collaborative open science learning experience aimed at exploring, creating, and promoting effective cloud-based science and applications workflows using NASA Earthdata Cloud data, tools, and services (among others), in support of Earth science data processing and analysis in the era of big data. Its goals are to:\n\nIntroduce Earth science data users to NASA Earthdata cloud-based data products, tools and services in order to increase awareness and support transition to cloud-based science and applications workflows.\nEnable science and applications workflows in the cloud that leverage NASA Earth Observations and capabilities (services) from within the NASA Earthdata Cloud, hosted in Amazon Web Services (AWS) cloud, thus increasing NASA Earthdata data utility and meaningfulness for science and applications use cases.\nFoster community engagement utilizing Earthdata cloud tools and services in support of open science and open data."
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- "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
+ "text": "These instructions are for anyone that would like to contribute tutorials that utilize (in part) NASA Earthdata to the PO.DAAC Cookbook. If you have cloned the PO.DAAC tutorials GitHub repository with all of the PO.DAAC Cookbook Notebooks, you can make changes on your copy of the tutorials and then create a pull request that will be reviewed by our team to potentially add your content. Follow these intructions on how to create a pull request in GitHub. If adding a new tutorial, within the Pull Request, state the section of the Cookbook you think your content would fit best and a member of PO.DAAC may link the tutorial so it renders within the Cookbook.\nAdded content will only be accepted if it follows these guidlines:\n\nFor Jupyter Notebook Tutorials, follow this template as a standard.\nFor tutorials outside of Jupyter Notebooks, the format must have the following sections:\n\nTitle\nAuthor Name/Affiliation\nSummary\nRequirements (the compute environment used, requirements to run the notebook (needed packages etc.))\nLearning Objectives\n\nThe tutorial must be tested successfully and reviewed by a PO.DAAC member non-author. (This will be done before merging the pull request.)"
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- "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
+ "text": "These instructions are for anyone that would like to contribute tutorials that utilize (in part) NASA Earthdata to the PO.DAAC Cookbook. If you have cloned the PO.DAAC tutorials GitHub repository with all of the PO.DAAC Cookbook Notebooks, you can make changes on your copy of the tutorials and then create a pull request that will be reviewed by our team to potentially add your content. Follow these intructions on how to create a pull request in GitHub. If adding a new tutorial, within the Pull Request, state the section of the Cookbook you think your content would fit best and a member of PO.DAAC may link the tutorial so it renders within the Cookbook.\nAdded content will only be accepted if it follows these guidlines:\n\nFor Jupyter Notebook Tutorials, follow this template as a standard.\nFor tutorials outside of Jupyter Notebooks, the format must have the following sections:\n\nTitle\nAuthor Name/Affiliation\nSummary\nRequirements (the compute environment used, requirements to run the notebook (needed packages etc.))\nLearning Objectives\n\nThe tutorial must be tested successfully and reviewed by a PO.DAAC member non-author. (This will be done before merging the pull request.)"
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- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\nSet up libraries needed to run demo\n\nimport os\nfrom harmony import BBox, Client, Collection, Request, Environment\nimport xarray as xr\nimport netCDF4 as nc\nimport matplotlib.pyplot as plt\n\nSet up collection to run concise and how many granules to concatenate\n\ncollection_id = 'C1940473819-POCLOUD'\nmax_results = 5\n\nSetup harmony client to make our harmony request.\nCreate our request with the collection we want to concatenate, set concatenate to true, how many granules we want to concatenate, set skip preview to true so job doesn’t pause, and the format output we want.\nCheck to make sure our harmony request is valid.\n\nharmony_client = Client(env=Environment.PROD)\n\ncollection = Collection(id=collection_id)\n\nrequest = Request(\n collection = collection,\n concatenate = True,\n max_results = max_results,\n skip_preview = True,\n format=\"application/x-netcdf4\",\n)\n\nrequest.is_valid()\n\nTrue\n\n\nNow that we have a valid request we simply need to call the submit function using the client we created earlier and pass in the request as a parameter.\nTip: if you want to see the request before submitting it, use the request_as_curl function on the client to get an equivalent curl command for the request that will be submitted.\n\nprint(harmony_client.request_as_curl(request))\n\njob1_id = harmony_client.submit(request)\n\ncurl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=d2e082a6b1cc5b8ebff64aba5ebfd18e' -H 'User-Agent: Darwin/20.6.0 python-requests/2.26.0 harmony-py/0.4.2 CPython/3.8.10' 'https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&format=application%2Fx-netcdf4&maxResults=5&concatenate=true&skipPreview=true'\n\n\nAfter submitting the request it is possible to retrieve the current processing status by using the job ID returned from the submission.\nIf the request is still running, we can wait until the Harmony request has finished processing. This cell will wait until the request has finised.\n\nprint(f'\\n{job1_id}')\n\nprint(harmony_client.status(job1_id))\n\nprint('\\nWaiting for the job to finish')\nresults = harmony_client.result_json(job1_id, show_progress=True)\n\n\n848c36db-0fe4-472e-b0e4-51abfba08101\n{'status': 'running', 'message': 'CMR query identified 2406552 granules, but the request has been limited to process only the first 5 granules because you requested 5 maxResults.', 'progress': 0, 'created_at': datetime.datetime(2022, 7, 27, 21, 37, 40, 347000, tzinfo=tzutc()), 'updated_at': datetime.datetime(2022, 7, 27, 21, 37, 40, 577000, tzinfo=tzutc()), 'created_at_local': '2022-07-27T14:37:40-07:00', 'updated_at_local': '2022-07-27T14:37:40-07:00', 'data_expiration': datetime.datetime(2022, 8, 26, 21, 37, 40, 347000, tzinfo=tzutc()), 'data_expiration_local': '2022-08-26T14:37:40-07:00', 'request': 'https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&format=application%2Fx-netcdf4&maxResults=5&concatenate=true&skipPreview=true', 'num_input_granules': 5}\n\nWaiting for the job to finish\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\nAfter the harmony job is finished we download the resulting concatenated granule file.\n\nprint('\\nDownloading results:')\nfilename = None\nfutures = harmony_client.download_all(job1_id, overwrite=True)\nfor f in futures:\n print(f)\n print(f.result()) # f.result() is a filename, in this case\n filename = f.result()\nprint('\\nDone downloading.')\n\n\nDownloading results:\n<Future at 0x108e8ac70 state=running>\nC1940473819-POCLOUD_merged.nc4\n\nDone downloading.\n\n\nWith the output file downloaded, now we can open concatenated granule using xarray to inspect some of the metadata.\nNote: In some of the collections the time variable has a time dimension and when we concatenate files we add a subset_index into the time dimension which causes the time variable have two dimension. Xarray doesn’t allow the time variable have two dimensions so when using xarray to open concatenated files the time variable might need to be dropped. The file can be open with netcdf library\n\n#some collections time variabe has a time dimension which can cause an exception when we concatenate and makes time two dimension\ntry:\n ds = xr.open_dataset(filename, decode_times=False)\nexcept xr.core.variable.MissingDimensionsError:\n ds = xr.open_dataset(filename, decode_times=False, drop_variables=['time'])\n\nprint(list(ds.variables))\n \nassert len(ds.coords['subset_index']) == max_results\n\n['subset_files', 'lat', 'lon', 'sea_surface_temperature', 'sst_dtime', 'quality_level', 'sses_bias', 'sses_standard_deviation', 'l2p_flags', 'sea_surface_temperature_4um', 'quality_level_4um', 'sses_bias_4um', 'sses_standard_deviation_4um', 'wind_speed', 'dt_analysis']\n\n\nAfter opening the file we can use matplotlib to create a plot for each subindex where each subindex represents the data for the granule file. We will plot sea_surface_temperature for each granule using subset_index dimension.\n\nvariable = None\nfor v in list(ds.variables):\n if v not in ['subset_files', 'lat', 'lon']:\n variable = v\n break;\n\nfor index in range(0, max_results):\n \n ds.isel(subset_index=index).plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=variable,\n s=1,\n levels=9,\n cmap=\"jet\",\n aspect=2.5,\n size=9\n )\n \n plt.xlim( 0., 360.)\n plt.ylim(-90., 90.)\n plt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWe can also plot out the entire granule file which would plot all the data of the concatenated files.\n\nds.plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=variable,\n s=1,\n levels=9,\n cmap=\"jet\",\n aspect=2.5,\n size=9\n)\n\nplt.xlim( 0., 360.)\nplt.ylim(-90., 90.)\nplt.show()"
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+ "text": "Instructions for PO.DAAC Contributors\n\nHow the Cookbook was Built\nThe PO.DAAC Cookbook is built using Quarto. To build this particular Quarto book, we used these instructions from NASA Openscapes. It’s a Quarto book tutorial on how to copy (fork or download) an existing Quarto website like this one and adapt it for your own uses.\n\n\nSetting up your Work Station\nAdmittedly, there are many ways to set up your work station to effectively accomplish contributing to or editing the PO.DAAC Cookbook quarto website. Steps 1, 2, 4, and 6 are universal to edit the cookbook, but steps 3 and 5 can have variations. The following is a method we have found to be helpful:\n1. Creat a GitHub Account - If you do not have one already, create a GitHub account here: https://github.com/.\n2. Join a team on the podaac repository - Once you have a GitHub account, in the podaac repository, request to join the PO.DAAC team that best suits your position here. This will give you the permissions required to contribute as a PO.DAAC Member.\n3. Set Up your Coding Workspace - On a local machine, I tend to deploy Jupyter Lab (where I do most of my coding) from Anaconda Navigator. RStudio, VS Code, etc. could also work, depending on which you prefer. The workflow would also work on a cloud environment, where I also tend to use the Jupyter Lab interface.\n4. Install Quarto - download and install Quarto’s latest version here.\n5. Pick your Prefered Method to Interface with GitHub in your Workspace - GitHub Desktop makes it easy for me to track my changes and create Pull Requests without needing to remember commands in a the command line interface. If you prefer to use the command line, this is a comprehensive and maintained list of git commands that may be useful.\n6. Clone the podaac/tutorials GitHub Repository - Instructions to do this are in the Tech Guides Section of this Cookbook. If using GitHub Desktop, say no when it asks you if you would like to fork the repository. A fork creates a completely independent copy of the repository while a clone creates a linked copy that will continue to synchronize with the tutorials repository.\nIf you’ve completed the above, you should have all the necessary ingredients to contribute to the PO.DAAC Cookbook!\n\n\nTutorials Repository Organization\nEach chapter in our Cookbook is a separate .qmd markdown file within the quarto_text directory. The notebooks directory holds all of our internal tutorials that get rendered in the Cookbook. images contains all figures embedded within the Cookbook files.\nThe Cookbook structure (i.e. the order of sections and chapters) is determined in the _quarto.yml file in the root directory. We can shuffle chapter order by editing the _quarto.yml file, and add new chapters by adding to the _quarto.yml and creating a new file in the appropriate sub-directory that is indicated in _quarto.yml.\n\n\nHow to Edit Cookbook Content\n1. Create a New Branch in GitHub - In a GitHub repository, the main source of code for that repo is deployed in what is called the “Master” branch. This is the branch that the PO.DAAC Cookbook is rendered from. To edit, creating a new branch is important so changes are not overwritten on the master branch if another individual is working on the same file as you. It helps to have your own branch to work on (named however you like) to make changes and then merge those changes with the master branch after changes are done. After merging, it is common practice to delete the branch you created. I like to create a new branch from GitHub Desktop like so:\n\n\n\n\n\n2. Navigate to the tutorials Folder in your Coding Workspace - once you’ve created a new branch, any code you modify in your coding workspace from the repository you cloned should have tracked changes in your new branch. You can open any file in the tutorials folder and start editing! Here is what my Jupyter Lab workspace looks like:\n\n\n\n\n\n3. Tip for Previewing Changes in the Cookbook - To implement changes in the actual Cookbook, usually those changes need to be committed to your new branch and pulled into the master branch using a Pull Request (outlined below). Most of the time though, it is nice to see what your changes would look like visually in the Cookbook before you commit to them. To open up a preview page of the cookbook from your workspace, open up the terminal and change the directory to your tutorials folder location. Once there, type in quarto preview and another tab should open up in your browser that changes every time you save a change to your files. Here is a screenshot of my terminal opening the preview session:\n\n\n\n\n\nNote: the warnings about external files are fine. We do not host the external files in our repo, but link to them from other repos around GitHub, so they will not be rendered in the preview session. They will render in the actual Cookbook when your branch is merged with the master branch.\n4. Open the File you Wish to Edit - Most text within the Cookbook can be found in a .qmd file within the quarto_text folder\n5. Make Edits - GitHub should track your changes automatically. For example below, I have opened the ‘Contribute.qmd’ file in my Jupyter Lab and in the GitHub Desktop application, it shows all of the changes I have made in green and the old version in red. Here, I changed the text describing tutorial guidelines. Here is a helpful guide for Markdown Basics in Quarto.\n\n\n\n\n\n6. Commit Changes to your Branch and Push to Origin - I like to use GitHub Desktop for this, but you can also use the terminal using git commands.\n\n\n\n\n\n7. Create a Pull Request to Merge your Branch with the Master Branch - From the GitHub Desktop, you can then select “Create Pull Request” and it should open a browser window taking you to the tutorials repository in GitHub. In that browser window, if the information is not already populated from your commit, Add a descriptive title, outline any changes made, add reviewers within PO.DAAC that you think would be able to review your notebook, and then press “Create pull request.” A reviewer will look over your changes and either give feedback on improvements to be made before merging is enabled or accept the changes and merge your branch into the master branch.\n\n\n\n\n\n8. Delete your Branch after Merge is Complete - it is common practice to delete old branches and start again with new branches for new edits.\n\n\nHow to Add Tutorials and Display them in the Cookbook\nAdding tutorials to the podaac/tutorials GitHub repository as a PO.DAAC Contributor should follow the same instructions as those outside of PO.DAAC. See above.\nAfter a tutorial has been added to the repository, however, in order for it to display in the Cookbook, a couple more files need to be updated:\n1. The _quarto.yml file - This file is essentially the table of contents of the PO.DAAC Cookbook, telling quarto where to place a tutorial or file in the Cookbook. Write the path of the added tutorial in the appropriate desired location.\n2. The specific landing page .qmd file - This is the .qmd file that houses the section the tutorial will be in. I usually link the added tutorial on this homepage for the section.\nFor Example, here is a screenshot of the current ECCO portion of the _quarto.yml file and the ECCO.qmd file. The Use Case Demo notebook is hightlighted in both places it is linked. The notebook sits under multiple sections, first and formost, the “Tutorials” Section, and within that, the “Dataset Specific Examples” Section and finally, the “ECCO” page. In the .yml file, we gave the tutorial a title after the “text:” portion, which will be visible on the left hand side table of contents in the rendered Cookbook. Underneath the title, the notebook GitHub path is written out after “href:” as shown. The ECCO.qmd file hosts the information regarding the available ECCO tutorials, and somewhere within this page, the new tutorial should be linked. Note: this link may have a slightly different path starting point than the .yml file because the .qmd files are within a subfolder of the tutorials repo. You will likely need to add a “../” before the path in the .qmd file.\n\n\n\n\n\n\nGuidance for Dataset Specific Tutorials Section\nOnce a couple tutorials have been created for a particular mission, it is useful to add a page under the “Dataset Specific Tutorials” Section in the Cookbook for the tutorials. To add one, create a .qmd file in the quarto_text folder with the mission name as the file name. A good example for this would be the ECCO.qmd file or the SWOT.qmd file. Each Dataset Specific Landing Page should have the following sections:\n\nTitle of Mission\nBackground - a brief over view of the mission and products that links to the PO.DAAC webpage for the mission\nData Resources & Tutorials - this section can have sub-sections grouping resources)\nAdditional Resources - links to workshops or other useful materials relating to the mission)\n\n\n\n\nHow to Link to Notebook Tutorials Hosted in Other Repositories\nWe can include remote notebooks in the Cookbook by explicitly importing them with the following steps. This will create a local copy of them that have additional preamble inserted that includes the original urls and attribution for the notebook.\n\nNavigate to the _import directory.\nOpen assets.json in a text editor. Copy an existing example and use the same structure to indicate the remote notebook you’d like to include. You can write Markdown in the preamble.\n\ntitle: this will be the new title of the notebook\npreamble: this text will be inserted into the notebook below the new title. It should include any description and attribution of the notebook. The preamble is followed by two URL fields (next):\nsource: the url landing page of the specific notebook.\nurl: the raw url of the notebook. (i.e. it usually starts with https://raw.githubusercontent.com/ and can be found by clicking the raw button at the top of a GitHub file)\ntarget: the local filename to give the notebook. The notebook will be saved in the external folder in the root directory.\nprocess: true or false: whether or not to include the entire entry when running the quarto_import.py script\n\nAfter these updates to _import/assets.json, do the following in the terminal, which will return a confirmation of the file that has been processed:\n\ncd _import\nconda env update -f environment.yml\nconda activate quarto-import\npython quarto_import.py -f assets.json\n\nThen update _quarto.yml by adding your file (external/<target>) to the appropriate location in the Cookbook. Also link the external notebook in any .qmd file landing pages that are necessary (See “How to Add Tutorials and Display them in the Cookbook” above)."
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- "title": "This Notebook is no longer supported, a newer version exists here.",
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+ "href": "quarto_text/CloudOptimizedExamples.html",
+ "title": "Cloud Optimized Examples",
"section": "",
- "text": "This will demonstrate how to subset swath/L2 data with the data and services hosted on the cloud."
+ "text": "Tutorials highlighting Cloud Optimized Formats.\n\nExample: Zarr Access for NetCDF4 Files\nThis tutorial teaches about the Zarr format and library for accessing data in the cloud, building on prior knowledge from CMR and Earthdata Login tutorials, working through an example of using the EOSDIS Zarr Store to access data using XArray.\nZarr Hackathon Tutorial\n\n\nExample: Zarr Dataset\nThis tutorial opens PO.DAAC MUR dataset in a zarr format.\nZarr-eosdid-store Library\n\n\nExample: Opening NetCDF’s in Zarr Format\nThis tutorial leverages the Zarr reformatter service (available through Harmony API) to access ocean bottom pressure (OBP) data from ECCO V4r4 in Zarr format (instead of native netCDF4 file format).\nZarr2netCDF Example"
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- "title": "This Notebook is no longer supported, a newer version exists here.",
- "section": "Before Beginning",
- "text": "Before Beginning\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\n\nLearning Objective:\n\nSubset a specific file/granule that has already been found using the podaac L2 subsetter\n\n\nfrom harmony import BBox, Client, Collection, Request, Environment, LinkType\nfrom IPython.display import display, JSON\nimport tempfile\nimport shutil\nimport xarray as xr\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom pandas.plotting import register_matplotlib_converters\nimport numpy as np"
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+ "href": "quarto_text/GIS.html",
+ "title": "GIS",
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+ "text": "Exploring Water Surface Extent with Satellite Data\nSWOT: Through a GIS Lens\nUtilizing GRACE data over the Colorado River Basin\nThe Oceans & Melting Glaciers: OMG & GRACE"
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- "title": "This Notebook is no longer supported, a newer version exists here.",
- "section": "Subset of a PO.DAAC Granule",
- "text": "Subset of a PO.DAAC Granule\nWe build onto the root URL in order to actually perform a transformation. The first transformation is a subset of a selected granule. At this time, this requires discovering the granule id from CMR. That information can then be appended to the root URL and used to call Harmony with the help of the request library.\nNotes: The L2 subsetter current streams the data back to the user, and does not stage data in S3 for redirects. This is functionality we will be adding over time.\nCreate a Harmony-py client\n\nharmony_client = Client(env=Environment.PROD)\n\nWith the client created, we can contruct and validate the request. As this is a subsetting + concatenation request, we specify options on the request that define spatial bounds, variables we are interested in, temporal bounds, and indicated the result should be concatenated.\n\ncollection = Collection(id='C1940471193-POCLOUD') #Jason-1 GDR SSHA version E NetCDF\n\nrequest = Request(\n collection=collection,\n spatial=BBox(0,0,1,1), # 1 degree box\n granule_id='G1969371708-POCLOUD' #JA1_GPR_2PeP374_173_20120303_121639_20120303_125911.nc\n)\n\nrequest.is_valid()\n\nTrue\n\n\nNow that we have a valid request we simply need to call the submit function using the client we created earlier and pass in the request as a parameter.\nTip: if you want to see the request before submitting it, use the request_as_curl function on the client to get an equivalent curl command for the request that will be submitted.\n\nprint(harmony_client.request_as_curl(request))\njob_id = harmony_client.submit(request)\nprint(f'Job ID: {job_id}')\n\ncurl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=0c2f471216e220fc8ef81d7f18a5ddfb' -H 'User-Agent: Windows/10 CPython/3.8.12 harmony-py/0.4.2 python-requests/2.25.1' 'https://harmony.earthdata.nasa.gov/C1940471193-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%280%3A1%29&subset=lon%280%3A1%29&granuleId=G1969371708-POCLOUD'\nJob ID: 8fad49e8-c95f-4a98-8e99-d5b053d86de7\n\n\n\nprint(harmony_client.status(job_id))\n\nprint('\\nWaiting for the job to finish')\nresults = harmony_client.result_json(job_id, show_progress=True)\n\n{'status': 'running', 'message': 'The job is being processed', 'progress': 0, 'created_at': datetime.datetime(2022, 10, 25, 17, 5, 0, 76000, tzinfo=tzutc()), 'updated_at': datetime.datetime(2022, 10, 25, 17, 5, 0, 438000, tzinfo=tzutc()), 'created_at_local': '2022-10-25T10:05:00-07:00', 'updated_at_local': '2022-10-25T10:05:00-07:00', 'data_expiration': datetime.datetime(2022, 11, 24, 17, 5, 0, 76000, tzinfo=tzutc()), 'data_expiration_local': '2022-11-24T09:05:00-08:00', 'request': 'https://harmony.earthdata.nasa.gov/C1940471193-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat(0%3A1)&subset=lon(0%3A1)&granuleId=G1969371708-POCLOUD', 'num_input_granules': 1}\n\nWaiting for the job to finish\n\n\n [ Processing: 0% ] | | [|]\n\n\nConnectionError: ('Connection aborted.', TimeoutError(10060, 'A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond', None, 10060, None))\n\n\n\ntemp_dir = tempfile.mkdtemp()\nfutures = harmony_client.download_all(job_id, directory=temp_dir, overwrite=True)\nfile_names = [f.result() for f in futures]\nfile_names\n\n\nds = xr.open_dataset(file_names[0])\nds\n\n\nds.ssha.plot()"
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+ "href": "quarto_text/GIS.html#gis-storymaps-of-select-datasets",
+ "title": "GIS",
+ "section": "",
+ "text": "Exploring Water Surface Extent with Satellite Data\nSWOT: Through a GIS Lens\nUtilizing GRACE data over the Colorado River Basin\nThe Oceans & Melting Glaciers: OMG & GRACE"
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- "href": "notebooks/Cloud L2SS subset and plot - JH.html#verify-the-subsetting-worked",
- "title": "This Notebook is no longer supported, a newer version exists here.",
- "section": "Verify the subsetting worked",
- "text": "Verify the subsetting worked\nBounds are defined earlier\n\nlat_max = ds.lat.max()\nlat_min = ds.lat.min()\n\nlon_min = ds.lon.min()\nlon_max = ds.lon.max()\n\n\nif lat_max < bblat_max and lat_min > bblat_min:\n print(\"Successful Latitude subsetting\")\nelse:\n assert false\n\n \nif lon_max < bblon_max and lon_min > bblon_min:\n print(\"Successful Longitude subsetting\")\nelse:\n assert false"
+ "objectID": "quarto_text/GIS.html#gis-jupyter-notebook-tutorials",
+ "href": "quarto_text/GIS.html#gis-jupyter-notebook-tutorials",
+ "title": "GIS",
+ "section": "GIS Jupyter Notebook Tutorials",
+ "text": "GIS Jupyter Notebook Tutorials\n\nGIS SWOT shapefile exploration\nNetCDF to Geotiff Conversion: SWOT Data Example"
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- "title": "This Notebook is no longer supported, a newer version exists here.",
- "section": "Plot swath onto a map",
- "text": "Plot swath onto a map\n\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\n\nplt.scatter(ds.lon, ds.lat, lw=2, c=ds.ssha)\nplt.colorbar()\nplt.clim(-0.3, 0.3)\n\nplt.show()"
+ "objectID": "quarto_text/Webinars.html",
+ "href": "quarto_text/Webinars.html",
+ "title": "Webinars",
+ "section": "",
+ "text": "2020: Making Waves: PO.DAAC’s Journey from Servers to a Cloud Environment - Our move to the cloud, what it means for the data, services, and resources PO.DAAC provides, and what it means for the data user community.\n2021: Surfing Ocean Data in the Cloud - The Beginner’s Guide to PO.DAAC in the NASA Earthdata Cloud.\n2022: Moving Code to the Data: Analyzing Sea Level Rise Using Earth Data in the Cloud - How in-cloud analysis can be achieved with minimal knowledge of AWS cloud walking through a Jupyter Notebook tutorial."
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- "title": "Use Case: Study Amazon Estuaries with Data from the EOSDIS Cloud",
+ "objectID": "quarto_text/Experimental.html",
+ "href": "quarto_text/Experimental.html",
+ "title": "In Development/Experimental",
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- "text": "Read more about the EOSDIS Cloud at NASA Earthdata."
+ "text": "Content coming soon!"
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- "title": "Use Case: Study Amazon Estuaries with Data from the EOSDIS Cloud",
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- "text": "Overview\nThis tutorial uses satellite data products to analyze the relationships between river height and land water equivalent thickness in the Amazon River estuary. Users can expand on these examples to also include sea surface salinity, sea surface temperature, and ocean color for example, for a more comprehensive exploration of the Amazon river basin’s estuary and coastal region. The contents are useful for the ocean, coastal and terrestrial hydrosphere disciplines, showcasing how to use on premises and Earthdata cloud datasets, existing Earthdata cloud services and functionalities, and Earthdata User Interface (UI) and Application Programming Interfaces (API).\n\nLearning objectives:\n\nSearch for land water equivalent (LWE) thickness (GRACE/GRACE-FO) and river discharge data (MEaSUREs Pre-SWOT)\nAccess LWE thickeness dataset in Zarr format from Earthdata Cloud (AWS) using the Harmony API (specifically the Zarr reformatted service)\nAccess discharge dataset from PODAAC on premise (server) data archive\nSubset both, plot and compare coincident data.\n\n\n\nDatasets\nThe tutorial uses a combination of cloud and on premises datasets: - JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06 Version 02 - Provides land water equivalent (LWE) thickness for observing seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, pointing to a wet season. Source data are from GRACE and GRACE-FO. - Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2 - Provides virtual gauges to stand in for discharge data from Surface Water and Ocean Topography (SWOT). MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute. Data were produced for the Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program."
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+ "href": "quarto_text/SMAP.html",
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+ "text": "The Soil Moisture Active Passive (SMAP) satellite is designed to principally measure soil moisture and freeze/thaw state from space for all non-liquid water surfaces globally within the top layer of the Earth. The mission additionally provides a value-added Level 4 terrestrial carbon dataset derived from SMAP observations. More information can be found on PO.DAAC’s SMAP webpage."
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- "title": "Use Case: Study Amazon Estuaries with Data from the EOSDIS Cloud",
- "section": "Requirements",
- "text": "Requirements\nThis notebook was developed to run in the AWS cloud (us-west-2 region), next to the Earthdata Cloud (PO.DAAC) data holdings, to leverage cloud optimized data formats (e.g. Zarr) and the Earthdata Harmony API, specifically the Zarr refomrating service. For more informaion on Harmony, please see https://harmony.earthdata.nasa.gov/ .\n\nBefore you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\nIn this notebook, we will be calling the authentication in the below cell, a work around if you do not yet have a netrc file.\nThis notebook was developed in Python 3.6.\n\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nfrom getpass import getpass\nimport netrc\nfrom platform import system\nfrom os.path import join, isfile, basename, abspath, expanduser\n\ndef setup_earthdata_login_auth(endpoint: str='urs.earthdata.nasa.gov'):\n netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n try:\n username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n print('Please provide your Earthdata Login credentials for access.')\n print('Your info will only be passed to %s and will not be exposed in Jupyter.' % (endpoint))\n username = input('Username: ')\n password = getpass('Password: ')\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n \nsetup_earthdata_login_auth('urs.earthdata.nasa.gov')\n\nPlease provide your Earthdata Login credentials for access.\nYour info will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter.\n\n\nUsername: nickles\nPassword: ···········\n\n\n\nimport time\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom urllib import request\nfrom urllib.request import urlopen\nfrom harmony import BBox, Client, Collection, Request, Environment, LinkType\nimport cartopy.crs as ccrs\nimport cartopy\nimport zarr\nimport s3fs\nfrom IPython.display import HTML\nfrom json import dumps, loads\n\n\n\nEndpoints\nSet a few endpoints for use during the remainder of the workflow:\n\ncmr = \"cmr.earthdata.nasa.gov\"\nurs = \"urs.earthdata.nasa.gov\"\nharmony = \"harmony.earthdata.nasa.gov\"\n\ncmr, urs, harmony\n\n('cmr.earthdata.nasa.gov',\n 'urs.earthdata.nasa.gov',\n 'harmony.earthdata.nasa.gov')"
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+ "href": "quarto_text/SMAP.html#background",
+ "title": "SMAP",
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+ "text": "The Soil Moisture Active Passive (SMAP) satellite is designed to principally measure soil moisture and freeze/thaw state from space for all non-liquid water surfaces globally within the top layer of the Earth. The mission additionally provides a value-added Level 4 terrestrial carbon dataset derived from SMAP observations. More information can be found on PO.DAAC’s SMAP webpage."
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- "title": "Use Case: Study Amazon Estuaries with Data from the EOSDIS Cloud",
- "section": "Cloud data from JPL GRACE and GRACE-FO Mascon",
- "text": "Cloud data from JPL GRACE and GRACE-FO Mascon\n\n\n\ngrace mascon\n\n\nJPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06 Version 02\nThis dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (Version2/RL06). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. This version of the data employs a Coastal Resolution Improvement (CRI) filter that reduces signal leakage errors across coastlines. The water storage/height anomalies are given in equivalent water thickness units (cm). The solution provided here is derived from solving for monthly gravity field variations in terms of geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the solution inversion to intrinsically remove correlated error. Thus, these Mascon grids do not need to be destriped or smoothed, like traditional spherical harmonic gravity solutions. The complete Mascon solution consists of 4,551 relatively independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. A subset of these individual mascons span coastlines, and contain mixed land and ocean mass change signals.\nFor more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/.\nFor a detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. For a detailed description of the CRI filter implementation, please see Wiese et al., 2016, doi: 10.1002/2016WR019344.\n\nMetadata\nData from the TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2 dataset can be obtained from AWS S3. Use its ShortName to retrieve the collection metadata from CMR:\n\ngrace_ShortName = \"TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\"\ngrace_ShortName\n\n'TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2'\n\n\n\nCollection (dataset)\nGet the UMM Collection metadata using requests.get:\n\nr = requests.get(url=f\"https://{cmr}/search/collections.umm_json\", \n params={'provider': \"POCLOUD\", \n 'ShortName': grace_ShortName})\n\ngrace_coll = r.json()\ngrace_coll['hits']\n\n1\n\n\nThere should be only one result. Select and print its CMR Search metadata:\n\ngrace_coll_meta = grace_coll['items'][0]['meta']\ngrace_coll_meta\n\n{'revision-id': 21,\n 'deleted': False,\n 'format': 'application/vnd.nasa.cmr.umm+json',\n 'provider-id': 'POCLOUD',\n 'user-id': 'chen5510',\n 'has-formats': True,\n 'associations': {'variables': ['V2079350316-POCLOUD',\n 'V2079362050-POCLOUD',\n 'V2112016640-POCLOUD',\n 'V2112016643-POCLOUD',\n 'V2112016651-POCLOUD',\n 'V2112016653-POCLOUD',\n 'V2112016655-POCLOUD',\n 'V2112016657-POCLOUD'],\n 'services': ['S2004184019-POCLOUD'],\n 'tools': ['TL2108419875-POCLOUD']},\n 's3-links': ['podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/',\n 'podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/'],\n 'has-spatial-subsetting': True,\n 'native-id': 'JPL+GRACE+and+GRACE-FO+Mascon+Ocean,+Ice,+and+Hydrology+Equivalent+Water+Height+Coastal+Resolution+Improvement+(CRI)+Filtered+Release+06+Version+02',\n 'has-transforms': False,\n 'has-variables': True,\n 'concept-id': 'C1938032626-POCLOUD',\n 'revision-date': '2022-09-30T20:16:20.101Z',\n 'has-temporal-subsetting': True,\n 'concept-type': 'collection'}\n\n\n\n\nGranule (file)\nGet the UMM Granule metadata using requests.get:\n\nr = requests.get(url=f\"https://{cmr}/search/granules.umm_json\", \n params={'provider': \"POCLOUD\", \n 'ShortName': grace_ShortName})\n\ngrace_gran = r.json()\ngrace_gran['hits']\n\n1\n\n\nAs you can see, one result was returned (one hit). Print the CMR Search metadata for the granule (meta):\n\ngrace_gran['items'][0]['meta']\n\n{'concept-type': 'granule',\n 'concept-id': 'G2435507181-POCLOUD',\n 'revision-id': 1,\n 'native-id': 'GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI',\n 'provider-id': 'POCLOUD',\n 'format': 'application/vnd.nasa.cmr.umm+json',\n 'revision-date': '2022-09-02T17:44:24.135Z'}\n\n\nThe other component in each result (from the list of items) is the UMM metadata, accessible from the umm key. Print the RelatedUrls metadata field for the granule:\n\ngrace_gran['items'][0]['umm']['RelatedUrls']\n\n[{'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc',\n 'Description': 'Download GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc',\n 'Type': 'GET DATA'},\n {'URL': 's3://podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc',\n 'Description': 'This link provides direct download access via S3 to the granule',\n 'Type': 'GET DATA VIA DIRECT ACCESS'},\n {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc.md5',\n 'Description': 'Download GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc.md5',\n 'Type': 'EXTENDED METADATA'},\n {'URL': 's3://podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc.md5',\n 'Description': 'This link provides direct download access via S3 to the granule',\n 'Type': 'EXTENDED METADATA'},\n {'URL': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'Description': 'api endpoint to retrieve temporary credentials valid for same-region direct s3 access',\n 'Type': 'VIEW RELATED INFORMATION'},\n {'URL': 'https://opendap.earthdata.nasa.gov/collections/C1938032626-POCLOUD/granules/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI',\n 'Type': 'USE SERVICE API',\n 'Subtype': 'OPENDAP DATA',\n 'Description': 'OPeNDAP request URL'}]\n\n\nWe want the URL corresponding to 'Type': 'GET DATA'. Select the URL from appropriate item in the list, then print:\n\ngrace_url = grace_gran['items'][0]['umm']['RelatedUrls'][0]['URL']\ngrace_url\n\n'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.nc'\n\n\nThen, to do a regular HTTPS download:\nr = requests.get(grace_url)\nwith open('tutorial7_data_GRACEFO.nc', 'wb') as f:\n f.write(r.content)\n\n!ncdump -h tutorial7_data_GRACEFO.nc\nBut we’ll use the Harmony API’s Zarr Reformatter service instead of downloading the entire granule. The zarr format will allow us to open and download/read just the data that we require for our Amazon Basin study area.\n\n\n\nRequest to Harmony API: Zarr Reformatter\nIf you have a jobID you’d like to re-visit instead of running this command again, modify the cell below to set the async_jobId then skip to Format and display the complete url to the Harmony API job:.\nIf you are running for the first time, proceed to the next cells to submit the harmony request.\n\n#async_jobId = \"dfefc536-768c-4db3-a7d2-c326e9253042\" # jobId belongs to dev. You wont have access.\nasync_jobId = None \n\nSee important usage note below if this is your first time submitting a request to the Zarr Reformatter service.\nThe Zarr Reformatter service operates on an input Collection concept-id (a CMR construct). The service will accept more user-friendly inputs (like a Collection ShortName) in future releases. Here’s how you identify the CMR concept-id for the JPL GRACE/GRACE-FO Mascon dataset:\n\ncollection_concept_id = grace_coll_meta['concept-id']\ncollection_concept_id\n\n'C1938032626-POCLOUD'\n\n\nMost of this next cell will only evaluate if there’s NOT a valid job identifier set to the async_jobId variable above. It submits the Harmony request, and prints the JSON response.\n\nharmony_client = Client(env=Environment.PROD)\n\ncollection_id = Collection(collection_concept_id) \n\nrequest = Request(\n collection=collection_id,\n format='application/x-zarr'\n)\n\nrequest.is_valid()\n\nTrue\n\n\n\nif async_jobId is None:\n print(harmony_client.request_as_curl(request))\n async_jobId = harmony_client.submit(request)\n print(f'Job ID: {async_jobId}')\n print('\\n Waiting for the job to finish. . .\\n')\n response = harmony_client.result_json(async_jobId, show_progress=True)\n print(\"\\n. . .DONE!\")\n\ncurl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate, br' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=9dd333f7ecce24b1bb77b97e8f6f7a12' -H 'User-Agent: python-requests/2.28.0 harmony-py/0.4.2 Linux/5.4.156-83.273.amzn2.x86_64 CPython/3.9.13' 'https://harmony.earthdata.nasa.gov/C1938032626-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&format=application%2Fx-zarr'\nJob ID: 9c93ee8e-1b5d-4d65-bacd-a91070e0c44c\n\n Waiting for the job to finish. . .\n\n\n. . .DONE!\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\nQuery for the job status and links in case the request is still processing:\n\nwhile True:\n job_json = harmony_client.status(async_jobId)\n if job_json['status'] != 'running':\n break\n print(f\"# Job status is running. Progress is {job_json['progress']}. Trying again.\")\n time.sleep(5)\n\nlinks = []\nif job_json['status'] == 'successful' and job_json['progress'] == 100:\n print(\"# Job progress is 100%. Links to job outputs are displayed below:\")\n links = [link['href'] for link in response['links']]\n display(links)\nelse:\n print(job_json)\n\n# Job progress is 100%. Links to job outputs are displayed below:\n\n\n['https://harmony.earthdata.nasa.gov/stac/9c93ee8e-1b5d-4d65-bacd-a91070e0c44c/',\n 'https://harmony.earthdata.nasa.gov/cloud-access.sh',\n 'https://harmony.earthdata.nasa.gov/cloud-access',\n 's3://harmony-prod-staging/public/harmony/netcdf-to-zarr/9298ce19-2552-4803-a812-a10cdcde55aa/',\n 's3://harmony-prod-staging/public/harmony/netcdf-to-zarr/9298ce19-2552-4803-a812-a10cdcde55aa/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.zarr',\n 'https://harmony.earthdata.nasa.gov/jobs/9c93ee8e-1b5d-4d65-bacd-a91070e0c44c?linktype=https&page=1&limit=2000']\n\n\nAccess url for the output zarr file\nThe new zarr dataset is staged for us in an S3 bucket. The url is the second to last one in the list shown above.\nSelect the url and display below:\n\nzarr_url = links[-2]\nzarr_url\n\n's3://harmony-prod-staging/public/harmony/netcdf-to-zarr/9298ce19-2552-4803-a812-a10cdcde55aa/GRCTellus.JPL.200204_202207.GLO.RL06M.MSCNv02CRI.zarr'\n\n\nAccess credentials for the output zarr file\nCredentials provided at the third and fourth urls in the list grant authenticated access to your staged S3 resources.\nGrab the credentials as a JSON string, load to a Python dictionary, and display their expiration date:\n\nfrom urllib import request\nfrom urllib.request import urlopen\n\nwith request.urlopen(f\"https://{harmony}/cloud-access\") as f:\n creds = loads(f.read())\n\ncreds['Expiration']\n\n'2022-11-08T07:42:41.000Z'\n\n\n\n\nOpen staged zarr file with s3fs\nWe use the AWS s3fs package to get metadata about the zarr data store and list its contents:\n\nzarr_fs = s3fs.S3FileSystem(\n key=creds['AccessKeyId'],\n secret=creds['SecretAccessKey'],\n token=creds['SessionToken'],\n client_kwargs={'region_name':'us-west-2'},\n)\nzarr_store = zarr_fs.get_mapper(root=zarr_url, check=False)\nzarr_dataset = zarr.open(zarr_store)\n\nprint(zarr_dataset.tree())\n\n/\n ├── lat (360,) float64\n ├── lat_bounds (360, 2) float64\n ├── lon (720,) float64\n ├── lon_bounds (720, 2) float64\n ├── lwe_thickness (211, 360, 720) float64\n ├── time (211,) float64\n ├── time_bounds (211, 2) float64\n └── uncertainty (211, 360, 720) float64\n\n\nNow print metadata for the lwe_thickness variable:\n\nprint(zarr_dataset.lwe_thickness.info)\n\nName : /lwe_thickness\nType : zarr.core.Array\nData type : float64\nShape : (211, 360, 720)\nChunk shape : (125, 125, 125)\nOrder : C\nRead-only : False\nCompressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\nStore type : zarr.storage.KVStore\nNo. bytes : 437529600 (417.3M)\nNo. bytes stored : 76942675 (73.4M)\nStorage ratio : 5.7\nChunks initialized : 36/36\n\n\n\n\n\nOpen staged zarr file with xarray\nHere’s the documentation for xarray’s zarr reader: http://xarray.pydata.org/en/stable/generated/xarray.open_zarr.html\nOpen the zarr dataset and print the dataset:\n\nds_GRACE = xr.open_dataset(zarr_store, engine=\"zarr\")\nprint(ds_GRACE)\n\nSubset by Latitude/Longitude\nOnce we have obtained all the data, to make processing quicker, we are going to subset datasets by latitude/longitude for the Amazon River estuary.\nOnce we have obtained the GRACE-FO data, we should spatial subset the data to the minimal area covering the Amazon River estuary. This will reduce processing load and reduce cloud costs for the user.\nMake a GRACE-FO subset and display the min, max of the lat and lon variables:\n\nsubset_GRACE = ds_GRACE.sel(lat=slice(-18, 10), lon=slice(275, 330))\nprint(subset_GRACE.lat.min().data, \n subset_GRACE.lat.max().data,\n subset_GRACE.lon.min().data,\n subset_GRACE.lon.max().data)\n\nSelect the variable for Land Water Equivalent Thickness (lwe_thickness)\nGrab the land water equivalent thickness variable from the GRACE subset:\n\nlwe = subset_GRACE.lwe_thickness\nprint(lwe)\n\n\n\nPlots\nWe will create an animation from sequential GRACE-FO plots over the Amazon Rainforest in the following cells. Define two functions to make the process a bit more convenient:\n\ndef setup_map(ax, pmap, ds_subset, x, y, var, t, cmap, levels, extent):\n title = str(pd.to_datetime(ds_subset.time[t].values))\n pmap.set_title(title, fontsize=14)\n pmap.coastlines()\n pmap.set_extent(extent)\n pmap.add_feature(cartopy.feature.RIVERS)\n variable_desired = var[t,:,:]\n cont = pmap.contourf(x, y, variable_desired, cmap=cmap, levels=levels, zorder=1)\n return cont\n\ndef animate_ts(framenumber, ax, pmap, ds_subset, x, y, var, t, cmap, levels, extent):\n cont = setup_map(ax, pmap, ds_subset, x, y, var, t + framenumber, cmap, levels, extent) \n return cont\n\nPlot the first timestep in the JPL GRACE/GRACE-FO Mascon time series:\n\n# Initialize a matplotlib plot object and add subplot:\nfig = plt.figure(figsize=[13,9]) \nax = fig.add_subplot(1, 1, 1)\n\n# Configure axes to display projected data using PlateCarree crs:\npmap = plt.axes(projection=ccrs.PlateCarree())\n\n# Get arrays of x and y to label the plot axes:\nx,y = np.meshgrid(subset_GRACE.lon, subset_GRACE.lat) \n\n# Set a few constants for plotting the GRACE-FO time series:\ntime_start = 168\ncmap_name = \"bwr_r\"\ncmap_levels = np.linspace(-100., 100., 14)\nmap_extent = [-85, -30, -16, 11]\n\n# Plot the first timestep: \ncont = setup_map(ax, pmap, subset_GRACE, x, y, lwe, time_start, cmap_name, cmap_levels, map_extent)\n\nfig.colorbar(cont, ticks=cmap_levels, orientation='horizontal', label='Land Water Equivalent Thickness (cm)')\n\nPlot all the 2019 timesteps sequentially to create an animation of land water equivalent thickness for the Amazon Rainforest territories:\n\nani = animation.FuncAnimation(fig, animate_ts, frames=range(0,12), fargs=(\n ax, pmap, subset_GRACE, x, y, lwe, time_start, cmap_name, cmap_levels, map_extent\n), interval=500)\n\nHTML(ani.to_html5_video())\n\nUser note: You will need to install ‘ffmpeg’ in the cmd prompt to save the .mpg to disk. Use the following command to install from the conda-forge channel:\nconda install -c conda-forge ffmpeg\nUncomment, run the next cell to save the animation to MP4:\n\n#ani.save(\"tutorial7_animation_GRACEFO.mp4\", writer=animation.FFMpegWriter())"
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+ "title": "SMAP",
+ "section": "Data Resources & Tutorials",
+ "text": "Data Resources & Tutorials\n\nVisualizing Ocean Salinity as Powerful Storms Wreak Havoc in California - Data in Action Use Case Notebook Tutorial with a local machine workflow\n\nThe Data in Action Story\n\n\n\nMonitoring Changes in the Arctic Using SMAP Satellite data and Saildrone - AWS cloud tutorial that compares salinity from the SMAP satellite and Saildrone in-situ measurements"
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- "title": "Use Case: Study Amazon Estuaries with Data from the EOSDIS Cloud",
- "section": "On-prem hydro data from Pre-SWOT MEaSUREs program",
- "text": "On-prem hydro data from Pre-SWOT MEaSUREs program\nData from PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2 are not currently available on the cloud, but we can access via the PO.DAAC’s on-prem OPeNDAP service (Hyrax) instead.\n\nThe guidebook explains the details of the Pre-SWOT MEaSUREs data: https://podaac-tools.jpl.nasa.gov/drive/files/allData/preswot_hydrology/L2/rivers/docs/GRRATS_user_handbookV2.pdf\nAccess URL for PO.DAAC on-prem OPeNDAP service\nIdentify an appropriate OPeNDAP endpoint through the following steps:\n\nGo to the project/mission page on the PO.DAAC portal (e.g. for Pre-SWOT MEaSUREs: https://podaac.jpl.nasa.gov/MEaSUREs-Pre-SWOT)\nChoose the dataset of interest. Go to the “Data Access” tab of the corresponding dataset landing page, which should like the OPeNDAP access link (for compatible datasets, e.g. for the daily river heights from virtual stations: https://podaac-opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/).\nNavigate to the desired NetCDF file and copy the endpoint (e.g. for our Amazon Basin use case we choose the South America file: https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc).\n\n\nOpen netCDF file with xarray\nOpen the netCDF dataset via OPeNDAP using xarray:\n\nds_MEaSUREs = xr.open_dataset('https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc')\nprint(ds_MEaSUREs)\n\n<xarray.Dataset>\nDimensions: (X: 3311, Y: 3311, distance: 3311, time: 9469,\n charlength: 26)\nCoordinates:\n * time (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...\nDimensions without coordinates: X, Y, distance, charlength\nData variables:\n lon (X) float64 ...\n lat (Y) float64 ...\n FD (distance) float64 ...\n height (distance, time) float64 ...\n sat (charlength) |S64 ...\n storage (distance, time) float64 ...\n LakeFlag (distance) float64 ...\n Storage_uncertainty (distance, time) float64 ...\n IceFlag (time) float64 ...\nAttributes: (12/40)\n title: GRRATS (Global River Radar Altimetry Time ...\n Conventions: CF-1.6, ACDD-1.3\n institution: Ohio State University, School of Earth Sci...\n source: MEaSUREs OSU Storage toolbox 2018\n keywords: EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n keywords_vocabulary: Global Change Master Directory (GCMD)\n ... ...\n geospatial_lat_max: -0.6550700975069503\n geospatial_lat_units: degree_north\n geospatial_vertical_max: 92.7681246287056\n geospatial_vertical_min: -3.563409518163376\n geospatial_vertical_units: m\n geospatial_vertical_positive: up\n\n\nOur desired variable is height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Amazon estuary. Time is between April 8, 1993 and April 20, 2019.\n\n\nPlot\nAmazon River heights for March 16, 2018\nPlot the river distances and associated heights on the map at time t=9069:\n\nfig = plt.figure(figsize=[13,9]) \nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent([-85, -30, -20, 20])\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()\n\n\n\n\nFor GRACE-FO, plotting lwe_thickness[107:179,34,69] indicates time, latitude, and longitude indices corresponding to the pixel for the time period 1/2019 to 12/2019 at lat/lon (-0.7, -50). For the 2019 year, measurements of LWE thickness followd expected patterns of high volume of water from the river output into the estuary.\n2011-2019 Seasonality Plots (WIP)\nFor GRACE-FO, plotting lwe_thickness[107:179,34,69] indicates time, latitude, and longitude indices corresponding to the pixel for the time period 8/2011 to 12/2019 at lat/lon (-0.7, -50).\n\n#plot variables associated with river\nfig, ax1 = plt.subplots(figsize=[12,7])\n#plot river height\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\n\n#plot LWE thickness on secondary axis\nax2 = ax1.twinx()\nax2.plot(subset_GRACE.time[107:179], subset_GRACE.lwe_thickness[107:179,34,69], color = 'darkorange')\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax1.set_ylabel('River Height (m)', color='darkblue')\nax2.legend(['GRACE-FO'], loc='upper right')\nax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')\n\nplt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')\n\nplt.show()"
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+ "href": "quarto_text/SMAP.html#additional-resources",
+ "title": "SMAP",
+ "section": "Additional Resources",
+ "text": "Additional Resources\nNASA Mission Page"
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- "href": "notebooks/HUC Feature Translation Service Examples.html",
- "title": "A newer version of this Notebook exists here.",
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- "text": "This Jupyter Notebook contains examples related to querying the HUC Feature Translation Service (FTS), previewing (viusualizing) the queried region of interest, and using FTS results to query NASA’s Common Metadata Repository (CMR).\nExample Use Case: Check if data is available over my region of interest using HUCs. In this example we are using FTS to geospatially define our region of interest, namely the Upper Tuolumne River Basin in the San Joaquin River Basin in California’s Sierra Nevada, searching by HUC or region name, and then using those geospatial bounds (coordinates) to query Sentinel-1 data in CMR. 1. use FTS to define geographic region of interest (query by partial or exact HUC or HUC region name) 2. preview query 3. use coordinates returned by FTS to query Sentinel-1 data in CMR, by polygon or bounding box.\nResources\nUSGS Hydrologic unit map to help identifiy region of interest (e.g. HUC value or name) can be found here: https://water.usgs.gov/GIS/regions.html\n\n%%capture\n\nimport sys\n!{sys.executable} -m pip install bs4 requests\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport json\n\ncloudfront_url = \"https://d3fu1wb0xptl0v.cloudfront.net\""
+ "text": "The Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) is a NASA Earth Venture Suborbital (EVS-3) mission that utilizes research aircraft equipped with state-of-the-art remote sensing instruments, a research vessel, Wave Gliders, Saildrones, and many other in situ assets. These instruments contribute to an unprecedented view of the physics of submesoscale eddies and fronts, and their effects on vertical transport in the upper ocean. The S-MODE investigation is composed of a Pilot Campaign (Fall 2021) and two Intensive Operating Periods (IOP-1 and IOP-2, Fall 2022 and Spring 2023). Each field campaign is between 3-4 weeks in duration. The scientific area of interest is located about 150 miles off the coast of San Francisco\nMore information can be found on PO.DAAC’s SMODE webpage."
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- "title": "A newer version of this Notebook exists here.",
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+ "href": "quarto_text/SMODE.html#background",
+ "title": "S-MODE",
"section": "",
- "text": "This Jupyter Notebook contains examples related to querying the HUC Feature Translation Service (FTS), previewing (viusualizing) the queried region of interest, and using FTS results to query NASA’s Common Metadata Repository (CMR).\nExample Use Case: Check if data is available over my region of interest using HUCs. In this example we are using FTS to geospatially define our region of interest, namely the Upper Tuolumne River Basin in the San Joaquin River Basin in California’s Sierra Nevada, searching by HUC or region name, and then using those geospatial bounds (coordinates) to query Sentinel-1 data in CMR. 1. use FTS to define geographic region of interest (query by partial or exact HUC or HUC region name) 2. preview query 3. use coordinates returned by FTS to query Sentinel-1 data in CMR, by polygon or bounding box.\nResources\nUSGS Hydrologic unit map to help identifiy region of interest (e.g. HUC value or name) can be found here: https://water.usgs.gov/GIS/regions.html\n\n%%capture\n\nimport sys\n!{sys.executable} -m pip install bs4 requests\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport json\n\ncloudfront_url = \"https://d3fu1wb0xptl0v.cloudfront.net\""
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- "title": "A newer version of this Notebook exists here.",
- "section": "Search Feature Translation Service for Partial Region Matches",
- "text": "Search Feature Translation Service for Partial Region Matches\nIf you are unsure what the corresponding HUC is for your region of interest, you can query the FTS for partial region matches.\n\n###################\n\n# Querying partial matches with region \"San Joaquin\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"San Jo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with HUC 1805000301\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 11,\n \"time\": \"5.689 ms.\",\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false\n },\n \"results\": {\n \"San Joaquin\": {\n \"HUC\": \"1804\",\n \"Bounding Box\": 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+ "text": "The Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) is a NASA Earth Venture Suborbital (EVS-3) mission that utilizes research aircraft equipped with state-of-the-art remote sensing instruments, a research vessel, Wave Gliders, Saildrones, and many other in situ assets. These instruments contribute to an unprecedented view of the physics of submesoscale eddies and fronts, and their effects on vertical transport in the upper ocean. The S-MODE investigation is composed of a Pilot Campaign (Fall 2021) and two Intensive Operating Periods (IOP-1 and IOP-2, Fall 2022 and Spring 2023). Each field campaign is between 3-4 weeks in duration. The scientific area of interest is located about 150 miles off the coast of San Francisco\nMore information can be found on PO.DAAC’s SMODE webpage."
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- "href": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-exact-huc-matches",
- "title": "A newer version of this Notebook exists here.",
- "section": "Search Feature Translation Service for Exact HUC Matches",
- "text": "Search Feature Translation Service for Exact HUC Matches\nHere we can define a HUC, or hydrologic unit code, and use this to query the HUC FTS. By defining the parameter EXACT = True, we tell the query to not search for partial matches.\nBased on the partial response in the previous step, we can now do an exact search for SJRB, using its HUC (1804).\n\n###################\n\n# Querying exact matches for HUC \"1804\" = San Joaquin RB\n\nHUC = \"1804\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.452 ms.\",\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true\n },\n \"results\": {\n \"1804\": {\n \"Region Name\": \"San Joaquin\",\n \"Bounding Box\": \"-121.93679916804501,36.36688239563472,-118.65438684397327,38.757297326299295\",\n \"Convex Hull Polygon\": 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+ "objectID": "quarto_text/SMODE.html#data-resources-tutorials",
+ "href": "quarto_text/SMODE.html#data-resources-tutorials",
+ "title": "S-MODE",
+ "section": "Data Resources & Tutorials",
+ "text": "Data Resources & Tutorials\n\nScience Case Study Airborne\nThe DownloadDopplerScattData.ipynb notebook walks through creating the .netrc file and downloading the Dopplerscatt data used in this case study. The VisualizeDopplerScattData.ipynb notebook contains the Airborne Science Case Study data visualization and discussion. Instructions for installing the airborne material in a conda environment are contained in this Airborne Case Study README.\n\n\nScience Case Study In Situ\nThe insitu_dataviz_demo.ipynb notebook contains the In Situ Science Case Study data visualization and discussion. This notebook also contains sample code to run the PO.DAAC Data Downloader to download Saildrone data. Instructions for installing the necessary Python packages, and more information on obtaining S-MODE data are in the In Situ Case Study README."
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- "href": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-partial-region-matches-1",
- "title": "A newer version of this Notebook exists here.",
- "section": "Search Feature Translation Service for Partial Region Matches",
- "text": "Search Feature Translation Service for Partial Region Matches\nBut we are specifically interested in Tuolumne RB within the San Joaquin, so let’s do a partial search for “Upper Tuo”, given we may not know the exact region name.\n\n###################\n\n# Querying partial matches with region \"Upper Tuo\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"Upper Tuo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with HUC 1805000301\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.549 ms.\",\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false\n },\n \"results\": {\n \"Upper Tuolumne\": {\n \"HUC\": \"18040009\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}"
+ "objectID": "quarto_text/SMODE.html#additional-resources",
+ "href": "quarto_text/SMODE.html#additional-resources",
+ "title": "S-MODE",
+ "section": "Additional Resources",
+ "text": "Additional Resources\n\n2022 S-MODE Open Data Workshop\nhttps://espo.nasa.gov/s-mode/content/S-MODE_2022_Open_Data_Workshop - Recordings and Presentations\nThe Submesoscale Ocean Dynamics and Vertical Transport Experiment (S-MODE) science team is hosted a virtual Open Data Workshop on 1 December 2022 from 11:00am – 1:00pm ET to share about the S-MODE mission, to learn about its instrumentation, and find out how to access and use its data products."
},
{
- "objectID": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-exact-named-region-matches",
- "href": "notebooks/HUC Feature Translation Service Examples.html#search-feature-translation-service-for-exact-named-region-matches",
- "title": "A newer version of this Notebook exists here.",
- "section": "Search Feature Translation Service for Exact Named Region Matches",
- "text": "Search Feature Translation Service for Exact Named Region Matches\nGiven the above response, or that we already know an exact region name or HUC in USGS’s Watershed Boundary Dataset (WBD), we can use this instead of a partial search. Below is an example of exact matches by HUC (18040009), and then by region name (“Upper Tuolumne”).\n\n###################\n\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.516 ms.\",\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true\n },\n \"results\": {\n \"18040009\": {\n \"Region Name\": \"Upper Tuolumne\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}\n\n\n\n###################\n\n# Querying exact matches with region \"Upper Tuolumne\"\n\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\n# Note the change in endpoint from \"/prod/huc\" to \"/prod/region\"\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exact matches with region \"Woods Creek-Skykomish River\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.665 ms.\",\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": true\n },\n \"results\": {\n \"Upper Tuolumne\": {\n \"HUC\": \"18040009\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}"
+ "objectID": "quarto_text/GHRSST.html",
+ "href": "quarto_text/GHRSST.html",
+ "title": "GHRSST",
+ "section": "",
+ "text": "The Group for High Resolution Sea Surface Temperature (GHRSST) was established in 2002 to foster an international focus and coordination for the development of a new generation of global, multi-sensor, high-resolution near real time SST datasets. It brings together international space agencies, research institutes, universities, and government agencies to collectively address the scientific, logistical and managerial challenges posed by creating the SST datasets and services within the Project. The overall aim of the GHRSST is to provide the best quality sea surface temperature data for applications in short, medium and decadal/climate time scales in the most cost effective and efficient manner through international collaboration and scientific innovation. More information can be found on PO.DAAC’s GHRSST webpage."
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- "objectID": "notebooks/HUC Feature Translation Service Examples.html#function-for-visualization",
- "href": "notebooks/HUC Feature Translation Service Examples.html#function-for-visualization",
- "title": "A newer version of this Notebook exists here.",
- "section": "Function for Visualization",
- "text": "Function for Visualization\nBelow is a function created specifically to visualize the output of the HUC Feature Translation Service.\n\ndef visualize(fts_response):\n \n regions = []\n bounding_boxes = []\n convex_hull_polygons = []\n visvalingam_polygons = []\n for element in fts_response['results']:\n for heading in fts_response['results'][element]:\n if heading == \"Bounding Box\":\n bounding_boxes.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Convex Hull Polygon\":\n convex_hull_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Visvalingam Polygon\":\n visvalingam_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"HUC\":\n regions.append(\"Region Name: \" + element + \"\\n\" + \"HUC: \" + fts_response['results'][element][heading])\n elif heading == \"Region Name\":\n regions.append(\"Region Name: \" + fts_response['results'][element][heading] + \"\\n\" + \"HUC: \" + element)\n else:\n continue\n\n bounding_boxes = [box(e[0], e[1], e[2], e[3]) for e in bounding_boxes]\n convex_hull_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in convex_hull_polygons]\n visvalingam_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in visvalingam_polygons]\n \n for i in range(len(bounding_boxes)):\n ax = gpd.GeoSeries(bounding_boxes[i]).plot(alpha=0.2, cmap='Pastel1', figsize=(10,10))\n gpd.GeoSeries(convex_hull_polygons[i]).plot(ax = ax, cmap='Pastel2')\n gpd.GeoSeries(visvalingam_polygons[i]).plot(alpha=0.5, ax=ax, cmap='viridis')\n\n plt.title(regions[i])\n plt.show()"
+ "objectID": "quarto_text/GHRSST.html#background",
+ "href": "quarto_text/GHRSST.html#background",
+ "title": "GHRSST",
+ "section": "",
+ "text": "The Group for High Resolution Sea Surface Temperature (GHRSST) was established in 2002 to foster an international focus and coordination for the development of a new generation of global, multi-sensor, high-resolution near real time SST datasets. It brings together international space agencies, research institutes, universities, and government agencies to collectively address the scientific, logistical and managerial challenges posed by creating the SST datasets and services within the Project. The overall aim of the GHRSST is to provide the best quality sea surface temperature data for applications in short, medium and decadal/climate time scales in the most cost effective and efficient manner through international collaboration and scientific innovation. More information can be found on PO.DAAC’s GHRSST webpage."
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- "objectID": "notebooks/HUC Feature Translation Service Examples.html#visualization",
- "href": "notebooks/HUC Feature Translation Service Examples.html#visualization",
- "title": "A newer version of this Notebook exists here.",
- "section": "Visualization",
- "text": "Visualization\nWe can take that response and pass it to the visualize() function created above.\n\n#visualize FTS response\nvisualize(response)\n\n\n\n\n\n###################\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"hits\": 1,\n \"time\": \"1.556 ms.\",\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true\n },\n \"results\": {\n \"18040009\": {\n \"Region Name\": \"Upper Tuolumne\",\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": \"-121.105517801627,37.57291785522102,-120.51777999837259,37.58160878749919,-119.26845687218679,37.73942430183757,-119.26095827844847,37.741190162251485,-119.26079495969867,37.74128122475133,-119.25581474616479,37.7450598684955,-119.25563206491506,37.74520087891193,-119.25521361804067,37.745555179953044,-119.20452512020273,37.79316755800414,-119.20311483687158,37.794898117376476,-119.20297581291345,37.79511513091779,-119.20108320354137,37.801137019450096,-119.20096521291657,37.803876760070864,-119.19927543166921,37.88483115890352,-119.19931234937746,37.885001276611604,-119.20064394937538,37.88738135160793,-119.31090541587093,38.044980644071586,-119.3277000731365,38.0651666159153,-119.32796109605277,38.06544024091488,-119.34908448143665,38.08655395234041,-119.62508146642494,38.22905559795254,-119.65624842470987,38.22952896670182,-119.65829346949835,38.22947615316025,-119.79473757241158,38.21799358859471,-119.99491475022586,38.196920114669126,-120.38613654232694,38.056378609678916,-121.15444382863438,37.62884831659255,-121.15500076925849,37.6284224540932,-121.15993039529252,37.62332076451776,-121.16822139007132,37.61386883849076,-121.17452907235321,37.605445134337174,-121.17462853797804,37.60522817287921,-121.17469632131127,37.60502320725453,-121.17471004943627,37.60496802808791,-121.17476593797784,37.604743358296616,-121.17472602131124,37.60443736142207,-121.1743974786034,37.603737121839856,-121.17385444318757,37.603213931215635,-121.12495024430518,37.575249448967384,-121.1206057318119,37.57340581772024,-121.1184699109819,37.573299354178744,-121.105517801627,37.57291785522102\",\n \"Visvalingam Polygon\": \"-119.65612154137676,38.229472830243594,-119.65887392887248,38.21611789588928,-119.68748882882807,38.20072575945488,-119.74423195478164,38.21583016359807,-119.79473757241158,38.21799358859471,-119.80777226405803,38.20387888444998,-119.83634931818034,38.19900379279085,-119.8794751608217,38.205164957364616,-119.92926093053609,38.1903575073876,-119.95874486382365,38.194300821964816,-119.9915583491894,38.18745202718378,-119.99491475022586,38.196920114669126,-120.05974666158357,38.15445730952666,-120.10528053234623,38.13442047414111,-120.12843912918527,38.10262073148215,-120.20521266552441,38.056065841971076,-120.26427926855774,38.061807551337154,-120.34283367885251,38.04442165553081,-120.36616220485797,38.05864021280041,-120.38138515275097,38.04379106074015,-120.39399451523144,38.00852514516987,-120.41557226103123,38.00413815246833,-120.4432863495299,37.97081393481176,-120.45616958284324,37.92842260675252,-120.46878266719864,37.921885701554345,-120.4624829120001,37.891430354726594,-120.479954646348,37.871879250590325,-120.48053337030541,37.82415609128935,-120.5431307066666,37.84913992354228,-120.55227857644405,37.86382160997783,-120.57047251079081,37.845455260006304,-120.5664340514221,37.82240133087544,-120.59755003054045,37.81161053401718,-120.64595511379866,37.78181727677179,-120.67580313979397,37.7973594465393,-120.7399643115694,37.77691864136273,-120.75337800946522,37.73604807163446,-120.78855502295232,37.75464512473059,-120.82306428227372,37.739964848711736,-120.82298294894053,37.72367104352867,-120.84114918537068,37.706087340431,-120.8673865186633,37.69917305085835,-120.87598703427494,37.68470263317249,-120.91724289983591,37.65975447800287,-120.94840845082916,37.657548763422994,-121.0014800642885,37.642007069697115,-121.06141474752877,37.632796530128076,-121.15500076925849,37.6284224540932,-121.17472602131124,37.60443736142207,-121.15475178384224,37.60555392496201,-121.12649717971942,37.5761612791743,-121.105517801627,37.57291785522102,-121.06195388086127,37.58432554999496,-121.051833277752,37.59549621664428,-120.9623811789325,37.616090631195675,-120.93801052688701,37.61182627286894,-120.86057590200721,37.62129079368759,-120.85730386034561,37.613155902033554,-120.78087282192257,37.61414406661538,-120.73817309594716,37.63053679471494,-120.70606299078867,37.6253131207647,-120.65312547628753,37.630845649922776,-120.64372085026048,37.617530275985075,-120.59897548366325,37.61838293640045,-120.58566304618392,37.6280897895104,-120.55437374519079,37.61949203744041,-120.53035751501977,37.62416281034979,-120.52267756711501,37.58373637916253,-120.47482846302262,37.59087335206817,-120.45815308909016,37.620518593688814,-120.4371221932895,37.63718915616295,-120.3849402642038,37.635048797832894,-120.3755883360933,37.65339462697108,-120.39417890064777,37.66818361028146,-120.392313883984,37.68359100504921,-120.3559603048738,37.67552716860342,-120.32610607262848,37.648966585311314,-120.30639988828403,37.66573735924362,-120.3455439350983,37.72512544352645,-120.31539807993676,37.733894229971156,-120.28665821956469,37.72927809560332,-120.28245683519623,37.74541342682829,-120.26069274460497,37.73358365601331,-120.25387803732389,37.749232223697334,-120.200135472824,37.76372567054983,-120.17357968640687,37.79608365070794,-120.12741327606187,37.78170633927192,-120.08906888341306,37.81273366005712,-120.07891344384547,37.82866939649068,-120.05533048450707,37.812757440265386,-120.02518823247055,37.81132716005931,-119.96359235131615,37.78073240906514,-119.94507216905322,37.76542058617224,-119.90693944932076,37.75781682576735,-119.86549272230178,37.77222938512,-119.85359450461186,37.758767389307536,-119.83160174943771,37.76963435595735,-119.80589665676928,37.75608133722835,-119.75060675164673,37.77341942574316,-119.7359645225028,37.78635874968137,-119.6978660861036,37.78960109342637,-119.64907657576265,37.81516770484501,-119.65722697262504,37.83230492565173,-119.59708603105173,37.86135878810666,-119.58892104668939,37.88872669535584,-119.57247042275657,37.8998297015886,-119.53527651760601,37.90190377762701,-119.49696578120711,37.86409281310239,-119.47533168332404,37.85892923602705,-119.45447817606475,37.871394138091034,-119.44539026566218,37.858937875610366,-119.4045319969756,37.85021429541558,-119.40293857510306,37.833821247524384,-119.37314428244099,37.83849664855876,-119.35155096789117,37.82452854545545,-119.3552763335104,37.812805713182,-119.32340220126821,37.79368655279501,-119.29229722319144,37.762878678884476,-119.2876598648653,37.74535544245333,-119.26052065344913,37.74159433725089,-119.24428567430766,37.76834668616766,-119.22055414726117,37.77924966531742,-119.20108320354137,37.801137019450096,-119.21738830351609,37.8183151860901,-119.20449054936944,37.82981189961396,-119.21625690560114,37.847411426669964,-119.21512584414461,37.87042564434256,-119.19927543166921,37.88483115890352,-119.23787860140095,37.911280908862466,-119.26486444510903,37.91263911719369,-119.26807310656238,37.92880876300194,-119.30948290962311,37.94616478068332,-119.31574230232172,37.96621302648555,-119.30533157629623,38.02416955035392,-119.34927245018639,38.08565116171684,-119.35845034913046,38.08266815651314,-119.39810467927725,38.1068175096006,-119.43127595110076,38.11332130542388,-119.4403859021283,38.09636985024184,-119.46399499479998,38.09838383773871,-119.4692413104168,38.12798441894279,-119.48819819267908,38.132729004352086,-119.50246159786525,38.159339980352456,-119.50459633952858,38.140964939755975,-119.54763344883679,38.14419101891764,-119.54624260196397,38.15397065015242,-119.5773162810824,38.15780512931315,-119.57980050712018,38.17791634178195,-119.62908996641869,38.196015076128845,-119.62508146642494,38.22905559795254,-119.65612154137676,38.229472830243594\",\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n }\n }\n }\n}\n\n\n\n#visualize FTS response\nvisualize(response)"
+ "objectID": "quarto_text/GHRSST.html#data-resources-tutorials",
+ "href": "quarto_text/GHRSST.html#data-resources-tutorials",
+ "title": "GHRSST",
+ "section": "Data Resources & Tutorials",
+ "text": "Data Resources & Tutorials\n\nMapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions - Utilizes the PO.DAAC Data Downloader to download SSTA from the GHRSST MUR climatology dataset and plot on a local machine.\n\n\nUsing OPeNDAP to Access the MUR Sea Surface Temperature dataset - opening and visualizing the GRHSST MUR SST dataset using OPeNDAP."
},
{
- "objectID": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-polygon",
- "href": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-polygon",
- "title": "A newer version of this Notebook exists here.",
- "section": "Query CMR by Polygon",
- "text": "Query CMR by Polygon\nHere is a more useful example of the Feature Translation Service. We can use results obtained from the FTS to then directly and automatically query CMR. Below I’m extracting the polygon representing Upper Tuolumne River Basin within the San Joaquin River Basin, and using it to search for granules available through the Sentinel-1 mission.\n\n\n###################\n\nCOLLECTION_ID = \"C1522341104-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain convex hull polygon from response\npolygon = response['results'][REGION]['Convex Hull Polygon']\n#polygon = response['results'][REGION]['Visvalingam Polygon']\n\n# Query CMR\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?polygon={}&echo_collection_id={}&pretty=True\".format(polygon, COLLECTION_ID))\n\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2019-11-26T18:24:02.850Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?polygon=-121.105517801627,37.57291785522102,-120.51777999837259,37.58160878749919,-119.26845687218679,37.73942430183757,-119.26095827844847,37.741190162251485,-119.26079495969867,37.74128122475133,-119.25581474616479,37.7450598684955,-119.25563206491506,37.74520087891193,-119.25521361804067,37.745555179953044,-119.20452512020273,37.79316755800414,-119.20311483687158,37.794898117376476,-119.20297581291345,37.79511513091779,-119.20108320354137,37.801137019450096,-119.20096521291657,37.803876760070864,-119.19927543166921,37.88483115890352,-119.19931234937746,37.885001276611604,-119.20064394937538,37.88738135160793,-119.31090541587093,38.044980644071586,-119.3277000731365,38.0651666159153,-119.32796109605277,38.06544024091488,-119.34908448143665,38.08655395234041,-119.62508146642494,38.22905559795254,-119.65624842470987,38.22952896670182,-119.65829346949835,38.22947615316025,-119.79473757241158,38.21799358859471,-119.99491475022586,38.196920114669126,-120.38613654232694,38.056378609678916,-121.15444382863438,37.62884831659255,-121.15500076925849,37.6284224540932,-121.15993039529252,37.62332076451776,-121.16822139007132,37.61386883849076,-121.17452907235321,37.605445134337174,-121.17462853797804,37.60522817287921,-121.17469632131127,37.60502320725453,-121.17471004943627,37.60496802808791,-121.17476593797784,37.604743358296616,-121.17472602131124,37.60443736142207,-121.1743974786034,37.603737121839856,-121.17385444318757,37.603213931215635,-121.12495024430518,37.575249448967384,-121.1206057318119,37.57340581772024,-121.1184699109819,37.573299354178744,-121.105517801627,37.57291785522102&echo_collection_id=C1522341104-NSIDC_ECS&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5\",\n \"time_start\": \"2015-04-01T01:19:59.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:141648389\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:31.000Z\",\n \"id\": \"G1565814828-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8805513382\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.5845451 -121.9242706 36.5554848 -121.9242706 36.5554848 -118.6670151 38.5845451 -118.6670151 38.5845451 -121.9242706\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2018.10.26/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5.iso.xml\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/SMAP/SPL2SMAP_S.002/\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C1522341104-NSIDC_ECS&m=-38.109375!23.34375!1!1!0!0%2C2&tl=1518545080!4!!&q=SPL2SMAP_S\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R16010_001.h5\",\n \"time_start\": 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\"2015-08-15T14:54:24.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:135494953\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-08-15T14:55:27.000Z\",\n \"id\": \"G1540860984-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"4.5890922546\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"39.0352478 -124.071579 36.9062119 -124.071579 36.9062119 -120.8143158 39.0352478 -120.8143158 39.0352478 -124.071579\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": 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\"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:135505613\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-08-22T14:19:12.000Z\",\n \"id\": \"G1540928643-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.5922441483\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.9448853 -122.0176315 36.9062119 -122.0176315 36.9062119 -118.760376 38.9448853 -118.760376 38.9448853 -122.0176315\"\n ]\n ],\n \"collection_concept_id\": \"C1522341104-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.002/2015.08.22/SMAP_L2_SM_SP_1AIWDV_20150822T141807_20150823T015907_120W37N_R16010_001.h5\"\n },\n {\n \"rel\": 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\"https://search.earthdata.nasa.gov/search/granules?p=C1522341104-NSIDC_ECS&m=-38.109375!23.34375!1!1!0!0%2C2&tl=1518545080!4!!&q=SPL2SMAP_S\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/KE1CSVXMI95Y\"\n }\n ]\n }\n ]\n }\n}"
+ "objectID": "quarto_text/GHRSST.html#additional-resources",
+ "href": "quarto_text/GHRSST.html#additional-resources",
+ "title": "GHRSST",
+ "section": "Additional Resources",
+ "text": "Additional Resources\nGHRSST Project Page"
},
{
- "objectID": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-bounding-box",
- "href": "notebooks/HUC Feature Translation Service Examples.html#query-cmr-by-bounding-box",
- "title": "A newer version of this Notebook exists here.",
- "section": "Query CMR by Bounding Box",
- "text": "Query CMR by Bounding Box\nInstead of querying via polygon, we can extract the bounding box of the region and use this to query CMR. Similarly to above, we’re extracting information (this time the bounding box) from the Upper Tuolumne River Basin and using this to search for granules available through the Sentinel-1 mission.\nHere we query by region in these two examples, however it would be equally valid to query by HUC.\n\n###################\n\nCOLLECTION_ID = \"C1522341104-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\nREGION = \"Upper Tuolumne\"\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(cloudfront_url + \"/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain bounding box from response\nbbox = response['results'][REGION]['Bounding Box']\n\n# Query CMR\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?bounding_box={}&echo_collection_id={}&pretty=True\".format(bbox, COLLECTION_ID))\n\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2019-11-26T18:24:03.927Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?bounding_box=-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182&echo_collection_id=C1522341104-NSIDC_ECS&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R16010_001.h5\",\n \"time_start\": \"2015-04-01T01:19:59.000Z\",\n \"updated\": \"2019-07-12T16:31:30.636Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.002:141648389\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:31.000Z\",\n \"id\": \"G1565814828-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8805513382\",\n 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+ "href": "quarto_text/Sentinel6MF.html",
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+ "section": "",
+ "text": "The Sentinel-6 Michael Freilich satellite measures the height of the ocean. In addition, an instrument on board the satellite uses the Global Navigation Satellite System Radio-Occultation sounding technique, which analyses changes in signals from international global navigation system satellites to determine atmospheric temperature and humidity. More information can be found on PO.DAAC’s Sentinel-6 Michael Freilich webpage."
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- "href": "notebooks/SearchDownload_SWOTviaCMR.html",
- "title": "Search and Download Simulated SWOT Data via earthaccess",
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+ "href": "quarto_text/Sentinel6MF.html#background",
+ "title": "Sentinel-6 Michael Freilich",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
+ "text": "The Sentinel-6 Michael Freilich satellite measures the height of the ocean. In addition, an instrument on board the satellite uses the Global Navigation Satellite System Radio-Occultation sounding technique, which analyses changes in signals from international global navigation system satellites to determine atmospheric temperature and humidity. More information can be found on PO.DAAC’s Sentinel-6 Michael Freilich webpage."
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- "href": "notebooks/SearchDownload_SWOTviaCMR.html#summary",
- "title": "Search and Download Simulated SWOT Data via earthaccess",
- "section": "Summary",
- "text": "Summary\nThis notebook will find and download simulated SWOT data programmatically via earthaccess. For more information about earthaccess visit: https://nsidc.github.io/earthaccess/"
+ "objectID": "quarto_text/Sentinel6MF.html#data-resources-tutorials",
+ "href": "quarto_text/Sentinel6MF.html#data-resources-tutorials",
+ "title": "Sentinel-6 Michael Freilich",
+ "section": "Data Resources & Tutorials",
+ "text": "Data Resources & Tutorials\n\nData Access\n\nAccess by Cycle/Pass\n\nAccess Near Real-Time Data\n\nOPeNDAP Access"
},
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- "title": "Search and Download Simulated SWOT Data via earthaccess",
- "section": "Requirements",
- "text": "Requirements\n\n1. Compute environment\nThis tutorial can be run in the following environments: - Local compute environment e.g. laptop, server: this tutorial can be run on your local machine\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nImport libraries\n\nimport requests\nimport json\nimport geopandas as gpd\nimport glob\nfrom pathlib import Path\nimport pandas as pd\nimport os\nimport zipfile\nfrom urllib.request import urlretrieve\nfrom json import dumps\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\nIn this notebook, we will be calling the authentication in the below cell.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)\n\n\n\nSearch for SWOT sample data links\nWe want to find the SWOT sample files that will cross over our region of interest, in the case, a bounding box of the United States.\nEach dataset has it’s own unique collection ID. For the SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1 dataset, we find the collection ID here.\nSample SWOT Hydrology Datasets and Associated Collection IDs: 1. River Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1 - C2263384307-POCLOUD\n\nLake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1 - C2263384453-POCLOUD\nRaster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1 - C2263383790-POCLOUD\nWater Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1 - C2263383386-POCLOUD\nWater Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1 - C2263383657-POCLOUD\n\n\n#earthaccess data search\nQuery = DataGranules().concept_id(\"C2263384307-POCLOUD\").bounding_box(-124.848974,24.396308,-66.885444,49.384358)\nprint(f\"Granule hits: {Query.hits()}\")\n\nGranule hits: 46\n\n\n\ngranules = Query.get()\n\n\n#extract the data links from the granules\ndata_links = [g.data_links(access=\"on_prem\") for g in granules]\n\n\ndata_links[0]\n\n['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1/SWOT_L2_HR_RiverSP_Node_007_022_NA_20220804T224145_20220804T224402_PGA0_01.zip']\n\n\n\n\nGet Download links from earthaccess search results\n\n#add desired links to a list\n#if the link has \"Reach\" instead of \"Node\" in the name, we want to download it for the swath use case\ndownloads = []\nfor r in data_links:\n for l in r:\n if 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/' in l:\n if 'Reach' in l:\n downloads.append(l)\n \nprint(len(downloads))\n\n23\n\n\nThis leaves us with half of the original links from our search.\n\n\nDownload the Data into a folder\n\n#Create folder to house downloaded data \nfolder = Path(\"SWOT_sample_files\")\n#newpath = r'SWOT_sample_files' \nif not os.path.exists(folder):\n os.makedirs(folder)\n\n\n#download data\nStore(auth).get(downloads, \"./SWOT_sample_files\")\n\n\n\n\n\n\n\n\n\n\n['SWOT_L2_HR_RiverSP_Reach_007_022_NA_20220804T224145_20220804T224402_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_104_NA_20220807T205936_20220807T210016_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_132_NA_20220808T210018_20220808T210252_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_162_NA_20220809T224722_20220809T225058_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_175_NA_20220810T101607_20220810T101940_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_177_NA_20220810T120102_20220810T120420_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_203_NA_20220811T101614_20220811T102211_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_287_NA_20220814T101759_20220814T102333_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_343_NA_20220816T101844_20220816T102323_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_371_NA_20220817T101846_20220817T102530_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_427_NA_20220819T101956_20220819T102559_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_440_NA_20220819T210905_20220819T211311_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_453_NA_20220820T083815_20220820T084053_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_468_NA_20220820T211105_20220820T211330_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip']\n\n\n\n\nShapefiles come in a .zip format, and need to be unzipped in the existing folder\n\nfor item in os.listdir(folder): # loop through items in dir\n if item.endswith(\".zip\"): # check for \".zip\" extension\n zip_ref = zipfile.ZipFile(f\"{folder}/{item}\") # create zipfile object\n zip_ref.extractall(folder) # extract file to dir\n zip_ref.close() # close file\n\n\nos.listdir(folder)\n\n['SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_175_NA_20220810T101607_20220810T101940_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_440_NA_20220819T210905_20220819T211311_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.shp.xml',\n 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- "text": "This tutorial uses multiple satellite data products to explore the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region over several years. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use on-premises and cloud datasets and services. The notebook can be executed locally, or in AWS (in us-west-2 region where the cloud data is located)."
+ "text": "imported on: 2023-07-05\n\nThis notebook is from NASA’s Zarr EOSDIS store notebook\n\n\nThe original source for this document is https://github.com/nasa/zarr-eosdis-store/blob/main/presentation/example.ipynb\n\n\nzarr-eosdis-store example\nInstall dependencies\n\nimport sys\n\n# zarr and zarr-eosdis-store, the main libraries being demoed\n!{sys.executable} -m pip install zarr zarr-eosdis-store\n\n# Notebook-specific libraries\n!{sys.executable} -m pip install matplotlib\n\nImportant: To run this, you must first create an Earthdata Login account (https://urs.earthdata.nasa.gov) and place your credentials in ~/.netrc e.g.:\n machine urs.earthdata.nasa.gov login YOUR_USER password YOUR_PASSWORD\nNever share or commit your password / .netrc file!\nBasic usage. After these lines, we work with ds as though it were a normal Zarr dataset\n\nimport zarr\nfrom eosdis_store import EosdisStore\n\nurl = 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210715090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'\n\nds = zarr.open(EosdisStore(url))\n\nView the file’s variable structure\n\nprint(ds.tree())\n\n/\n ├── analysed_sst (1, 17999, 36000) int16\n ├── analysis_error (1, 17999, 36000) int16\n ├── dt_1km_data (1, 17999, 36000) int16\n ├── lat (17999,) float32\n ├── lon (36000,) float32\n ├── mask (1, 17999, 36000) int16\n ├── sea_ice_fraction (1, 17999, 36000) int16\n ├── sst_anomaly (1, 17999, 36000) int16\n └── time (1,) int32\n\n\nFetch the latitude and longitude arrays and determine start and end indices for our area of interest. In this case, we’re looking at the Great Lakes, which have a nice, recognizeable shape. Latitudes 41 to 49, longitudes -93 to 76.\n\nlats = ds['lat'][:]\nlons = ds['lon'][:]\nlat_range = slice(lats.searchsorted(41), lats.searchsorted(49))\nlon_range = slice(lons.searchsorted(-93), lons.searchsorted(-76))\n\nGet the analysed sea surface temperature variable over our area of interest and apply scale factor and offset from the file metadata. In a future release, scale factor and add offset will be automatically applied.\n\nvar = ds['analysed_sst']\nanalysed_sst = var[0, lat_range, lon_range] * var.attrs['scale_factor'] + var.attrs['add_offset']\n\nDraw a pretty picture\n\nfrom matplotlib import pyplot as plt\n\nplt.rcParams[\"figure.figsize\"] = [16, 8]\nplt.imshow(analysed_sst[::-1, :])\nNone\n\n\n\n\nIn a dozen lines of code and a few seconds, we have managed to fetch and visualize the 3.2 megabyte we needed from a 732 megabyte file using the original archive URL and no processing services"
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"section": "",
- "text": "This tutorial uses multiple satellite data products to explore the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region over several years. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use on-premises and cloud datasets and services. The notebook can be executed locally, or in AWS (in us-west-2 region where the cloud data is located)."
+ "text": "The original source for this document is https://nasa-openscapes.github.io/2021-Cloud-Workshop-AGU/tutorials/01_Earthdata_Search.html\nThis tutorial guides you through how to use Earthdata Search for NASA Earth observations search and discovery, and how to connect the search output (e.g. download or access links) to a programmatic workflow (locally or from within the cloud).\n\nStep 1. Go to Earthdata Search and Login\nGo to Earthdata Search https://search.earthdata.nasa.gov and use your Earthdata login credentials to log in. If you do not have an Earthdata account, please see the Workshop Prerequisites for guidance.\n\n\nStep 2. Search for dataset of interest\nUse the search box in the upper left to type key words. In this example we are interested in the ECCO dataset, hosted by the PO.DAAC. This dataset is available from the NASA Earthdata Cloud archive hosted in AWS cloud.\nClick on the “Available from AWS Cloud” filter option on the left. Here, 104 matching collections were found with the basic ECCO search.\n\n\n\nFigure caption: Search for ECCO data available in AWS cloud in Earthdata Search portal\n\n\nLet’s refine our search further. Let’s search for ECCO monthly SSH in the search box (which will produce 39 matching collections), and for the time period for year 2015. The latter can be done using the calendar icon on the left under the search box.\nScroll down the list of returned matches until we see the dataset of interest, in this case ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4).\nWe can click on the (i) icon for the dataset to read more details, including the dataset shortname (helpful for programmatic workflows) just below the dataset name; here ECCO_L4_SSH_05DEG_MONTHLY_V4R4.\n\n\n\nFigure caption: Refine search, set temporal bounds, get more information\n\n\n\n\nStep 3. Explore the dataset details, including Cloud Access information\nOnce we clicked the (i), scrolling down the info page for the dataset we will see Cloud Access information, such as:\n\nwhether the dataset is available in the cloud\nthe cloud Region (all NASA Earthdata Cloud data is/will be in us-west-2 region)\nthe S3 storage bucket and object prefix where this data is located\nlink that generates AWS S3 Credentials for in-cloud data access (we will cover this in the Direct Data Access Tutorials)\nlink to documentation describing the In-region Direct S3 Access to Buckets. Note: these will be unique depending on the DAAC where the data is archived. (We will show examples of direct in-region access in Tutorial 3.)\n\n\n\n\nFigure caption: Cloud access info in EDS\n\n\n\n\n\nFigure caption: Documentation describing the In-region Direct S3 Access to Buckets\n\n\nPro Tip: Clicking on “For Developers” to exapnd will provide programmatic endpoints such as those for the CMR API, and more. CMR API and CMR STAC API tutorials can be found on the 2021 Cloud Hackathon website.\nFor now, let’s say we are intersted in getting download link(s) or access link(s) for specific data files (granules) within this collection.\nAt the top of the dataset info section, click on Search Results, which will take us back to the list of datasets matching our search parameters. Clicking on the dataset (here again it’s the same ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4)) we now see a list of files (granules) that are part of the dataset (collection).\n\n\nStep 4. Customize the download or data access\nClick on the green + symbol to add a few files to our project. Here we added the first 3 listed for 2015. Then click on the green button towards the bottom that says “Download”. This will take us to another page with options to customize our download or access link(s).\n\n\n\nFigure caption: Select granules and click download\n\n\n\n4.a. Entire file content\nLet’s stay we are interested in the entire file content, so we select the “Direct Download” option (as opposed to other options to subset or transform the data):\n\n\n\nFigure caption: Customize your download or access\n\n\nClicking the green Download Data button again, will take us to the final page for instructions to download and links for data access in the cloud. You should see three tabs: Download Files, AWS S3 Access, Download Script:\n \nThe Download Files tab provides the https:// links for downloading the files locally. E.g.: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc\nThe AWS S3 Access tab provides the S3:// links, which is what we would use to access the data directly in-region (us-west-2) within the AWS cloud (an example will be shown in Tutorial 3). E.g.: s3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc where s3 indicates data is stored in AWS S3 storage, podaac-ops-cumulus-protected is the bucket, and ECCO_L4_SSH_05DEG_MONTHLY_V4R4 is the object prefix (the latter two are also listed in the dataset collection information under Cloud Access (step 3 above)).\nTip: Another quicker way to find the bucket and object prefix is from the list of data files the search returns. Next to the + green button is a grey donwload symbol. Click on that to see the Download Files https:// links or on the AWS S3 Access to get the direct S3:// access links, which contain the bucket and object prefix where data is stored.\n\n\n4.b. Subset or transform before download or access\nDAAC tools and services are also being migrated or developed in the cloud, next to that data. These include the Harmony API and OPeNDAP in the cloud, as a few examples.\nWe can leverage these cloud-based services on cloud-archived data to reduce or transform the data (depending on need) before getting the access links regardless of whether we prefer to download the data and work on a local machine or whether we want to access the data in the cloud (from a cloud workspace). These can be useful data reduction services that support a faster time to science.\nHarmony\nHarmony allows you to seamlessly analyze Earth observation data from different NASA data centers. These services (API endpoints) provide data reduction (e.g. subsetting) and transfromation services (e.g. convert netCDF data to Zarr cloud optimized format).\n\n\n\nFigure caption: Leverage Harmony cloud-based data transformation services\n\n\nWhen you click the final green Download button, the links provided are to data that had been transformed based on our selections on the previous screen (here chosing to use the Harmony service to reformat the data to Zarr). These data are staged for us in an S3 bucket in AWS, and we can use the s3:// links to access those specific data. This service also provides STAC access links. This particular example is applicable if your workflow is in the AWS us-west-2 region.\n\n\n\nFigure caption: Harmony-staged data in S3\n\n\n\n\n\nStep 5. Integrate file links into programmatic workflow, locally or in the AWS cloud.\nIn tutorial 3 Direct Data Access, we will work programmatically in the cloud to access datasets of interest, to get us set up for further scientific analysis of choice. There are several ways to do this. One way to connect the search part of the workflow we just did in Earthdata Search to our next steps working in the cloud is to simply copy/paste the s3:// links provides in Step 4 above into a JupyterHub notebook or script in our cloud workspace, and continue the data analysis from there.\nOne could also copy/paste the s3:// links and save them in a text file, then open and read the text file in the notebook or script in the JupyterHub in the cloud.\nTutorial 3 will pick up from here and cover these next steps in more detail."
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- "text": "Datasets\nThe tutorial itself will use five different datasets, which represent a combination of cloud^^- and on premise- archived datasets:\n\nTELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\n\nDOI: [https://doi.org/10.5067/TEMSC-3JC62](https://doi.org/10.5067/TEMSC-3JC62) \n\nThe Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) satellite land water equivalent (LWE) thicknesses will be used to observe seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, thus highlighting a wet season. \n\nPRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\n\nDOI: [https://doi.org/10.5067/PSGRA-DA2V2](https://doi.org/10.5067/PSGRA-DA2V2)\n\nThe NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual gauges will be used as a proxy for Surface Water and Ocean Topography (SWOT) discharge until SWOT products are available. MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute.\n\nSMAP_JPL_L3_SSS_CAP_MONTHLY_V5\n\nDOI: [https://doi.org/10.5067/SMP50-3TMCS](https://doi.org/10.5067/SMP50-3TMCS) \n\nSea surface salinity is obtained from the Soil Moisture Active Passive (SMAP) satellite (2015-2019).\n\nAQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5\n\nDOI: [https://doi.org/10.5067/AQR50-3Y1CE](https://doi.org/10.5067/AQR50-3Y1CE)\n\nSea surface salinity is obtained from the Aquarius/SAC-D satellite (2011-2015).\n\nMODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0\n\nDOI: [https://doi.org/10.5067/MODAM-MO9N9](https://doi.org/10.5067/MODAM-MO9N9)\n\nSea surface temperature is obtained from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the Aqua satellite. \nThe tutorial will show how each of these datasets are accessed and subset for our specific location, the Amazon River estuary. Graphs and images of river height (river discharge when SWOT data is available), LWE thickness, salinity, and sea surface temperature will be created and shown side by side to enable the exploration of relationships between the data.\n^^During 2021 PO.DAAC is in the process of migrating its data and services to the Earthdata Cloud in Amazon Web Services (AWS). As such some data will be available for early access from or within the Earthdata Cloud, while also being available from the on-premise archive. One such Cloud Pathfinder dataset is from the GRACE and GRACE-FO missions. In this example we access GRACE/FO data from the Earthdata Cloud. As a user, during the migration period (in 2021), you will need early access to be able to access cloud-based Pathfinder datasets such as GRACE. To gain that early access, please contact podaac@podaac.jpl.nasa.gov with your request for Early Access to cloud data. Please include your Earthdata Login username. Once you’ve been added to the early access list, you can then see the available collections after logging into the PO.DAAC Cloud Earthdata Search Portal or run this notebook. For more information on the PO.DAAC transition to the cloud, please visit: https://podaac.jpl.nasa.gov/cloud-datasets/about"
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+ "href": "external/zarr_access.html",
+ "title": "Zarr Access for NetCDF4 files",
+ "section": "",
+ "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
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- "section": "Needed Packages",
- "text": "Needed Packages\n\nimport time\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport cartopy.crs as ccrs\nimport cartopy\nfrom json import dumps, loads\nimport json\nfrom IPython.display import HTML\nfrom os.path import isfile, basename, abspath"
+ "objectID": "external/zarr_access.html#timing",
+ "href": "external/zarr_access.html#timing",
+ "title": "Zarr Access for NetCDF4 files",
+ "section": "Timing:",
+ "text": "Timing:\n\nExercise: 45 minutes"
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- "title": "This Notebook is no longer up to date, a newer version exists here.",
- "section": "Set the CMR, URS, and Harmony endpoints",
- "text": "Set the CMR, URS, and Harmony endpoints\nCMR, or the Earthdata Common Metadata Repository, is a high-performance, high-quality, continuously evolving metadata system that catalogs Earth Science data and associated service metadata records. URS is the Earthdata login system, that allows (free) download access to Earthdata data. Harmony API allows you to seamlessly analyze Earth observation data from different NASA data centers.\n\ncmr = \"cmr.earthdata.nasa.gov\"\nurs = \"urs.earthdata.nasa.gov\"\nharmony = \"harmony.earthdata.nasa.gov\"\n\ncmr, urs, harmony\n\n('cmr.earthdata.nasa.gov',\n 'urs.earthdata.nasa.gov',\n 'harmony.earthdata.nasa.gov')"
+ "objectID": "external/zarr_access.html#summary",
+ "href": "external/zarr_access.html#summary",
+ "title": "Zarr Access for NetCDF4 files",
+ "section": "Summary",
+ "text": "Summary\nZarr is an open source library for storing N-dimensional array data. It supports multidimensional arrays with attributes and dimensions similar to NetCDF4, and it can be read by XArray. Zarr is often used for data held in cloud object storage (like Amazon S3), because it is better optimized for these situations than NetCDF4.\nThe zarr-eosdis-store library allows NASA EOSDIS NetCDF4 files to be read more efficiently by transferring only file metadata and data needed for computation in a small number of requests, rather than moving the whole file or making many small requests. It works by making the files directly readable by the Zarr Python library and XArray across a network. To use it, files must have a corresponding metadata file ending in .dmrpp, which increasingly true for cloud-accessible EOSDIS data. https://github.com/nasa/zarr-eosdis-store\nThe zarr-eosdis-store library provides several benefits over downloading EOSDIS data files and accessing them using XArray, NetCDF4, or HDF5 Python libraries:\n\nIt only downloads the chunks of data you actually read, so if you don’t read all variables or the full spatiotemporal extent of a file, you usually won’t spend time downloading those portions of the file\nIt parallelizes and optimizes downloads for the portions of files you do read, so download speeds can be faster in general\nIt automatically interoperates with Earthdata Login if you have a .netrc file set up\nIt is aware of some EOSDIS cloud implementation quirks and provides caching that can save time for repeated requests to individual files\n\nIt can also be faster than using XArray pointing NetCDF4 files with s3:// URLs, depending on the file’s internal structure, and is often more convenient.\nConsider using this library when: 1. The portion of the data file you need to use is much smaller than the full file, e.g. in cases of spatial subsets or reading a single variable from a file containing several 1. s3:// URLs are not readily available 1. Code need to run outside of the AWS cloud or us-west-2 region or in a hybrid cloud / non-cloud manner 1. s3:// access using XArray seems slower than you would expect (possibly due to unoptimized internal file structure) 1. No readily-available, public, cloud-optimized version of the data exists already. The example we show is also available as an AWS Public Dataset: https://registry.opendata.aws/mur/ 1. Adding “.dmrpp” to the end of a data URL returns a file\n\nObjectives\n\nBuild on prior knowledge from CMR and Earthdata Login tutorials\nWork through an example of using the EOSDIS Zarr Store to access data using XArray\nLearn about the Zarr format and library for accessing data in the cloud"
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- "section": "NASA Earthdata Login Setup",
- "text": "NASA Earthdata Login Setup\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\nThe setup_earthdata_login_auth function will allow Python scripts to log into any Earthdata Login application programmatically. To avoid being prompted for credentials every time you run and also allow clients such as curl to log in, you can add the following to a .netrc (_netrc on Windows) file in your home directory:\n machine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nMake sure that this file is only readable by the current user or you will receive an error stating “netrc access too permissive.”\n$ chmod 0600 ~/.netrc\nYou will be prompted for your username and password if you dont have a netrc file. Note: these imports are all in the Python 3.6+ standard library.\n\nfrom platform import system\nfrom netrc import netrc\nfrom getpass import getpass\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nfrom os.path import join, expanduser\n\nTOKEN_DATA = (\"<token>\"\n \"<username>%s</username>\"\n \"<password>%s</password>\"\n \"<client_id>PODAAC CMR Client</client_id>\"\n \"<user_ip_address>%s</user_ip_address>\"\n \"</token>\")\n\n\ndef setup_earthdata_login_auth(urs: str='urs.earthdata.nasa.gov', cmr: str='cmr.earthdata.nasa.gov'):\n\n # GET URS LOGIN INFO FROM NETRC OR USER PROMPTS:\n netrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n try:\n username, _, password = netrc(file=join(expanduser('~'), netrc_name)).authenticators(urs)\n print(\"# Your URS credentials were securely retrieved from your .netrc file.\")\n except (FileNotFoundError, TypeError):\n print('# Please provide your Earthdata Login credentials for access.')\n print('# Your info will only be passed to %s and will not be exposed in Jupyter.' % (urs))\n username = input('Username: ')\n password = getpass('Password: ')\n\n # SET UP URS AUTHENTICATION FOR HTTP DOWNLOADS:\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, urs, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n\n # GET TOKEN TO ACCESS RESTRICTED CMR METADATA:\n ip = requests.get(\"https://ipinfo.io/ip\").text.strip()\n r = requests.post(\n url=\"https://%s/legacy-services/rest/tokens\" % cmr,\n data=TOKEN_DATA % (str(username), str(password), ip),\n headers={'Content-Type': 'application/xml', 'Accept': 'application/json'}\n )\n return r.json()['token']['id']\n\n\n# Provide URS credentials for HTTP download auth & CMR token retrieval:\n_token = setup_earthdata_login_auth(urs=urs, cmr=cmr)\n\n# Your URS credentials were securely retrieved from your .netrc file.\n\n\n\nCloud data: GRACE Liquid Water Equivalent (LWE)\n\nSearch for GRACE LWE Thickness data\nGRACE/GRACE-FO data can be obtained from the Earthdata Cloud, as described in the introduction section of this notebook.\n\nHow to find a collection ID from the dataset landing page\nSuppose we are interested in LWE data from the dataset (DOI:10.5067/TEMSC-3JC62) described on this PO.DAAC dataset landing page: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\nFrom the landing page, we see the dataset Short Name under the Information tab. Copy that to paste and search later. Log in to your Earthdata account at: https://search.earthdata.nasa.gov. Enter the Short Name and search. Clicking on one of the search results brings us to a list of granules. Within that URL, we can grab the concept-id, a string starting with “C” and ending with “-POCLOUD”. For this dataset, it is “C1938032626-POCLOUD”.\n\n\n\nCollection search in CMR\nHere we use the requests library to search in collections by either short name or concept-id, which returns exactly one dataset, or one “hit”, in a JSON format.\n\ngrace_ShortName = \"TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2\"\nr = requests.get(url=f\"https://{cmr}/search/collections.umm_json\",\n params = \n {\n 'provider': \"POCLOUD\",\n 'token': _token,\n 'concept-id': \"C1938032626-POCLOUD\"\n #'ShortName': grace_ShortName,\n }\n \n )\ngrace_coll = r.json()\ngrace_coll['hits']\n\n1\n\n\n\n\nSee collection metadata\n\ngrace_coll_meta = grace_coll['items'][0]['meta']\ngrace_coll_meta\n\n{'revision-id': 4,\n 'deleted': False,\n 'format': 'application/vnd.nasa.cmr.umm+json',\n 'provider-id': 'POCLOUD',\n 'user-id': 'chen5510',\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'native-id': 'JPL+GRACE+and+GRACE-FO+Mascon+Ocean,+Ice,+and+Hydrology+Equivalent+Water+Height+Coastal+Resolution+Improvement+(CRI)+Filtered+Release+06+Version+02',\n 'has-transforms': False,\n 'has-variables': False,\n 'concept-id': 'C1938032626-POCLOUD',\n 'revision-date': '2021-05-21T15:29:56.854Z',\n 'granule-count': 0,\n 'has-temporal-subsetting': False,\n 'concept-type': 'collection'}\n\n\n\n\nGranule search\nHere we use the requests library to search for granules in the collection. It returns 7 “hits”, or 7 granules.\n\nr = requests.get(url=f\"https://{cmr}/search/granules.umm_json\", \n params={'provider': \"POCLOUD\", \n 'ShortName': grace_ShortName, \n 'token': _token})\n\ngrace_gran = r.json()\ngrace_gran['hits']\n\n8\n\n\n\n\nFor GRACE, when there are multiple granules, take the latest monthly granule (automate finding of most recent month?)\n\nlatest_granule = grace_gran['hits']-1 # If not true, then sort grace_gran by 'native-id'\ngrace_gran['items'][latest_granule]['meta']\n\n{'concept-type': 'granule',\n 'concept-id': 'G2050191643-POCLOUD',\n 'revision-id': 1,\n 'native-id': 'GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI',\n 'provider-id': 'POCLOUD',\n 'format': 'application/vnd.nasa.cmr.umm+json',\n 'revision-date': '2021-05-11T03:30:34.686Z'}\n\n\n\n\nGet download link\nThe download link to the .nc file is one of the RelatedURLs.\n\ngrace_gran['items'][latest_granule]['umm']['RelatedUrls']\n\n[{'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc.md5',\n 'Type': 'EXTENDED METADATA',\n 'Description': 'File to download'},\n {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.cmr.json',\n 'Type': 'EXTENDED METADATA',\n 'Description': 'File to download'},\n {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc',\n 'Type': 'GET DATA',\n 'Description': 'File to download'}]\n\n\n\n# From above, select the link to the .nc file (links are not always listed in the same order)\nlink_num = 2\ngrace_url = grace_gran['items'][latest_granule]['umm']['RelatedUrls'][link_num]['URL']\ngrace_url\n\n'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2/GRCTellus.JPL.200204_202103.GLO.RL06M.MSCNv02CRI.nc'\n\n\n\n\nDownload the .nc file from the Earthdata cloud\nAnd display overview of contents (metadata) using ncdump. This will download the GRACE data to your local machine, or whereever you are running this notebook from.\n\nr = requests.get(grace_url)\nwith open('iosos_demo_GRACEFO.nc', 'wb') as f:\n f.write(r.content)\n\n!ncdump -h iosos_demo_GRACEFO.nc\n\nnetcdf iosos_demo_GRACEFO {\ndimensions:\n lon = 720 ;\n lat = 360 ;\n time = 195 ;\n bounds = 2 ;\nvariables:\n double lon(lon) ;\n lon:units = \"degrees_east\" ;\n lon:long_name = \"longitude\" ;\n lon:standard_name = \"longitude\" ;\n lon:axis = \"X\" ;\n lon:valid_min = 0.25 ;\n lon:valid_max = 359.75 ;\n lon:bounds = \"lon_bounds\" ;\n double lat(lat) ;\n lat:units = \"degrees_north\" ;\n lat:long_name = \"latitude\" ;\n lat:standard_name = \"latitude\" ;\n lat:axis = \"Y\" ;\n lat:valid_min = -89.75 ;\n lat:valid_max = 89.75 ;\n lat:bounds = \"lat_bounds\" ;\n double time(time) ;\n time:units = \"days since 2002-01-01T00:00:00Z\" ;\n time:long_name = \"time\" ;\n time:standard_name = \"time\" ;\n time:axis = \"T\" ;\n time:calendar = \"gregorian\" ;\n time:bounds = \"time_bounds\" ;\n double lwe_thickness(time, lat, lon) ;\n lwe_thickness:units = \"cm\" ;\n lwe_thickness:long_name = \"Liquid_Water_Equivalent_Thickness\" ;\n lwe_thickness:standard_name = \"Liquid_Water_Equivalent_Thickness\" ;\n lwe_thickness:coordinates = \"time lat lon\" ;\n lwe_thickness:grid_mapping = \"WGS84\" ;\n lwe_thickness:_FillValue = -99999. ;\n lwe_thickness:valid_min = -1772.14897730884 ;\n lwe_thickness:valid_max = 767.736827711678 ;\n lwe_thickness:comment = \"Coastline Resolution Improvement (CRI) filter is applied\" ;\n double uncertainty(time, lat, lon) ;\n uncertainty:units = \"cm\" ;\n uncertainty:long_name = \"uncertainty\" ;\n uncertainty:standard_name = \"uncertainty\" ;\n uncertainty:coordinates = \"time lat lon\" ;\n uncertainty:grid_mapping = \"WGS84\" ;\n uncertainty:_FillValue = -99999. ;\n uncertainty:valid_min = 0.158623647924877 ;\n uncertainty:valid_max = 53.3446959856009 ;\n uncertainty:comment = \"1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate\" ;\n double lat_bounds(lat, bounds) ;\n lat_bounds:long_name = \"latitude boundaries\" ;\n lat_bounds:units = \"degrees_north\" ;\n lat_bounds:comment = \"latitude values at the north and south bounds of each pixel\" ;\n double lon_bounds(lon, bounds) ;\n lon_bounds:long_name = \"longitude boundaries\" ;\n lon_bounds:units = \"degrees_east\" ;\n lon_bounds:comment = \"longitude values at the west and east bounds of each pixel\" ;\n double time_bounds(time, bounds) ;\n time_bounds:long_name = \"time boundaries\" ;\n time_bounds:units = \"days since 2002-01-01T00:00:00Z\" ;\n time_bounds:comment = \"time bounds for each time value, i.e. the first day and last day included in the monthly solution\" ;\n\n// global attributes:\n :Conventions = \"CF-1.6, ACDD-1.3, ISO 8601\" ;\n :Metadata_Conventions = \"Unidata Dataset Discovery v1.0\" ;\n :standard_name_vocabulary = \"NetCDF Climate and Forecast (CF) Metadata Convention-1.6\" ;\n :title = \"JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI\" ;\n :summary = \"Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06Mv2 mascon solution - with CRI filter applied\" ;\n :keywords = \"Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thickness\" ;\n :keywords_vocabulary = \"NASA Global Change Master Directory (GCMD) Science Keywords\" ;\n :platform = \"GRACE and GRACE-FO\" ;\n :institution = \"NASA/JPL\" ;\n :creator_name = \"David Wiese\" ;\n :creator_email = \"grace@podaac.jpl.nasa.gov\" ;\n :creator_url = \"https://grace.jpl.nasa.gov\" ;\n :creator_type = \"group\" ;\n :creator_institution = \"NASA/JPL\" ;\n :publisher_name = \"Physical Oceanography Distributed Active Archive Center\" ;\n :publisher_email = \"podaac@jpl.nasa.gov\" ;\n :publisher_url = \"https://podaac.jpl.nasa.gov\" ;\n :publisher_type = \"group\" ;\n :publisher_institution = \"NASA/JPL\" ;\n :project = \"NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)\" ;\n :program = \"NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Program\" ;\n :id = \"10.5067/TEMSC-3JC62\" ;\n :naming_authority = \"org.doi.dx\" ;\n :source = \"GRACE and GRACE-FO JPL RL06Mv2-CRI\" ;\n :processing_level = \"2 and 3\" ;\n :acknowledgement = \"GRACE is a joint mission of NASA (USA) and DLR (Germany). GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAC\" ;\n :license = \"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy\" ;\n :product_version = \"v2.0\" ;\n :time_epoch = \"2002-01-01T00:00:00Z\" ;\n :time_coverage_start = \"2002-04-16T00:00:00Z\" ;\n :time_coverage_end = \"2021-03-16T23:59:59Z\" ;\n :geospatial_lat_min = -89.75 ;\n :geospatial_lat_max = 89.75 ;\n :geospatial_lat_units = \"degrees_north\" ;\n :geospatial_lat_resolution = \"0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons\" ;\n :geospatial_lon_min = 0.25 ;\n :geospatial_lon_max = 359.75 ;\n :geospatial_lon_units = \"degrees_east\" ;\n :geospatial_lon_resolution = \"0.5 degree grid; however the native resolution of the data is 3-degree equal-area mascons\" ;\n :time_mean_removed = \"2004.000 to 2009.999\" ;\n :months_missing = \"2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09\" ;\n :postprocess_1 = \" OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLY\" ;\n :postprocess_2 = \"Water density used to convert to equivalent water height: 1000 kg/m^3\" ;\n :postprocess_3 = \"Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlines\" ;\n :GIA_removed = \"ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.\" ;\n :geocenter_correction = \"We use a version of TN-13 based on the JPL mascons\" ;\n :C_20_substitution = \"TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929\" ;\n :C_30_substitution = \"TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929. This substitution is made for all months after August 2016.\" ;\n :user_note_1 = \"The accelerometer on the GRACE-B spacecraft was turned off after August 2016. After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure. This has led to a subsequent degradation in the quality of the gravity fields derived. The uncertainties in this file have been scaled to accomodate this degradation.\" ;\n :user_note_2 = \"The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018. After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure. The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.\" ;\n :journal_reference = \"Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth\\'s time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. \" ;\n :CRI_filter_journal_reference = \"Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. \" ;\n :date_created = \"2021-04-27T21:04:06Z\" ;\n}\n\n\n\n\nOpen file using xarray.\n\nds_GRACE = xr.open_dataset('iosos_demo_GRACEFO.nc')\nds_GRACE\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (bounds: 2, lat: 360, lon: 720, time: 195)\nCoordinates:\n * lon (lon) float64 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8\n * lat (lat) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * time (time) datetime64[ns] 2002-04-17T12:00:00 ... 2021-03-16T1...\nDimensions without coordinates: bounds\nData variables:\n lwe_thickness (time, lat, lon) float64 ...\n uncertainty (time, lat, lon) float64 ...\n lat_bounds (lat, bounds) float64 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0\n lon_bounds (lon, bounds) float64 0.0 0.5 0.5 1.0 ... 359.5 359.5 360.0\n time_bounds (time, bounds) datetime64[ns] 2002-04-04 ... 2021-03-31T23...\nAttributes:\n Conventions: CF-1.6, ACDD-1.3, ISO 8601\n Metadata_Conventions: Unidata Dataset Discovery v1.0\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata ...\n title: JPL GRACE and GRACE-FO MASCON RL06Mv2 CRI\n summary: Monthly gravity solutions from GRACE and G...\n keywords: Solid Earth, Geodetics/Gravity, Gravity, l...\n keywords_vocabulary: NASA Global Change Master Directory (GCMD)...\n platform: GRACE and GRACE-FO\n institution: NASA/JPL\n creator_name: David Wiese\n creator_email: grace@podaac.jpl.nasa.gov\n creator_url: https://grace.jpl.nasa.gov\n creator_type: group\n creator_institution: NASA/JPL\n publisher_name: Physical Oceanography Distributed Active A...\n publisher_email: podaac@jpl.nasa.gov\n publisher_url: https://podaac.jpl.nasa.gov\n publisher_type: group\n publisher_institution: NASA/JPL\n project: NASA Gravity Recovery and Climate Experime...\n program: NASA Earth Science System Pathfinder and N...\n id: 10.5067/TEMSC-3JC62\n naming_authority: org.doi.dx\n source: GRACE and GRACE-FO JPL RL06Mv2-CRI\n processing_level: 2 and 3\n acknowledgement: GRACE is a joint mission of NASA (USA) and...\n license: https://science.nasa.gov/earth-science/ear...\n product_version: v2.0\n time_epoch: 2002-01-01T00:00:00Z\n time_coverage_start: 2002-04-16T00:00:00Z\n time_coverage_end: 2021-03-16T23:59:59Z\n geospatial_lat_min: -89.75\n geospatial_lat_max: 89.75\n geospatial_lat_units: degrees_north\n geospatial_lat_resolution: 0.5 degree grid; however the native resolu...\n geospatial_lon_min: 0.25\n geospatial_lon_max: 359.75\n geospatial_lon_units: degrees_east\n geospatial_lon_resolution: 0.5 degree grid; however the native resolu...\n time_mean_removed: 2004.000 to 2009.999\n months_missing: 2002-06;2002-07;2003-06;2011-01;2011-06;20...\n postprocess_1: OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MON...\n postprocess_2: Water density used to convert to equivalen...\n postprocess_3: Coastline Resolution Improvement (CRI) fil...\n GIA_removed: ICE6G-D; Peltier, W. R., D. F. Argus, and ...\n geocenter_correction: We use a version of TN-13 based on the JPL...\n C_20_substitution: TN-14; Loomis et al., 2019, Geophys. Res. ...\n C_30_substitution: TN-14; Loomis et al., 2019, Geophys. Res. ...\n user_note_1: The accelerometer on the GRACE-B spacecraf...\n user_note_2: The accelerometer on the GRACE-D spacecraf...\n journal_reference: Watkins, M. M., D. N. Wiese, D.-N. Yuan, C...\n CRI_filter_journal_reference: Wiese, D. N., F. W. Landerer, and M. M. Wa...\n date_created: 2021-04-27T21:04:06Zxarray.DatasetDimensions:bounds: 2lat: 360lon: 720time: 195Coordinates: (3)lon(lon)float640.25 0.75 1.25 ... 359.2 359.8units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xvalid_min :0.25valid_max :359.75bounds :lon_boundsarray([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,\n 3.5975e+02])lat(lat)float64-89.75 -89.25 ... 89.25 89.75units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yvalid_min :-89.75valid_max :89.75bounds :lat_boundsarray([-89.75, -89.25, -88.75, ..., 88.75, 89.25, 89.75])time(time)datetime64[ns]2002-04-17T12:00:00 ... 2021-03-...long_name :timestandard_name :timeaxis :Tbounds :time_boundsarray(['2002-04-17T12:00:00.000000000', '2002-05-10T12:00:00.000000000',\n '2002-08-16T12:00:00.000000000', '2002-09-16T00:00:00.000000000',\n '2002-10-16T12:00:00.000000000', '2002-11-16T00:00:00.000000000',\n '2002-12-16T12:00:00.000000000', '2003-01-16T12:00:00.000000000',\n '2003-02-15T00:00:00.000000000', '2003-03-16T12:00:00.000000000',\n '2003-04-16T00:00:00.000000000', '2003-05-11T12:00:00.000000000',\n '2003-07-16T12:00:00.000000000', '2003-08-16T12:00:00.000000000',\n '2003-09-16T00:00:00.000000000', '2003-10-16T00:00:00.000000000',\n '2003-11-16T00:00:00.000000000', '2003-12-16T12:00:00.000000000',\n '2004-01-07T12:00:00.000000000', '2004-02-17T00:00:00.000000000',\n '2004-03-16T12:00:00.000000000', '2004-04-16T00:00:00.000000000',\n '2004-05-16T12:00:00.000000000', '2004-06-16T00:00:00.000000000',\n '2004-07-16T12:00:00.000000000', '2004-08-16T12:00:00.000000000',\n '2004-09-16T00:00:00.000000000', '2004-10-16T12:00:00.000000000',\n '2004-11-16T00:00:00.000000000', '2004-12-16T12:00:00.000000000',\n '2005-01-16T12:00:00.000000000', '2005-02-15T00:00:00.000000000',\n '2005-03-16T12:00:00.000000000', '2005-04-16T00:00:00.000000000',\n '2005-05-16T12:00:00.000000000', '2005-06-16T00:00:00.000000000',\n '2005-07-16T12:00:00.000000000', '2005-08-16T12:00:00.000000000',\n '2005-09-16T00:00:00.000000000', '2005-10-16T12:00:00.000000000',\n '2005-11-16T00:00:00.000000000', '2005-12-16T12:00:00.000000000',\n '2006-01-16T12:00:00.000000000', '2006-02-15T00:00:00.000000000',\n '2006-03-16T12:00:00.000000000', '2006-04-16T00:00:00.000000000',\n '2006-05-16T12:00:00.000000000', '2006-06-16T00:00:00.000000000',\n '2006-07-16T12:00:00.000000000', '2006-08-16T12:00:00.000000000',\n '2006-09-16T00:00:00.000000000', '2006-10-16T12:00:00.000000000',\n '2006-11-16T00:00:00.000000000', '2006-12-16T12:00:00.000000000',\n '2007-01-16T12:00:00.000000000', '2007-02-15T00:00:00.000000000',\n '2007-03-16T12:00:00.000000000', '2007-04-16T00:00:00.000000000',\n '2007-05-16T12:00:00.000000000', '2007-06-16T00:00:00.000000000',\n '2007-07-16T12:00:00.000000000', '2007-08-16T12:00:00.000000000',\n '2007-09-16T00:00:00.000000000', '2007-10-16T12:00:00.000000000',\n '2007-11-16T00:00:00.000000000', '2007-12-16T12:00:00.000000000',\n '2008-01-16T12:00:00.000000000', '2008-02-15T12:00:00.000000000',\n '2008-03-16T12:00:00.000000000', '2008-04-16T00:00:00.000000000',\n '2008-05-16T12:00:00.000000000', '2008-06-16T00:00:00.000000000',\n '2008-07-16T12:00:00.000000000', '2008-08-16T12:00:00.000000000',\n '2008-09-16T00:00:00.000000000', '2008-10-16T12:00:00.000000000',\n '2008-11-16T00:00:00.000000000', '2008-12-16T12:00:00.000000000',\n '2009-01-16T12:00:00.000000000', '2009-02-15T00:00:00.000000000',\n '2009-03-16T12:00:00.000000000', '2009-04-16T00:00:00.000000000',\n '2009-05-16T12:00:00.000000000', '2009-06-16T00:00:00.000000000',\n '2009-07-16T12:00:00.000000000', '2009-08-16T12:00:00.000000000',\n '2009-09-16T00:00:00.000000000', '2009-10-16T12:00:00.000000000',\n '2009-11-16T00:00:00.000000000', '2009-12-16T12:00:00.000000000',\n '2010-01-16T12:00:00.000000000', '2010-02-15T00:00:00.000000000',\n '2010-03-16T12:00:00.000000000', '2010-04-16T00:00:00.000000000',\n '2010-05-16T12:00:00.000000000', '2010-06-16T00:00:00.000000000',\n '2010-07-16T12:00:00.000000000', '2010-08-16T12:00:00.000000000',\n '2010-09-16T00:00:00.000000000', '2010-10-16T12:00:00.000000000',\n '2010-11-16T00:00:00.000000000', '2010-12-14T12:00:00.000000000',\n '2011-02-18T12:00:00.000000000', '2011-03-16T12:00:00.000000000',\n '2011-04-16T00:00:00.000000000', '2011-05-16T12:00:00.000000000',\n '2011-07-18T12:00:00.000000000', '2011-08-16T12:00:00.000000000',\n '2011-09-16T00:00:00.000000000', '2011-10-16T12:00:00.000000000',\n '2011-11-01T12:00:00.000000000', '2012-01-01T00:00:00.000000000',\n '2012-01-16T12:00:00.000000000', '2012-02-15T12:00:00.000000000',\n '2012-03-16T12:00:00.000000000', '2012-04-04T12:00:00.000000000',\n '2012-06-16T00:00:00.000000000', '2012-07-16T12:00:00.000000000',\n '2012-08-16T12:00:00.000000000', '2012-09-13T00:00:00.000000000',\n '2012-11-18T12:00:00.000000000', '2012-12-16T12:00:00.000000000',\n '2013-01-16T12:00:00.000000000', '2013-02-14T00:00:00.000000000',\n '2013-04-21T00:00:00.000000000', '2013-05-16T12:00:00.000000000',\n '2013-06-16T00:00:00.000000000', '2013-07-16T12:00:00.000000000',\n '2013-10-16T12:00:00.000000000', '2013-11-16T00:00:00.000000000',\n '2013-12-16T12:00:00.000000000', '2014-01-09T12:00:00.000000000',\n '2014-03-16T12:00:00.000000000', '2014-04-16T00:00:00.000000000',\n '2014-05-16T12:00:00.000000000', '2014-06-13T00:00:00.000000000',\n '2014-08-16T12:00:00.000000000', '2014-09-16T00:00:00.000000000',\n '2014-10-16T12:00:00.000000000', '2014-11-17T00:00:00.000000000',\n '2015-01-22T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n '2015-03-16T12:00:00.000000000', '2015-04-16T00:00:00.000000000',\n '2015-04-27T00:00:00.000000000', '2015-07-15T12:00:00.000000000',\n '2015-08-16T12:00:00.000000000', '2015-09-14T12:00:00.000000000',\n '2015-12-23T12:00:00.000000000', '2016-01-16T12:00:00.000000000',\n '2016-02-14T00:00:00.000000000', '2016-03-16T12:00:00.000000000',\n '2016-05-20T00:00:00.000000000', '2016-06-16T00:00:00.000000000',\n '2016-07-15T12:00:00.000000000', '2016-08-21T12:00:00.000000000',\n '2016-11-27T12:00:00.000000000', '2016-12-24T12:00:00.000000000',\n '2017-01-21T00:00:00.000000000', '2017-03-31T12:00:00.000000000',\n '2017-04-24T12:00:00.000000000', '2017-05-12T12:00:00.000000000',\n '2017-06-11T00:00:00.000000000', '2018-06-16T00:00:00.000000000',\n '2018-07-10T00:00:00.000000000', '2018-10-31T12:00:00.000000000',\n '2018-11-16T00:00:00.000000000', '2018-12-16T12:00:00.000000000',\n '2019-01-16T12:00:00.000000000', '2019-02-14T00:00:00.000000000',\n '2019-03-16T12:00:00.000000000', '2019-04-16T00:00:00.000000000',\n '2019-05-16T12:00:00.000000000', '2019-06-16T00:00:00.000000000',\n '2019-07-16T12:00:00.000000000', '2019-08-16T12:00:00.000000000',\n '2019-09-16T00:00:00.000000000', '2019-10-16T12:00:00.000000000',\n '2019-11-16T00:00:00.000000000', '2019-12-16T12:00:00.000000000',\n '2020-01-16T12:00:00.000000000', '2020-02-15T12:00:00.000000000',\n '2020-03-16T12:00:00.000000000', '2020-04-16T00:00:00.000000000',\n '2020-05-16T12:00:00.000000000', '2020-06-16T00:00:00.000000000',\n '2020-07-16T12:00:00.000000000', '2020-08-16T12:00:00.000000000',\n '2020-09-16T00:00:00.000000000', '2020-10-16T12:00:00.000000000',\n '2020-11-16T00:00:00.000000000', '2020-12-16T12:00:00.000000000',\n '2021-01-16T12:00:00.000000000', '2021-02-15T00:00:00.000000000',\n '2021-03-16T12:00:00.000000000'], dtype='datetime64[ns]')Data variables: (5)lwe_thickness(time, lat, lon)float64...units :cmlong_name :Liquid_Water_Equivalent_Thicknessstandard_name :Liquid_Water_Equivalent_Thicknessgrid_mapping :WGS84valid_min :-1772.1489773088445valid_max :767.7368277116782comment :Coastline Resolution Improvement (CRI) filter is applied[50544000 values with dtype=float64]uncertainty(time, lat, lon)float64...units :cmlong_name :uncertaintystandard_name :uncertaintygrid_mapping :WGS84valid_min :0.1586236479248768valid_max :53.34469598560085comment :1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate[50544000 values with dtype=float64]lat_bounds(lat, bounds)float64...long_name :latitude boundariesunits :degrees_northcomment :latitude values at the north and south bounds of each pixelarray([[-90. , -89.5],\n [-89.5, -89. ],\n [-89. , -88.5],\n ...,\n [ 88.5, 89. ],\n [ 89. , 89.5],\n [ 89.5, 90. ]])lon_bounds(lon, bounds)float64...long_name :longitude boundariesunits :degrees_eastcomment :longitude values at the west and east bounds of each pixelarray([[ 0. , 0.5],\n [ 0.5, 1. ],\n [ 1. , 1.5],\n ...,\n [358.5, 359. ],\n [359. , 359.5],\n [359.5, 360. ]])time_bounds(time, bounds)datetime64[ns]...long_name :time boundariescomment :time bounds for each time value, i.e. the first day and last day included in the monthly solutionarray([['2002-04-04T00:00:00.000000000', '2002-04-30T23:59:59.913600000'],\n ['2002-05-02T00:00:00.000000000', '2002-05-18T23:59:59.913600000'],\n ['2002-08-01T00:00:00.000000000', '2002-08-31T23:59:59.913600000'],\n ...,\n ['2021-01-01T00:00:00.000000000', '2021-01-31T23:59:59.913600000'],\n ['2021-02-01T00:00:00.000000000', '2021-02-28T23:59:59.913600000'],\n ['2021-03-01T00:00:00.000000000', '2021-03-31T23:59:59.913600000']],\n dtype='datetime64[ns]')Attributes: (53)Conventions :CF-1.6, ACDD-1.3, ISO 8601Metadata_Conventions :Unidata Dataset Discovery v1.0standard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Convention-1.6title :JPL GRACE and GRACE-FO MASCON RL06Mv2 CRIsummary :Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06Mv2 mascon solution - with CRI filter appliedkeywords :Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thicknesskeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsplatform :GRACE and GRACE-FOinstitution :NASA/JPLcreator_name :David Wiesecreator_email :grace@podaac.jpl.nasa.govcreator_url :https://grace.jpl.nasa.govcreator_type :groupcreator_institution :NASA/JPLpublisher_name :Physical Oceanography Distributed Active Archive Centerpublisher_email :podaac@jpl.nasa.govpublisher_url :https://podaac.jpl.nasa.govpublisher_type :grouppublisher_institution :NASA/JPLproject :NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)program :NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Programid :10.5067/TEMSC-3JC62naming_authority :org.doi.dxsource :GRACE and GRACE-FO JPL RL06Mv2-CRIprocessing_level :2 and 3acknowledgement :GRACE is a joint mission of NASA (USA) and DLR (Germany). GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAClicense :https://science.nasa.gov/earth-science/earth-science-data/data-information-policyproduct_version :v2.0time_epoch :2002-01-01T00:00:00Ztime_coverage_start :2002-04-16T00:00:00Ztime_coverage_end :2021-03-16T23:59:59Zgeospatial_lat_min :-89.75geospatial_lat_max :89.75geospatial_lat_units :degrees_northgeospatial_lat_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconsgeospatial_lon_min :0.25geospatial_lon_max :359.75geospatial_lon_units :degrees_eastgeospatial_lon_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconstime_mean_removed :2004.000 to 2009.999months_missing :2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09postprocess_1 : OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLYpostprocess_2 :Water density used to convert to equivalent water height: 1000 kg/m^3postprocess_3 :Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlinesGIA_removed :ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.geocenter_correction :We use a version of TN-13 based on the JPL masconsC_20_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929C_30_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929. This substitution is made for all months after August 2016.user_note_1 :The accelerometer on the GRACE-B spacecraft was turned off after August 2016. After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure. This has led to a subsequent degradation in the quality of the gravity fields derived. The uncertainties in this file have been scaled to accomodate this degradation.user_note_2 :The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018. After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure. The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.journal_reference :Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. CRI_filter_journal_reference :Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. date_created :2021-04-27T21:04:06Z\n\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data to plot.\n\nlat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\nds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_GRACE_subset\n\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.add_feature(cartopy.feature.RIVERS)\nds_GRACE_subset.lwe_thickness[193,:,:].plot(cmap = 'bwr_r') #106: July 2011\nplt.show()\n\n\n\n\n\n\n\nOn-premise data via OPeNDAP: River heights\nUse xarray and OPeNDAP link to see an overview of the dataset.\nCurrently, Pre-SWOT MEaSUREs data do not have the tools required to access them on the cloud, but methods are in the works! Here, we will obtain this dataset using OPeNDAP. OPeNDAP provides an API on the host server to access data without downloading it. OPeNDAP will also have a cloud component in the future, so this method of access can be used for both on-premise and cloud-based data in the near future and moving forward.\nTo find the OPeNDAP links needed to open the data (using the python package xarray), go to the specific satellite’s page on PO.DAAC (ex. Pre-SWOT MEaSUREs’s site).\nClick on the dataset you want (e.g. https://podaac.jpl.nasa.gov/dataset/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2) and click the “Data Access” tab. This will give you a link to where you can find the data in OPeNDAP (ex. https://podaac-opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/).\nFrom there, navigate to the desired NetCDF file and copy its link (e.g. for MEaSUREs, we want the Amazon estuary, so we choose the South America Amazon file: https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc).\nThe guidebook explains the details of the Pre-SWOT MEaSUREs data.\nOur desired variable is height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Amazon estuary. Time is between April 8, 1993 and April 20, 2019.\nLet’s look at this example file to see how the data is organized:\n\nds_MEaSUREs = xr.open_dataset('https://opendap.jpl.nasa.gov/opendap/allData/preswot_hydrology/L2/rivers/daily/South_America_Amazon1kmdaily.nc')\nds_MEaSUREs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (X: 3311, Y: 3311, charlength: 26, distance: 3311, time: 9469)\nCoordinates:\n * time (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...\nDimensions without coordinates: X, Y, charlength, distance\nData variables:\n lon (X) float64 -51.04 -51.05 -51.06 ... -73.35 -73.35\n lat (Y) float64 -0.6559 -0.6553 -0.6551 ... -4.179 -4.187\n FD (distance) float64 104.9 1.105e+03 ... 3.31e+06\n height (distance, time) float64 ...\n sat (charlength) |S64 b'--------------------------------...\n storage (distance, time) float64 ...\n LakeFlag (distance) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n Storage_uncertainty (distance, time) float64 ...\n IceFlag (time) float64 nan nan nan nan nan ... nan nan nan nan\nAttributes:\n title: GRRATS (Global River Radar Altimetry Time ...\n Conventions: CF-1.6, ACDD-1.3\n institution: Ohio State University, School of Earth Sci...\n source: MEaSUREs OSU Storage toolbox 2018\n keywords: EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n keywords_vocabulary: Global Change Master Directory (GCMD)\n cdm_data_type: station\n creator_name: Coss,Steve\n creator_email: Coss.31@osu.edu\n project: MEaSUREs OSU\n program: NASA Earth Science Data Systems (ESDS)\n publisher_name: PO.DAAC (Physical Oceanography Distributed...\n publisher_email: podaac@podaac.jpl.nasa.gov\n publisher_url: podaac.jpl.nasa.gov\n publisher_type: Institution\n publisher_institution: PO.DAAC\n processing_level: L2\n doi: 10.5067/PSGRA-DA2V2\n history: This GRRATS product adds data river surfac...\n platform: ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Ja...\n platform_vocabulary: NASA/GCMD Platform Keywords. Version 8.6\n instrument: RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(...\n instrument_vocabulary: NASA/GCMD Platform Keywords. Version 8.6\n references: in review :doi.org/10.5194/essd-2019-84\n id: GRRATS(Global River Radar Altimeter Time S...\n summary: The Global River Radar Altimeter Time Seri...\n time_coverage_resolution: 1 day\n date_created: 2021-05-17T21:26:51\n time_coverage_start: 1992-04-08T15:20:40\n time_coverage_end: 2018-04-20T03:39:13\n geospatial_lon_min: -73.35433106652545\n geospatial_lon_max: -51.0426448887506\n geospatial_lon_units: degree_east\n geospatial_lat_min: -4.380427586763687\n geospatial_lat_max: -0.6550700975069503\n geospatial_lat_units: degree_north\n geospatial_vertical_max: 92.7681246287056\n geospatial_vertical_min: -3.563409518163376\n geospatial_vertical_units: m\n geospatial_vertical_positive: upxarray.DatasetDimensions:X: 3311Y: 3311charlength: 26distance: 3311time: 9469Coordinates: (1)time(time)datetime64[ns]1993-04-08T15:20:40.665117184 .....long_name :timestandard_name :timeaxis :Tarray(['1993-04-08T15:20:40.665117184', '1993-04-09T15:20:40.665117184',\n '1993-04-10T15:20:40.665117184', ..., '2019-04-18T03:39:13.243964928',\n '2019-04-19T03:39:13.243964928', '2019-04-20T03:39:13.243964928'],\n dtype='datetime64[ns]')Data variables: (9)lon(X)float64...units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xarray([-51.042645, -51.051273, -51.06017 , ..., -73.354331, -73.351882,\n -73.348082])lat(Y)float64...units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yarray([-0.655885, -0.655342, -0.65507 , ..., -4.170956, -4.179361, -4.187492])FD(distance)float64...long_name :distance_from_river_mouthunits :kmcomment :This is the distance along the river centerline from the river mouth to this VS.array([1.049362e+02, 1.104936e+03, 2.104936e+03, ..., 3.308105e+06,\n 3.309105e+06, 3.310105e+06])height(distance, time)float64...units :mpositive :uplong_name :interpolated_heightsstandard_name :heightvalid_min :-3.563409518163376valid_max :92.7681246287056comment :A time flow distance grid of river water heights with respect to the EGM08 Geoid).-9999 fill values are for missing data, while -9995 fill values are for lakes and reservoirs.[31351859 values with dtype=float64]sat(charlength)|S64...long_name :satellitecomment :The satellite the measurement is derived from.string_length :9469array([b'----------------------------------------------------------------',\n b'EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE',\n b'RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR',\n b'SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS',\n b'1111111111111111111111111111111111111111111111111111111111111111',\n b'cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' ',\n b' '],\n dtype='|S64')storage(distance, time)float64...units :km3positive :uplong_name :river_channel_storagecomment :A time flow distance grid of river channel storage values.[31351859 values with dtype=float64]LakeFlag(distance)float64...long_name :Lake_Flagvalid_range :0, 1flag_masks :1flag_meaning :River sections that are lakes or reservoirscomment : This is a flag for masking out setions that are lakes or reservoirs.array([0., 0., 0., ..., 0., 0., 0.])Storage_uncertainty(distance, time)float64...units :km3positive :uplong_name :river_channel_storage_uncertaintycomment :A time flow distance grid of river channel storage uncertainty values.[31351859 values with dtype=float64]IceFlag(time)float64...long_name :Ice_Flagvalid_range :0, 1flag_masks :1flag_meaning :Time of ice covercomment : This is a flag for masking out times of Ice Cover.array([nan, nan, nan, ..., nan, nan, nan])Attributes: (40)title :GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Amazon RiverConventions :CF-1.6, ACDD-1.3institution :Ohio State University, School of Earth Sciencessource :MEaSUREs OSU Storage toolbox 2018keywords :EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHTkeywords_vocabulary :Global Change Master Directory (GCMD)cdm_data_type :stationcreator_name :Coss,Stevecreator_email :Coss.31@osu.eduproject :MEaSUREs OSUprogram :NASA Earth Science Data Systems (ESDS)publisher_name :PO.DAAC (Physical Oceanography Distributed Active Archive Center)publisher_email :podaac@podaac.jpl.nasa.govpublisher_url :podaac.jpl.nasa.govpublisher_type :Institutionpublisher_institution :PO.DAACprocessing_level :L2doi :10.5067/PSGRA-DA2V2history :This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements. platform :ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)platform_vocabulary :NASA/GCMD Platform Keywords. Version 8.6instrument :RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)instrument_vocabulary :NASA/GCMD Platform Keywords. Version 8.6references :in review :doi.org/10.5194/essd-2019-84id :GRRATS(Global River Radar Altimeter Time Series) 1km/dailysummary :The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.time_coverage_resolution :1 daydate_created :2021-05-17T21:26:51time_coverage_start :1992-04-08T15:20:40time_coverage_end :2018-04-20T03:39:13geospatial_lon_min :-73.35433106652545geospatial_lon_max :-51.0426448887506geospatial_lon_units :degree_eastgeospatial_lat_min :-4.380427586763687geospatial_lat_max :-0.6550700975069503geospatial_lat_units :degree_northgeospatial_vertical_max :92.7681246287056geospatial_vertical_min :-3.563409518163376geospatial_vertical_units :mgeospatial_vertical_positive :up\n\n\n\n\nOn-premise data via OPeNDAP: sea surface salinity (SMAP and Aquarius) and temperature (MODIS)\nPre-SWOT MEaSUREs data resides in one NetCDF file for the whole time period, but the same cannot be said for SMAP, Aquarius and MODIS data. They have one file per month for their monthly datasets. SMAP has salinity data from April 2015 - present, Aquarius has salinity data from August 2011 - June 2015, and MODIS has SST data for the entire 2011-2019 time period.\nFirst, we create strings of OPeNDAP links in lists for each satellite product so we can obtain them and merge them into one file. The links change depending on the date, so the pattern of how the links change needs to be observed and then looped over and appended to the links file list. For example, SMAP has month and year numbers in its links, while Aquarius has start day and end day of the year in its links, so writing their strings takes different logic.\n\n# Initialize SMAP file list\nfile_list_SMAP = []\n#create an array of the months of the year and an array for the years\ncounter_month = np.arange(1,13)\ncounter_year = np.arange(2015, 2020)\n\n# Make list of file paths\nfor j in counter_year:\n for i in counter_month:\n if j == 2015: #data starts in april for 2015\n if i > 3:\n if i < 10: #for single digit months, only one number needs to be changed\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d0%d_MONTHLY_V5.0.nc' % (j, j, i))\n else: #for double digit months, 2 numbers in the string need to change\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d%d_MONTHLY_V5.0.nc' % (j, j, i))\n else:\n if i < 10: #for single digit months, only one number needs to be changed\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d0%d_MONTHLY_V5.0.nc' % (j, j, i))\n else: #for double digit months, 2 numbers in the string need to change\n file_list_SMAP.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/smap/L3/JPL/V5.0/monthly/%d/SMAP_L3_SSS_%d%d_MONTHLY_V5.0.nc' % (j, j, i))\n\n# Initialize Aquarius file list\nfile_list_Aq = []\n# Create an array of the months of the year and an array for the years\ncounter_month = np.arange(0,12)\ncounter_year = np.arange(2011, 2016)\n\n# Make list of file paths\nfor j in counter_year:\n for i in counter_month:\n if j == 2012: #leap-year\n d1 = ['001', '032', '061', '092', '122', '153', '183', '214', '245', '275', '306', '336']\n d2 = ['031', '060', '091', '121', '152', '182', '213', '244', '274', '305', '335', '366']\n else: #all other years\n d1 = ['001', '032', '060', '091', '121', '152', '182', '213', '244', '274', '305', '335']\n d2 = ['031', '059', '090', '120', '151', '181', '212', '243', '273', '304', '334', '365']\n \n if j == 2011: #data starts in the 8th month (index 7)\n if i > 6:\n file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n elif j == 2015: #data ends in the 6th month (index 5)\n if i < 6:\n file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n else:\n file_list_Aq.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/monthly/SCI/%d/Q%d%s%d%s.L3m_MO_SCI_V5.0_SSS_1deg.bz2' % (j, j, d1[i], j, d2[i]))\n\n \n# Initialize MODIS file list for sea surface temperature\nfile_list_MODIS = []\n# Create an array of the months of the year and an array for the years\ncounter_month = np.arange(0,12)\ncounter_year = np.arange(2011, 2020)\n\n# Make list of file paths\nfor j in counter_year:\n for i in counter_month:\n if j == 2012 or j == 2016: #leap-year\n d1 = ['0101', '0201', '0301', '0401', '0501', '0601', '0701', '0801', '0901', '1001', '1101', '1201']\n d2 = ['0131', '0229', '0331', '0430', '0531', '0630', '0731', '0831', '0930', '1031', '1130', '1231']\n else: #all other years\n d1 = ['0101', '0201', '0301', '0401', '0501', '0601', '0701', '0801', '0901', '1001', '1101', '1201']\n d2 = ['0131', '0228', '0331', '0430', '0531', '0630', '0731', '0831', '0930', '1031', '1130', '1231']\n \n if j == 2011: #data starts in the 8th month (index 7)\n if i > 6:\n file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n elif j == 2019: #data ends in the 11th month (index 10)\n if i < 11:\n file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n else:\n file_list_MODIS.append('https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/%d/AQUA_MODIS.%d%s_%d%s.L3m.MO.SST4.sst4.9km.nc' % (j, j, d1[i], j, d2[i]))\n\n# Un comment to see links lists\n# file_list_SMAP\n# file_list_Aq\nfile_list_MODIS\n\n['https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20110801_20110831.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20110901_20110930.L3m.MO.SST4.sst4.9km.nc',\n 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2011/AQUA_MODIS.20111001_20111031.L3m.MO.SST4.sst4.9km.nc',\n 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'https://podaac-opendap.jpl.nasa.gov/opendap/allData/modis/L3/aqua/4um/v2019.0/9km/monthly/2019/AQUA_MODIS.20191101_20191130.L3m.MO.SST4.sst4.9km.nc']\n\n\n\n# For OPeNDAP, we need to set the number of files that can be opened at once to 10, \n# So that xa.open_mfdataset() actually reads all links (see https://github.com/pydata/xarray/issues/4082)\nxr.set_options(file_cache_maxsize=10)\n\n# To use xa.open_mfdataset which combines netCDF files\nds_SMAP = xr.open_mfdataset(file_list_SMAP, combine='by_coords')\nds_SMAP\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (latitude: 720, longitude: 1440, time: 57)\nCoordinates:\n * longitude (longitude) float32 -179.875 -179.625 ... 179.875\n * latitude (latitude) float32 89.875 89.625 ... -89.625 -89.875\n * time (time) datetime64[ns] 2015-04-16 ... 2019-12-16T12:...\nData variables:\n smap_sss (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n anc_sss (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n anc_sst (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n smap_spd (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n smap_high_spd (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n weight (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n land_fraction (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n ice_fraction (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n smap_sss_uncertainty (time, latitude, longitude) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\nAttributes:\n title: SMAP 0.25x0.25 deg grid averaged monthly SSS...\n institution: Jet Propulsion Laboratory\n source: SMAP L2B SSS\n history: DATA_SOURCE_VERSION V5.0 L2B SMAP SSS/WSPD\n comment: Gaussian-weighted map gridding of SMAP L2B S...\n Gaussian_window_radius: 45.0\n Gaussian_window_half_power: 30.0\n revs_used: [ 870 871 872 873 874 875 876 877 87...\n revs_missing: [ 881 882 908 909 910 911 933 934 95...\n l2b_files: SMAP_L2B_SSS_00870_20150401T004402_R17000_V5...\n TB_CRID: R17000\n Year: 2015\n Month: 4\n Conventions: CF-1.6, ACDD-1.3\n processing_level: 3\n cdm_data_type: Grid\n date_issued: 2020-293T18:28:59.223\n date_created: 2020-293T18:28:59.223\n time_coverage_start: 2015-091T00:00:00.000\n time_coverage_end: 2015-121T00:00:00.000\n geospatial_lat_max: 90.0\n geospatial_lat_min: -90.0\n geospatial_lon_max: 180.0\n geospatial_lon_min: -180.0\n geospatial_lat_units: degrees_north\n geospatial_lon_units: degrees_east\n platform: SMAP\n sensor: SMAP\n project: SMAP\n product_version: V5.0\n keywords_vocabulary: http://gcmd.gsfc.nasa.gov/Resources/valids/g...\n keywords: SEA SURFACE SALINITY, SALINITY, SMAP, Jet Pr...\n creator_name: JPL\n creator_email: fore@jpl.nasa.gov\n publisher_name: Alexander G. Fore\n publisher_email: fore@jpl.nasa.gov\n contributor_name: Alexander Fore, Simon Yueh, Wenqing Tang, Ak...\n references: 10.1109/TGRS.2016.2601486, 10.1109/TGRS.2016...xarray.DatasetDimensions:latitude: 720longitude: 1440time: 57Coordinates: (3)longitude(longitude)float32-179.875 -179.625 ... 179.875units :degrees_eaststandard_name :longitudelong_name :longitude of grid cellaxis :Xcoverage_content_type :coordinatearray([-179.875, -179.625, -179.375, ..., 179.375, 179.625, 179.875],\n dtype=float32)latitude(latitude)float3289.875 89.625 ... -89.625 -89.875units :degrees_northstandard_name :latitudelong_name :latitude of grid cellaxis :Ycoverage_content_type :coordinatearray([ 89.875, 89.625, 89.375, ..., -89.375, -89.625, -89.875],\n dtype=float32)time(time)datetime64[ns]2015-04-16 ... 2019-12-16T12:00:00long_name :Reference time of sss fieldstandard_name :timecomment :Midpoint of time interval of analyzed fieldscoverage_content_type :coordinatearray(['2015-04-16T00:00:00.000000000', '2015-05-16T12:00:00.000000000',\n '2015-06-16T00:00:00.000000000', '2015-07-16T12:00:00.000000000',\n '2015-08-16T12:00:00.000000000', '2015-09-16T00:00:00.000000000',\n '2015-10-16T12:00:00.000000000', '2015-11-16T00:00:00.000000000',\n '2015-12-16T12:00:00.000000000', '2016-01-16T12:00:00.000000000',\n '2016-02-15T12:00:00.000000000', '2016-03-16T12:00:00.000000000',\n '2016-04-16T00:00:00.000000000', '2016-05-16T12:00:00.000000000',\n '2016-06-16T00:00:00.000000000', '2016-07-16T12:00:00.000000000',\n '2016-08-16T12:00:00.000000000', '2016-09-16T00:00:00.000000000',\n '2016-10-16T12:00:00.000000000', '2016-11-16T00:00:00.000000000',\n '2016-12-16T12:00:00.000000000', '2017-01-16T12:00:00.000000000',\n '2017-02-15T00:00:00.000000000', '2017-03-16T12:00:00.000000000',\n '2017-04-16T00:00:00.000000000', '2017-05-16T12:00:00.000000000',\n '2017-06-16T00:00:00.000000000', '2017-07-16T12:00:00.000000000',\n '2017-08-16T12:00:00.000000000', '2017-09-16T00:00:00.000000000',\n '2017-10-16T12:00:00.000000000', '2017-11-16T00:00:00.000000000',\n '2017-12-16T12:00:00.000000000', '2018-01-16T12:00:00.000000000',\n '2018-02-15T00:00:00.000000000', '2018-03-16T12:00:00.000000000',\n '2018-04-16T00:00:00.000000000', '2018-05-16T12:00:00.000000000',\n '2018-06-16T00:00:00.000000000', '2018-07-16T12:00:00.000000000',\n '2018-08-16T12:00:00.000000000', '2018-09-16T00:00:00.000000000',\n '2018-10-16T12:00:00.000000000', '2018-11-16T00:00:00.000000000',\n '2018-12-16T12:00:00.000000000', '2019-01-16T12:00:00.000000000',\n '2019-02-15T00:00:00.000000000', '2019-03-16T12:00:00.000000000',\n '2019-04-16T00:00:00.000000000', '2019-05-16T12:00:00.000000000',\n '2019-06-16T00:00:00.000000000', '2019-07-16T12:00:00.000000000',\n '2019-08-16T12:00:00.000000000', '2019-09-16T00:00:00.000000000',\n '2019-10-16T12:00:00.000000000', '2019-11-16T00:00:00.000000000',\n '2019-12-16T12:00:00.000000000'], dtype='datetime64[ns]')Data variables: (9)smap_sss(time, latitude, longitude)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>long_name :SMAP sea surface salinityunits :1e-3standard_name :sea_surface_salinityvalid_min :0valid_max :45coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanc_sss\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nHYCOM sea surface salinity\n\nunits :\n\n1e-3\n\nstandard_name :\n\nsea_surface_salinity\n\nvalid_min :\n\n0\n\nvalid_max :\n\n45\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nanc_sst\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSea surface temperature\n\nunits :\n\nK\n\nstandard_name :\n\nsea_surface_temperature\n\nvalid_min :\n\n0\n\nvalid_max :\n\n340\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsmap_spd\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSMAP 10 m wind speed\n\nunits :\n\nm s-1\n\nstandard_name :\n\nwind_speed\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 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Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nland_fraction\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nvalid_min :\n\n0\n\nvalid_max :\n\n1\n\nlong_name :\n\nAverage land fraction\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nice_fraction\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nvalid_min :\n\n0\n\nvalid_max :\n\n1\n\nlong_name :\n\nAverage ice fraction\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsmap_sss_uncertainty\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 720, 1440), meta=np.ndarray>\n\n\n\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0\n\nvalid_max :\n\n50\n\nlong_name :\n\nSMAP SSS uncertainty\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n236.39 MB\n4.15 MB\n\n\nShape\n(57, 720, 1440)\n(1, 720, 1440)\n\n\nCount\n228 Tasks\n57 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (38)title :SMAP 0.25x0.25 deg grid averaged monthly SSS/WSPDinstitution :Jet Propulsion Laboratorysource :SMAP L2B SSShistory :DATA_SOURCE_VERSION V5.0 L2B SMAP SSS/WSPDcomment :Gaussian-weighted map gridding of SMAP L2B SSS ProductGaussian_window_radius :45.0Gaussian_window_half_power :30.0revs_used :[ 870 871 872 873 874 875 876 877 878 879 880 883 884 885\n 886 887 888 889 890 891 892 893 894 895 896 897 898 899\n 900 901 902 903 904 905 906 907 912 913 914 915 916 917\n 918 919 920 921 922 923 924 925 926 927 928 929 930 931\n 932 935 936 937 938 939 940 941 942 943 944 945 946 947\n 948 949 950 951 952 953 954 955 956 959 960 961 962 963\n 964 965 966 967 968 969 970 971 972 973 974 975 976 977\n 978 979 980 981 982 983 984 985 986 987 988 989 990 991\n 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005\n 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019\n 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1032 1033 1034\n 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048\n 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062\n 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076\n 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1090 1091\n 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105\n 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119\n 1120 1121 1122 1123 1124 1125 1127 1128 1129 1130 1131 1132 1133 1134\n 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148\n 1149 1150 1151 1152 1153 1154 1155 1157 1158 1159 1160 1161 1162 1163\n 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1178 1179\n 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193\n 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207\n 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221\n 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235\n 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249\n 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263\n 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277\n 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291\n 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305\n 1306 1307 1308]revs_missing :[ 881 882 908 909 910 911 933 934 957 958 1031 1089 1126 1156\n 1176 1177]l2b_files 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MAP_L2B_SSS_00978_20150408T095727_R17000_V5.0.h5\nSMAP_L2B_SSS_00979_20150408T113555_R17000_V5.0.h5\nSMAP_L2B_SSS_00980_20150408T131422_R17000_V5.0.h5\nSMAP_L2B_SSS_00981_20150408T145250_R17000_V5.0.h5\nSMAP_L2B_SSS_00982_20150408T163117_R17000_V5.0.h5\nSMAP_L2B_SSS_00983_20150408T180945_R17000_V5.0.h5\nSMAP_L2B_SSS_00984_20150408T194812_R17000_V5.0.h5\nSMAP_L2B_SSS_00985_20150408T212639_R17000_V5.0.h5\nSMAP_L2B_SSS_00986_20150408T230507_R17000_V5.0.h5\nSMAP_L2B_SSS_00987_20150409T004334_R17000_V5.0.h5\nSMAP_L2B_SSS_00988_20150409T022202_R17000_V5.0.h5\nSMAP_L2B_SSS_00989_20150409T040029_R17000_V5.0.h5\nSMAP_L2B_SSS_00990_20150409T053857_R17000_V5.0.h5\nSMAP_L2B_SSS_00991_20150409T071724_R17000_V5.0.h5\nSMAP_L2B_SSS_00992_20150409T085551_R17000_V5.0.h5\nSMAP_L2B_SSS_00993_20150409T103419_R17000_V5.0.h5\nSMAP_L2B_SSS_00994_20150409T121246_R17000_V5.0.h5\nSMAP_L2B_SSS_00995_20150409T135114_R17000_V5.0.h5\nSMAP_L2B_SSS_00996_20150409T152941_R17000_V5.0.h5\nSMAP_L2B_SSS_00997_20150409T170809_R17000_V5.0.h5\nSMAP_L2B_SSS_00998_20150409T184636_R17000_V5.0.h5\nSMAP_L2B_SSS_00999_20150409T202503_R17000_V5.0.h5\nSMAP_L2B_SSS_01000_20150409T220331_R17000_V5.0.h5\nSMAP_L2B_SSS_01001_20150409T234158_R17000_V5.0.h5\nSMAP_L2B_SSS_01002_20150410T012026_R17000_V5.0.h5\nSMAP_L2B_SSS_01003_20150410T025853_R17000_V5.0.h5\nSMAP_L2B_SSS_01004_20150410T043721_R17000_V5.0.h5\nSMAP_L2B_SSS_01005_20150410T061548_R17000_V5.0.h5\nSMAP_L2B_SSS_01006_20150410T075416_R17000_V5.0.h5\nSMAP_L2B_SSS_01007_20150410T093243_R17000_V5.0.h5\nSMAP_L2B_SSS_01008_20150410T111110_R17000_V5.0.h5\nSMAP_L2B_SSS_01009_20150410T124938_R17000_V5.0.h5\nSMAP_L2B_SSS_01010_20150410T142806_R17000_V5.0.h5\nSMAP_L2B_SSS_01011_20150410T160633_R17000_V5.0.h5\nSMAP_L2B_SSS_01012_20150410T174500_R17000_V5.0.h5\nSMAP_L2B_SSS_01013_20150410T192328_R17000_V5.0.h5\nSMAP_L2B_SSS_01014_20150410T210155_R17000_V5.0.h5\nSMAP_L2B_SSS_01015_20150410T224022_R17000_V5.0.h5\nSMAP_L2B_SSS_01016_20150411T001850_R17000_V5.0.h5\nSMAP_L2B_SSS_01017_20150411T015718_R17000_V5.0.h5\nSMAP_L2B_SSS_01018_20150411T033545_R17000_V5.0.h5\nSMAP_L2B_SSS_01019_20150411T051412_R17000_V5.0.h5\nSMAP_L2B_SSS_01020_20150411T065240_R17000_V5.0.h5\nSMAP_L2B_SSS_01021_20150411T083107_R17000_V5.0.h5\nSMAP_L2B_SSS_01022_20150411T100934_R17000_V5.0.h5\nSMAP_L2B_SSS_01023_20150411T114802_R17000_V5.0.h5\nSMAP_L2B_SSS_01024_20150411T132630_R17000_V5.0.h5\nSMAP_L2B_SSS_01025_20150411T150457_R17000_V5.0.h5\nSMAP_L2B_SSS_01026_20150411T164324_R17000_V5.0.h5\nSMAP_L2B_SSS_01027_20150411T182152_R17000_V5.0.h5\nSMAP_L2B_SSS_01028_20150411T200019_R17000_V5.0.h5\nSMAP_L2B_SSS_01029_20150411T213846_R17000_V5.0.h5\nSMAP_L2B_SSS_01030_20150411T231714_R17000_V5.0.h5\nSMAP_L2B_SSS_01032_20150412T023409_R17000_V5.0.h5\nSMAP_L2B_SSS_01033_20150412T041236_R17000_V5.0.h5\nSMAP_L2B_SSS_01034_20150412T055104_R17000_V5.0.h5\nSMAP_L2B_SSS_01035_20150412T072931_R17000_V5.0.h5\nSMAP_L2B_SSS_01036_20150412T090758_R17000_V5.0.h5\nSMAP_L2B_SSS_01037_20150412T104626_R17000_V5.0.h5\nSMAP_L2B_SSS_01038_20150412T122453_R17000_V5.0.h5\nSMAP_L2B_SSS_01039_20150412T140321_R17000_V5.0.h5\nSMAP_L2B_SSS_01040_20150412T154148_R17000_V5.0.h5\nSMAP_L2B_SSS_01041_20150412T172016_R17000_V5.0.h5\nSMAP_L2B_SSS_01042_20150412T185843_R17000_V5.0.h5\nSMAP_L2B_SSS_01043_20150412T203710_R17000_V5.0.h5\nSMAP_L2B_SSS_01044_20150412T221538_R17000_V5.0.h5\nSMAP_L2B_SSS_01045_20150412T235405_R17000_V5.0.h5\nSMAP_L2B_SSS_01046_20150413T013233_R17000_V5.0.h5\nSMAP_L2B_SSS_01047_20150413T031100_R17000_V5.0.h5\nSMAP_L2B_SSS_01048_20150413T044928_R17000_V5.0.h5\nSMAP_L2B_SSS_01049_20150413T062755_R17000_V5.0.h5\nSMAP_L2B_SSS_01050_20150413T080622_R17000_V5.0.h5\nSMAP_L2B_SSS_01051_20150413T094450_R17000_V5.0.h5\nSMAP_L2B_SSS_01052_20150413T112317_R17000_V5.0.h5\nSMAP_L2B_SSS_01053_20150413T130145_R17000_V5.0.h5\nSMAP_L2B_SSS_01054_20150413T144012_R17000_V5.0.h5\nSMAP_L2B_SSS_01055_20150413T161840_R17000_V5.0.h5\nSMAP_L2B_SSS_01056_20150413T175707_R17000_V5.0.h5\nSMAP_L2B_SSS_01057_20150413T193534_R17000_V5.0.h5\nSMAP_L2B_SSS_01058_20150413T211402_R17000_V5.0.h5\nSMAP_L2B_SSS_01059_20150413T225229_R17000_V5.0.h5\nSMAP_L2B_SSS_01060_20150414T003056_R17000_V5.0.h5\nSMAP_L2B_SSS_01061_20150414T020924_R17000_V5.0.h5\nSMAP_L2B_SSS_01062_20150414T034751_R17000_V5.0.h5\nSMAP_L2B_SSS_01063_20150414T052619_R17000_V5.0.h5\nSMAP_L2B_SSS_01064_20150414T070446_R17000_V5.0.h5\nSMAP_L2B_SSS_01065_20150414T084313_R17000_V5.0.h5\nSMAP_L2B_SSS_01066_20150414T102141_R17000_V5.0.h5\nSMAP_L2B_SSS_01067_20150414T120008_R17000_V5.0.h5\nSMAP_L2B_SSS_01068_20150414T133836_R17000_V5.0.h5\nSMAP_L2B_SSS_01069_20150414T151703_R17000_V5.0.h5\nSMAP_L2B_SSS_01070_20150414T165531_R17000_V5.0.h5\nSMAP_L2B_SSS_01071_20150414T183358_R17000_V5.0.h5\nSMAP_L2B_SSS_01072_20150414T201225_R17000_V5.0.h5\nSMAP_L2B_SSS_01073_20150414T215053_R17000_V5.0.h5\nSMAP_L2B_SSS_01074_20150414T232920_R17000_V5.0.h5\nSMAP_L2B_SSS_01075_20150415T010748_R17000_V5.0.h5\nSMAP_L2B_SSS_01076_20150415T024615_R17000_V5.0.h5\nSMAP_L2B_SSS_01077_20150415T042443_R17000_V5.0.h5\nSMAP_L2B_SSS_01078_20150415T060310_R17000_V5.0.h5\nSMAP_L2B_SSS_01079_20150415T074137_R17000_V5.0.h5\nSMAP_L2B_SSS_01080_20150415T092005_R17000_V5.0.h5\nSMAP_L2B_SSS_01081_20150415T105832_R17000_V5.0.h5\nSMAP_L2B_SSS_01082_20150415T123659_R17000_V5.0.h5\nSMAP_L2B_SSS_01083_20150415T141527_R17000_V5.0.h5\nSMAP_L2B_SSS_01084_20150415T155354_R17000_V5.0.h5\nSMAP_L2B_SSS_01085_20150415T173222_R17000_V5.0.h5\nSMAP_L2B_SSS_01086_20150415T191049_R17000_V5.0.h5\nSMAP_L2B_SSS_01087_20150415T204916_R17000_V5.0.h5\nSMAP_L2B_SSS_01088_20150415T222744_R17000_V5.0.h5\nSMAP_L2B_SSS_01090_20150416T014439_R17000_V5.0.h5\nSMAP_L2B_SSS_01091_20150416T032306_R17000_V5.0.h5\nSMAP_L2B_SSS_01092_20150416T050134_R17000_V5.0.h5\nSMAP_L2B_SSS_01093_20150416T064001_R17000_V5.0.h5\nSMAP_L2B_SSS_01094_20150416T081828_R17000_V5.0.h5\nSMAP_L2B_SSS_01095_20150416T095656_R17000_V5.0.h5\nSMAP_L2B_SSS_01096_20150416T113523_R17000_V5.0.h5\nSMAP_L2B_SSS_01097_20150416T131351_R17000_V5.0.h5\nSMAP_L2B_SSS_01098_20150416T145218_R17000_V5.0.h5\nSMAP_L2B_SSS_01099_20150416T163045_R17000_V5.0.h5\nSMAP_L2B_SSS_01100_20150416T180913_R17000_V5.0.h5\nSMAP_L2B_SSS_01101_20150416T194741_R17000_V5.0.h5\nSMAP_L2B_SSS_01102_20150416T212609_R17000_V5.0.h5\nSMAP_L2B_SSS_01103_20150416T230437_R17000_V5.0.h5\nSMAP_L2B_SSS_01104_20150417T004305_R17000_V5.0.h5\nSMAP_L2B_SSS_01105_20150417T022132_R17000_V5.0.h5\nSMAP_L2B_SSS_01106_20150417T040000_R17000_V5.0.h5\nSMAP_L2B_SSS_01107_20150417T053828_R17000_V5.0.h5\nSMAP_L2B_SSS_01108_20150417T071656_R17000_V5.0.h5\nSMAP_L2B_SSS_01109_20150417T085524_R17000_V5.0.h5\nSMAP_L2B_SSS_01110_20150417T103352_R17000_V5.0.h5\nSMAP_L2B_SSS_01111_20150417T121220_R17000_V5.0.h5\nSMAP_L2B_SSS_01112_20150417T135048_R17000_V5.0.h5\nSMAP_L2B_SSS_01113_20150417T152915_R17000_V5.0.h5\nSMAP_L2B_SSS_01114_20150417T170743_R17000_V5.0.h5\nSMAP_L2B_SSS_01115_20150417T184611_R17000_V5.0.h5\nSMAP_L2B_SSS_01116_20150417T202439_R17000_V5.0.h5\nSMAP_L2B_SSS_01117_20150417T220307_R17000_V5.0.h5\nSMAP_L2B_SSS_01118_20150417T234135_R17000_V5.0.h5\nSMAP_L2B_SSS_01119_20150418T012003_R17000_V5.0.h5\nSMAP_L2B_SSS_01120_20150418T025831_R17000_V5.0.h5\nSMAP_L2B_SSS_01121_20150418T043658_R17000_V5.0.h5\nSMAP_L2B_SSS_01122_20150418T061526_R17000_V5.0.h5\nSMAP_L2B_SSS_01123_20150418T075354_R17000_V5.0.h5\nSMAP_L2B_SSS_01124_20150418T093222_R17000_V5.0.h5\nSMAP_L2B_SSS_01125_20150418T111050_R17000_V5.0.h5\nSMAP_L2B_SSS_01127_20150418T142746_R17000_V5.0.h5\nSMAP_L2B_SSS_01128_20150418T160614_R17000_V5.0.h5\nSMAP_L2B_SSS_01129_20150418T174441_R17000_V5.0.h5\nSMAP_L2B_SSS_01130_20150418T192309_R17000_V5.0.h5\nSMAP_L2B_SSS_01131_20150418T210137_R17000_V5.0.h5\nSMAP_L2B_SSS_01132_20150418T224005_R17000_V5.0.h5\nSMAP_L2B_SSS_01133_20150419T001833_R17000_V5.0.h5\nSMAP_L2B_SSS_01134_20150419T015701_R17000_V5.0.h5\nSMAP_L2B_SSS_01135_20150419T033529_R17000_V5.0.h5\nSMAP_L2B_SSS_01136_20150419T051357_R17000_V5.0.h5\nSMAP_L2B_SSS_01137_20150419T065224_R17000_V5.0.h5\nSMAP_L2B_SSS_01138_20150419T083052_R17000_V5.0.h5\nSMAP_L2B_SSS_01139_20150419T100920_R17000_V5.0.h5\nSMAP_L2B_SSS_01140_20150419T114748_R17000_V5.0.h5\nSMAP_L2B_SSS_01141_20150419T132616_R17000_V5.0.h5\nSMAP_L2B_SSS_01142_20150419T150444_R17000_V5.0.h5\nSMAP_L2B_SSS_01143_20150419T164312_R17000_V5.0.h5\nSMAP_L2B_SSS_01144_20150419T182140_R17000_V5.0.h5\nSMAP_L2B_SSS_01145_20150419T200008_R17000_V5.0.h5\nSMAP_L2B_SSS_01146_20150419T213835_R17000_V5.0.h5\nSMAP_L2B_SSS_01147_20150419T231703_R17000_V5.0.h5\nSMAP_L2B_SSS_01148_20150420T005531_R17000_V5.0.h5\nSMAP_L2B_SSS_01149_20150420T023359_R17000_V5.0.h5\nSMAP_L2B_SSS_01150_20150420T041227_R17000_V5.0.h5\nSMAP_L2B_SSS_01151_20150420T055055_R17000_V5.0.h5\nSMAP_L2B_SSS_01152_20150420T072923_R17000_V5.0.h5\nSMAP_L2B_SSS_01153_20150420T090750_R17000_V5.0.h5\nSMAP_L2B_SSS_01154_20150420T104618_R17000_V5.0.h5\nSMAP_L2B_SSS_01155_20150420T122446_R17000_V5.0.h5\nSMAP_L2B_SSS_01157_20150420T154142_R17000_V5.0.h5\nSMAP_L2B_SSS_01158_20150420T172010_R17000_V5.0.h5\nSMAP_L2B_SSS_01159_20150420T185838_R17000_V5.0.h5\nSMAP_L2B_SSS_01160_20150420T203706_R17000_V5.0.h5\nSMAP_L2B_SSS_01161_20150420T221533_R17000_V5.0.h5\nSMAP_L2B_SSS_01162_20150420T235401_R17000_V5.0.h5\nSMAP_L2B_SSS_01163_20150421T013229_R17000_V5.0.h5\nSMAP_L2B_SSS_01164_20150421T031057_R17000_V5.0.h5\nSMAP_L2B_SSS_01165_20150421T044925_R17000_V5.0.h5\nSMAP_L2B_SSS_01166_20150421T062753_R17000_V5.0.h5\nSMAP_L2B_SSS_01167_20150421T080621_R17000_V5.0.h5\nSMAP_L2B_SSS_01168_20150421T094449_R17000_V5.0.h5\nSMAP_L2B_SSS_01169_20150421T112316_R17000_V5.0.h5\nSMAP_L2B_SSS_01170_20150421T130144_R17000_V5.0.h5\nSMAP_L2B_SSS_01171_20150421T144012_R17000_V5.0.h5\nSMAP_L2B_SSS_01172_20150421T161840_R17000_V5.0.h5\nSMAP_L2B_SSS_01173_20150421T175708_R17000_V5.0.h5\nSMAP_L2B_SSS_01174_20150421T193536_R17000_V5.0.h5\nSMAP_L2B_SSS_01175_20150421T211404_R17000_V5.0.h5\nSMAP_L2B_SSS_01178_20150422T020927_R17000_V5.0.h5\nSMAP_L2B_SSS_01179_20150422T034755_R17000_V5.0.h5\nSMAP_L2B_SSS_01180_20150422T052623_R17000_V5.0.h5\nSMAP_L2B_SSS_01181_20150422T070451_R17000_V5.0.h5\nSMAP_L2B_SSS_01182_20150422T084319_R17000_V5.0.h5\nSMAP_L2B_SSS_01183_20150422T102146_R17000_V5.0.h5\nSMAP_L2B_SSS_01184_20150422T120014_R17000_V5.0.h5\nSMAP_L2B_SSS_01185_20150422T133842_R17000_V5.0.h5\nSMAP_L2B_SSS_01186_20150422T151710_R17000_V5.0.h5\nSMAP_L2B_SSS_01187_20150422T165538_R17000_V5.0.h5\nSMAP_L2B_SSS_01188_20150422T183406_R17000_V5.0.h5\nSMAP_L2B_SSS_01189_20150422T201234_R17000_V5.0.h5\nSMAP_L2B_SSS_01190_20150422T215101_R17000_V5.0.h5\nSMAP_L2B_SSS_01191_20150422T232929_R17000_V5.0.h5\nSMAP_L2B_SSS_01192_20150423T010757_R17000_V5.0.h5\nSMAP_L2B_SSS_01193_20150423T024625_R17000_V5.0.h5\nSMAP_L2B_SSS_01194_20150423T042453_R17000_V5.0.h5\nSMAP_L2B_SSS_01195_20150423T060321_R17000_V5.0.h5\nSMAP_L2B_SSS_01196_20150423T074149_R17000_V5.0.h5\nSMAP_L2B_SSS_01197_20150423T092016_R17000_V5.0.h5\nSMAP_L2B_SSS_01198_20150423T105844_R17000_V5.0.h5\nSMAP_L2B_SSS_01199_20150423T123712_R17000_V5.0.h5\nSMAP_L2B_SSS_01200_20150423T141540_R17000_V5.0.h5\nSMAP_L2B_SSS_01201_20150423T155408_R17000_V5.0.h5\nSMAP_L2B_SSS_01202_20150423T173236_R17000_V5.0.h5\nSMAP_L2B_SSS_01203_20150423T191103_R17000_V5.0.h5\nSMAP_L2B_SSS_01204_20150423T204931_R17000_V5.0.h5\nSMAP_L2B_SSS_01205_20150423T222759_R17000_V5.0.h5\nSMAP_L2B_SSS_01206_20150424T000627_R17000_V5.0.h5\nSMAP_L2B_SSS_01207_20150424T014455_R17000_V5.0.h5\nSMAP_L2B_SSS_01208_20150424T032323_R17000_V5.0.h5\nSMAP_L2B_SSS_01209_20150424T050151_R17000_V5.0.h5\nSMAP_L2B_SSS_01210_20150424T064018_R17000_V5.0.h5\nSMAP_L2B_SSS_01211_20150424T081846_R17000_V5.0.h5\nSMAP_L2B_SSS_01212_20150424T095714_R17000_V5.0.h5\nSMAP_L2B_SSS_01213_20150424T113542_R17000_V5.0.h5\nSMAP_L2B_SSS_01214_20150424T131410_R17000_V5.0.h5\nSMAP_L2B_SSS_01215_20150424T145238_R17000_V5.0.h5\nSMAP_L2B_SSS_01216_20150424T163105_R17000_V5.0.h5\nSMAP_L2B_SSS_01217_20150424T180933_R17000_V5.0.h5\nSMAP_L2B_SSS_01218_20150424T194801_R17000_V5.0.h5\nSMAP_L2B_SSS_01219_20150424T212629_R17000_V5.0.h5\nSMAP_L2B_SSS_01220_20150424T230457_R17000_V5.0.h5\nSMAP_L2B_SSS_01221_20150425T004325_R17000_V5.0.h5\nSMAP_L2B_SSS_01222_20150425T022152_R17000_V5.0.h5\nSMAP_L2B_SSS_01223_20150425T040020_R17000_V5.0.h5\nSMAP_L2B_SSS_01224_20150425T053848_R17000_V5.0.h5\nSMAP_L2B_SSS_01225_20150425T071716_R17000_V5.0.h5\nSMAP_L2B_SSS_01226_20150425T085544_R17000_V5.0.h5\nSMAP_L2B_SSS_01227_20150425T103411_R17000_V5.0.h5\nSMAP_L2B_SSS_01228_20150425T121239_R17000_V5.0.h5\nSMAP_L2B_SSS_01229_20150425T135107_R17000_V5.0.h5\nSMAP_L2B_SSS_01230_20150425T152935_R17000_V5.0.h5\nSMAP_L2B_SSS_01231_20150425T170803_R17000_V5.0.h5\nSMAP_L2B_SSS_01232_20150425T184631_R17000_V5.0.h5\nSMAP_L2B_SSS_01233_20150425T202458_R17000_V5.0.h5\nSMAP_L2B_SSS_01234_20150425T220326_R17000_V5.0.h5\nSMAP_L2B_SSS_01235_20150425T234154_R17000_V5.0.h5\nSMAP_L2B_SSS_01236_20150426T012022_R17000_V5.0.h5\nSMAP_L2B_SSS_01237_20150426T025850_R17000_V5.0.h5\nSMAP_L2B_SSS_01238_20150426T043718_R17000_V5.0.h5\nSMAP_L2B_SSS_01239_20150426T061546_R17000_V5.0.h5\nSMAP_L2B_SSS_01240_20150426T075413_R17000_V5.0.h5\nSMAP_L2B_SSS_01241_20150426T093241_R17000_V5.0.h5\nSMAP_L2B_SSS_01242_20150426T111109_R17000_V5.0.h5\nSMAP_L2B_SSS_01243_20150426T124937_R17000_V5.0.h5\nSMAP_L2B_SSS_01244_20150426T142805_R17000_V5.0.h5\nSMAP_L2B_SSS_01245_20150426T160633_R17000_V5.0.h5\nSMAP_L2B_SSS_01246_20150426T174500_R17000_V5.0.h5\nSMAP_L2B_SSS_01247_20150426T192329_R17000_V5.0.h5\nSMAP_L2B_SSS_01248_20150426T210156_R17000_V5.0.h5\nSMAP_L2B_SSS_01249_20150426T224024_R17000_V5.0.h5\nSMAP_L2B_SSS_01250_20150427T001851_R17000_V5.0.h5\nSMAP_L2B_SSS_01251_20150427T015719_R17000_V5.0.h5\nSMAP_L2B_SSS_01252_20150427T033547_R17000_V5.0.h5\nSMAP_L2B_SSS_01253_20150427T051415_R17000_V5.0.h5\nSMAP_L2B_SSS_01254_20150427T065243_R17000_V5.0.h5\nSMAP_L2B_SSS_01255_20150427T083111_R17000_V5.0.h5\nSMAP_L2B_SSS_01256_20150427T100938_R17000_V5.0.h5\nSMAP_L2B_SSS_01257_20150427T114806_R17000_V5.0.h5\nSMAP_L2B_SSS_01258_20150427T132634_R17000_V5.0.h5\nSMAP_L2B_SSS_01259_20150427T150502_R17000_V5.0.h5\nSMAP_L2B_SSS_01260_20150427T164330_R17000_V5.0.h5\nSMAP_L2B_SSS_01261_20150427T182158_R17000_V5.0.h5\nSMAP_L2B_SSS_01262_20150427T200025_R17000_V5.0.h5\nSMAP_L2B_SSS_01263_20150427T213853_R17000_V5.0.h5\nSMAP_L2B_SSS_01264_20150427T231721_R17000_V5.0.h5\nSMAP_L2B_SSS_01265_20150428T005549_R17000_V5.0.h5\nSMAP_L2B_SSS_01266_20150428T023417_R17000_V5.0.h5\nSMAP_L2B_SSS_01267_20150428T041245_R17000_V5.0.h5\nSMAP_L2B_SSS_01268_20150428T055112_R17000_V5.0.h5\nSMAP_L2B_SSS_01269_20150428T072940_R17000_V5.0.h5\nSMAP_L2B_SSS_01270_20150428T090808_R17000_V5.0.h5\nSMAP_L2B_SSS_01271_20150428T104636_R17000_V5.0.h5\nSMAP_L2B_SSS_01272_20150428T122503_R17000_V5.0.h5\nSMAP_L2B_SSS_01273_20150428T140331_R17000_V5.0.h5\nSMAP_L2B_SSS_01274_20150428T154159_R17000_V5.0.h5\nSMAP_L2B_SSS_01275_20150428T172027_R17000_V5.0.h5\nSMAP_L2B_SSS_01276_20150428T185855_R17000_V5.0.h5\nSMAP_L2B_SSS_01277_20150428T203722_R17000_V5.0.h5\nSMAP_L2B_SSS_01278_20150428T221550_R17000_V5.0.h5\nSMAP_L2B_SSS_01279_20150428T235418_R17000_V5.0.h5\nSMAP_L2B_SSS_01280_20150429T013246_R17000_V5.0.h5\nSMAP_L2B_SSS_01281_20150429T031114_R17000_V5.0.h5\nSMAP_L2B_SSS_01282_20150429T044942_R17000_V5.0.h5\nSMAP_L2B_SSS_01283_20150429T062810_R17000_V5.0.h5\nSMAP_L2B_SSS_01284_20150429T080637_R17000_V5.0.h5\nSMAP_L2B_SSS_01285_20150429T094505_R17000_V5.0.h5\nSMAP_L2B_SSS_01286_20150429T112333_R17000_V5.0.h5\nSMAP_L2B_SSS_01287_20150429T130201_R17000_V5.0.h5\nSMAP_L2B_SSS_01288_20150429T144029_R17000_V5.0.h5\nSMAP_L2B_SSS_01289_20150429T161856_R17000_V5.0.h5\nSMAP_L2B_SSS_01290_20150429T175724_R17000_V5.0.h5\nSMAP_L2B_SSS_01291_20150429T193552_R17000_V5.0.h5\nSMAP_L2B_SSS_01292_20150429T211420_R17000_V5.0.h5\nSMAP_L2B_SSS_01293_20150429T225248_R17000_V5.0.h5\nSMAP_L2B_SSS_01294_20150430T003115_R17000_V5.0.h5\nSMAP_L2B_SSS_01295_20150430T020943_R17000_V5.0.h5\nSMAP_L2B_SSS_01296_20150430T034811_R17000_V5.0.h5\nSMAP_L2B_SSS_01297_20150430T052639_R17000_V5.0.h5\nSMAP_L2B_SSS_01298_20150430T070507_R17000_V5.0.h5\nSMAP_L2B_SSS_01299_20150430T084334_R17000_V5.0.h5\nSMAP_L2B_SSS_01300_20150430T102202_R17000_V5.0.h5\nSMAP_L2B_SSS_01301_20150430T120030_R17000_V5.0.h5\nSMAP_L2B_SSS_01302_20150430T133858_R17000_V5.0.h5\nSMAP_L2B_SSS_01303_20150430T151726_R17000_V5.0.h5\nSMAP_L2B_SSS_01304_20150430T165555_R17000_V5.0.h5\nSMAP_L2B_SSS_01305_20150430T183423_R17000_V5.0.h5\nSMAP_L2B_SSS_01306_20150430T201251_R17000_V5.0.h5\nSMAP_L2B_SSS_01307_20150430T215118_R17000_V5.0.h5\nSMAP_L2B_SSS_01308_20150430T232945_R17000_V5.0.h5TB_CRID :R17000Year :2015Month :4Conventions :CF-1.6, ACDD-1.3processing_level :3cdm_data_type :Griddate_issued :2020-293T18:28:59.223date_created :2020-293T18:28:59.223time_coverage_start :2015-091T00:00:00.000time_coverage_end :2015-121T00:00:00.000geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lon_max :180.0geospatial_lon_min :-180.0geospatial_lat_units :degrees_northgeospatial_lon_units :degrees_eastplatform :SMAPsensor :SMAPproject :SMAPproduct_version :V5.0keywords_vocabulary :http://gcmd.gsfc.nasa.gov/Resources/valids/gcmd_parameters.htmlkeywords :SEA SURFACE SALINITY, SALINITY, SMAP, Jet Propulsion Laboratory, NASA, https://smap.jpl.nasa.gov/, SMAP Radiometercreator_name :JPLcreator_email :fore@jpl.nasa.govpublisher_name :Alexander G. Forepublisher_email :fore@jpl.nasa.govcontributor_name :Alexander Fore, Simon Yueh, Wenqing Tang, Akiko Hayashi, Bryan Stilesreferences :10.1109/TGRS.2016.2601486, 10.1109/TGRS.2016.2600239, 10.1109/TGRS.2013.2266915, 10.1016/j.rse.2017.08.021\n\n\n\n#for OPeNDAP, we need to set the number of files that can be opened at once to 10, \n#so that xr.open_mfdataset() actually reads all links (see https://github.com/pydata/xarray/issues/4082)\nxr.set_options(file_cache_maxsize=10)\n\n#to use xr.open_mfdataset which combines netCDF files\nds_MODIS = xr.open_mfdataset(file_list_MODIS, combine='nested', concat_dim='time')\nds_MODIS\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (eightbitcolor: 256, lat: 2160, lon: 4320, rgb: 3, time: 100)\nCoordinates:\n * lat (lat) float32 89.958336 89.875 89.79167 ... -89.87501 -89.958336\n * lon (lon) float32 -179.95833 -179.875 ... 179.87502 179.95836\nDimensions without coordinates: eightbitcolor, rgb, time\nData variables:\n palette (time, rgb, eightbitcolor) int8 dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n qual_sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\nAttributes:\n product_name: AQUA_MODIS.20110801_20110831.L3m.MO.SST...\n instrument: MODIS\n title: MODISA Level-3 Standard Mapped Image\n project: Ocean Biology Processing Group (NASA/GS...\n platform: Aqua\n temporal_range: month\n processing_version: R2019.0\n date_created: 2019-12-17T18:29:09.000Z\n history: l3mapgen par=AQUA_MODIS.20110801_201108...\n l2_flag_names: LAND,~HISOLZEN\n time_coverage_start: 2011-07-31T13:00:01.000Z\n time_coverage_end: 2011-08-31T14:34:59.000Z\n start_orbit_number: 49153\n end_orbit_number: 49605\n map_projection: Equidistant Cylindrical\n latitude_units: degrees_north\n longitude_units: degrees_east\n northernmost_latitude: 90.0\n southernmost_latitude: -90.0\n westernmost_longitude: -180.0\n easternmost_longitude: 180.0\n geospatial_lat_max: 90.0\n geospatial_lat_min: -90.0\n geospatial_lon_max: 180.0\n geospatial_lon_min: -180.0\n latitude_step: 0.083333336\n longitude_step: 0.083333336\n sw_point_latitude: -89.958336\n sw_point_longitude: -179.95833\n spatialResolution: 9.28 km\n geospatial_lon_resolution: 0.083333336\n geospatial_lat_resolution: 0.083333336\n geospatial_lat_units: degrees_north\n geospatial_lon_units: degrees_east\n number_of_lines: 2160\n number_of_columns: 4320\n measure: Mean\n suggested_image_scaling_minimum: -2.0\n suggested_image_scaling_maximum: 45.0\n suggested_image_scaling_type: LINEAR\n suggested_image_scaling_applied: No\n _lastModified: 2019-12-17T18:29:09.000Z\n Conventions: CF-1.6 ACDD-1.3\n institution: NASA Goddard Space Flight Center, Ocean...\n standard_name_vocabulary: CF Standard Name Table v36\n naming_authority: gov.nasa.gsfc.sci.oceandata\n id: AQUA_MODIS.20110801_20110831.L3b.MO.SST...\n license: https://science.nasa.gov/earth-science/...\n creator_name: NASA/GSFC/OBPG\n publisher_name: NASA/GSFC/OBPG\n creator_email: data@oceancolor.gsfc.nasa.gov\n publisher_email: data@oceancolor.gsfc.nasa.gov\n creator_url: https://oceandata.sci.gsfc.nasa.gov\n publisher_url: https://oceandata.sci.gsfc.nasa.gov\n processing_level: L3 Mapped\n cdm_data_type: grid\n data_bins: Attribute edlided: Unsupported attribut...\n data_minimum: -1.665\n data_maximum: 35.065xarray.DatasetDimensions:eightbitcolor: 256lat: 2160lon: 4320rgb: 3time: 100Coordinates: (2)lat(lat)float3289.958336 89.875 ... -89.958336long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0array([ 89.958336, 89.875 , 89.79167 , ..., -89.791664, -89.87501 ,\n -89.958336], dtype=float32)lon(lon)float32-179.95833 -179.875 ... 179.95836long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0array([-179.95833, -179.875 , -179.79166, ..., 179.79167, 179.87502,\n 179.95836], dtype=float32)Data variables: (3)palette(time, rgb, eightbitcolor)int8dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n76.80 kB\n768 B\n\n\nShape\n(100, 3, 256)\n(1, 3, 256)\n\n\nCount\n400 Tasks\n100 Chunks\n\n\nType\nint8\nnumpy.ndarray\n\n\n\n\n\n\n\n\nsst4\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n\n\n\n\nlong_name :\n\n4um Sea Surface Temperature\n\nunits :\n\ndegree_C\n\nstandard_name :\n\nsea_surface_temperature\n\nvalid_min :\n\n-1000\n\nvalid_max :\n\n10000\n\ndisplay_scale :\n\nlinear\n\ndisplay_min :\n\n-2.0\n\ndisplay_max :\n\n45.0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.73 GB\n37.32 MB\n\n\nShape\n(100, 2160, 4320)\n(1, 2160, 4320)\n\n\nCount\n400 Tasks\n100 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nqual_sst4\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nQuality Levels, Sea Surface Temperature\n\nvalid_min :\n\n0\n\nvalid_max :\n\n5\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.73 GB\n37.32 MB\n\n\nShape\n(100, 2160, 4320)\n(1, 2160, 4320)\n\n\nCount\n400 Tasks\n100 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nAttributes: (59)product_name :AQUA_MODIS.20110801_20110831.L3m.MO.SST4.sst4.9km.ncinstrument :MODIStitle :MODISA Level-3 Standard Mapped Imageproject :Ocean Biology Processing Group (NASA/GSFC/OBPG)platform :Aquatemporal_range :monthprocessing_version :R2019.0date_created :2019-12-17T18:29:09.000Zhistory :l3mapgen par=AQUA_MODIS.20110801_20110831.L3m.MO.SST4.sst4.9km.nc.param l2_flag_names :LAND,~HISOLZENtime_coverage_start :2011-07-31T13:00:01.000Ztime_coverage_end :2011-08-31T14:34:59.000Zstart_orbit_number :49153end_orbit_number :49605map_projection :Equidistant Cylindricallatitude_units :degrees_northlongitude_units :degrees_eastnorthernmost_latitude :90.0southernmost_latitude :-90.0westernmost_longitude :-180.0easternmost_longitude :180.0geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lon_max :180.0geospatial_lon_min :-180.0latitude_step :0.083333336longitude_step :0.083333336sw_point_latitude :-89.958336sw_point_longitude :-179.95833spatialResolution :9.28 kmgeospatial_lon_resolution :0.083333336geospatial_lat_resolution :0.083333336geospatial_lat_units :degrees_northgeospatial_lon_units :degrees_eastnumber_of_lines :2160number_of_columns :4320measure :Meansuggested_image_scaling_minimum :-2.0suggested_image_scaling_maximum :45.0suggested_image_scaling_type :LINEARsuggested_image_scaling_applied :No_lastModified :2019-12-17T18:29:09.000ZConventions :CF-1.6 ACDD-1.3institution :NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Groupstandard_name_vocabulary :CF Standard Name Table v36naming_authority :gov.nasa.gsfc.sci.oceandataid :AQUA_MODIS.20110801_20110831.L3b.MO.SST4.nc/L3/AQUA_MODIS.20110801_20110831.L3b.MO.SST4.nclicense :https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/creator_name :NASA/GSFC/OBPGpublisher_name :NASA/GSFC/OBPGcreator_email :data@oceancolor.gsfc.nasa.govpublisher_email :data@oceancolor.gsfc.nasa.govcreator_url :https://oceandata.sci.gsfc.nasa.govpublisher_url :https://oceandata.sci.gsfc.nasa.govprocessing_level :L3 Mappedcdm_data_type :griddata_bins :Attribute edlided: Unsupported attribute type (NC_INT64)data_minimum :-1.665data_maximum :35.065\n\n\n\n#open and combine Aquarius files into a single .nc file\nxr.set_options(file_cache_maxsize=10)\n#to use xa.open_mfdataset which combines netCDF files\nds_Aq = xr.open_mfdataset(file_list_Aq, combine='nested', concat_dim='time')\nds_Aq\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (SSS_0: 180, SSS_1: 360, SSS_ran_unc_0: 180, SSS_ran_unc_1: 360, SSS_sys_unc_0: 180, SSS_sys_unc_1: 360, palette_0: 3, palette_1: 256, time: 47)\nDimensions without coordinates: SSS_0, SSS_1, SSS_ran_unc_0, SSS_ran_unc_1, SSS_sys_unc_0, SSS_sys_unc_1, palette_0, palette_1, time\nData variables:\n SSS (time, SSS_0, SSS_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n SSS_ran_unc (time, SSS_ran_unc_0, SSS_ran_unc_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n SSS_sys_unc (time, SSS_sys_unc_0, SSS_sys_unc_1) float32 dask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n palette (time, palette_0, palette_1) int8 dask.array<chunksize=(1, 3, 256), meta=np.ndarray>\nAttributes:\n product_name: Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg\n instrument: Aquarius\n title: Aquarius Level-3 Standard Mapped Image\n project: Ocean Biology Processing Group (NASA/G...\n platform: SAC-D Aquarius\n temporal_range: MO\n processing_version: V5.0\n date_created: 2017-10-21T21:22:21.000Z\n history: smigen par=Q20112132011243.L3m_MO_SCI_...\n l2_flag_names: POINTING,NAV,LANDRED,ICERED,REFL_1STOK...\n time_coverage_start: 2011-08-25T01:45:23.698Z\n time_coverage_end: 2011-09-01T01:30:15.207Z\n start_orbit_number: 1111\n end_orbit_number: 1213\n map_projection: Equidistant Cylindrical\n latitude_units: degrees_north\n longitude_units: degrees_east\n northernmost_latitude: 90.0\n southernmost_latitude: -90.0\n westernmost_longitude: -180.0\n easternmost_longitude: 180.0\n geospatial_lat_max: 90.0\n geospatial_lat_min: -90.0\n geospatial_lon_max: 180.0\n geospatial_lon_min: -180.0\n grid_mapping_name: latitude_longitude\n latitude_step: 1.0\n longitude_step: 1.0\n sw_point_latitude: -89.5\n sw_point_longitude: -179.5\n geospatial_lon_resolution: 1.0\n geospatial_lat_resolution: 1.0\n geospatial_lat_units: deg\n geospatial_lon_units: deg\n spatialResolution: 1.00 deg\n data_bins: 32001\n number_of_lines: 180\n number_of_columns: 360\n measure: Mean\n data_minimum: 3.82171\n data_maximum: 38.76831\n suggested_image_scaling_minimum: 0.0\n suggested_image_scaling_maximum: 70.0\n suggested_image_scaling_type: ATAN\n suggested_image_scaling_applied: No\n _lastModified: 2017-10-21T21:22:21.000Z\n Conventions: CF-1.6\n institution: NASA Goddard Space Flight Center, Ocea...\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metad...\n Metadata_Conventions: Unidata Dataset Discovery v1.0\n naming_authority: gov.nasa.gsfc.sci.oceandata\n id: Q20112132011243.L3b_MO_SCI_V5.0.main/L...\n license: http://science.nasa.gov/earth-science/...\n creator_name: NASA/GSFC/OBPG\n publisher_name: NASA/GSFC/OBPG\n creator_email: data@oceancolor.gsfc.nasa.gov\n publisher_email: data@oceancolor.gsfc.nasa.gov\n creator_url: http://oceandata.sci.gsfc.nasa.gov\n publisher_url: http://oceandata.sci.gsfc.nasa.gov\n processing_level: L3 Mapped\n cdm_data_type: grid\n identifier_product_doi_authority: http://dx.doi.org\n identifier_product_doi: http://dx.doi.org\n keywords: SURFACE SALINITY, SALINITY, AQUARIUS, ...\n keywords_vocabulary: NASA Global Change Master Directory (G...\n software_name: smigen\n software_version: 5.20\n source: Q20112132011243.L3b_MO_SCI_V5.0.mainxarray.DatasetDimensions:SSS_0: 180SSS_1: 360SSS_ran_unc_0: 180SSS_ran_unc_1: 360SSS_sys_unc_0: 180SSS_sys_unc_1: 360palette_0: 3palette_1: 256time: 47Coordinates: (0)Data variables: (4)SSS(time, SSS_0, SSS_1)float32dask.array<chunksize=(1, 180, 360), meta=np.ndarray>long_name :Sea Surface Salinityorigname :SSSfullnamepath :/SSS\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n12.18 MB\n259.20 kB\n\n\nShape\n(47, 180, 360)\n(1, 180, 360)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nSSS_ran_unc\n\n\n(time, SSS_ran_unc_0, SSS_ran_unc_1)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSea Surface Salinity Uncertainty (random)\n\norigname :\n\nSSS_ran_unc\n\nfullnamepath :\n\n/SSS_ran_unc\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n12.18 MB\n259.20 kB\n\n\nShape\n(47, 180, 360)\n(1, 180, 360)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nSSS_sys_unc\n\n\n(time, SSS_sys_unc_0, SSS_sys_unc_1)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 180, 360), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSea Surface Salinity Uncertainty (systematic)\n\norigname :\n\nSSS_sys_unc\n\nfullnamepath :\n\n/SSS_sys_unc\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n12.18 MB\n259.20 kB\n\n\nShape\n(47, 180, 360)\n(1, 180, 360)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\npalette\n\n\n(time, palette_0, palette_1)\n\n\nint8\n\n\ndask.array<chunksize=(1, 3, 256), meta=np.ndarray>\n\n\n\n\norigname :\n\npalette\n\nfullnamepath :\n\n/palette\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n36.10 kB\n768 B\n\n\nShape\n(47, 3, 256)\n(1, 3, 256)\n\n\nCount\n188 Tasks\n47 Chunks\n\n\nType\nint8\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (68)product_name :Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deginstrument :Aquariustitle :Aquarius Level-3 Standard Mapped Imageproject :Ocean Biology Processing Group (NASA/GSFC/OBPG)platform :SAC-D Aquariustemporal_range :MOprocessing_version :V5.0date_created :2017-10-21T21:22:21.000Zhistory :smigen par=Q20112132011243.L3m_MO_SCI_V5.0_SSS_1deg.paraml2_flag_names :POINTING,NAV,LANDRED,ICERED,REFL_1STOKESMOONRED,REFL_1STOKESGAL,TFTADIFFRED,RFI_REGION,SAOVERFLOW,COLDWATERRED,WINDRED,TBCONStime_coverage_start :2011-08-25T01:45:23.698Ztime_coverage_end :2011-09-01T01:30:15.207Zstart_orbit_number :1111end_orbit_number :1213map_projection :Equidistant Cylindricallatitude_units :degrees_northlongitude_units :degrees_eastnorthernmost_latitude :90.0southernmost_latitude :-90.0westernmost_longitude :-180.0easternmost_longitude :180.0geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lon_max :180.0geospatial_lon_min :-180.0grid_mapping_name :latitude_longitudelatitude_step :1.0longitude_step :1.0sw_point_latitude :-89.5sw_point_longitude :-179.5geospatial_lon_resolution :1.0geospatial_lat_resolution :1.0geospatial_lat_units :deggeospatial_lon_units :degspatialResolution :1.00 degdata_bins :32001number_of_lines :180number_of_columns :360measure :Meandata_minimum :3.82171data_maximum :38.76831suggested_image_scaling_minimum :0.0suggested_image_scaling_maximum :70.0suggested_image_scaling_type :ATANsuggested_image_scaling_applied :No_lastModified :2017-10-21T21:22:21.000ZConventions :CF-1.6institution :NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Groupstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata ConventionMetadata_Conventions :Unidata Dataset Discovery v1.0naming_authority :gov.nasa.gsfc.sci.oceandataid :Q20112132011243.L3b_MO_SCI_V5.0.main/L3/Q20112132011243.L3b_MO_SCI_V5.0.mainlicense :http://science.nasa.gov/earth-science/earth-science-data/data-information-policy/creator_name :NASA/GSFC/OBPGpublisher_name :NASA/GSFC/OBPGcreator_email :data@oceancolor.gsfc.nasa.govpublisher_email :data@oceancolor.gsfc.nasa.govcreator_url :http://oceandata.sci.gsfc.nasa.govpublisher_url :http://oceandata.sci.gsfc.nasa.govprocessing_level :L3 Mappedcdm_data_type :grididentifier_product_doi_authority :http://dx.doi.orgidentifier_product_doi :http://dx.doi.orgkeywords :SURFACE SALINITY, SALINITY, AQUARIUS, Jet Propulsion Laboratory, NASA, http://aquarius.nasa.gov/, AQUARIUS SAC-D, Aquarius Scatterometer, Aquarius Radiometerkeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordssoftware_name :smigensoftware_version :5.20source :Q20112132011243.L3b_MO_SCI_V5.0.main\n\n\n\nPreview the SMAP, Aquarius, and MODIS data over region of interest\n\n#SMAP\nlat_bnds, lon_bnds = [6, -2], [-52, -43] #switched lat directions from GRACE, and longitude has positives and negatives\nds_SMAP_subset = ds_SMAP.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))\n#ds_SMAP_subset\n\n#plot SMAP subset\nds_SMAP_subset.smap_sss[2,:,:].plot() #at time '2', indicating June 2015\n\nplt.show()\n\n\n\n\n\n#Aquarius\nlat_bnds, lon_bnds = [84, 92], [128, 137] #See how commented out plot is distorted, only positive numbers\nds_Aq_subset = ds_Aq.sel(SSS_0=slice(*lat_bnds), SSS_1=slice(*lon_bnds))\n#ds_Aq_subset\n\n#plot Aquarius subset\n#this map is inverted compared to SMAP, but still capturing the same area\nds_Aq_subset.SSS[10,:,:].plot() #at time '10' indicating June 2012\n#ds_Aq.SSS[10,:,:].plot() #whole world map view to see the inversion of the data\n\nplt.show()\n\n\n\n\n\n#MODIS SST\nlat_bnds, lon_bnds = [6, -2], [-52, -43] #like SMAP\nds_MODIS_subset = ds_MODIS.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\n#ds_MODIS_subset\n\n#plot MODIS subset\nds_MODIS_subset.sst4[2,:,:].plot() #at time '2', indicating Oct 2011\n\nplt.show()\n\n\n\n\n\n\nPlotting the river distances and associated heights on the map at time t=9069 (March 16, 2018)\n\n#Change the extent to capture the data of the netCDF file\nextent = [-85, -30, -20, 20]\n\n#Add basemap\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent(extent)\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()\n\n\n\n\n\n#GRACE-FO (different bounds than others because GRACE is over land)\nlat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\nds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_GRACE_subset\n\nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.add_feature(cartopy.feature.RIVERS)\nds_GRACE_subset.lwe_thickness[171,:,:].plot(cmap = 'bwr_r') # 171 for 2019-04\nplt.show()"
+ "objectID": "external/zarr_access.html#exercise",
+ "href": "external/zarr_access.html#exercise",
+ "title": "Zarr Access for NetCDF4 files",
+ "section": "Exercise",
+ "text": "Exercise\nIn this exercise, we will be using the eosdis-zarr-store library to aggregate and analyze a month of sea surface temperature for the Great Lakes region\n\nSet up\n\nImport Required Packages\n\n# Core libraries for this tutorial\n# Available via `pip install zarr zarr-eosdis-store`\nfrom eosdis_store import EosdisStore\nimport xarray as xr\n\n# Other Python libraries\nimport requests\nfrom pqdm.threads import pqdm\nfrom matplotlib import animation, pyplot as plt\nfrom IPython.core.display import display, HTML\n\n# Python standard library imports\nfrom pprint import pprint\n\nAlso set the width / height for plots we show\n\nplt.rcParams['figure.figsize'] = 12, 6\n\n\n\nSet Dataset, Time, and Region of Interest\nLook in PO.DAAC’s cloud archive for Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 Multiscale Ultrahigh Resolution (MUR) data\n\ndata_provider = 'POCLOUD'\nmur_short_name = 'MUR-JPL-L4-GLOB-v4.1'\n\nLooking for data from the month of September over the Great Lakes\n\nstart_time = '2021-09-01T21:00:00Z'\nend_time = '2021-09-30T20:59:59Z'\n\n# Bounding box around the Great Lakes\nlats = slice(41, 49)\nlons = slice(-93, -76)\n\n# Some other possibly interesting bounding boxes:\n\n# Hawaiian Islands\n# lats = slice(18, 22.5)\n# lons = slice(-161, -154)\n\n# Mediterranean Sea\n# lats = slice(29, 45)\n# lons = slice(-7, 37)\n\n\n\n\nFind URLs for the dataset and AOI\nSet up a CMR granules search for our area of interest, as we saw in prior tutorials\n\ncmr_url = 'https://cmr.earthdata.nasa.gov/search/granules.json'\n\nSearch for granules in our area of interest, expecting one granule per day of September\n\nresponse = requests.get(cmr_url, \n params={\n 'provider': data_provider,\n 'short_name': mur_short_name, \n 'temporal': f'{start_time},{end_time}',\n 'bounding_box': f'{lons.start},{lats.start},{lons.stop},{lats.stop}',\n 'page_size': 2000,\n }\n )\n\n\ngranules = response.json()['feed']['entry']\n\nfor granule in granules:\n print(granule['title'])\n\n20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210902090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210903090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210904090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210905090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210906090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210907090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210908090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210909090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210910090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210911090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210912090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210913090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210914090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210915090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210916090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210917090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210918090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210919090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210920090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210921090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210922090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210923090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210924090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210925090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210926090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210927090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210928090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210929090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n20210930090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n\n\n\npprint(granules[0])\n\n{'boxes': ['-90 -180 90 180'],\n 'browse_flag': False,\n 'collection_concept_id': 'C1996881146-POCLOUD',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 4 MUR Global Foundation Sea Surface Temperature '\n 'Analysis (v4.1)',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '9.059906005859375E-5',\n 'id': 'G2113241213-POCLOUD',\n 'links': [{'href': 's3://podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/s3#',\n 'title': 'This link provides direct download access via S3 to the '\n 'granule.'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'Download '\n '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.md5',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.md5'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'api endpoint to retrieve temporary credentials valid for '\n 'same-region direct s3 access'},\n {'href': 'https://opendap.earthdata.nasa.gov/collections/C1996881146-POCLOUD/granules/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/service#',\n 'title': 'OPeNDAP request URL'},\n {'href': 'https://github.com/nasa/podaac_tools_and_services/tree/master/subset_opendap',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://earthdata.nasa.gov/esds/competitive-programs/measures/mur-sst',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n {'href': 'http://journals.ametsoc.org/doi/abs/10.1175/1520-0426%281998%29015%3C0741:BSHWSS%3E2.0.CO;2',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://doi.org/10.1016/j.rse.2017.07.029',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://registry.opendata.aws/mur/#usageexa',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n {'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1996881146-POCLOUD ',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '300.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n {'href': ' '\n 'https://search.earthdata.nasa.gov/search/granules?p=C1996881146-POCLOUD ',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '700.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n {'href': 'https://podaac.jpl.nasa.gov/MEaSUREs-MUR',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/SWOT-EA-2021/Colocate_satellite_insitu_ocean.ipynb',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'time_end': '2021-09-01T21:00:00.000Z',\n 'time_start': '2021-08-31T21:00:00.000Z',\n 'title': '20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1',\n 'updated': '2021-09-10T07:29:40.511Z'}\n\n\n\nurls = []\nfor granule in granules:\n for link in granule['links']:\n if link['rel'].endswith('/data#'):\n urls.append(link['href'])\n break\npprint(urls)\n\n['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210901090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210902090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210903090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210904090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210905090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210906090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210907090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210908090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210909090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210910090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210911090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210912090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210913090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210914090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210915090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210916090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210917090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210918090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210919090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210920090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210921090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210922090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210923090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210924090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210925090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210926090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210927090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210928090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210929090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210930090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc']\n\n\n\n\nOpen and view our AOI without downloading a whole file\n\nCheck to see if we can use an efficient partial-access technique\n\nresponse = requests.head(f'{urls[0]}.dmrpp')\n\nprint('Can we use EosdisZarrStore and XArray to access these files more efficiently?')\nprint('Yes' if response.ok else 'No')\n\nCan we use EosdisZarrStore and XArray to access these files more efficiently?\nYes\n\n\nOpen our first URL using the Zarr library\n\nurl = urls[0]\n\nds = xr.open_zarr(EosdisStore(url), consolidated=False)\n\nThat’s it! No downloads, temporary credentials, or S3 filesystems. Hereafter, we interact with the ds variable as with any XArray dataset. We need not worry about the EosdisStore anymore.\nView the file’s variable structure\n\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 1, lat: 17999, lon: 36000)\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00\nData variables:\n analysed_sst (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n analysis_error (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n dt_1km_data (time, lat, lon) timedelta64[ns] dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n mask (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sea_ice_fraction (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sst_anomaly (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\nAttributes: (12/47)\n Conventions: CF-1.7\n title: Daily MUR SST, Final product\n summary: A merged, multi-sensor L4 Foundation SST anal...\n references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n institution: Jet Propulsion Laboratory\n history: created at nominal 4-day latency; replaced nr...\n ... ...\n project: NASA Making Earth Science Data Records for Us...\n publisher_name: GHRSST Project Office\n publisher_url: http://www.ghrsst.org\n publisher_email: ghrsst-po@nceo.ac.uk\n processing_level: L4\n cdm_data_type: gridxarray.DatasetDimensions:time: 1lat: 17999lon: 36000Coordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000'], dtype='datetime64[ns]')Data variables: (6)analysed_sst(time, lat, lon)float32dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanalysis_error\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated error standard deviation of analysed_sst\n\nunits :\n\nkelvin\n\nvalid_min :\n\n0\n\nvalid_max :\n\n32767\n\ncomment :\n\nuncertainty in \\\"analysed_sst\\\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\ndt_1km_data\n\n\n(time, lat, lon)\n\n\ntimedelta64[ns]\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime to most recent 1km data\n\nvalid_min :\n\n-127\n\nvalid_max :\n\n127\n\nsource :\n\nMODIS and VIIRS pixels ingested by MUR\n\ncomment :\n\nThe grid value is hours between the analysis time and the most recent MODIS or VIIRS 1km L2P datum within 0.01 degrees from the grid point. \\\"Fill value\\\" indicates absence of such 1km data at the grid point.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n4.83 GiB\n31.96 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n170 Tasks\n169 Chunks\n\n\nType\ntimedelta64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nmask\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea/land field composite mask\n\nvalid_min :\n\n1\n\nvalid_max :\n\n31\n\nflag_masks :\n\n[1, 2, 4, 8, 16]\n\nflag_meanings :\n\nopen_sea land open_lake open_sea_with_ice_in_the_grid open_lake_with_ice_in_the_grid\n\ncomment :\n\nmask can be used to further filter the data.\n\nsource :\n\nGMT \\\"grdlandmask\\\", ice flag from sea_ice_fraction data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n15.98 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n170 Tasks\n169 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsea_ice_fraction\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea ice area fraction\n\nstandard_name :\n\nsea_ice_area_fraction\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\nsource :\n\nEUMETSAT OSI-SAF, copyright EUMETSAT\n\ncomment :\n\nice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n15.98 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n170 Tasks\n169 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsst_anomaly\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSST anomaly from a seasonal SST climatology based on the MUR data over 2003-2014 period\n\nunits :\n\nkelvin\n\nvalid_min :\n\n-32767\n\nvalid_max :\n\n32767\n\ncomment :\n\nanomaly reference to the day-of-year average between 2003 and 2014\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (47)Conventions :CF-1.7title :Daily MUR SST, Final productsummary :A merged, multi-sensor L4 Foundation SST analysis product from JPL.references :http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SSTinstitution :Jet Propulsion Laboratoryhistory :created at nominal 4-day latency; replaced nrt (1-day latency) version.comment :MUR = \\\"Multi-scale Ultra-high Resolution\\\"license :These data are available free of charge under data policy of JPL PO.DAAC.id :MUR-JPL-L4-GLOB-v04.1naming_authority :org.ghrsstproduct_version :04.1uuid :27665bc0-d5fc-11e1-9b23-0800200c9a66gds_version_id :2.0netcdf_version_id :4.1date_created :20210910T072132Zstart_time :20210901T090000Zstop_time :20210901T090000Ztime_coverage_start :20210831T210000Ztime_coverage_end :20210901T210000Zfile_quality_level :3source :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAFplatform :Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Shipssensor :MODIS, AMSR2, AVHRR, in-situMetadata_Conventions :Unidata Observation Dataset v1.0metadata_link :http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1keywords :Oceans > Ocean Temperature > Sea Surface Temperaturekeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsouthernmost_latitude :-90.0northernmost_latitude :90.0westernmost_longitude :-180.0easternmost_longitude :180.0spatial_resolution :0.01 degreesgeospatial_lat_units :degrees northgeospatial_lat_resolution :0.009999999776geospatial_lon_units :degrees eastgeospatial_lon_resolution :0.009999999776acknowledgment :Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.creator_name :JPL MUR SST projectcreator_email :ghrsst@podaac.jpl.nasa.govcreator_url :http://mur.jpl.nasa.govproject :NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Programpublisher_name :GHRSST Project Officepublisher_url :http://www.ghrsst.orgpublisher_email :ghrsst-po@nceo.ac.ukprocessing_level :L4cdm_data_type :grid\n\n\n\nds.analysed_sst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 1, lat: 17999, lon: 36000)>\ndask.array<open_dataset-4d5a9a1e1fda090e80524b67b2e413c6analysed_sst, shape=(1, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 -89.96 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 1lat: 17999lon: 36000dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.41 GiB\n7.99 MiB\n\n\nShape\n(1, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n325 Tasks\n324 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000'], dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\nsst = ds.analysed_sst.sel(lat=lats, lon=lons)\nsst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 1, lat: 801, lon: 1701)>\ndask.array<getitem, shape=(1, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 41.0 41.01 41.02 41.03 ... 48.97 48.98 48.99 49.0\n * lon (lon) float32 -93.0 -92.99 -92.98 -92.97 ... -76.02 -76.01 -76.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 1lat: 801lon: 1701dask.array<chunksize=(1, 200, 1536), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n5.20 MiB\n3.52 MiB\n\n\nShape\n(1, 801, 1701)\n(1, 601, 1536)\n\n\nCount\n329 Tasks\n4 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float3241.0 41.01 41.02 ... 48.99 49.0long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([41. , 41.01, 41.02, ..., 48.98, 48.99, 49. ], dtype=float32)lon(lon)float32-93.0 -92.99 ... -76.01 -76.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-93. , -92.99, -92.98, ..., -76.02, -76.01, -76. ], dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000'], dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\nsst.plot()\n\n<matplotlib.collections.QuadMesh at 0x7f2d9848d4c0>\n\n\n\n\n\n\n\n\nAggregate and analyze 30 files\nSet up a function to open all of our URLs as XArrays in parallel\n\ndef open_as_zarr_xarray(url):\n return xr.open_zarr(EosdisStore(url), consolidated=False)\n\ndatasets = pqdm(urls, open_as_zarr_xarray, n_jobs=30)\n\n\n\n\n\n\n\n\n\n\nCombine the individual file-based datasets into a single xarray dataset with a time axis\n\nds = xr.concat(datasets, 'time')\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 30, lat: 17999, lon: 36000)\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-3...\nData variables:\n analysed_sst (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n analysis_error (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n dt_1km_data (time, lat, lon) timedelta64[ns] dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n mask (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sea_ice_fraction (time, lat, lon) float32 dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n sst_anomaly (time, lat, lon) float32 dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\nAttributes: (12/47)\n Conventions: CF-1.7\n title: Daily MUR SST, Final product\n summary: A merged, multi-sensor L4 Foundation SST anal...\n references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n institution: Jet Propulsion Laboratory\n history: created at nominal 4-day latency; replaced nr...\n ... ...\n project: NASA Making Earth Science Data Records for Us...\n publisher_name: GHRSST Project Office\n publisher_url: http://www.ghrsst.org\n publisher_email: ghrsst-po@nceo.ac.uk\n processing_level: L4\n cdm_data_type: gridxarray.DatasetDimensions:time: 30lat: 17999lon: 36000Coordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00 ... 2021-09-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000', '2021-09-02T09:00:00.000000000',\n '2021-09-03T09:00:00.000000000', '2021-09-04T09:00:00.000000000',\n '2021-09-05T09:00:00.000000000', '2021-09-06T09:00:00.000000000',\n '2021-09-07T09:00:00.000000000', '2021-09-08T09:00:00.000000000',\n '2021-09-09T09:00:00.000000000', '2021-09-10T09:00:00.000000000',\n '2021-09-11T09:00:00.000000000', '2021-09-12T09:00:00.000000000',\n '2021-09-13T09:00:00.000000000', '2021-09-14T09:00:00.000000000',\n '2021-09-15T09:00:00.000000000', '2021-09-16T09:00:00.000000000',\n '2021-09-17T09:00:00.000000000', '2021-09-18T09:00:00.000000000',\n '2021-09-19T09:00:00.000000000', '2021-09-20T09:00:00.000000000',\n '2021-09-21T09:00:00.000000000', '2021-09-22T09:00:00.000000000',\n '2021-09-23T09:00:00.000000000', '2021-09-24T09:00:00.000000000',\n '2021-09-25T09:00:00.000000000', '2021-09-26T09:00:00.000000000',\n '2021-09-27T09:00:00.000000000', '2021-09-28T09:00:00.000000000',\n '2021-09-29T09:00:00.000000000', '2021-09-30T09:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (6)analysed_sst(time, lat, lon)float32dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanalysis_error\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated error standard deviation of analysed_sst\n\nunits :\n\nkelvin\n\nvalid_min :\n\n0\n\nvalid_max :\n\n32767\n\ncomment :\n\nuncertainty in \\\"analysed_sst\\\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\ndt_1km_data\n\n\n(time, lat, lon)\n\n\ntimedelta64[ns]\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime to most recent 1km data\n\nvalid_min :\n\n-127\n\nvalid_max :\n\n127\n\nsource :\n\nMODIS and VIIRS pixels ingested by MUR\n\ncomment :\n\nThe grid value is hours between the analysis time and the most recent MODIS or VIIRS 1km L2P datum within 0.01 degrees from the grid point. \\\"Fill value\\\" indicates absence of such 1km data at the grid point.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n144.83 GiB\n31.96 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n10170 Tasks\n5070 Chunks\n\n\nType\ntimedelta64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nmask\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea/land field composite mask\n\nvalid_min :\n\n1\n\nvalid_max :\n\n31\n\nflag_masks :\n\n[1, 2, 4, 8, 16]\n\nflag_meanings :\n\nopen_sea land open_lake open_sea_with_ice_in_the_grid open_lake_with_ice_in_the_grid\n\ncomment :\n\nmask can be used to further filter the data.\n\nsource :\n\nGMT \\\"grdlandmask\\\", ice flag from sea_ice_fraction data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n15.98 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n10170 Tasks\n5070 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsea_ice_fraction\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea ice area fraction\n\nstandard_name :\n\nsea_ice_area_fraction\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\nsource :\n\nEUMETSAT OSI-SAF, copyright EUMETSAT\n\ncomment :\n\nice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n15.98 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1447, 2895)\n\n\nCount\n10170 Tasks\n5070 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsst_anomaly\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSST anomaly from a seasonal SST climatology based on the MUR data over 2003-2014 period\n\nunits :\n\nkelvin\n\nvalid_min :\n\n-32767\n\nvalid_max :\n\n32767\n\ncomment :\n\nanomaly reference to the day-of-year average between 2003 and 2014\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (47)Conventions :CF-1.7title :Daily MUR SST, Final productsummary :A merged, multi-sensor L4 Foundation SST analysis product from JPL.references :http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SSTinstitution :Jet Propulsion Laboratoryhistory :created at nominal 4-day latency; replaced nrt (1-day latency) version.comment :MUR = \\\"Multi-scale Ultra-high Resolution\\\"license :These data are available free of charge under data policy of JPL PO.DAAC.id :MUR-JPL-L4-GLOB-v04.1naming_authority :org.ghrsstproduct_version :04.1uuid :27665bc0-d5fc-11e1-9b23-0800200c9a66gds_version_id :2.0netcdf_version_id :4.1date_created :20210910T072132Zstart_time :20210901T090000Zstop_time :20210901T090000Ztime_coverage_start :20210831T210000Ztime_coverage_end :20210901T210000Zfile_quality_level :3source :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAFplatform :Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Shipssensor :MODIS, AMSR2, AVHRR, in-situMetadata_Conventions :Unidata Observation Dataset v1.0metadata_link :http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1keywords :Oceans > Ocean Temperature > Sea Surface Temperaturekeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsouthernmost_latitude :-90.0northernmost_latitude :90.0westernmost_longitude :-180.0easternmost_longitude :180.0spatial_resolution :0.01 degreesgeospatial_lat_units :degrees northgeospatial_lat_resolution :0.009999999776geospatial_lon_units :degrees eastgeospatial_lon_resolution :0.009999999776acknowledgment :Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.creator_name :JPL MUR SST projectcreator_email :ghrsst@podaac.jpl.nasa.govcreator_url :http://mur.jpl.nasa.govproject :NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Programpublisher_name :GHRSST Project Officepublisher_url :http://www.ghrsst.orgpublisher_email :ghrsst-po@nceo.ac.ukprocessing_level :L4cdm_data_type :grid\n\n\nLook at the Analysed SST variable metadata\n\nall_sst = ds.analysed_sst\nall_sst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 30, lat: 17999, lon: 36000)>\ndask.array<concatenate, shape=(30, 17999, 36000), dtype=float32, chunksize=(1, 1023, 2047), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 -89.99 -89.98 -89.97 -89.96 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 30lat: 17999lon: 36000dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n72.42 GiB\n7.99 MiB\n\n\nShape\n(30, 17999, 36000)\n(1, 1023, 2047)\n\n\nCount\n19470 Tasks\n9720 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00 ... 2021-09-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000', '2021-09-02T09:00:00.000000000',\n '2021-09-03T09:00:00.000000000', '2021-09-04T09:00:00.000000000',\n '2021-09-05T09:00:00.000000000', '2021-09-06T09:00:00.000000000',\n '2021-09-07T09:00:00.000000000', '2021-09-08T09:00:00.000000000',\n '2021-09-09T09:00:00.000000000', '2021-09-10T09:00:00.000000000',\n '2021-09-11T09:00:00.000000000', '2021-09-12T09:00:00.000000000',\n '2021-09-13T09:00:00.000000000', '2021-09-14T09:00:00.000000000',\n '2021-09-15T09:00:00.000000000', '2021-09-16T09:00:00.000000000',\n '2021-09-17T09:00:00.000000000', '2021-09-18T09:00:00.000000000',\n '2021-09-19T09:00:00.000000000', '2021-09-20T09:00:00.000000000',\n '2021-09-21T09:00:00.000000000', '2021-09-22T09:00:00.000000000',\n '2021-09-23T09:00:00.000000000', '2021-09-24T09:00:00.000000000',\n '2021-09-25T09:00:00.000000000', '2021-09-26T09:00:00.000000000',\n '2021-09-27T09:00:00.000000000', '2021-09-28T09:00:00.000000000',\n '2021-09-29T09:00:00.000000000', '2021-09-30T09:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\nCreate a dataset / variable that is only our area of interest and view its metadata\n\nsst = ds.analysed_sst.sel(lat=lats, lon=lons)\nsst\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'analysed_sst' (time: 30, lat: 801, lon: 1701)>\ndask.array<getitem, shape=(30, 801, 1701), dtype=float32, chunksize=(1, 601, 1536), chunktype=numpy.ndarray>\nCoordinates:\n * lat (lat) float32 41.0 41.01 41.02 41.03 ... 48.97 48.98 48.99 49.0\n * lon (lon) float32 -93.0 -92.99 -92.98 -92.97 ... -76.02 -76.01 -76.0\n * time (time) datetime64[ns] 2021-09-01T09:00:00 ... 2021-09-30T09:00:00\nAttributes:\n long_name: analysed sea surface temperature\n standard_name: sea_surface_foundation_temperature\n units: kelvin\n valid_min: -32767\n valid_max: 32767\n comment: \\\"Final\\\" version using Multi-Resolution Variational Anal...\n source: MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, A...xarray.DataArray'analysed_sst'time: 30lat: 801lon: 1701dask.array<chunksize=(1, 200, 1536), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n155.93 MiB\n3.52 MiB\n\n\nShape\n(30, 801, 1701)\n(1, 601, 1536)\n\n\nCount\n19590 Tasks\n120 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (3)lat(lat)float3241.0 41.01 41.02 ... 48.99 49.0long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([41. , 41.01, 41.02, ..., 48.98, 48.99, 49. ], dtype=float32)lon(lon)float32-93.0 -92.99 ... -76.01 -76.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-93. , -92.99, -92.98, ..., -76.02, -76.01, -76. ], dtype=float32)time(time)datetime64[ns]2021-09-01T09:00:00 ... 2021-09-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-09-01T09:00:00.000000000', '2021-09-02T09:00:00.000000000',\n '2021-09-03T09:00:00.000000000', '2021-09-04T09:00:00.000000000',\n '2021-09-05T09:00:00.000000000', '2021-09-06T09:00:00.000000000',\n '2021-09-07T09:00:00.000000000', '2021-09-08T09:00:00.000000000',\n '2021-09-09T09:00:00.000000000', '2021-09-10T09:00:00.000000000',\n '2021-09-11T09:00:00.000000000', '2021-09-12T09:00:00.000000000',\n '2021-09-13T09:00:00.000000000', '2021-09-14T09:00:00.000000000',\n '2021-09-15T09:00:00.000000000', '2021-09-16T09:00:00.000000000',\n '2021-09-17T09:00:00.000000000', '2021-09-18T09:00:00.000000000',\n '2021-09-19T09:00:00.000000000', '2021-09-20T09:00:00.000000000',\n '2021-09-21T09:00:00.000000000', '2021-09-22T09:00:00.000000000',\n '2021-09-23T09:00:00.000000000', '2021-09-24T09:00:00.000000000',\n '2021-09-25T09:00:00.000000000', '2021-09-26T09:00:00.000000000',\n '2021-09-27T09:00:00.000000000', '2021-09-28T09:00:00.000000000',\n '2021-09-29T09:00:00.000000000', '2021-09-30T09:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (7)long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\\\"Final\\\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\nXArray reads data lazily, i.e. only when our code actually needs it. Up to this point, we haven’t read any data values, only metadata. The next line will force XArray to read the portions of the source files containing our area of interest. Behind the scenes, the eosdis-zarr-store library is ensuring data is fetched as efficiently as possible.\nNote: This line isn’t strictly necessary, since XArray will automatically read the data we need the first time our code tries to use it, but calling this will make sure that we can read the data multiple times later on without re-fetching anything from the source files.\nThis line will take several seconds to complete, but since it is retrieving only about 50 MB of data from 22 GB of source files, several seconds constitutes a significant time, bandwidth, and disk space savings.\n\nsst.load();\n\nNow we can start looking at aggregations across the time dimension. In this case, plot the standard deviation of the temperature at each point to get a visual sense of how much temperatures fluctuate over the course of the month.\n\n# We expect a warning here, from finding the standard deviation of arrays that contain all N/A values.\n# numpy produces N/A for these points, though, which is exactly what we want.\nstdev_sst = sst.std('time')\nstdev_sst.name = 'stdev of analysed_sst [Kelvin]'\nstdev_sst.plot();\n\n/srv/conda/envs/notebook/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1670: RuntimeWarning: Degrees of freedom <= 0 for slice.\n var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n\n\n\n\n\n\nInteractive animation of a month of data\nThis section isn’t as important to fully understand. It shows us a way to get an interactive animation to see what we have retrieved so far\nDefine an animation function to plot the ith time step. We need to make sure each plot is using the same color scale, set by vmin and vmax so the animation is consistent\n\nsst_min = sst.min()\nsst_max = sst.max()\n\ndef show_time_step(i):\n plt.clf()\n res = sst[i].plot.imshow(vmin=sst_min, vmax=sst_max)\n return (res,)\n\nRender each time slice once and show it as an HTML animation with interactive controls\n\n#anim = animation.FuncAnimation(plt.gcf(), func=show_time_step, frames=len(sst))\n#display(HTML(anim.to_jshtml()))\n#plt.close()\n\n\n\n\nSupplemental: What’s happening here?\nFor EOSDIS data in the cloud, we have begun producing a metadata sidecar file in a format called DMR++ that extracts all of the information about arrays, variables, and dimensions from data files, as well as the byte offsets in the NetCDF4 file where data can be found. This information is sufficient to let the Zarr library read data from our NetCDF4 files, but it’s in the wrong format. zarr-eosdis-store knows how to fetch the sidecar file and transform it into something the Zarr library understands. Passing it when reading Zarr using XArray or the Zarr library lets these libraries interact with EOSDIS data exactly as if they were Zarr stores in a way that’s more optimal for reading data in the cloud. Beyond this, the zarr-eosdis-store library makes some optimizations in the way it reads data to help make up for situations where the NetCDF4 file is not internally arranged well for cloud-based access patterns."
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- "text": "Time Series Comparison\nPlot each dataset for the time period 2011-2019.\nFirst, we need to average all pixels in the subset lat/lon per time for sea surface salinity across both satellites, and sea surface temperature to set up for the graphs. This could take a few minutes each.\n\n#SMAP\ntime_smap = np.arange('2015-04', '2020-01', dtype='datetime64[M]')\nsss_smap_mean = []\nfor t in np.arange(len(ds_SMAP_subset.time)):\n sss_smap_mean.append(np.nanmean(ds_SMAP_subset.smap_sss[t,:,:].values))\n \n#sss_smap_mean\n\n\n#Aquarius\ntime_Aq = np.arange('2011-08', '2015-07', dtype='datetime64[M]')\nsss_Aq_mean =[]\nfor t in np.arange(len(ds_Aq_subset.time)):\n sss_Aq_mean.append(np.nanmean(ds_Aq_subset.SSS[t,:,:].values))\n\n#sss_Aq_mean \n\n\n#MODIS\ntime_MODIS = np.arange('2011-08', '2019-12', dtype='datetime64[M]')\nsst_MODIS_mean = []\nfor t in np.arange(len(ds_MODIS_subset.time)):\n sst_MODIS_mean.append(np.nanmean(ds_MODIS_subset.sst4[t,:,:].values))\n \n#sst_MODIS_mean\n\n\nCombined timeseries plot of river height and LWE thickness\n\n#plot river height and land water equivalent thickness\nfig, ax1 = plt.subplots(figsize=[12,7])\n\n#plot river height\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\n\n#plot LWE thickness on secondary axis\nax2 = ax1.twinx()\nax2.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax1.set_ylabel('River Height (m)', color='darkblue')\nax2.legend(['GRACE-FO'], loc='upper right')\nax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')\n\nplt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')\nax1.grid()\nplt.show()\n\n\n\n\nLWE thickness captures the seasonality of Pre-SWOT MEaSUREs river heights well, and so LWE thickness can be compared to all other variables as a representative of the seasonality of both measurements for the purpose of this notebook.\n\n\nCombined timeseries plots of salinity and LWE thickness, followed by temperature\n\n#Combined Subplots\nfig = plt.figure(figsize=(10,10))\n\nax1 = fig.add_subplot(211)\nplt.title('Amazon Estuary, 2011-2019')\nax2 = ax1.twinx()\nax3 = plt.subplot(212)\n\n#lwe thickness\nax1.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\nax1.set_ylabel('LWE Thickness (cm)', color='darkorange')\nax1.grid()\n\n#sea surface salinity\nax2.plot(time_Aq, sss_Aq_mean, 'g-')\nax2.plot(time_smap, sss_smap_mean, 'g--')\nax2.set_ylabel('SSS (psu)', color='g')\nax2.legend(['Aquarius', 'SMAP'], loc='upper right')\n\n#sea surface temperature\nax3.plot(time_MODIS, sst_MODIS_mean, 'darkred')\nax3.set_ylabel('SST (deg C)', color='darkred')\nax3.grid()\nax3.legend(['MODIS'], loc='upper right')\n\n<matplotlib.legend.Legend at 0x7ff593314d50>\n\n\n\n\n\n\n\nA close-up view of salinity and LWE thickness in 2019\n\n#plot SSS and LWE thickness\n\nfig, ax1 = plt.subplots(figsize=[10,6])\n#plot LWE thickness\nax1.plot(ds_GRACE_subset.time[167:179], ds_GRACE_subset.lwe_thickness[167:179,34,69], color = 'darkorange')\n\n#plot SSS on secondary axis\nax2 = ax1.twinx()\nax2.plot(time_smap[45:], sss_smap_mean[45:], 'g-') # 45:\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Sea Surface Salinity (psu)', color='g')\nax1.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax2.legend(['SMAP'], loc='upper left')\nax1.legend(['GRACE-FO'], loc='lower left')\nplt.title('Measurements Near the Amazon Estuary for 2019')\nax1.grid()\nplt.show()\n\n\n\n\nFor the 2019 year, measurements of LWE thickness and SSS follow expected patterns. When lwe thickness is at its peak, indicating a large amount of water in the river from the wet season between March and June, SSS is at its lowest. The high volume of water from the river output into the estuary decreases the salinity. Points on the graph do not line up exactly month by month because GRACE-FO has specific dates for their monthly dataset whereas SMAP’s monthly dataset is calculated via averaging multiple measurements over the course of the month, so it does not have a specific day, but only a specific month."
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+ "text": "The PO.DAAC Data downloader is a python-based tool for bulk and one-off (or non-often) downloading of data from the PO.DAAC archive. Use this script if you want to download data based on a space or time every once and a while.\nFor installation and dependency information, please see the top-level README.\n$> podaac-data-downloader -h\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--limit LIMIT] [--dry-run]\n\noptional arguments:\n -h, --help show this help message and exit\n -c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n -d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\n --cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles\n -sd STARTDATE, --start-date STARTDATE\n The ISO date time after which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n -ed ENDDATE, --end-date ENDDATE\n The ISO date time before which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n -f, --force Flag to force downloading files that are listed in CMR query, even if the file exists and checksum matches\n -b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from the command line.\n Default: \"-180,-90,180,90\".\n -dc Flag to use cycle number for directory where data products will be downloaded.\n -dydoy Flag to use start time (Year/DOY) of downloaded data for directory where data products will be downloaded.\n -dymd Flag to use start time (Year/Month/Day) of downloaded data for directory where data products will be downloaded.\n -dy Flag to use start time (Year) of downloaded data for directory where data products will be downloaded.\n --offset OFFSET Flag used to shift timestamp. Units are in hours, e.g. 10 or -10.\n -e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\n -gr GRANULENAME, --granule-name GRANULENAME\n Flag to download specific granule from a collection. This parameter can only be used if you know the granule name. Only one granule name can be supplied\n --process PROCESS_CMD\n Processing command to run on each downloaded file (e.g., compression). Can be specified multiple times.\n --version Display script version information and exit.\n --verbose Verbose mode.\n -p PROVIDER, --provider PROVIDER\n Specify a provider for collection search. Default is POCLOUD.\n --limit LIMIT Integer limit for number of granules to download. Useful in testing. Defaults to no limit.\n --dry-run Search and identify files to download, but do not actually download them\n\n\n\nUsage:\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose]\n [-p PROVIDER] [--limit LIMIT] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n--cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --cycle must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the downloader.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nUnlike the ‘subscriber’, no ‘state’ is maintained for the downloader. if you re-run the downloader you’ll re-download all of the files again, unlike the subscriber which will download newly ingested data since the last run.\n\n\n\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token\n\n\n\n\n\nIf you’re aware of a file you want to download, you can use the -gr option to download by a filename. The -c (COLLECTION) and -d (directory) options are still required.\nThe -gr option works by taking the file name, removing the suffix and searching for a CMR entry called the granuleUR. Some examples of this include:\n\n\n\n\n\n\n\n\nCollection\nFilename\nCMR GranuleUR\n\n\n\n\nMUR25-JPL-L4-GLOB-v04.2\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2\n\n\nJASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07.nc\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07\n\n\n\nBecause of this behavior, granules without data suffixes and granules where the the UR does not directly follow this convention may not work as anticipated. We will be adding the ability to download by granuleUR in a future enhancement.\n\n\n\nSome PO.DAAC datasets are better suited for cycles based search instead of start and end times. To enabled this, we’ve added ‘cycle’ based downloading to the data-downloader. The following example will download data from cycle 42:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42\nThe cycle parameter can be repeated to specify multiple cycles:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42 --cycle 43 --cycle 44\n\n\n\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z -ed 2014-07-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the downloader is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the downloader will skip downloading that file.\nThis can drastically reduce the time for the downloader to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the downloader to always download matching files, by using –force/-f.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -f\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-downloader -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\" -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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- "text": "More Visualizing Data on the Map\n\nTimelapses\nTo visualize GRACE and SMAP data, timelapses have been created for the year 2019:\n\n########################### Defining needed functions ###########################\n\n#create map for specific timestep function\ndef setup_map(ax, map, ds_subset, x, y, var, t, cmap, levels, title, extent):\n map.set_title(title, fontsize=14)\n map.coastlines()\n map.set_extent(extent)\n map.add_feature(cartopy.feature.RIVERS)\n variable_desired = var[t,:,:]\n title = str(pd.to_datetime(ds_subset.time[t].values))\n cont = map.contourf(x, y, variable_desired, cmap=cmap, levels=levels, zorder=1)\n return cont\n\n#create animation function for all timesteps, outlines what needs to change\ndef animate_ts(framenumber, ax, map, ds_subset, x, y, var, t, cmap, levels, title, extent):\n ax.clear()\n # change to next timestep\n t = t + framenumber\n title = str(pd.to_datetime(ds_subset.time[t].values))\n cont = setup_map(ax, map, ds_subset, x, y, var, t, cmap, levels, title, extent) \n return cont\n\n##################################################################################\n\n\nfig = plt.figure(figsize=[13,9]) \nax = fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)\nmap = plt.axes(projection=ccrs.PlateCarree())\n\n#Necessary Variables for functions\nextent = [-85, -30, -16, 11] #lat/lon extents of map\nx,y = np.meshgrid(ds_GRACE_subset.lon, ds_GRACE_subset.lat) #x, y lat/lon values for functions \nlevels = np.linspace(-100., 100., 14) #number of levels for color differentiation\ncmap='bwr_r' #blue white red color scheme\nt=168 #time to start with\nvar = ds_GRACE_subset.lwe_thickness #variable we will be subsetting from the GRACE-FO data\ntitle = str(pd.to_datetime(ds_GRACE_subset.time[t].values)) #Time of specific time step\n\n#Set up first time step\ncont = setup_map(ax, map, ds_GRACE_subset, x, y, var, t, cmap, levels, title, extent) \n\n#Make a color bar\nfig.colorbar(cont, cmap=cmap, boundaries=levels, ticks=levels, \n orientation='horizontal', label='Land Water Equivalent Thickness (cm)')\n\n#Create animation for 2019\nani = animation.FuncAnimation(fig, animate_ts, frames=range(0,12),\n fargs=(ax, map, ds_GRACE_subset, x, y, var, t, cmap, levels, title, extent), interval=500)\n\n#Will need to install 'ffmpeg' in the cmd prompt to save the .mpg (ie. conda install -c conda-forge ffmpeg)\nani.save(\"GRACE-FO_animation.mp4\", writer=animation.FFMpegWriter())\n\nHTML(ani.to_html5_video())\n\n\n \n Your browser does not support the video tag.\n\n\n\n\n\n\n\n\nSMAP Timelapse\n\n#A new figure window\nfig = plt.figure(figsize=[10,8]) \nax = fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)\nmap = plt.axes(projection=ccrs.PlateCarree())\n\n#Necessary Variables for functions\nextent = [-52, -43, -2, 6] #lat/lon extents of map\nx,y = np.meshgrid(ds_SMAP_subset.longitude, ds_SMAP_subset.latitude) #x, y lat/lon values for functions \nlevels = np.linspace(0., 45., 10) #number of levels for color differentiation\ncmap='viridis' #color scheme\nt=0 #time to start with\nvar = ds_SMAP_subset.smap_sss #variable we will be subsetting from the GRACE-FO data\ntitle = str(pd.to_datetime(ds_SMAP_subset.time[t].values)) #Time of specific time step\n\n#Set up first time step\ncont = setup_map(ax, map, ds_SMAP_subset, x, y, var, t, cmap, levels, title, extent) \n\n#Make a color bar\nfig.colorbar(cont, cmap=cmap, boundaries=levels, ticks=levels, \n orientation='horizontal', label='Sea Surface Salinity (psu)')\n\n#Create animation for the 2019 year (change the frame range for different time periods)\nani = animation.FuncAnimation(fig, animate_ts, frames=range(45,57),\n fargs=(ax, map, ds_SMAP_subset, x, y, var, t, cmap, levels, title, extent), interval=400)\n\n#Will need to install 'ffmpeg' in the cmd prompt to save the .mpg (ie. conda install -c conda-forge ffmpeg)\nani.save(\"SMAP_animation.mp4\", writer=animation.FFMpegWriter())\n\nHTML(ani.to_html5_video())\n\n\n \n Your browser does not support the video tag."
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+ "text": "Usage:\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose]\n [-p PROVIDER] [--limit LIMIT] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n--cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --cycle must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the downloader.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nUnlike the ‘subscriber’, no ‘state’ is maintained for the downloader. if you re-run the downloader you’ll re-download all of the files again, unlike the subscriber which will download newly ingested data since the last run."
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- "text": "Future Modifications\nThis is not a static notebook and can be altered as more cloud data and services become available.\nIn the future, when the upcoming Surface Water and Ocean Topography (SWOT) satellite has been launched, data products for discharge can be added to analyze the impact discharge specifically has on the coastal environment.\nAll these datasets will be able to be accessed through the cloud in the future; OPeNDAP will have a cloud interface. Check back on PO.DAAC’s Cloud Data page for updates."
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+ "text": "The netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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+ "title": "Data Downloader: Bulk or one-time Scripted Access to PODAAC data",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis Jupyter Notebook contains examples related to querying river reaches (segments) using the SWOT River Database (SWORD) and visualizing results and then querying related datasets for the identified spatial extent through NASA’s Common Metadata Repository (CMR) search.\nExample Use Case: In this example, we geospatially search a single river reach, multiple reaches, and river nodes within the database. We then use geospatial coordinates of the features (here river reaches/node along the Kasai River, a tributary of the Congo River in Africa) to query against a dataset in CMR, namely Pre SWOT Hydrology.\nNote: PO.DAAC is in the process of publishing SWOT sample data to the POCLOUD archive (expected June 2022). Once this is complete, the example below will be updated to work with this sample data as well. SWOT is expected to launch Nov 2022.\nResources - SWOT River Database (SWORD) data can be found here: https://zenodo.org/record/4917236#.YTKLPd9lCST - Other SWOT SWORD documentation can be found here: https://swot.jpl.nasa.gov/documents/4031/ - MEaSUREs - Pre-Surface Water and Ocean Topography (Pre-SWOT) Hydrology data can be found here: https://podaac.jpl.nasa.gov/MEaSUREs-Pre-SWOT?sections=data"
+ "text": "If you’re aware of a file you want to download, you can use the -gr option to download by a filename. The -c (COLLECTION) and -d (directory) options are still required.\nThe -gr option works by taking the file name, removing the suffix and searching for a CMR entry called the granuleUR. Some examples of this include:\n\n\n\n\n\n\n\n\nCollection\nFilename\nCMR GranuleUR\n\n\n\n\nMUR25-JPL-L4-GLOB-v04.2\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2\n\n\nJASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07.nc\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07\n\n\n\nBecause of this behavior, granules without data suffixes and granules where the the UR does not directly follow this convention may not work as anticipated. We will be adding the ability to download by granuleUR in a future enhancement.\n\n\n\nSome PO.DAAC datasets are better suited for cycles based search instead of start and end times. To enabled this, we’ve added ‘cycle’ based downloading to the data-downloader. The following example will download data from cycle 42:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42\nThe cycle parameter can be repeated to specify multiple cycles:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42 --cycle 43 --cycle 44\n\n\n\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z -ed 2014-07-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the downloader is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the downloader will skip downloading that file.\nThis can drastically reduce the time for the downloader to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the downloader to always download matching files, by using –force/-f.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -f\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-downloader -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\" -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
},
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- "href": "notebooks/SWORD_River_Demo.html#query-cmr-by-coordinates",
- "title": "SWORD River Demo",
- "section": "Query CMR by Coordinates",
- "text": "Query CMR by Coordinates\nWe can use results obtained from the FTS query to then directly and automatically query data using CMR. We will use the coordinate information of a single reach to search for granules (files) available through the Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2 data, which has the collection ID C2036882359-POCLOUD.\nWe query FTS using the previously used reach ID of 13227000061 over Kasai River, a tributary of the Congo River in Africa.\n\nresponse = requests.get(\"https://fts.podaac.earthdata.nasa.gov/rivers/reach/13227000061\")\nfeatureCollection = response_to_FeatureCollection(response)\n\npprint.pprint(response.json(), compact=True, width=60, depth=2)\n\n{'hits': 1,\n 'results': {'13227000061': {...}},\n 'search on': {'exact': False,\n 'page_number': 1,\n 'page_size': 100,\n 'parameter': 'reach'},\n 'status': '200 OK',\n 'time': '5.389 ms.'}\n\n\nThe next cell queries CMR using the coordinates of the reach. Note that coordinates should be listed in the format lon1, lat1, lon2, lat2, lon3, lat3, and so on. The CMR json response proivides a link to the data file (granule) from the Pre SWOT Hydroology GRRATS Daily River Heights data collection that overlaps the geospatial search from FTS-SWORD for the river reaches of interest, e.g. \"href\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc\"\n\nCOLLECTION_ID = \"C2036882359-POCLOUD\" # Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2\n\n# derive lon,lat bounds of nodes along the reach\nlats = [xy[1] for feature in featureCollection['features'] for xy in feature['coordinates']]\nlons = [xy[0] for feature in featureCollection['features'] for xy in feature['coordinates']]\n\n# find max and min of lat and lon\nmaxlat, maxlon, minlat, minlon = max(lats), max(lons), min(lats), min(lons)\n\n# create one single list of lon,lat coordinates that creates a bounding box of extent\ncoord_list = [maxlon, maxlat, maxlon, minlat, minlon, maxlat, minlon, minlat]\n\n# create a string of the list to input into CMR\ncoord_list_string = str(coord_list)[1:-1]\nlonlat_bbox = coord_list_string.replace(\" \", \"\")\n\n# query CMR\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?line={}&echo_collection_id={}&pretty=True\".format(lonlat_bbox, COLLECTION_ID))\n\n# Print out results\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2022-06-08T14:11:18.434Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?line=21.005094,-4.611049,21.005094,-4.633254,20.917191,-4.611049,20.917191,-4.633254&echo_collection_id=C2036882359-POCLOUD&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"boxes\": [\n \"-6.013 12.708 2.185 25.948\"\n ],\n \"time_start\": \"1992-05-01T20:48:54.000Z\",\n \"updated\": \"2022-03-11T19:11:49.948Z\",\n \"dataset_id\": \"Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2\",\n \"data_center\": \"POCLOUD\",\n \"title\": \"Africa_Congo1kmdaily\",\n \"coordinate_system\": \"CARTESIAN\",\n \"day_night_flag\": \"UNSPECIFIED\",\n \"time_end\": \"2018-04-19T22:01:11.000Z\",\n \"id\": \"G2105958909-POCLOUD\",\n \"original_format\": \"UMM_JSON\",\n \"granule_size\": \"5.435943603515625E-5\",\n \"browse_flag\": false,\n \"collection_concept_id\": \"C2036882359-POCLOUD\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"title\": \"Download Africa_Congo1kmdaily.nc\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/s3#\",\n \"title\": \"This link provides direct download access via S3 to the granule\",\n \"hreflang\": \"en-US\",\n \"href\": \"s3://podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"title\": \"Download Africa_Congo1kmdaily.nc.md5\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc.md5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"title\": \"This link provides direct download access via S3 to the granule\",\n \"hreflang\": \"en-US\",\n \"href\": \"s3://podaac-ops-cumulus-public/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc.md5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"title\": \"api endpoint to retrieve temporary credentials valid for same-region direct s3 access\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://archive.podaac.earthdata.nasa.gov/s3credentials\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/service#\",\n \"title\": \"OPeNDAP request URL\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://opendap.earthdata.nasa.gov/collections/C2036882359-POCLOUD/granules/Africa_Congo1kmdaily\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://podaac-tools.jpl.nasa.gov/drive/files/allData/preswot_hydrology/L2/rivers/docs/GRRATS_user_handbookV2.pdf\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://github.com/podaac/data-readers\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://podaac.jpl.nasa.gov/CitingPODAAC\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0MB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C2036882359-POCLOUD\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0MB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C2036882359-POCLOUD\"\n }\n ]\n }\n ]\n }\n}\n\n\nFrom all that was printed out above, we want to hone in specifically on the granule file from the Pre-SWOT MEaSUREs dataset that gives us the data over our desired region.\n\ngranule = cmr_response.json()['feed']['entry'][0]['id']\ngranule\n\n'G2105958909-POCLOUD'\n\n\nIf we want to direct download to our local machine, we want the link with the title Download Africa_Congo1kmdaily.nc. If we want to directly access this granule in the cloud, we want the link entitled, This link provides direct download access via S3 to the granule. Here, we access the netCDF file links and print them out respectively. From here, you’re ready to access the data either locally or via cloud direct access!\nIt should be noted that the links are not always in the same order across datasets (collections), and thus referencing other datasets with the ‘[0]’ and ‘[1]’ indexes may not work for the download and s3 links respectively. For direct download, the link should always start with “https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/”, and for direct cloud access to PO.DAAC data, the link should start with “s3://podaac-ops-cumulus-protected/” no matter the dataset.\n\ngranule_download_link = cmr_response.json()['feed']['entry'][0]['links'][0]['href']\ngranule_cloud_s3_link = cmr_response.json()['feed']['entry'][0]['links'][1]['href']\ngranule_download_link, granule_cloud_s3_link\n\n('https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc',\n 's3://podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/Africa_Congo1kmdaily.nc')\n\n\n(Note: the cell above just prints the links of interest for either downloading the file, or accessing from the cloud. It didn’t yet download or access the data. That would be your next step.)"
+ "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html",
+ "href": "external/Direct_Access_SWOT_sim_Oceanography.html",
+ "title": "Access Sample SWOT Oceanography Data in the Cloud",
+ "section": "",
+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SWOT-OCEAN-Cloud-Workshop"
+ },
+ {
+ "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#getting-started",
+ "href": "external/Direct_Access_SWOT_sim_Oceanography.html#getting-started",
+ "title": "Access Sample SWOT Oceanography Data in the Cloud",
+ "section": "Getting Started",
+ "text": "Getting Started\nIn this notebook will show direct access of PO.DAAC archived products in the Earthdata Cloud in AWS Simple Storage Service (S3). In this demo, we will showcase the usage of SWOT Simulated Level-2 KaRIn SSH from GLORYS for Science Version 1. More information on the datasets can be found at https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1.\nWe will access the data from inside the AWS cloud (us-west-2 region, specifically) and load a time series made of multiple netCDF files into a single xarray dataset. This approach leverages S3 native protocols for efficient access to the data.\nIn the future, if you want to use this notebook as a reference, please note that we are not doing collection discovery here - we assume the collection of interest has been determined.\n\nRequirements\nThis can run in the Small openscapes instance, that is, it only needs 8GB of memory and ~2 CPU.\nIf you want to run this in your own AWS account, you can use a t2.large instance, which also has 2 CPU and 8GB memory. It’s improtant to note that all instances using direct S3 access to PO.DAAC or Earthdata data are required to run in us-west-2, or the Oregon region.\nThis instance will cost approximately $0.0832 per hour. The entire demo can run in considerably less time.\n\n\nImports\nMost of these imports are from the Python standard library. However, you will need to install these packages into your Python 3 environment if you have not already done so:\nboto3\ns3fs\nxarray\nmatplotlib\ncartopy"
+ },
+ {
+ "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#learning-objectives",
+ "href": "external/Direct_Access_SWOT_sim_Oceanography.html#learning-objectives",
+ "title": "Access Sample SWOT Oceanography Data in the Cloud",
+ "section": "Learning Objectives",
+ "text": "Learning Objectives\n\nimport needed libraries\nauthenticate for NASA Earthdata archive (Earthdata Login) (here this takes place as part of obtaining the AWS credentials step)\nobtain AWS credentials for Earthdata DAAC archive in AWS S3\naccess DAAC data by downloading directly into your cloud workspace from S3 within US-west 2 and operating on those files.\naccess DAAC data directly from the in-region S3 bucket without moving or downloading any files to your local (cloud) workspace\nplot the first time step in the data\n\nNote: no files are being donwloaded off the cloud, rather, we are working with the data in the AWS cloud.\n\nimport boto3\nimport json\nimport xarray as xr\nimport s3fs\nimport os\nimport requests\nimport cartopy.crs as ccrs\nfrom matplotlib import pyplot as plt\nfrom os import path\n%matplotlib inline"
+ },
+ {
+ "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#get-a-temporary-aws-access-key-based-on-your-earthdata-login-user-id",
+ "href": "external/Direct_Access_SWOT_sim_Oceanography.html#get-a-temporary-aws-access-key-based-on-your-earthdata-login-user-id",
+ "title": "Access Sample SWOT Oceanography Data in the Cloud",
+ "section": "Get a temporary AWS Access Key based on your Earthdata Login user ID",
+ "text": "Get a temporary AWS Access Key based on your Earthdata Login user ID\nDirect S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects (i.e. data) from applicable Earthdata Cloud buckets (storage space). For now, each NASA DAAC has different AWS credentials endpoints. Below are some of the credential endpoints to various DAACs.\nThe below methods (get_temp_dreds) requires the user to have a ‘netrc’ file in the users home directory.\n\ns3_cred_endpoint = {\n 'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'gesdisc': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',\n 'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\n 'ornldaac': 'https://data.ornldaac.earthdata.nasa.gov/s3credentials',\n 'ghrcdaac': 'https://data.ghrc.earthdata.nasa.gov/s3credentials'\n}\n\ndef get_temp_creds(provider):\n return requests.get(s3_cred_endpoint[provider]).json()\n\nWe will now get a credential for the ‘PO.DAAC’ provider and set up our environment to use those values.\nNOTE if you see an error like ‘HTTP Basic: Access denied.’ It means the username/password you’ve entered is incorrect.\nNOTE2 If you get what looks like a long HTML page in your error message (e.g. \n\n…), the right netrc ‘machine’ might be missing.\n\ncreds = get_temp_creds('podaac')\n\nos.environ[\"AWS_ACCESS_KEY_ID\"] = creds[\"accessKeyId\"]\nos.environ[\"AWS_SECRET_ACCESS_KEY\"] = creds[\"secretAccessKey\"]\nos.environ[\"AWS_SESSION_TOKEN\"] = creds[\"sessionToken\"]\n\ns3 = s3fs.S3FileSystem(anon=False)"
+ },
+ {
+ "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#location-of-data-in-the-po.daac-s3-archive",
+ "href": "external/Direct_Access_SWOT_sim_Oceanography.html#location-of-data-in-the-po.daac-s3-archive",
+ "title": "Access Sample SWOT Oceanography Data in the Cloud",
+ "section": "Location of data in the PO.DAAC S3 Archive",
+ "text": "Location of data in the PO.DAAC S3 Archive\nWe need to determine the path for our products of interest. We can do this through several mechanisms. Those are described in the Finding_collection_concept_ids.ipynb notebook, or the Pre-Workshop material, https://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/prerequisites/01_Earthdata_Search.html.\nAfter using the Finding_collection_concept_ids.ipynb guide to find our S3 location, we end up with:\n{\n ...\n \"DirectDistributionInformation\": {\n \"Region\": \"us-west-2\",\n \"S3BucketAndObjectPrefixNames\": [\n \"podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/\",\n \"podaac-ops-cumulus-public/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/\"\n ],\n \"S3CredentialsAPIEndpoint\": \"https://archive.podaac.earthdata.nasa.gov/s3credentials\",\n \"S3CredentialsAPIDocumentationURL\": \"https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME\"\n },\n ...\n}"
+ },
+ {
+ "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#now-that-we-have-the-s3-bucket-location-for-the-data-of-interest",
+ "href": "external/Direct_Access_SWOT_sim_Oceanography.html#now-that-we-have-the-s3-bucket-location-for-the-data-of-interest",
+ "title": "Access Sample SWOT Oceanography Data in the Cloud",
+ "section": "Now that we have the S3 bucket location for the data of interest…",
+ "text": "Now that we have the S3 bucket location for the data of interest…\nIt’s time to find our data! Below we are using a glob to find file names matching a pattern. Here, we want any files matching the pattern used below; here this equates, in science, terms, to Cycle 001 and the first 10 passes. This information can be gleaned from product description documents. Another way of finding specific data files would be to search on cycle/pass from CMR or Earthdata Search GUI and use the S3 links provided in the resulting metadata or access links, respectively, directly instead of doing a glob (essentially an ‘ls’).\nThe files we are looking at are about 11-13 MB each. So the 10 we’re looking to access are about ~100 MB total.\n\ns3path = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_00*.nc'\nremote_files = s3.glob(s3path)\n\n\nremote_files\n\n['podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_001_20140412T120000_20140412T125126_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_002_20140412T125126_20140412T134253_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_003_20140412T134253_20140412T143420_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_004_20140412T143420_20140412T152546_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_005_20140412T152547_20140412T161713_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_006_20140412T161714_20140412T170840_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_007_20140412T170840_20140412T180007_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_008_20140412T180008_20140412T185134_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_009_20140412T185134_20140412T194301_DG10_01.nc']"
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+ "section": "A final word…",
+ "text": "A final word…\nAccessing data completely from S3 and in memory are affected by various things.\n\nThe format of the data - archive formats like NetCDF, GEOTIFF, HDF vs cloud optimized data structures (Zarr, kerchunk, COG). Cloud formats are made for accessing only the pieces of data of interest needed at the time of the request (e.g. a subset, timestep, etc).\nThe internal structure of the data. Tools like xarray make a lot of assumptions about how to open and read a file. Sometimes the internals don’t fit the xarray ‘mould’ and we need to continue to work with data providers and software providers to make these two sides work together. Level 2 data (non-gridded), specifically, suffers from some of the assumptions made."
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+ "title": "Access to ECCO V4r4 Datasets on a Local Machine",
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+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.\nDuped+slightly modified version of the s3 access ipynb. Tested on JPL-issued macbook and my linux box. It starts by setting up a most trusted strategy for batch downloads behind URS ussing curl/wget. Will attempt to add line(s) to your netrc file if needed btw; then it writes your urs cookies to a local file that should effectively “pre-authenticate” future download sessions for those sub domains."
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+ "title": "Access to ECCO V4r4 Datasets on a Local Machine",
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+ "text": "Quick Start\nA key takeaway in this notebook… follow these instructions on the Earthdata Wiki to authenticate and store your URS cookies in a local file. You can batch download really efficiently this way, effectively “pre-authenticated” through your previous session.\nAvoid lines that begin with % or %% in code cells. Those are IPython “magic functions” that tell a line or cell to evaluate in some special mode like by bash instead of py3.\n\nConfigure your .netrc file\nGood idea to back up your existing netrc file, if you have one. And while youre at it check for these entries because they might exist in there already:\n\n%cp ~/.netrc ~/bak.netrc\n\n%cat ~/.netrc | grep '.earthdata.nasa.gov' | cut -f-5 -d\" \"\n\nmachine urs.earthdata.nasa.gov login jmcnelis password\nmachine uat.urs.earthdata.nasa.gov login jmcnelis password\nmachine opendap.earthdata.nasa.gov login jmcnelis password\n\n\n\nAdd entries to your netrc for these two earthdata.nasa.gov sub domains, at a minimum:\nmachine urs.earthdata.nasa.gov login jmcnelis password ***\nmachine opendap.earthdata.nasa.gov login jmcnelis password ***\nand replace jmcnelis and *** with your Earthdata Login username and password, respectively…\n\nReplace jmcnelis and *** with your Earthdata username and password, and then run the cell to append these two lines to your netrc file, if one exists. Otherwise write them to a new one. (all set up by -a)\n\n%%file -a ~/.netrc\nmachine urs.earthdata.nasa.gov login jmcnelis password ***\nmachine opendap.earthdata.nasa.gov login jmcnelis password ***\n\nAppending to /Users/jmcnelis/.netrc\n\n\nDump the netrc again sans passwords to confirm that it worked:\n\n!cat ~/.netrc | grep '.earthdata.nasa.gov' | cut -f-5 -d\" \"\n\nmachine urs.earthdata.nasa.gov login jmcnelis password\nmachine uat.urs.earthdata.nasa.gov login jmcnelis password\nmachine opendap.earthdata.nasa.gov login jmcnelis password\nmachine urs.earthdata.nasa.gov login jmcnelis password\nmachine opendap.earthdata.nasa.gov login jmcnelis password\n\n\nFinally, you need to make sure to limit access to the netrc file because it stores your plain text password. Simple on MacOS and Linux:\n\n!chmod 0600 ~/.netrc\n\nNo outputs expected.\n\n\nDownload a sample data file, get your URS cookies, and write them to a local file\nNow I’ll download a random file that’s protected by URS/Earthdata Login authentication so that I can grab my URS cookies.\nI chose to download a file containing ECCO grid geometries for the 0.5-degree latitude/longitude grid. It’s small and it may prove useful for downstream analysis of the SSH data. (Again, any protected data file will work.)\n\n%%bash\nwget --no-verbose \\\n --no-clobber \\\n --load-cookies ~/.urs_cookies \\\n --save-cookies ~/.urs_cookies \\\n --keep-session-cookies \\\n https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_GEOMETRY_05DEG_V4R4/GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg.nc\n\nI used –quiet mode so wget would not dump tons of garbage into the notebook. Confirm that the cookies file exists in your home directory:\n\n!ls ~/.urs_cookies\n\n/Users/jmcnelis/.urs_cookies\n\n\nAnd see if the file downloaded successfully to double-confirm it worked as expected (and be aware that the ncdump output is truncated to the first 50 lines):\n\n!ncdump -h GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg.nc | head -25\n\nnetcdf GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg {\ndimensions:\n Z = 50 ;\n latitude = 360 ;\n longitude = 720 ;\n nv = 2 ;\nvariables:\n float Z(Z) ;\n Z:axis = \"Z\" ;\n Z:bounds = \"Z_bnds\" ;\n Z:comment = \"Non-uniform vertical spacing.\" ;\n Z:coverage_content_type = \"coordinate\" ;\n Z:long_name = \"depth of grid cell center\" ;\n Z:positive = \"up\" ;\n Z:standard_name = \"depth\" ;\n Z:units = \"m\" ;\n float latitude(latitude) ;\n latitude:axis = \"Y\" ;\n latitude:bounds = \"latitude_bnds\" ;\n latitude:comment = \"uniform grid spacing from -89.75 to 89.75 by 0.5\" ;\n latitude:coverage_content_type = \"coordinate\" ;\n latitude:long_name = \"latitude at grid cell center\" ;\n latitude:standard_name = \"latitude\" ;\n latitude:units = \"degrees_north\" ;\n float longitude(longitude) ;\n\n\nAnd that’s it! You should now be able to use wget and curl to download URS-protected data from PODAAC cloud without providing your creds each time.\n\n\nPrepare a list of files to download\nNow the only step that remains is to get a list of URLs to pass to wget or curl for downloading. There’s a lot of ways to do this – even more so for ECCO V4r4 data because the files/datasets follow well-structured naming conventions – but we will rely on Earthdata Search to do this from the browser for the sake of simplicity.\n1. Find the collection/dataset of interest in Earthdata Search.\nStart from this complete list of ECCO collections in Earthdata Search (79 in total), and refine the results until you see your dataset of interest. In this case we want monthly sea surface height grids provided at 0.5-degree cell resolution on an interpolated latitude/longitude grid.\n2. Pick your collection, then click the green Download All button on the next page.\nClick the big green button identified by the red arrow/box in the screenshot below.\n\nThat will add all the granules in the collection to your “shopping cart” and then redirect you straight there and present you with the available options for customizing the data prior to download. We will ignore those because they’re mostly in active development and because we want to download all data in the collection.\n\n\nThe screenshot above shows the download customization interface (i.e. “shopping cart”)\n\n3. Click Download Data to get your list of download urls (bottom-left, another green button)\nThe Download Data button takes you to one final page that provides the list of urls from which to download the files matching your search parameters and any customization options that you selected in the steps that followed. This page will be retained in your User History in case you need to return to it later.\n\nThere are several ways that you could get the list of urls into a text file that’s accessible from Jupyter or your local shell. I simply clicked the save button in my browser and downloaded them as a text file to a subdirectory called resources inside this workspace. (You could also copy them into a new notebook cell and write them to a file like we did with the netrc file above.)\n\n\nDownload files in a batch with GNU Wget\nI find wget options to be convenient and easy to remember. There are only a handful that I use with any regularity.\nThe most important wget option for our purpose is set by the -i argument, which takes a path to the input text file containing our download urls. Another nice feature of wget is the ability to continue downloads where you left of during a previously-interuppted download session. That option is turned on by passing the -c argument.\nNow run wget against a list of files retrieve from Earthdata Search and see what happens.\n\n!ls resources/*.txt\n\nresources/5237392644-download.txt\n\n\nGo ahead and create a data/ directory to keep the downloaded files, and then start the downloads into that location by including the -P argument:\n\n%%bash\n\nmkdir -p data\n\nwget --no-verbose \\\n --no-clobber \\\n --continue \\\n -i resources/5237392644-download.txt -P data/\n\nWait a long time if you have to… then count the number of netCDF files in the data directory:\n\n!ls -1 data/*.nc | wc -l\n\n 312\n\n\nAdd a folder for outputs, if needed:\n\n!mkdir -p outputs/\n\nGet a list of netCDF files at the data directory and print the count + the first filename in the list:\n\nfiles = !ls data/*.nc\n\nlen(files), files[0]\n\n(312, 'data/SEA_SURFACE_HEIGHT_mon_mean_1992-01_ECCO_V4r4_latlon_0p50deg.nc')\n\n\nThis is the first time you’ll need any Python in this notebook… Install Python 3 requirements (I am on version 3.8.)\n\n#!conda install -c conda-forge numpy dask netCDF4 xarray, cartopy, ffmpeg\nimport xarray as xr\nimport matplotlib.animation as animation\nimport matplotlib.pyplot as plt\nimport cartopy.feature as cfeat\nimport cartopy.crs as ccrs\nimport cartopy\n\nOpen the netCDFs as one multi-file dataset with xarray:\n\nds = xr.open_mfdataset(\n paths=files,\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks={'latitude': 360, 'longitude': 720, 'time': 5}\n)\n\nssh = ds.SSH\n\nprint(ssh)\n\n<xarray.DataArray 'SSH' (time: 312, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(312, 360, 720), dtype=float32, chunksize=(1, 360, 720), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 1992-01-16T18:00:00 ... 2017-12-16T06:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: -1.8805772066116333\n valid_max: 1.4207719564437866\n\n\n\n\nPlot gridded sea surface height time series\nBut only the timesteps beginning in 2015:\n\nssh_after_201x = ssh[ssh['time.year']>=2015,:,:]\nprint(ssh_after_201x)\n\n<xarray.DataArray 'SSH' (time: 36, latitude: 360, longitude: 720)>\ndask.array<getitem, shape=(36, 360, 720), dtype=float32, chunksize=(1, 360, 720), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-16T12:00:00 ... 2017-12-16T06:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: -1.8805772066116333\n valid_max: 1.4207719564437866\n\n\nPlot the grid for the first time step using a Robinson projection. Define a helper function for consistency throughout the notebook:\n\ndef make_figure(proj):\n fig = plt.figure(figsize=(16,6))\n ax = fig.add_subplot(1, 1, 1, projection=proj)\n ax.add_feature(cfeat.LAND)\n ax.add_feature(cfeat.OCEAN)\n ax.add_feature(cfeat.COASTLINE)\n ax.add_feature(cfeat.BORDERS, linestyle='dotted')\n return fig, ax\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\nssh_after_201x.isel(time=0).plot(ax=ax, transform=ccrs.PlateCarree(), cmap='Spectral_r')\n\n<cartopy.mpl.geocollection.GeoQuadMesh at 0x1a344a0a0>\n\n\n\n\n\nNow plot the whole time series (post-2010) in an animation and write it to an mp4 file called ecco_monthly_ssh_grid_2015_to_x.mp4:\n\ndef get_animation(var, cmap: str=\"Spectral_r\"):\n \"\"\"Get time series animation for input xarray dataset\"\"\"\n\n def draw_map(i: int, add_colorbar: bool):\n data = var[i]\n m = data.plot(ax=ax, \n transform=ccrs.PlateCarree(),\n add_colorbar=add_colorbar,\n vmin=var.valid_min, \n vmax=var.valid_max,\n cmap=cmap)\n plt.title(str(data.time.values)[:7])\n return m\n\n def init():\n return draw_map(0, add_colorbar=True)\n \n def animate(i):\n return draw_map(i, add_colorbar=False)\n\n return init, animate\n\nNow make the animation using the function:\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\ninit, animate = get_animation(ssh_after_201x)\n\nani = animation.FuncAnimation(fig=fig, \n func=animate, \n frames=ssh_after_201x.time.size, \n init_func=init, \n interval=0.2, \n blit=False, \n repeat=False)\n\n# Now save the animation to an MP4 file:\nani.save('outputs/ecco_monthly_ssh_grid_2015_to_x.mp4', writer=animation.FFMpegWriter(fps=8))\n\nplt.close(fig)\n\nRender the animation in the ipynb:\n\n#HTML(ani.to_html5_video())"
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+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\nimport glob\nfrom netCDF4 import Dataset\nimport xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport gsw"
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+ "title": "S-MODE Workshop: Science Case Study In Situ",
+ "section": "Compare saildrone ADCP data with R/V Oceanus data",
+ "text": "Compare saildrone ADCP data with R/V Oceanus data\nA major goal of the S-MODE Pilot was to compare saildrone ADCP data. Here’s one example of a simple saildrone-Oceanus velocity comparison.\n\n# this is not working for the Oceanus ADCP\nstart_time = '2021-10-20T00:00:00Z'\nstart_time = '2021-08-01T00:00:00Z'\nend_time = '2021-11-08T00:00:00Z'\n\nshort_name = 'SMODE_LX_SHIPBOARD_ADCP_V1'\n!podaac-data-downloader -c $short_name -d data/$short_name --start-date $start_time --end-date $end_time -e .nc4\n\n/Users/crocha/miniconda3/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.0.0)/charset_normalizer (2.0.4) doesn't match a supported version!\n warnings.warn(\"urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported \"\n[2022-11-28 19:46:35,848] {podaac_data_downloader.py:243} INFO - Found 2 total files to download\n[2022-11-28 19:46:35,885] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:46:35.885322 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_os75nb.nc4\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:268} INFO - 2022-11-28 19:46:35.967540 SKIPPED: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:287} INFO - Downloaded Files: 0\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:288} INFO - Failed Files: 0\n[2022-11-28 19:46:35,967] {podaac_data_downloader.py:289} INFO - Skipped Files: 2\n[2022-11-28 19:46:36,245] {podaac_access.py:122} INFO - CMR token successfully deleted\n[2022-11-28 19:46:36,245] {podaac_data_downloader.py:299} INFO - END\n\n\n\n\n\nds = xr.open_dataset('data/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4',drop_variables='depth').isel(trajectory=0)\nds['depth'] = Dataset('data/SMODE_LX_SHIPBOARD_ADCP_V1/S-MODE_PFC_OC2108A_adcp_wh300.nc4')['depth'][0]\n\n\nsubset_oceanus = ds.where((ds.time>=t0)&(ds.time<=t1),drop=True) \n\n\n# interpolate to subset72 time\n# TODO: try to resample with averaging to 5 minutes before interpolating (e.g., subset_oceanus.resample(time='5min').mean())\nsubset_oceanus = subset_oceanus.interp({'time': subset72.time})\n\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imin = index.get_loc(minval, method=\"nearest\")\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imax = index.get_loc(maxval, method=\"nearest\")\n\n\n\n# interpolate to saildrone depth to oceanus depth\nsubset72['depth'] = subset72.cell_depth + 1.9 # add depth of the instrument 1.9\nsubset72 = subset72.swap_dims({'cell_depth': 'depth'})\nsubset72 = subset72.interp({'depth': subset_oceanus.depth})\n\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imin = index.get_loc(minval, method=\"nearest\")\n/Users/crocha/miniconda3/lib/python3.9/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n imax = index.get_loc(maxval, method=\"nearest\")\n\n\n\nVisual comparison\n\ndepth_bin = 1\nsc = 6\n\nfig, ax = plt.subplots(figsize=(12,8))\n\nq1 = plt.quiver(\n subset72.longitude,\n subset72.latitude,\n subset72.vel_east.isel(depth=depth_bin),\n subset72.vel_north.isel(depth=depth_bin),\n color='C00',scale=sc\n)\n\n\nq2 = plt.quiver(\n subset_oceanus.longitude,\n subset_oceanus.latitude,\n subset_oceanus.zonal_velocity_component.isel(depth=depth_bin),\n subset_oceanus.meridional_velocity_component.isel(depth=depth_bin),\n color='C02', scale=sc\n)\n\nplt.quiverkey(q1, .1, 0.815, .5, 'SD-1072',)\nplt.quiverkey(q2, .1, 0.620, .5, 'R/V Oceanus',)\n\nax.set_ylim(37.18,37.26)\nax.set_aspect(np.cos(37.22*np.pi/180))\n\n\n\n\n\nfig, axs = plt.subplots(2,1,figsize=(12,14))\n\nkw = {'vmin': -.4,'vmax': +.4,'cmap': 'RdBu_r'}\n\nsubset_oceanus.zonal_velocity_component.plot(x='time',y='depth',ax=axs[0],**kw)\nsubset72.vel_east.plot(x='time',y='depth',ax=axs[1])\n\naxs[0].set_title('R/V Oceanus')\naxs[1].set_title('SD-1072')\n\n[ax.set_ylim(80,0) for ax in axs]\n\n[(80.0, 0.0), (80.0, 0.0)]\n\n\n\n\n\n\n\nQuantitative comparison: calculate and plot velocity differences\n\nsubset_oceanus = subset_oceanus.where(subset_oceanus.depth<=70)\nsubset72 = subset72.where(subset_oceanus.depth<=70)\n\n\ndu = subset_oceanus.zonal_velocity_component-subset72.vel_east\ndv = subset_oceanus.meridional_velocity_component-subset72.vel_north\n\n\n_ = plt.hist(dv.values.flatten(),bins=np.arange(-.15,.156,.01))\n\n\n\n\n\ndu.mean().values, dv.mean().values\n\n(array(-0.01207606), array(0.00744287))\n\n\n\ndu.std().values, dv.std().values\n\n(array(0.03616049), array(0.04748247))"
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+ "title": "S-MODE Workshop: Science Case Study Airborne Part 2",
+ "section": "",
+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\n%load_ext autoreload\n%autoreload 2\nimport sys\nsys.path.append('../src')\nfrom matplotlib import pyplot as plt\n%matplotlib inline\nfrom pathlib import Path\nimport numpy as np\nimport rioxarray\nimport xarray as xr\nfrom plot_dopplerscatt_data import make_streamplot_image\nimport warnings\nwarnings.simplefilter('ignore')"
+ },
+ {
+ "objectID": "external/VisualizeDopplerScattData.html#apply-the-good-data-mask-for-all-current-observations",
+ "href": "external/VisualizeDopplerScattData.html#apply-the-good-data-mask-for-all-current-observations",
+ "title": "S-MODE Workshop: Science Case Study Airborne Part 2",
+ "section": "Apply the good data mask for all current observations",
+ "text": "Apply the good data mask for all current observations\nOnly accept estimates that use a minimum number of observations. The current recommended number is 4. Use the variable nobs_all_lines to make a mask and then mask all variables\n\ndef mask_velocity_all_lines(ds, minobs, data_vars, vthresh=0.1):\n \"\"\"Mask all measurements with fewer than minobs observations.\"\"\"\n bad = ( (ds.nobs_all_lines.data < minobs) |\n (ds.u_current_error_all_lines.data > vthresh) |\n (ds.v_current_error_all_lines.data > vthresh) )\n for v in data_vars:\n if np.issubdtype(ds[v].dtype, np.floating):\n ds[v].data[bad] = np.nan\n return ds\n\n\nminobs = 4\nvthresh =0.1\ndata_vars = [\n 'u_current_all_lines',\n 'v_current_all_lines',\n 'u_current_error_all_lines',\n 'v_current_error_all_lines']\nds_all = mask_velocity_all_lines(ds_all, minobs, data_vars, vthresh)"
},
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@@ -1316,1375 +1267,1424 @@
"text": "About PO.DAAC\nThe Physical Oceanography Distributed Active Archive Center (PO.DAAC) is a NASA Earth Observing System Data and Information System (EOSDIS) data center managed by the Earth Science Data and Information System (ESDIS) Project. The PO.DAAC is operated by Jet Propulsion Laboratory (JPL) in Pasadena, California.\nFor a brief overview of PO.DAAC’s history, see The Evolution of the PO.DAAC: Seasat to SWOT."
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- "href": "notebooks/podaac_cmr_tutorial.html",
- "title": "Introduction to Programmatic Common Metadata Repository Search",
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+ "href": "external/cof-zarr-reformat.html",
+ "title": "COF Zarr Access via Reformat",
"section": "",
- "text": "This notebook will introduce you to programmatic Common Metadata Repository (CMR) search in python, using PO.DAAC Data as the example of data we’re interested in. While these tutorials focus on PO.DAAC data, the same strategies and code snippets can be used for other earthdata collections."
+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
},
{
- "objectID": "notebooks/podaac_cmr_tutorial.html#api-documentation",
- "href": "notebooks/podaac_cmr_tutorial.html#api-documentation",
- "title": "Introduction to Programmatic Common Metadata Repository Search",
- "section": "API Documentation",
- "text": "API Documentation\nThis tutorial is not meant to be a replacement for the official CMR documentation. Its features are well documented and that should be the first place to go for information. It can be found at https://cmr.earthdata.nasa.gov/search. Some users may find it easier to navigate the Earthdata Search interface, find data of interest, and then automate the results using scripts. We’d suggest visiting https://search.earthdata.nasa.gov/"
+ "objectID": "external/cof-zarr-reformat.html#getting-started",
+ "href": "external/cof-zarr-reformat.html#getting-started",
+ "title": "COF Zarr Access via Reformat",
+ "section": "Getting Started",
+ "text": "Getting Started\nWe will access monthly ocean bottom pressure (OBP) data from ECCO V4r4 (10.5067/ECG5M-OBP44), which are provided as a monthly time series on a 0.5-degree latitude/longitude grid.\nThe data are archived in netCDF format. However, this notebook demonstration will request conversion to Zarr format for files covering the period between 2010 and 2018. Upon receiving our request, Harmony’s backend will convert the files and stage them in S3 for native access in AWS (us-west-2 region, specifically). We will access the new Zarr datasets as an aggregated dataset using xarray, and leverage the S3 native protocols for direct access to the data in an efficient manner.\n\n\nRequirements\n\nAWS\nThis notebook should be running in an EC2 instance in AWS region us-west-2, as previously mentioned. We recommend using an EC2 with at least 8GB of memory available.\nThe notebook was developed and tested using a t2.large instance (2 cpus; 8GB memory).\n\n\nPython 3\nMost of these imports are from the Python standard library. However, you will need to install these packages into your Python 3 environment if you have not already done so:\n\ns3fs\nrequests\npandas\nxarray\nmatplotlib\n\n\n\n\nRequirements\n\nimport matplotlib.pyplot as plt\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport requests\nimport json\nimport time\nimport s3fs\n\nShortName = \"ECCO_L4_OBP_05DEG_MONTHLY_V4R4\"\n\n\n\nStudy period\nSet some “master” inputs to define the time and place contexts for our case studies in the ipynb. This example will be requesting time subsets and receiving global data back from the Harmony API.\n\nstart_date = \"2010-01-01\"\nend_date = \"2018-12-31\"\n\n\n\nData Access\nSome features in the Harmony API require us to identify the target dataset/collection by its concept-id (which uniquely idenfifies it among the other datasets in the Common Metadata Repository). Support for selection by the dataset ShortName will be added in a future release.\n\nCommon Metadata Repository (CMR)\nFor now, we will need to get the concept-id that corresponds to our dataset by accessing its metadata from the CMR. Read more about the CMR at: https://cmr.earthdata.nasa.gov/\nRequest the UMM Collection metadata (i.e. metadata about the dataset) from the CMR and select the concept-id as a new variable ccid.\n\nresponse = requests.get(\n url='https://cmr.earthdata.nasa.gov/search/collections.umm_json', \n params={'provider': \"POCLOUD\",\n 'ShortName': ShortName,\n 'page_size': 1}\n)\n\nummc = response.json()['items'][0]\n\nccid = ummc['meta']['concept-id']\n\nccid\n\n'C1990404791-POCLOUD'\n\n\n\n\nHarmony API\nAnd get the Harmony API endpoint and zarr parameter like we did for SMAP before:\n\nbase = f\"https://harmony.earthdata.nasa.gov/{ccid}\"\nhreq = f\"{base}/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset\"\nrurl = f\"{hreq}?format=application/x-zarr\"\n\nprint(rurl)\n\nhttps://harmony.earthdata.nasa.gov/C1990404791-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?format=application/x-zarr\n\n\nECCO monthly collections have 312 granules in V4r4 (you can confirm with the granule listing from CMR Search API) so we can get the entire time series for 2010 to 2018 with one request to the Harmony API.\nFormat a string of query parameters to limit the processing to the desired time period. Then, append the string of time subset parameters to the variable rurl.\n\nsubs = '&'.join([f'subset=time(\"{start_date}T00:00:00.000Z\":\"{end_date}T23:59:59.999Z\")'])\n\nrurl = f\"{rurl}&{subs}\"\n\nprint(rurl)\n\nhttps://harmony.earthdata.nasa.gov/C1990404791-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?format=application/x-zarr&subset=time(\"2010-01-01T00:00:00.000Z\":\"2018-12-31T23:59:59.999Z\")\n\n\nSubmit the request and monitor the processing status in a while loop, breaking it on completion of the request job:\n\nresponse = requests.get(url=rurl).json()\n\n# Monitor status in a while loop. Wait 10 seconds for each check.\nwait = 10\nwhile True:\n response = requests.get(url=response['links'][0]['href']).json()\n if response['status']!='running':\n break\n print(f\"Job in progress ({response['progress']}%)\")\n time.sleep(wait)\n\nprint(\"DONE!\")\n\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nDONE!\n\n\nAccess the staged cloud datasets over native AWS interfaces\nCheck the message field in the response for clues about how to proceed:\n\nprint(response['message'])\n\nThe job has completed successfully. Contains results in AWS S3. Access from AWS us-west-2 with keys from https://harmony.earthdata.nasa.gov/cloud-access.sh\n\n\nThe third item in the list of links contains the shell script from the job status message printed above. Let’s download the same information in JSON format. It should be the fourth item; check to be sure:\n\nlen(response['links'])\n\n102\n\n\nSelect the url and download the json, then load to Python dictionary and print the keys:\n\nwith requests.get(response['links'][3]['href']) as r:\n creds = r.json()\n\nprint(creds.keys())\n\ndict_keys(['AccessKeyId', 'SecretAccessKey', 'SessionToken', 'Expiration'])\n\n\nCheck the expiration timestamp for the temporary credentials:\n\ncreds['Expiration']\n\n'2021-06-11T02:36:29.000Z'\n\n\nOpen zarr datasets with s3fs and xarray\nGet the s3 output directory and list of zarr datasets from the list of links. The s3 directory should be the fifth item; the urls are from item six onward:\n\ns3_dir = response['links'][4]['href']\n\nprint(s3_dir)\n\ns3://harmony-prod-staging/public/harmony/netcdf-to-zarr/2295236b-8086-4543-9482-f524a9f2d0c3/\n\n\nNow select the URLs for the staged files and print the first one:\n\ns3_urls = [u['href'] for u in response['links'][5:]]\n\nprint(s3_urls[0])\n\ns3://harmony-prod-staging/public/harmony/netcdf-to-zarr/2295236b-8086-4543-9482-f524a9f2d0c3/OCEAN_BOTTOM_PRESSURE_mon_mean_2009-12_ECCO_V4r4_latlon_0p50deg.zarr\n\n\nUse the AWS s3fs package and your temporary aws_creds to open the zarr directory storage:\n\ns3 = s3fs.S3FileSystem(\n key=creds['AccessKeyId'],\n secret=creds['SecretAccessKey'],\n token=creds['SessionToken'],\n client_kwargs={'region_name':'us-west-2'},\n)\n\nlen(s3.ls(s3_dir))\n\n97\n\n\nPlot the first Ocean Bottom Pressure dataset\nCheck out the documentation for xarray’s open_zarr method at this link. Open the first dataset and plot the OBP variable:\n\nds0 = xr.open_zarr(s3.get_mapper(s3_urls[0]), decode_cf=True, mask_and_scale=True)\n\n# Mask the dataset where OBP is not within the bounds of the variable's valid min/max:\nds0_masked = ds0.where((ds0.OBP>=ds0.OBP.valid_min) & (ds0.OBP<=ds0.OBP.valid_max))\n\n# Plot the masked dataset\nds0_masked.OBP.isel(time=0).plot.imshow(size=10)\n\n<matplotlib.image.AxesImage at 0x7f28ed2ba4c0>\n\n\n\n\n\nLoad the zarr datasets into one large xarray dataset\nLoad all the datasets in a loop and concatenate them:\n\nzds = xr.concat([xr.open_zarr(s3.get_mapper(u)) for u in s3_urls], dim=\"time\")\n\nprint(zds)\n\n<xarray.Dataset>\nDimensions: (latitude: 360, longitude: 720, nv: 2, time: 97)\nCoordinates:\n * latitude (latitude) float64 -89.75 -89.25 -88.75 ... 89.25 89.75\n latitude_bnds (latitude, nv) float64 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0\n * longitude (longitude) float64 -179.8 -179.2 -178.8 ... 179.2 179.8\n longitude_bnds (longitude, nv) float64 -180.0 -179.5 -179.5 ... 179.5 180.0\n * time (time) datetime64[ns] 2009-12-16T12:00:00 ... 2017-12-16T...\n time_bnds (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>\nDimensions without coordinates: nv\nData variables:\n OBP (time, latitude, longitude) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n OBPGMAP (time, latitude, longitude) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\nAttributes: (12/57)\n Conventions: CF-1.8, ACDD-1.3\n acknowledgement: This research was carried out by the Jet Pr...\n author: Ian Fenty and Ou Wang\n cdm_data_type: Grid\n comment: Fields provided on a regular lat-lon grid. ...\n coordinates_comment: Note: the global 'coordinates' attribute de...\n ... ...\n time_coverage_duration: P1M\n time_coverage_end: 2010-01-01T00:00:00\n time_coverage_resolution: P1M\n time_coverage_start: 2009-12-01T00:00:00\n title: ECCO Ocean Bottom Pressure - Monthly Mean 0...\n uuid: 297c8df0-4158-11eb-b208-0cc47a3f687b\n\n\nReference OBP and mask the dataset according to the valid minimum and maximum:\n\nobp = zds.OBP\n\nprint(obp)\n\n<xarray.DataArray 'OBP' (time: 97, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(97, 360, 720), dtype=float64, chunksize=(1, 360, 720), chunktype=numpy.ndarray>\nCoordinates:\n * latitude (latitude) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float64 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\n * time (time) datetime64[ns] 2009-12-16T12:00:00 ... 2017-12-16T06:00:00\nAttributes:\n comment: OBP excludes the contribution from global mean at...\n coverage_content_type: modelResult\n long_name: Ocean bottom pressure given as equivalent water t...\n units: m\n valid_max: 72.07011413574219\n valid_min: -1.7899188995361328\n\n\nGet the valid min and max from the corresponding CF attributes:\n\nobp_vmin, obp_vmax = obp.valid_min, obp.valid_max\n\nobp_vmin, obp_vmax\n\n(-1.7899188995361328, 72.07011413574219)\n\n\nMask the dataset according to the OBP min and max and plot a series:\n\n# Mask dataset where not inside OBP variable valid min/max:\nzds_masked = zds.where((obp>=obp_vmin)&(obp<=obp_vmax))\n\n# Plot SSH again for the first 12 time slices:\nobpp = zds_masked.OBP.isel(time=slice(0, 6)).plot(\n x=\"longitude\", \n y=\"latitude\", \n col=\"time\",\n levels=8,\n col_wrap=3, \n add_colorbar=False,\n figsize=(14, 8)\n)\n\n# Plot a colorbar on a secondary axis\nmappable = obpp.axes[0][0].collections[0]\ncax = plt.axes([0.05, -0.04, 0.95, 0.04])\ncbar1 = plt.colorbar(mappable, cax=cax, orientation='horizontal')"
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- "href": "notebooks/podaac_cmr_tutorial.html#cmr-background-information",
- "title": "Introduction to Programmatic Common Metadata Repository Search",
- "section": "CMR Background information",
- "text": "CMR Background information\nCMR houses metadata for the 12 different DAACs. These come in the following forms:\n\nCollections\nGranules\nVariables\nServices\nVisualizations\nTools\n\nThis tutorial will focus on Collections and Granules. for more information, see the https://earthdata.nasa.gov/learn/user-resources/glossary"
+ "objectID": "external/ECCO_cloud_direct_access_s3.html",
+ "href": "external/ECCO_cloud_direct_access_s3.html",
+ "title": "Direct Access to ECCO V4r4 Datasets in the Cloud",
+ "section": "",
+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
},
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- "objectID": "notebooks/podaac_cmr_tutorial.html#collection-dataset-series",
- "href": "notebooks/podaac_cmr_tutorial.html#collection-dataset-series",
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- "section": "Collection / Dataset Series",
- "text": "Collection / Dataset Series\nCollection of datasets sharing the same product specification. They are synonym of EO collections. They are named dataset series as they may be mapped to ‘dataset series’ according to the terminology defined in ISO 19113, ISO 19114 and ISO 19115."
+ "objectID": "external/ECCO_cloud_direct_access_s3.html#getting-started",
+ "href": "external/ECCO_cloud_direct_access_s3.html#getting-started",
+ "title": "Direct Access to ECCO V4r4 Datasets in the Cloud",
+ "section": "Getting Started",
+ "text": "Getting Started\nIn this notebook, we will access monthly sea surface height from ECCO V4r4 (10.5067/ECG5D-SSH44). The data are provided as a time series of monthly netCDFs on a 0.5-degree latitude/longitude grid.\nWe will access the data from inside the AWS cloud (us-west-2 region, specifically) and load a time series made of multiple netCDF datasets into a single xarray dataset. This approach leverages S3 native protocols for efficient access to the data.\n\n\nRequirements\n\nAWS\nThis notebook should be running in an EC2 instance in AWS region us-west-2, as previously mentioned. We recommend using an EC2 with at least 8GB of memory available.\nThe notebook was developed and tested using a t2.large instance (2 cpus; 8GB memory).\n\n\nPython 3\nMost of these imports are from the Python standard library. However, you will need to install these packages into your Python 3 environment if you have not already done so:\n\ns3fs\nrequests\npandas\nxarray\nmatplotlib\ncartopy\n\n\nimport s3fs\nimport requests\nimport numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport cartopy\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeat\nfrom json import dumps\nfrom io import StringIO\nfrom os.path import dirname, join\nfrom IPython.display import HTML\n\nplt.rcParams.update({'font.size': 14})\n\nMake a folder to write some outputs, if needed:\n\n!mkdir -p outputs/\n\n\n\n\nInputs\nConfigure one input: the ShortName of the desired dataset from ECCO V4r4. In this case it’s the following string that unique identifies the collection of monthly, 0.5-degree sea surface height data.\n\nShortName = \"ECCO_L4_SSH_05DEG_MONTHLY_V4R4\"\n\n\n\nEarthdata Login\nYou should have a .netrc file set up like:\nmachine urs.earthdata.nasa.gov login <username> password <password>\n\n\nDirect access from S3\nSet up an s3fs session for authneticated access to ECCO netCDF files in s3:\n\ndef begin_s3_direct_access(url: str=\"https://archive.podaac.earthdata.nasa.gov/s3credentials\"):\n response = requests.get(url).json()\n return s3fs.S3FileSystem(key=response['accessKeyId'],\n secret=response['secretAccessKey'],\n token=response['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})\n\nfs = begin_s3_direct_access()\n\ntype(fs)\n\ns3fs.core.S3FileSystem"
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- "section": "Granule",
- "text": "Granule\nThe smallest aggregation of data which is independently managed (i. e. described, inventoried, retrievable). Granules may be managed as logical granules and/or physical granules. See also Scene.\nNote that granule is often equivalent to Data Set."
+ "objectID": "external/ECCO_cloud_direct_access_s3.html#datasets",
+ "href": "external/ECCO_cloud_direct_access_s3.html#datasets",
+ "title": "Direct Access to ECCO V4r4 Datasets in the Cloud",
+ "section": "Datasets",
+ "text": "Datasets\n\nsea surface height (0.5-degree gridded, monthly)\nECCO_L4_SSH_05DEG_MONTHLY_V4R4\nGet a list of netCDF files located at the S3 path corresponding to the ECCO V4r4 monthly sea surface height dataset on the 0.5-degree latitude/longitude grid.\n\nssh_Files = fs.glob(join(\"podaac-ops-cumulus-protected/\", ShortName, \"*2015*.nc\"))\n\nlen(ssh_Files)\n\n12\n\n\nOpen with the netCDF files using the s3fs package, then load them all at once into a concatenated xarray dataset.\n\nssh_Dataset = xr.open_mfdataset(\n paths=[fs.open(f) for f in ssh_Files],\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks={'latitude': 60, # These were chosen arbitrarily. You must specify \n 'longitude': 120, # chunking that is suitable to the data and target\n 'time': 100} # analysis.\n)\n\nssh = ssh_Dataset.SSH\n\nprint(ssh)\n\n<xarray.DataArray 'SSH' (time: 12, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(12, 360, 720), dtype=float32, chunksize=(1, 60, 120), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-16T12:00:00 ... 2015-12-16T12:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: [-1.88057721]\n valid_max: [1.42077196]\n\n\n\n\nPlot the gridded sea surface height time series\nBut only the timesteps beginning in 2015:\n\nssh_after_201x = ssh[ssh['time.year']>=2015,:,:]\n\nprint(ssh_after_201x)\n\n<xarray.DataArray 'SSH' (time: 12, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(12, 360, 720), dtype=float32, chunksize=(1, 60, 120), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-16T12:00:00 ... 2015-12-16T12:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: [-1.88057721]\n valid_max: [1.42077196]\n\n\nPlot the grid for the first time step using a Robinson projection. Define a helper function for consistency throughout the notebook:\n\ndef make_figure(proj):\n fig = plt.figure(figsize=(16,6))\n ax = fig.add_subplot(1, 1, 1, projection=proj)\n ax.add_feature(cfeat.LAND)\n ax.add_feature(cfeat.OCEAN)\n ax.add_feature(cfeat.COASTLINE)\n ax.add_feature(cfeat.BORDERS, linestyle='dotted')\n return fig, ax\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\nssh_after_201x.isel(time=0).plot(ax=ax, transform=ccrs.PlateCarree(), cmap='Spectral_r')\n\n<matplotlib.collections.QuadMesh at 0x7fae2533d730>\n\n\n\n\n\nNow plot the whole time series (post-2010) in an animation and write it to an mp4 file called ecco_monthly_ssh_grid_2015_to_x.mp4:\n\ndef get_animation(var, cmap: str=\"Spectral_r\"):\n \"\"\"Get time series animation for input xarray dataset\"\"\"\n\n def draw_map(i: int, add_colorbar: bool):\n data = var[i]\n m = data.plot(ax=ax, \n transform=ccrs.PlateCarree(),\n add_colorbar=add_colorbar,\n vmin=var.valid_min, \n vmax=var.valid_max,\n cmap=cmap)\n plt.title(str(data.time.values)[:7])\n return m\n\n def init():\n return draw_map(0, add_colorbar=True)\n \n def animate(i):\n return draw_map(i, add_colorbar=False)\n\n return init, animate\n\nNow make the animation using the function:\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\ninit, animate = get_animation(ssh_after_201x)\n\nani = animation.FuncAnimation(fig=fig, \n func=animate, \n frames=ssh_after_201x.time.size, \n init_func=init, \n interval=0.2, \n blit=False, \n repeat=False)\n\n# Now save the animation to an MP4 file:\nani.save('outputs/ecco_monthly_ssh_grid_2015_to_x.mp4', writer=animation.FFMpegWriter(fps=8))\n\nplt.close(fig)\n\nRender the animation in the ipynb:\n\n#HTML(ani.to_html5_video())\n\n\n\ntflux (0.5-degree gridded, daily)\nNow we will do something similar to access daily, gridded (0.5-degree) ocean and sea-ice surface heat fluxes (10.5067/ECG5D-HEA44). Read more about the dataset and the rest of the ECCO V4r4 product suite on the PO.DAAC Web Portal.\nUse a “glob” pattern when listing the S3 bucket contents such that only netCDFs from January 2015 are represented in the resulting list of paths.\n\ntflux_Files = fs.glob(join(\"podaac-ops-cumulus-protected/\", \"ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4\", \"*2015-01*.nc\"))\n\nlen(tflux_Files)\n\n31\n\n\nNow open them all as one xarray dataset just like before. Open and pass the 365 netCDF files to the xarray.open_mfdataset constructor so that we can operate on them as a single aggregated dataset.\n\ntflux_Dataset = xr.open_mfdataset(\n paths=[fs.open(f) for f in tflux_Files],\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks={'latitude': 60, # These were chosen arbitrarily. You must specify \n 'longitude': 120, # chunking that is suitable to the data and target\n 'time': 100} # analysis.\n)\n\ntflux = tflux_Dataset.TFLUX\n\nprint(tflux)\n\n<xarray.DataArray 'TFLUX' (time: 31, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(31, 360, 720), dtype=float32, chunksize=(1, 60, 120), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-01T12:00:00 ... 2015-01-31T12:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n direction: >0 increases potential temperature (THETA)\n long_name: Rate of change of ocean heat content per m2 accou...\n units: W m-2\n comment: The rate of change of ocean heat content due to h...\n valid_min: [-1713.51220703]\n valid_max: [870.31304932]\n\n\nSelect a region over the Gulf of Mexico and spatially subset it from the larger dataset by slicing on the latitude and longitude axes.\n\ntflux_gom = tflux.sel(latitude=slice(15, 40), \n longitude=slice(-105, -70))\n\nprint(tflux_gom.shape)\n\n(31, 50, 70)\n\n\n\ntflux_gom.isel(time=0).plot()\n\n<matplotlib.collections.QuadMesh at 0x7fae1689a550>\n\n\n\n\n\nPlot the Jan 2015 surface heat flux as a gridded time series animation over the GOM study region.\n\nfig, ax = make_figure(proj=ccrs.Mercator())\n\nax.coastlines()\nax.set_extent([tflux_gom.longitude.min(), \n tflux_gom.longitude.max(), \n tflux_gom.latitude.min(), \n tflux_gom.latitude.max()])\n\ninit, animate = get_animation(tflux_gom, cmap=\"RdBu\")\n\n# Plot a time series animation write it to an mp4 file:\nani = animation.FuncAnimation(fig=fig, \n func=animate, \n frames=tflux_gom.time.size, \n init_func=init, \n interval=0.2, \n blit=False, \n repeat=False)\n\nani.save('outputs/ecco_daily_tflux_gom_2015.mp4', writer=animation.FFMpegWriter(fps=8))\n\nplt.close(fig)"
},
{
- "objectID": "notebooks/podaac_cmr_tutorial.html#data-set",
- "href": "notebooks/podaac_cmr_tutorial.html#data-set",
- "title": "Introduction to Programmatic Common Metadata Repository Search",
- "section": "Data Set",
- "text": "Data Set\nA logically meaningful grouping or collection of similar or related data. Data having all of the same characteristics (source or class of source, processing level, resolution, etc.) but different independent variable ranges and/or responding to a specific need are normally considered part of a single data set. A data set is typically composed by products from several missions, gathered together to respond to the overall coverage or revisit requirements from a specific group of users.\nIn the context of EO data preservation a data set consists of the data records of one mission, sensor, and product type and the associated knowledge(information, tools). See collection."
+ "objectID": "external/DownloadDopplerScattData.html",
+ "href": "external/DownloadDopplerScattData.html",
+ "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
+ "section": "",
+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop"
},
{
- "objectID": "notebooks/podaac_cmr_tutorial.html#what-does-all-of-this-mean",
- "href": "notebooks/podaac_cmr_tutorial.html#what-does-all-of-this-mean",
- "title": "Introduction to Programmatic Common Metadata Repository Search",
- "section": "What does all of this mean?",
- "text": "What does all of this mean?\nFor the most part, users want to discover collections of interest to them, usually defined by parameter (Sea Surface Temperature, Ocean Winds, Sea Surface Height, etc), Level, spatial and temporal coverage, etc. Lets show an example."
+ "objectID": "external/DownloadDopplerScattData.html#create-a-netrc-file-if-non-existent.",
+ "href": "external/DownloadDopplerScattData.html#create-a-netrc-file-if-non-existent.",
+ "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
+ "section": "Create a netrc file, if non-existent.",
+ "text": "Create a netrc file, if non-existent.\nPrior to doing this, you must obtain an account with NASA Earthdata.\n\nnetrc_file = setup_netrc_file()\n\nnetrc file not found, please login into NASA Earthdata:\nnterc file written to /Users/erodrigu/.netrc\n\n\nEnter NASA Earthdata Login Username: ········\nEnter NASA Earthdata Login Password: ········"
},
{
- "objectID": "notebooks/podaac_cmr_tutorial.html#find-collections-by-parameter",
- "href": "notebooks/podaac_cmr_tutorial.html#find-collections-by-parameter",
- "title": "Introduction to Programmatic Common Metadata Repository Search",
- "section": "Find collections by parameter",
- "text": "Find collections by parameter\n\nfrom urllib import request\nimport json\nimport pprint\n\ncmr_url = \"https://cmr.earthdata.nasa.gov/search/\"\n\nwith request.urlopen(cmr_url+\"collections.umm_json?science_keywords[0][topic]=OCEANS\") as response:\n data = response.read()\n encoding = response.info().get_content_charset('utf-8')\n JSON_object = json.loads(data.decode(encoding))\n pp = pprint.PrettyPrinter(indent=2)\n pp.pprint(JSON_object)\n\n{ 'hits': 10904,\n 'items': [ { 'meta': { 'concept-id': 'C1214305813-AU_AADC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'ASAC_2201_HCL_0.5',\n 'provider-id': 'AU_AADC',\n 'revision-date': '2019-12-12T16:00:14Z',\n 'revision-id': 9,\n 'user-id': 'sritz'},\n 'umm': { 'Abstract': 'These results are for the 0.5 hour '\n 'extraction of HCl.\\n'\n '\\n'\n 'See also the metadata records for the 4 '\n 'hour extraction of HCl, and the time '\n 'trial data for 1 M HCl extractions.\\n'\n '\\n'\n 'A regional survey of potential '\n 'contaminants in marine or estuarine '\n 'sediments is often one of the first steps '\n 'in a post-disturbance environmental '\n 'impact assessment. Of the many different '\n 'chemical extraction or digestion '\n 'procedures that have been proposed to '\n 'quantify metal contamination, partial '\n 'acid extractions are probably the best '\n 'overall compromise between selectivity, '\n 'sensitivity, precision, cost and '\n 'expediency. The extent to which measured '\n 'metal concentrations relate to the '\n 'anthropogenic fraction that is '\n 'bioavailable is contentious, but is one '\n 'of the desired outcomes of an assessment '\n 'or prediction of biological impact. As '\n 'part of a regional survey of metal '\n \"contamination associated with Australia's \"\n 'past waste management activities in '\n 'Antarctica, we wanted to identify an acid '\n 'type and extraction protocol that would '\n 'allow a reasonable definition of the '\n 'anthropogenic bioavailable fraction for a '\n 'large number of samples. From a kinetic '\n 'study of the 1 M HCl extraction of two '\n 'certified Certified Reference Materials '\n '(MESS-2 and PACS-2) and two Antarctic '\n 'marine sediments, we concluded that a 4 '\n 'hour extraction time allows the '\n 'equilibrium dissolution of relatively '\n 'labile metal contaminants, but does not '\n 'favour the extraction of natural geogenic '\n 'metals. In a regional survey of 88 '\n 'marine samples from the Casey Station '\n 'area of East Antarctica, the 4 h '\n 'extraction procedure correlated best with '\n 'biological data, and most clearly '\n 'identified those sediments thought to be '\n 'contaminated by runoff from abandoned '\n 'waste disposal sites. Most importantly '\n 'the 4 hour extraction provided better '\n 'definition of the low to moderately '\n 'contaminated locations by picking up '\n 'small differences in anthropogenic metal '\n 'concentrations. For the purposes of '\n 'inter-regional comparison, we recommend a '\n '4 hour 1 M HCl acid extraction as a '\n 'standard method for assessing metal '\n 'contamination in Antarctica.\\n'\n '\\n'\n 'The fields in this dataset are\\n'\n '\\n'\n 'Location\\n'\n 'Site\\n'\n 'Replicate\\n'\n 'Antimony\\n'\n 'Arsenic\\n'\n 'Cadmium\\n'\n 'Chromium\\n'\n 'Copper\\n'\n 'Iron\\n'\n 'Lead\\n'\n 'Manganese\\n'\n 'Nickel\\n'\n 'Silver\\n'\n 'Tin\\n'\n 'Zinc',\n 'AccessConstraints': { 'Description': 'The data are '\n 'available for '\n 'download from '\n 'the url given '\n 'below.'},\n 'AncillaryKeywords': [ 'ANTIMONY',\n 'ARSENIC',\n 'BIOAVAILABLE METALS',\n 'CADMIUM',\n 'CHROMIUM',\n 'COPPER',\n 'IRON',\n 'KINETICS',\n 'LEAD',\n 'LOCATION',\n 'MANGANESE',\n 'MESS',\n 'MULTIVARIATE ANALYSIS',\n 'NICKEL',\n 'PACS',\n 'REPLICATE',\n 'SILVER',\n 'SITE',\n 'TIN',\n 'WINDMILL ISLANDS',\n 'ZINC'],\n 'CollectionCitations': [ { 'Creator': 'Snape, I., '\n 'Riddle, M.J., '\n 'Gore, D., '\n 'Stark, J.S., '\n 'Scouller, R. '\n 'and Stark, S.C.',\n 'OnlineResource': { 'Linkage': 'https://data.aad.gov.au/metadata/records/ASAC_2201_HCL_0.5'},\n 'Publisher': 'Australian '\n 'Antarctic '\n 'Data Centre',\n 'ReleaseDate': '2004-08-02T00:00:00.000Z',\n 'SeriesName': 'CAASM '\n 'Metadata',\n 'Title': '0.5 hour 1 M HCl '\n 'extraction data '\n 'for the Windmill '\n 'Islands marine '\n 'sediments',\n 'Version': '1'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'ian.snape@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3158'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3591'}]},\n 'FirstName': 'IAN',\n 'LastName': 'SNAPE',\n 'Roles': [ 'Investigator',\n 'Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'martin.riddle@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3573'}]},\n 'FirstName': 'MARTIN',\n 'LastName': 'RIDDLE',\n 'MiddleName': 'J.',\n 'Roles': ['Investigator']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'dave.connell@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3244'}]},\n 'FirstName': 'DAVE',\n 'LastName': 'CONNELL',\n 'MiddleName': 'J.',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Marsfield',\n 'Country': 'Australia',\n 'PostalCode': '2109',\n 'StateProvince': 'New '\n 'South '\n 'Wales',\n 'StreetAddresses': [ 'Department '\n 'of '\n 'Physical '\n 'Geography',\n 'Macquarie '\n 'University']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'damian.gore@mq.edu.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '2 '\n '9850 '\n '8420'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '2 '\n '9850 '\n '8391'}]},\n 'FirstName': 'DAMIAN',\n 'LastName': 'GORE',\n 'Roles': ['Investigator']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'jonny.stark@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3158'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3589'}]},\n 'FirstName': 'JONATHAN',\n 'LastName': 'STARK',\n 'MiddleName': 'SEAN',\n 'Roles': ['Investigator']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'beck.scouller@aad.gov.au'}]},\n 'FirstName': 'REBECCA',\n 'LastName': 'SCOULLER',\n 'Roles': [ 'Investigator',\n 'Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'scott.stark@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3169'}]},\n 'FirstName': 'SCOTT',\n 'LastName': 'STARK',\n 'MiddleName': 'CHARLES',\n 'Roles': ['Investigator']}],\n 'DOI': {'DOI': 'doi:10.4225/15/5747A30D1F767'},\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://data.aad.gov.au',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'metadata@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3244'}]},\n 'FirstName': 'DATA '\n 'OFFICER',\n 'LastName': 'AADC',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Australian Antarctic '\n 'Data Centre, Australia',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'AU/AADC'}],\n 'DataLanguage': 'eng',\n 'DirectoryNames': [ {'ShortName': 'AMD/AU'},\n {'ShortName': 'CEOS'},\n {'ShortName': 'AMD'}],\n 'EntryTitle': '0.5 hour 1 M HCl extraction data for '\n 'the Windmill Islands marine sediments',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION',\n 'OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Windmill '\n 'Islands',\n 'Type': 'ANTARCTICA'},\n { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'POLAR'}],\n 'MetadataDates': [ { 'Date': '2004-07-30T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-26T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ShortName': 'SEDIMENT '\n 'CORERS'}],\n 'ShortName': 'Not provided'},\n {'ShortName': 'LABORATORY'},\n {'ShortName': 'FIELD SURVEYS'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'PublicationReferences': [ { 'Author': 'Snape, I., '\n 'Scouller, '\n 'R.C., Stark, '\n 'S.C., Stark, '\n 'J., Riddle, '\n 'M.J., Gore, '\n 'D.B.',\n 'DOI': { 'DOI': 'doi:10.1016/j.chemosphere.2004.05.042'},\n 'Issue': '6',\n 'Pages': '491-504',\n 'PublicationDate': '2004-01-01T00:00:00.000Z',\n 'Series': 'Chemosphere',\n 'Title': 'Characterisation '\n 'of the dilute '\n 'HCl extraction '\n 'method for the '\n 'identification '\n 'of metal '\n 'contamination '\n 'in Antarctic '\n 'marine '\n 'sediments',\n 'Volume': '57'}],\n 'Quality': 'The dates provided in temporal coverage '\n 'are approximate only. Years are correct.\\n'\n '\\n'\n 'See the referenced paper for full details '\n 'on steps taken to ensure quality of data.\\n'\n '\\n'\n 'To assess extraction efficiency for a '\n 'range of sediment types, four marine '\n 'sediments were analysed in detail. Two '\n 'international certified reference '\n 'materials (CRMs) and two '\n 'well-characterised Antarctic sediments '\n 'were chosen to compare and contrast '\n 'moderately to strongly contaminated '\n 'samples (based on total metal digest), '\n 'with clean samples of similar matrices. '\n 'One CRM was an uncontaminated continental '\n 'shelf mud (MESS-2), and the other a '\n 'contaminated harbour mud (PACS-2) (NRCC, '\n '2002). The two Antarctic sediments were '\n 'collected as part of a regional '\n 'hierarchical survey (Stark et al., 2003). '\n 'One Antarctic sample was from an area of '\n 'known metal pollution in Brown Bay (BB), '\n \"which is adjacent to the 'Old' Casey \"\n 'Station waste disposal site (Snape et al., '\n '2001; Stark et al., 2003). The second '\n 'Antarctic sample was from a non-impacted '\n \"control site from O'Brien Bay (OBB), 3 km \"\n 'south of Casey Station and the disposal '\n 'site (Fig. 1). The Antarctic samples, OBB '\n 'and BB, have similar matrices, proportions '\n 'of mud (less than 63 microns; 19% and 22% '\n 'respectively) and total organic carbon '\n 'contents (1.9% and 2.3% respectively). '\n 'Both MESS and PACS are sieved, homogenised '\n 'and dried CRMs that have been ground to '\n '~50 microns (NRCC, 2002). In contrast, '\n 'OBB and BB were only sieved to less than 2 '\n 'mm, thereby removing only the very largest '\n 'particles (less than or equal to 3%). The '\n 'Antarctic samples were collected using '\n 'acid-washed PVC coring tubes. The samples '\n 'were kept frozen at -20 degrees C until '\n 'wet-sieved with a small amount of clean '\n 'filtered (0.45 microns cellulose nitrate) '\n \"O'Brien Bay seawater through 2 mm nylon \"\n 'mesh held in a plastic sieve unit. The '\n 'sediments were then oven-dried to constant '\n 'weight at 103 degrees C (Loring and '\n 'Rantala 1992), and stored in Nalgene HDPE '\n 'bottles until analysis.',\n 'RelatedUrls': [ { 'Description': 'Download point for '\n 'the data',\n 'Type': 'GET DATA',\n 'URL': 'http://data.aad.gov.au/aadc/portal/download_file.cfm?file_id=1677',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Public information '\n 'for ASAC project '\n '2201',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://secure3.aad.gov.au/proms/public/projects/report_project_public.cfm?project_no=2201',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Citation reference '\n 'for this metadata '\n 'record and dataset',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=ASAC_2201_HCL_0.5',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ENVIRONMENTAL IMPACTS',\n 'Topic': 'HUMAN DIMENSIONS',\n 'VariableLevel1': 'HEAVY METALS '\n 'CONCENTRATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MARINE SEDIMENTS',\n 'Topic': 'OCEANS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MARINE SEDIMENTS',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEDIMENT '\n 'CHEMISTRY'}],\n 'ShortName': 'ASAC_2201_HCL_0.5',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 110.0,\n 'NorthBoundingCoordinate': -66.0,\n 'SouthBoundingCoordinate': -66.0,\n 'WestBoundingCoordinate': 110.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1997-10-01T00:00:00.000Z',\n 'EndingDateTime': '1999-03-31T23:59:59.999Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'This '\n 'data '\n 'set '\n 'conforms '\n 'to '\n 'the '\n 'CCBY '\n 'Attribution '\n 'License\\n'\n '(http://creativecommons.org/licenses/by/4.0/).\\n'\n '\\n'\n 'Please '\n 'follow '\n 'instructions '\n 'listed '\n 'in '\n 'the '\n 'citation '\n 'reference '\n 'provided '\n 'at '\n 'http://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=ASAC_2201_HCL_0.5 '\n 'when '\n 'using '\n 'these '\n 'data.'}},\n 'Version': '1'}},\n { 'meta': { 'concept-id': 'C1214422215-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'OES_CEOB_106_MILE',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-08T12:53:39Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The 106-Mile Dumpsite Oceanographic '\n 'project is response to the Ocean Dumping\\n'\n 'Act '\n '(http://www.epa.gov/history/topics/mprsa/02.htm) '\n 'which directs the National\\n'\n 'Oceanic and Atmospheric Administration '\n '(NOAA), the US Environmental Protection\\n'\n 'Agency (EPA), and the US Coast Guard to '\n 'perform research, monitoring, and\\n'\n 'surveillance until ocean dumping of '\n 'municipal sewage sludge is ended in '\n '1992. \\n'\n 'NOAA is responsible for research and '\n 'monitoring of the far-field and '\n 'long-term\\n'\n 'effects of dumping at the dumpsite, which '\n 'is located in the Mid-Atlantic Bight\\n'\n '(MAB).\\n'\n '\\n'\n 'Four 1991 seasonal deployments of four '\n 'satellite-tracked drifters each and a\\n'\n 'single 1990 deployment of eight drifters '\n 'were conducted across the continental\\n'\n 'shelf break and Dumpsite. The tracking '\n 'and processing of near-surface drifters\\n'\n 'continues. Coupled with EPA weekly '\n 'drifter deployments, the CEOB drifter '\n 'study\\n'\n 'provides information on the relationship '\n 'between suspended sludge dispersal to\\n'\n 'the near-surface circulation and '\n 'interaction of shelf water, slope water, '\n 'and\\n'\n 'Gulf Stream over the continental margin '\n 'in the MAB.\\n'\n '\\n'\n 'Hydrographic studies include '\n 'quasi-synoptic conductivity and '\n 'temperature\\n'\n 'profile and expendable bathythermograph '\n '(XBT) surveys of the MAB and dumpsite.\\n'\n 'These surveys were conducted in support '\n 'of biological and chemical sampling and\\n'\n 'in conjunction with the deployment of '\n 'drifters.\\n'\n '\\n'\n 'Weekly transects were taken across the '\n 'shelf, slope, and northern tip of the\\n'\n 'Dumpsite to the Gulf Stream from the ship '\n 'of opportunity, M/V OLEANDER; this\\n'\n 'was an expansion of the monthly program '\n 'managed by National Marine Fisheries\\n'\n \"Service's Northeast Fisheries Center, in \"\n 'Narragansett, Rhode Island.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-01 '\n '12:48:25'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'AncillaryKeywords': [ 'OES/CEOB_106_MILE',\n 'CONSERVATION'],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3281',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'NOAA/National '\n 'Ocean '\n 'Service',\n '1305 '\n 'East-West '\n 'Highway, '\n 'Room '\n '6543']}],\n 'ContactMechanisms': [ { 'Type': 'Fax',\n 'Value': '301-713-4501'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-2809'}]},\n 'FirstName': 'FRANK',\n 'LastName': 'AIKMAN',\n 'Roles': ['Investigator']}],\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://coastalscience.noaa.gov/about/centers/ccma',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3281',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'National '\n 'Centers '\n 'for '\n 'Coastal '\n 'Ocean '\n 'Science',\n 'Center '\n 'for '\n 'Coastal '\n 'Monitoring '\n 'and '\n 'Assessment',\n 'NOAA/National '\n 'Ocean '\n 'Survey',\n '1305 '\n 'East-West '\n 'Highway, '\n 'SSMC4']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'harris.white@noaa.gov'},\n { 'Type': 'Fax',\n 'Value': '301-713-4338'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-3034'}]},\n 'FirstName': 'HARRIS',\n 'LastName': 'WHITE',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Center for Coastal '\n 'Monitoring and '\n 'Assessment, National '\n 'Ocean Service, NOAA, '\n 'U.S. Department of '\n 'Commerce',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'DOC/NOAA/NOS/NCCOS/CCMA'}],\n 'DirectoryNames': [ {'ShortName': 'USA/NOAA'},\n {'ShortName': 'CEOS'}],\n 'EntryTitle': '106-Mile Dumpsite Oceanographic Project '\n '(Mid Atlantic Bight); Surface Drifters '\n 'and Hydrographic Measurements; NOAA/NOS',\n 'ISOTopicCategories': [ 'ELEVATION',\n 'ENVIRONMENT',\n 'OCEANS'],\n 'LocationKeywords': [ { 'Category': 'OCEAN',\n 'Type': 'ATLANTIC OCEAN'},\n { 'Category': 'CONTINENT',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Mid-Atlantic '\n 'Bight',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'NEW YORK',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Narragansett',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'RHODE ISLAND',\n 'Type': 'NORTH AMERICA'}],\n 'MetadataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-20T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ShortName': 'BATHYTHERMOGRAPHS'},\n { 'LongName': 'Conductivity, '\n 'Temperature, '\n 'Depth',\n 'ShortName': 'CTD'},\n { 'ShortName': 'DRIFTING '\n 'BUOYS'},\n { 'LongName': 'Salinity, '\n 'Temperature, '\n 'Depth',\n 'ShortName': 'STD'},\n { 'LongName': 'Expendable '\n 'Bathythermographs',\n 'ShortName': 'XBT'}],\n 'ShortName': 'BUOYS'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'RelatedUrls': [ { 'Description': 'Information on the '\n '106-mile dumpsite',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.oar.noaa.gov/spotlite/archive/spot_oceandumping.html',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SLUDGE '\n 'DISPERSAL',\n 'Term': 'ENVIRONMENTAL IMPACTS',\n 'Topic': 'HUMAN DIMENSIONS',\n 'VariableLevel1': 'SEWAGE '\n 'DISPOSAL'},\n { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'CONTINENTAL '\n 'RISES/SLOPES',\n 'Term': 'BATHYMETRY/SEAFLOOR '\n 'TOPOGRAPHY',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'CONTINENTAL '\n 'MARGINS'},\n { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'CONTINENTAL '\n 'SHELVES',\n 'Term': 'BATHYMETRY/SEAFLOOR '\n 'TOPOGRAPHY',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'CONTINENTAL '\n 'MARGINS'},\n { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'GULF '\n 'STREAM',\n 'Term': 'OCEAN CIRCULATION',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'OCEAN '\n 'CURRENTS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN CIRCULATION',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WIND-DRIVEN '\n 'CIRCULATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SALINITY/DENSITY',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'CONDUCTIVITY'}],\n 'ShortName': 'OES_CEOB_106_MILE',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -70.0,\n 'NorthBoundingCoordinate': 41.0,\n 'SouthBoundingCoordinate': 37.0,\n 'WestBoundingCoordinate': -74.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1970-01-01T00:00:00.000Z'}]}],\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214422266-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'CS0005',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-08T13:12:29Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The database includes 1951-88 monthly '\n 'cumulative streamflow (mouth of\\n'\n 'Chesapeake Bay) in cubic feet per '\n 'second. The data were digitized\\n'\n 'from data provided by the U.S. Geological '\n 'Survey.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-01 '\n '12:48:33'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'AncillaryKeywords': [ 'CHESAPEAKE BAY',\n 'ESTUARY',\n 'RUNOFF'],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3282',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'NOAA/NOS '\n 'N/SCI2',\n '1315 '\n 'East-West '\n 'Hwy']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'Michael.Dowgiallo@noaa.gov'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-3338 '\n 'x129'}]},\n 'FirstName': 'MICHAEL',\n 'LastName': 'DOWGIALLO',\n 'MiddleName': 'J.',\n 'Roles': [ 'Investigator',\n 'Metadata Author']}],\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.cop.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3282',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'NOAA/NOS '\n 'N/SCI2',\n '1315 '\n 'East-West '\n 'Hwy']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'Michael.Dowgiallo@noaa.gov'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-3338 '\n 'x129'}]},\n 'FirstName': 'MICHAEL',\n 'LastName': 'DOWGIALLO',\n 'MiddleName': 'J.',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Center for Sponsored '\n 'Coastal Ocean '\n 'Research, National '\n 'Centers for Coastal '\n 'Ocean Science, '\n 'National Ocean '\n 'Service, NOAA, U.S. '\n 'Department of Commerce',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'DOC/NOAA/NOS/NCCOS/CSCOR'}],\n 'DirectoryNames': [ {'ShortName': 'USA/NOAA'},\n {'ShortName': 'CEOS'}],\n 'EntryTitle': '1951-88 Monthly Cumulative Streamflow '\n 'at the Mouth of the Chesapeake Bay',\n 'ISOTopicCategories': [ 'GEOSCIENTIFIC INFORMATION',\n 'INLAND WATERS',\n 'OCEANS'],\n 'LocationKeywords': [ { 'Category': 'OCEAN',\n 'DetailedLocation': 'CHESAPEAKE '\n 'BAY',\n 'Type': 'ATLANTIC OCEAN'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'CHESAPEAKE '\n 'BAY',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Chesapeake '\n 'Bay',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'MARYLAND',\n 'Type': 'NORTH AMERICA'}],\n 'MetadataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-20T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [{'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Projects': [ { 'LongName': 'Coastal Ocean Program',\n 'ShortName': 'COP'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE WATER',\n 'Topic': 'TERRESTRIAL '\n 'HYDROSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WATER '\n 'FEATURES',\n 'VariableLevel2': 'RIVERS/STREAMS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE WATER',\n 'Topic': 'TERRESTRIAL '\n 'HYDROSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WATER '\n 'PROCESSES/MEASUREMENTS',\n 'VariableLevel2': 'DISCHARGE/FLOW'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'COASTAL PROCESSES',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ESTUARIES'}],\n 'ShortName': 'CS0005',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -75.0,\n 'NorthBoundingCoordinate': 40.0,\n 'SouthBoundingCoordinate': 36.0,\n 'WestBoundingCoordinate': -78.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1951-01-01T00:00:00.000Z',\n 'EndingDateTime': '1988-12-31T23:59:59.999Z'}]}],\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214621676-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'darling_sst_82-93',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-12T15:31:27Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'Seawater Surface Temperature Data '\n 'Collected between the years 1982-1989 '\n 'and\\n'\n '1993 off the dock at the Darling Marine '\n 'Center, Walpole, Maine',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-02 '\n '13:18:42'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://dmc.umaine.edu/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Walpole',\n 'Country': 'USA',\n 'PostalCode': '04573',\n 'StateProvince': 'ME',\n 'StreetAddresses': [ '193 '\n \"Clark's \"\n 'Cove '\n 'Road']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'darling@maine.edu'},\n { 'Type': 'Fax',\n 'Value': '207-563-3119'},\n { 'Type': 'Telephone',\n 'Value': '207-563-3146'}]},\n 'FirstName': 'KEVIN',\n 'LastName': 'ECKELBARGER',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Darling Marine Center, '\n 'School of Marine '\n 'Science, University of '\n 'Maine',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'UMAINE/SMS/DMC'}],\n 'DataLanguage': 'eng',\n 'DirectoryNames': [{'ShortName': 'USA/NASA'}],\n 'EntryTitle': '1982-1989 and 1993 Seawater '\n 'Temperatures at the Darling Marine '\n 'Center',\n 'ISOTopicCategories': ['OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'MAINE',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'OCEAN',\n 'Subregion1': 'NORTH ATLANTIC '\n 'OCEAN',\n 'Subregion2': 'GULF OF MAINE',\n 'Type': 'ATLANTIC OCEAN'}],\n 'MetadataDates': [ { 'Date': '2002-11-07T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-24T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [{'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Projects': [ { 'LongName': 'Gulf of Maine Ocean Data '\n 'Partnership',\n 'ShortName': 'GOMODP'}],\n 'RelatedUrls': [ { 'Description': 'Seawater Surface '\n 'Temperature Data',\n 'Type': 'GET DATA',\n 'URL': 'http://server.dmc.maine.edu/dmctemps1980s.html',\n 'URLContentType': 'DistributionURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE'}],\n 'ShortName': 'darling_sst_82-93',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -66.74,\n 'NorthBoundingCoordinate': 47.67,\n 'SouthBoundingCoordinate': 42.85,\n 'WestBoundingCoordinate': -71.31}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1982-03-01T00:00:00.000Z',\n 'EndingDateTime': '1989-09-14T23:59:59.999Z'},\n { 'BeginningDateTime': '1993-01-29T00:00:00.000Z',\n 'EndingDateTime': '1993-12-31T23:59:59.999Z'}]}],\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214609006-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'PASSCAL_FOGO',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-07-30T15:20:13Z',\n 'revision-id': 3,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The data were acquired during July 1991 '\n 'in conjunction with LITHOPROBE\\n'\n 'East. Three marine airgun lines were shot '\n 'on the northeast\\n'\n 'Newfoundland shelf and recorded on Fogo '\n 'Island off the north coast of\\n'\n 'Newfoundland. The source was an untuned '\n 'array of five 1000\\n'\n 'cu. in. airguns.\\n'\n '\\n'\n 'The data were recorded with a 13 element '\n 'array of 3-component\\n'\n 'receivers. 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'Description': '\\n'\n ' '\n 'Data '\n 'may '\n 'not '\n 'be '\n 'used '\n 'for '\n 'commercial '\n 'applications\\n'\n ' '}},\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214587159-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': '1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-05-07T15:38:17Z',\n 'revision-id': 3,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'A series of measurements in water '\n 'temperature, conductivity and depth was '\n 'carried out during the austral summer of '\n '1998/99 within and the north of Prydz '\n 'Bay, the southern Indian Ocean.34 '\n 'oceanographic stations were successfully '\n 'completed and 3.77MB CTD data were '\n 'obtained.',\n 'AccessConstraints': { 'Description': '\\n'\n ' '\n 'Public and '\n 'free\\n'\n ' '},\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-02 '\n '13:31:52'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'AncillaryKeywords': ['CTD'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'txt',\n 'FormatType': 'Native',\n 'Media': [ 'online']}]},\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'PostalCode': '200136'}]},\n 'FirstName': 'ZHAOQIAN',\n 'LastName': 'DONG',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'PostalCode': '266003'}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'ysea@public.qd.sd.cn'},\n { 'Type': 'Fax',\n 'Value': '086-0532-8899911'},\n { 'Type': 'Telephone',\n 'Value': '086-0532-8897474'}]},\n 'FirstName': 'YUTIAN',\n 'LastName': 'JIAO',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.chinare.org.cn/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Shanghai',\n 'Country': 'P.R.C.',\n 'PostalCode': '200136',\n 'StateProvince': 'Shanghai',\n 'StreetAddresses': [ '451, '\n 'Jinqiao '\n 'Road']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'zhangjie@pric.gov.cn'},\n { 'Type': 'Telephone',\n 'Value': '(8621) '\n '5871-7576'}]},\n 'FirstName': 'JIE',\n 'LastName': 'ZHANG',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'National Antarctic and '\n 'Arctic Data Center, '\n 'China',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'CN/NADC',\n 'Uuid': 'c2a07368-c030-4045-85e0-8c8b1f60353a'}],\n 'DirectoryNames': [ {'ShortName': 'AMD/CN'},\n {'ShortName': 'CEOS'},\n {'ShortName': 'AMD'},\n {'ShortName': 'ARCTIC'}],\n 'EntryTitle': '1998-1999 Raw data of CTD in Prydz Bay '\n 'region of the southern Indian Ocean, '\n 'CHINARE-15',\n 'ISOTopicCategories': ['OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Prydz Bay',\n 'Type': 'ANTARCTICA'},\n { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'ARCTIC'},\n { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'POLAR'}],\n 'MetadataDates': [ { 'Date': '2007-08-16T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-20T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Conductivity, '\n 'Temperature, '\n 'Depth',\n 'ShortName': 'CTD'}],\n 'ShortName': 'SHIPS',\n 'Type': 'In Situ Ocean-based '\n 'Platforms'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Quality': '\\n'\n ' This instrument is used as '\n 'routine observation, and the data set is '\n 'in good quality in general.\\n'\n ' ',\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'BATHYMETRY/SEAFLOOR '\n 'TOPOGRAPHY',\n 'Topic': 'OCEANS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SALINITY/DENSITY',\n 'Topic': 'OCEANS'}],\n 'ShortName': '1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 77.0,\n 'NorthBoundingCoordinate': -62.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': 70.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL_VERTICAL',\n 'VerticalSpatialDomains': [ { 'Type': 'Minimum '\n 'Depth',\n 'Value': '0M'},\n { 'Type': 'Maximum '\n 'Depth',\n 'Value': '3500'}]},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1999-01-01T00:00:00.000Z',\n 'EndingDateTime': '1999-02-01T23:59:59.999Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': '\\n'\n ' '\n 'Data '\n 'may '\n 'not '\n 'be '\n 'used '\n 'for '\n 'commercial '\n 'applications\\n'\n ' '}},\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214612327-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'darling_sst_99',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-12T15:31:29Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': '1999 Seawater Surface Temperature Data '\n 'collected off the dock at the Darling\\n'\n 'Marine Center Walpole, Maine.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-02 '\n '12:54:13'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://dmc.umaine.edu/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Walpole',\n 'Country': 'USA',\n 'PostalCode': '04573',\n 'StateProvince': 'ME',\n 'StreetAddresses': [ '193 '\n \"Clark's \"\n 'Cove '\n 'Road']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'darling@maine.edu'},\n { 'Type': 'Fax',\n 'Value': '207-563-3119'},\n { 'Type': 'Telephone',\n 'Value': '207-563-3146'}]},\n 'FirstName': 'KEVIN',\n 'LastName': 'ECKELBARGER',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Darling Marine Center, '\n 'School of Marine '\n 'Science, University of '\n 'Maine',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'UMAINE/SMS/DMC'}],\n 'DataLanguage': 'eng',\n 'DirectoryNames': [{'ShortName': 'USA/NASA'}],\n 'EntryTitle': '1999 Seawater Temperatures at the '\n 'Darling Marine Center',\n 'ISOTopicCategories': ['OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'MAINE',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'OCEAN',\n 'Subregion1': 'NORTH ATLANTIC '\n 'OCEAN',\n 'Subregion2': 'GULF OF MAINE',\n 'Type': 'ATLANTIC OCEAN'}],\n 'MetadataDates': [ { 'Date': '2002-11-07T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [{'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Projects': [ { 'LongName': 'Gulf of Maine Ocean Data '\n 'Partnership',\n 'ShortName': 'GOMODP'}],\n 'RelatedUrls': [ { 'Description': '1999 Seawater '\n 'Surface Temperature '\n 'Data',\n 'Type': 'GET DATA',\n 'URL': 'http://server.dmc.maine.edu/dmctemp99.html',\n 'URLContentType': 'DistributionURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE'}],\n 'ShortName': 'darling_sst_99',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -66.74,\n 'NorthBoundingCoordinate': 47.67,\n 'SouthBoundingCoordinate': 42.85,\n 'WestBoundingCoordinate': -71.31}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1999-01-01T00:00:00.000Z',\n 'EndingDateTime': '1999-12-31T23:59:59.999Z'}]}],\n 'Version': 'Not provided'}}],\n 'took': 17}\n\n\nThere’s a lot going on here. First off, the url:\nhttps://cmr.earthdata.nasa.gov/search/collections.umm_json?science_keywords[0][topic]=OCEANS\nThe basic premise is this: We are asking for all collections (../search/collections) that fall under the ‘OCEANS’ science topic as defined by GCMD. We are requesting this in the umm_json format (.umm_json). What we get back is a listing of those collections matching this. When last run, this was over 10900 collections! that’s a lot. Let’s get that down a bit…\n\nwith request.urlopen(cmr_url+\"collections.umm_json?science_keywords[0][topic]=OCEANS&science_keywords[0][term]=Ocean%20Temperature&has_granules_or_cwic=true&page_size=50\") as response:\n data = response.read()\n encoding = response.info().get_content_charset('utf-8')\n JSON_object = json.loads(data.decode(encoding))\n pp = pprint.PrettyPrinter(indent=2)\n pp.pprint(JSON_object)\n\n{ 'hits': 483,\n 'items': [ { 'meta': { 'concept-id': 'C1597928934-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-VIIRS_N20-OSPO-L2P',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:50:50Z',\n 'revision-id': 2,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'NOAA-20 (N20/JPSS-1/J1) is the second '\n 'satellite in the US NOAA latest '\n 'generation Joint Polar Satellite System '\n '(JPSS). N20 was launched on November 18, '\n '2017. In conjunction with the first US '\n 'satellite in JPSS series, Suomi National '\n 'Polar-orbiting Partnership (S-NPP) '\n 'satellite launched on October 28, 2011, '\n 'N20 form the new NOAA polar '\n 'constellation. NOAA is responsible for '\n 'all JPSS products, including SST from the '\n 'Visible Infrared Imaging Radiometer Suite '\n '(VIIRS). VIIRS is a whiskbroom scanning '\n 'radiometer, which takes measurements in '\n 'the cross-track direction within a field '\n 'of view of 112.56-deg using 16 detectors '\n 'and a double-sided mirror assembly. At a '\n 'nominal altitude of 829 km, the swath '\n 'width is 3,060 km, providing global daily '\n 'coverage for both day and night passes. '\n 'VIIRS has 22 spectral bands, covering the '\n 'spectrum from 0.4-12 um, including 16 '\n 'moderate resolution bands (M-bands). \\n'\n '\\n'\n 'The L2P SST product is derived at the '\n 'native sensor resolution (~0.75 km at '\n 'nadir, ~1.5 km at swath edge) using '\n \"NOAA's Advanced Clear-Sky Processor for \"\n 'Ocean (ACSPO) system, and reported in 10 '\n 'minute granules in netCDF4 format, '\n 'compliant with the GHRSST Data '\n 'Specification version 2 (GDS2). There are '\n '144 granules per 24hr interval, with a '\n 'total data volume of 27GB/day. In '\n 'addition to pixel-level earth locations, '\n 'Sun-sensor geometry, and ancillary data '\n 'from the NCEP global weather forecast, '\n 'ACSPO outputs include four brightness '\n 'temperatures (BTs) in M12 (3.7um), M14 '\n '(8.6um), M15 (11um), and M16 (12um) '\n 'bands, and two reflectances in M5 '\n '(0.67um) and M7 (0.87um) bands. The '\n 'reflectances are used for cloud '\n 'identification. Beginning with ACSPO '\n 'v2.60, all BTs and reflectances are '\n 'destriped (Bouali and Ignatov, 2014) and '\n 'resampled (Gladkova et al., 2016), to '\n 'minimize the effect of bow-tie '\n 'distortions and deletions. SSTs are '\n 'retrieved from destriped BTs. \\n'\n '\\n'\n 'SSTs are derived from BTs using the '\n 'Multi-Channel SST (MCSST; night) and '\n 'Non-Linear SST (NLSST; day) algorithms '\n '(Petrenko et al., 2014). ACSPO clear-sky '\n 'mask (ACSM) is provided in each pixel as '\n 'part of variable l2p_flags, which also '\n 'includes day/night, land, ice, twilight, '\n 'and glint flags (Petrenko et al., 2010). '\n 'Fill values are reported in all pixels '\n 'with >5 km inland. For each valid water '\n 'pixel (defined as ocean, sea, lake or '\n 'river, and up to 5 km inland), four BTs '\n 'in M12/14/15/16 (included for those users '\n 'interested in direct \"radiance '\n 'assimilation\", e.g., NOAA NCEP, NASA '\n 'GMAO, ECMWF) and two refelctances in M5/7 '\n 'are reported, along with derived SST. '\n 'Other variables include NCEP wind speed '\n 'and ACSPO SST minus reference SST '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). '\n 'Only ACSM confidently clear pixels are '\n 'recommended (equivalent to GDS2 quality '\n 'level=5). Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with QL=5. Note that users of ACSPO data '\n 'have the flexibility to ignore the ACSM '\n 'and derive their own clear-sky mask, and '\n 'apply it to BTs and SSTs. They may also '\n 'ignore ACSPO SSTs, and derive their own '\n 'SSTs from the original BTs. \\n'\n '\\n'\n 'The L2P product is monitored and '\n 'validated against quality controlled in '\n 'situ data provided by NOAA in situ SST '\n 'Quality Monitor system (iQuam; Xu and '\n 'Ignatov, 2014), using another NOAA '\n 'system, SST Quality Monitor (SQUAM; Dash '\n 'et al, 2010). Corresponding clear-sky BTs '\n 'are validated against RTM simulation in '\n 'the Monitoring IR Clear-sky Radiances '\n 'over Ocean for SST system (MICROS; Liang '\n 'and Ignatov, 2011). A reduced size '\n '(1GB/day), equal-angle gridded '\n '(0.02-deg), ACSPO L3U product is also '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-OSPO-L3U-v2.61, '\n 'where gridded L2P SSTs with QL=5 only are '\n 'reported, and BT layers omitted.',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': '',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'Name': 'Office '\n 'of '\n 'Satellite '\n 'and '\n 'Product '\n 'Operations '\n 'website',\n 'Protocol': 'HTTP'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 2P '\n 'OSPO dataset '\n 'v2.61 from VIIRS '\n 'on the NOAA-20 '\n 'satellite (GDS '\n 'v2) (GDS version '\n '2)',\n 'Version': '2.61'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}],\n 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov',\n 'URLContentType': 'DataContactURL'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'NonDataCenterAffiliation': 'Technical '\n 'Contact',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Kihai,',\n 'LastName': 'Yury',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Petrenko,',\n 'LastName': 'Boris',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Jonasson,',\n 'LastName': 'Olafur',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Pennybacker,',\n 'LastName': 'Matthew',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Gladkova,',\n 'LastName': 'Irina',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Asheville',\n 'Country': 'USA',\n 'PostalCode': '28801-5001',\n 'StateProvince': 'NC',\n 'StreetAddresses': [ 'Federal '\n 'Building '\n '151 '\n 'Patton '\n 'Avenue']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-713-3277'},\n { 'Type': 'Fax',\n 'Value': '301-713-3300'},\n { 'Type': 'Email',\n 'Value': 'NODC.DataOfficer@noaa.gov'}],\n 'RelatedUrls': [ { 'Description': 'Main '\n 'NCEI '\n 'website '\n 'providing '\n 'links '\n 'to '\n 'access '\n 'data '\n 'and '\n 'data '\n 'services.',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.ncei.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'National Centers for '\n 'Environmental '\n 'Information, NESDIS, '\n 'NOAA, U.S. Department '\n 'of Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-08-07T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P OSPO dataset v2.61 from '\n 'VIIRS on the NOAA-20 satellite (GDS v2) '\n '(GDS version 2)',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'OCEANS',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'BIOTA',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL OCEAN'}],\n 'MetadataAssociations': [ { 'EntryId': 'GHRSST Level '\n '2P OSPO '\n 'dataset v2.61 '\n 'from VIIRS on '\n 'the NOAA-20 '\n 'satellite (GDS '\n 'v2)',\n 'Type': 'COLLECTION',\n 'Version': '2.61'}],\n 'Platforms': [ { 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-20',\n 'ShortName': 'NOAA-20'},\n { 'Instruments': [ { 'LongName': 'Visible '\n 'Infrared '\n 'Imaging '\n 'Radiometer '\n 'Suite',\n 'ShortName': 'VIIRS'}],\n 'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Purpose': 'This dataset is available to the public '\n 'for a wide variety of uses including '\n 'scientific research and analysis.',\n 'RelatedUrls': [ { 'Description': 'Navigate directly '\n 'to the URL for a '\n 'descriptive web '\n 'page with download '\n 'links.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://doi.org/10.25921/sfs7-9688',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Search for data '\n 'granules belonging '\n 'to this collection '\n '(a granule is the '\n 'smallest '\n 'aggregation of data '\n 'that can be '\n 'independently '\n 'described and '\n 'retrieved).',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://www.nodc.noaa.gov/search/granule/rest/find/document?searchText=fileIdentifier%3AGHRSST-VIIRS_N20-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_N20-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_N20-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_N20-OSPO-L2P*&start=1&max=100&f=searchPage',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through a '\n 'variety of services '\n 'via a THREDDS '\n '(Thematic Real-time '\n 'Environmental '\n 'Distributed Data '\n 'Services) Data '\n 'Server (TDS). '\n 'Depending on the '\n 'dataset, the TDS '\n 'can provide WMS, '\n 'WCS, DAP, HTTP, and '\n 'other data access '\n 'and metadata '\n 'services as well. '\n 'For more '\n 'information on the '\n 'TDS, see '\n 'http://www.unidata.ucar.edu/software/thredds/current/tds/.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/thredds/catalog/ghrsst/L2P/VIIRS_N20/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Navigate directly '\n 'to the URL for data '\n 'access and direct '\n 'download.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. 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'\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/L2P/VIIRS_N20/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Preview graphic',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://data.nodc.noaa.gov/cgi-bin/gfx?id=gov.noaa.nodc:GHRSST-VIIRS_N20-OSPO-L2P',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Samples, Interface '\n 'Control Document '\n 'describing file '\n 'contents, '\n 'background ppt and '\n 'other info',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/ACSPO/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'and A. Heidinger, '\n '2010: Clear-Sky '\n 'Mask for ACSPO. '\n 'JTech, 27, '\n '1609-1623',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://journals.ametsoc.org/doi/abs/10.1175/2010JTECHA1413.1',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'J. Stroup, P. Dash, '\n '2014: Evaluation '\n 'and Selection of '\n 'SST Regression '\n 'Algorithms for JPSS '\n 'VIIRS. 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Kihai',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'oceanography',\n 'Term': 'Not provided',\n 'Topic': 'Not provided'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS',\n 'VariableLevel2': 'WIND SPEED'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SEA ICE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ICE EXTENT'}],\n 'ShortName': '10.25921/sfs7-9688',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2018-11-07T00:00:00.000Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'Distribution '\n 'liability: '\n 'NOAA '\n 'and '\n 'NCEI '\n 'make '\n 'no '\n 'warranty, '\n 'expressed '\n 'or '\n 'implied, '\n 'regarding '\n 'these '\n 'data, '\n 'nor '\n 'does '\n 'the '\n 'fact '\n 'of '\n 'distribution '\n 'constitute '\n 'such '\n 'a '\n 'warranty. '\n 'NOAA '\n 'and '\n 'NCEI '\n 'cannot '\n 'assume '\n 'liability '\n 'for '\n 'any '\n 'damages '\n 'caused '\n 'by '\n 'any '\n 'errors '\n 'or '\n 'omissions '\n 'in '\n 'these '\n 'data. '\n 'If '\n 'appropriate, '\n 'NCEI '\n 'can '\n 'only '\n 'certify '\n 'that '\n 'the '\n 'data '\n 'it '\n 'distributes '\n 'are '\n 'an '\n 'authentic '\n 'copy '\n 'of '\n 'the '\n 'records '\n 'that '\n 'were '\n 'accepted '\n 'for '\n 'inclusion '\n 'in '\n 'the '\n 'NCEI '\n 'archives.'}},\n 'Version': '2.61'}},\n { 'meta': { 'concept-id': 'C1597928333-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-MSG03-OSPO-L2P',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:38:50Z',\n 'revision-id': 2,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The Meteosat Second Generation (MSG-3) '\n 'satellites are spin stabilized '\n 'geostationary satellites operated by the '\n 'European Organization for the '\n 'Exploitation of Meteorological Satellites '\n '(EUMETSAT) to provide accurate weather '\n 'monitoring data through its primary '\n 'instrument the Spinning Enhanced Visible '\n 'and InfraRed Imager (SEVIRI), which has '\n 'the capacity to observe the Earth in 12 '\n 'spectral channels. Eight of these '\n 'channels are in the thermal infrared, '\n 'providing among other information, '\n 'observations of the temperatures of '\n 'clouds, land and sea surfaces at '\n 'approximately 5 km resolution with a 15 '\n 'minute duty cycle. This Group for High '\n 'Resolution Sea Surface Temperature '\n '(GHRSST) dataset produced by the US '\n 'National Oceanic and Atmospheric '\n 'Administration (NOAA) National '\n 'Environmental Satellite, Data, and '\n 'Information Service (NESDIS) is derived '\n 'from the SEVIRI instrument on the second '\n 'MSG satellite (also known as Meteosat-9) '\n 'that was launched on 22 December 2005. '\n 'Skin sea surface temperature (SST) data '\n 'are calculated from the infrared channels '\n 'of SEVIRI at full resolution every 15 '\n 'minutes. L2P data products with Single '\n 'Sensor Error Statistics (SSES) are then '\n 'derived following the GHRSST-PP Data '\n 'Processing Specification (GDS) version '\n '2.0.',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': 'Koner, Prabhat',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'https://podaac.jpl.nasa.gov/',\n 'Name': 'NASA '\n 'JPL '\n 'PO.DAAC '\n 'website',\n 'Protocol': 'HTTPS'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 2P '\n 'Atlantic Regional '\n 'Skin Sea Surface '\n 'Temperature from '\n 'the Spinning '\n 'Enhanced Visible '\n 'and InfraRed '\n 'Imager (SEVIRI) '\n 'on the Meteosat '\n 'Second Generation '\n '(MSG-3) satellite '\n '(GDS version 2)',\n 'Version': '1.0'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-763-8102 '\n 'x172'},\n { 'Type': 'Fax',\n 'Value': '301-763-8572'},\n { 'Type': 'Email',\n 'Value': 'Eileen.Maturi@noaa.gov'}]},\n 'FirstName': 'Eileen',\n 'LastName': 'Maturi',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-763-8102 '\n 'x172'},\n { 'Type': 'Fax',\n 'Value': '301-763-8572'},\n { 'Type': 'Email',\n 'Value': 'Eileen.Maturi@noaa.gov'}]},\n 'FirstName': 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'Avenue']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-713-3277'},\n { 'Type': 'Fax',\n 'Value': '301-713-3300'},\n { 'Type': 'Email',\n 'Value': 'NODC.DataOfficer@noaa.gov'}],\n 'RelatedUrls': [ { 'Description': 'Main '\n 'NCEI '\n 'website '\n 'providing '\n 'links '\n 'to '\n 'access '\n 'data '\n 'and '\n 'data '\n 'services.',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.ncei.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'National Centers for '\n 'Environmental '\n 'Information, NESDIS, '\n 'NOAA, U.S. Department '\n 'of Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-01-03T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P Atlantic Regional Skin '\n 'Sea Surface Temperature from the '\n 'Spinning Enhanced Visible and InfraRed '\n 'Imager (SEVIRI) on the Meteosat Second '\n 'Generation (MSG-3) satellite (GDS '\n 'version 2)',\n 'ISOTopicCategories': [ 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'\n 'NOAA '\n 'and '\n 'NCEI '\n 'cannot '\n 'assume '\n 'liability '\n 'for '\n 'any '\n 'damages '\n 'caused '\n 'by '\n 'any '\n 'errors '\n 'or '\n 'omissions '\n 'in '\n 'these '\n 'data. '\n 'If '\n 'appropriate, '\n 'NCEI '\n 'can '\n 'only '\n 'certify '\n 'that '\n 'the '\n 'data '\n 'it '\n 'distributes '\n 'are '\n 'an '\n 'authentic '\n 'copy '\n 'of '\n 'the '\n 'records '\n 'that '\n 'were '\n 'accepted '\n 'for '\n 'inclusion '\n 'in '\n 'the '\n 'NCEI '\n 'archives.'}},\n 'Version': '1.0'}},\n { 'meta': { 'concept-id': 'C1597990368-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-VIIRS_NPP-OSPO-L3U',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:52:23Z',\n 'revision-id': 2,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The Joint Polar Satellite System (JPSS), '\n 'starting with S-NPP launched on 28 '\n 'October 2011, is the new generation of '\n 'the US Polar Operational Environmental '\n 'Satellites (POES). The Suomi National '\n 'Polar-orbiting Partnership (S-NPP) is a '\n 'collaboration between NASA and NOAA. \\n'\n '\\n'\n 'The ACSPO SNPP/VIIRS L3U (Level 3 '\n 'Uncollated) product is a gridded version '\n 'of the ACSPO SNPP/VIIRS L2P product '\n 'available here '\n 'https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-OSPO-L2P-v2.61. '\n 'The L3U output files are 10-minute '\n 'granules in netCDF4 format, compliant '\n 'with the GHRSST Data Specification '\n 'version 2 (GDS2). There are 144 granules '\n 'per 24hr interval, with a total data '\n 'volume of 500MB/day. Fill values are '\n 'reported at all invalid pixels, including '\n 'pixels with >5 km inland. For each valid '\n 'water pixel (defined as ocean, sea, lake '\n 'or river, and up to 5 km inland), the '\n 'following layers are reported: SSTs, '\n 'ACSPO clear-sky mask (ACSM; provided in '\n 'each grid as part of l2p_flags, which '\n 'also includes day/night, land, ice, '\n 'twilight, and glint flags), NCEP wind '\n 'speed, and ACSPO SST minus reference '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 '\n '). Only L2P SSTs with QL=5 were gridded, '\n 'so all valid SSTs are recommended for the '\n 'users. Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with valid SST. The ACSPO VIIRS L3U '\n 'product is monitored and validated '\n 'against iQuam in situ data (Xu and '\n 'Ignatov, 2014) in SQUAM (Dash et al, '\n '2010).',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': '',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'Name': 'Office '\n 'of '\n 'Satellite '\n 'and '\n 'Product '\n 'Operations '\n 'website',\n 'Protocol': 'HTTP'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 3U '\n 'OSPO dataset '\n 'v2.61 from VIIRS '\n 'on S-NPP '\n 'Satellite (GDS '\n 'v2) (GDS version '\n '2)',\n 'Version': '2.61'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}],\n 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.star.nesdis.noaa.gov',\n 'URLContentType': 'DataContactURL'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'NonDataCenterAffiliation': 'Technical '\n 'Contact',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Pennybacker,',\n 'LastName': 'Matthew',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Gladkova,',\n 'LastName': 'Irina',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Kihai,',\n 'LastName': 'Yury',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Petrenko,',\n 'LastName': 'Boris',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Jonasson,',\n 'LastName': 'Olafur',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Asheville',\n 'Country': 'USA',\n 'PostalCode': '28801-5001',\n 'StateProvince': 'NC',\n 'StreetAddresses': [ 'Federal '\n 'Building '\n '151 '\n 'Patton '\n 'Avenue']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-713-3277'},\n { 'Type': 'Fax',\n 'Value': '301-713-3300'},\n { 'Type': 'Email',\n 'Value': 'NODC.DataOfficer@noaa.gov'}],\n 'RelatedUrls': [ { 'Description': 'Main '\n 'NCEI '\n 'website '\n 'providing '\n 'links '\n 'to '\n 'access '\n 'data '\n 'and '\n 'data '\n 'services.',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.ncei.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'National Centers for '\n 'Environmental '\n 'Information, NESDIS, '\n 'NOAA, U.S. Department '\n 'of Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-08-11T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 3U OSPO dataset v2.61 from '\n 'VIIRS on S-NPP Satellite (GDS v2) (GDS '\n 'version 2)',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'OCEANS',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'BIOTA',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL OCEAN'}],\n 'MetadataAssociations': [ { 'EntryId': 'GHRSST Level '\n '3U OSPO '\n 'dataset v2.61 '\n 'from VIIRS on '\n 'S-NPP '\n 'Satellite (GDS '\n 'v2)',\n 'Type': 'COLLECTION',\n 'Version': '2.61'}],\n 'Platforms': [ { 'LongName': 'Suomi National '\n 'Polar-orbiting '\n 'Partnership',\n 'ShortName': 'SUOMI-NPP'},\n { 'Instruments': [ { 'LongName': 'Visible '\n 'Infrared '\n 'Imaging '\n 'Radiometer '\n 'Suite',\n 'ShortName': 'VIIRS'}],\n 'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Purpose': 'This dataset is available to the public '\n 'for a wide variety of uses including '\n 'scientific research and analysis.',\n 'RelatedUrls': [ { 'Description': 'Navigate directly '\n 'to the URL for a '\n 'descriptive web '\n 'page with download '\n 'links.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://doi.org/10.7289/v5kk98s8',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Search for data '\n 'granules belonging '\n 'to this collection '\n '(a granule is the '\n 'smallest '\n 'aggregation of data '\n 'that can be '\n 'independently '\n 'described and '\n 'retrieved).',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://www.nodc.noaa.gov/search/granule/rest/find/document?searchText=fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*&start=1&max=100&f=searchPage',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through a '\n 'variety of services '\n 'via a THREDDS '\n '(Thematic Real-time '\n 'Environmental '\n 'Distributed Data '\n 'Services) Data '\n 'Server (TDS). '\n 'Depending on the '\n 'dataset, the TDS '\n 'can provide WMS, '\n 'WCS, DAP, HTTP, and '\n 'other data access '\n 'and metadata '\n 'services as well. '\n 'For more '\n 'information on the '\n 'TDS, see '\n 'http://www.unidata.ucar.edu/software/thredds/current/tds/.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/thredds/catalog/ghrsst/L3U/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Navigate directly '\n 'to the URL for data '\n 'access and direct '\n 'download.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/L3U/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through '\n 'the File Transfer '\n 'Protocol (FTP). You '\n 'may use any FTP '\n 'client to download '\n 'these data.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/L3U/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Preview graphic',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://data.nodc.noaa.gov/cgi-bin/gfx?id=gov.noaa.nodc:GHRSST-VIIRS_NPP-OSPO-L3U',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/miscellaneous-documents/GHRSSTUserGuidev91.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/governance-documents/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dissemination '\n 'reports log',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.nodc.noaa.gov/SatelliteData/ghrsst/logs/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/ACSPO/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Journal Article',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://dx.doi.org/10.1016/j.rse.2015.01.003',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Journal Article',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://dx.doi.org/10.1002/2013JD020637',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'SST products '\n 'monitored',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Samples, Interface '\n 'Control Document '\n 'describing file '\n 'contents, '\n 'background ppt and '\n 'other info',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/ACSPO/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'and A. Heidinger, '\n '2010: Clear-Sky '\n 'Mask for ACSPO. '\n 'JTech, 27, '\n '1609-1623',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://journals.ametsoc.org/doi/abs/10.1175/2010JTECHA1413.1',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'J. Stroup, P. Dash, '\n '2014: Evaluation '\n 'and Selection of '\n 'SST Regression '\n 'Algorithms for JPSS '\n 'VIIRS. JGR, 119, '\n '4580-4599, doi: '\n '10.1002/2013JD020637',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://onlinelibrary.wiley.com/doi/10.1002/2013JD020637/abstract',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Read software',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/sw/IDL/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': '(Search Granule)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHVRS-3UO61',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Xu, F.',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/iquam/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Liang, X.',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/micros/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dash, P., A. '\n 'Ignatov, Y. Kihai',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'oceanography',\n 'Term': 'Not provided',\n 'Topic': 'Not provided'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS',\n 'VariableLevel2': 'WIND SPEED'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SEA ICE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ICE EXTENT'}],\n 'ShortName': '10.7289/v5kk98s8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2015-05-19T00:00:00.000Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'Distribution '\n 'liability: '\n 'NOAA '\n 'and '\n 'NCEI '\n 'make '\n 'no '\n 'warranty, '\n 'expressed '\n 'or '\n 'implied, '\n 'regarding '\n 'these '\n 'data, '\n 'nor '\n 'does '\n 'the '\n 'fact '\n 'of '\n 'distribution '\n 'constitute '\n 'such '\n 'a '\n 'warranty. '\n 'NOAA '\n 'and '\n 'NCEI '\n 'cannot '\n 'assume '\n 'liability '\n 'for '\n 'any '\n 'damages '\n 'caused '\n 'by '\n 'any '\n 'errors '\n 'or '\n 'omissions '\n 'in '\n 'these '\n 'data. '\n 'If '\n 'appropriate, '\n 'NCEI '\n 'can '\n 'only '\n 'certify '\n 'that '\n 'the '\n 'data '\n 'it '\n 'distributes '\n 'are '\n 'an '\n 'authentic '\n 'copy '\n 'of '\n 'the '\n 'records '\n 'that '\n 'were '\n 'accepted '\n 'for '\n 'inclusion '\n 'in '\n 'the '\n 'NCEI '\n 'archives.'}},\n 'Version': '2.61'}},\n { 'meta': { 'concept-id': 'C1224519979-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-VIIRS_NPP-OSPO-L2P',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:51:43Z',\n 'revision-id': 7,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The Joint Polar Satellite System (JPSS), '\n 'starting with S-NPP launched on 28 '\n 'October 2011, is the new generation of '\n 'the US Polar Operational Environmental '\n 'Satellites (POES). The Suomi National '\n 'Polar-orbiting Partnership (S-NPP) is a '\n 'collaboration between NASA and NOAA. NOAA '\n 'is responsible for all JPSS products, '\n 'including SST from the Visible Infrared '\n 'Imaging Radiometer Suite (VIIRS). VIIRS '\n 'is a whiskbroom scanning radiometer, '\n 'which takes measurements in the '\n 'cross-track direction within a field of '\n 'view of 112.56-deg using 16 detectors and '\n 'a double-sided mirror assembly. At a '\n 'nominal altitude of 829 km, the swath '\n 'width is 3,060 km, providing global daily '\n 'coverage for both day and night passes. '\n 'VIIRS has 22 spectral bands covering the '\n 'spectrum from 0.4-12 um, including 16 '\n 'moderate resolution bands (M-bands). \\n'\n '\\n'\n 'The L2P SST product is derived at the '\n 'native sensor resolution (~0.75 km at '\n 'nadir, ~1.5 km at swath edge) using '\n \"NOAA's Advanced Clear-Sky Processor for \"\n 'Ocean (ACSPO) system, and reported in '\n '10-minute granules in netCDF4 format, '\n 'compliant with the GHRSST Data '\n 'Specification version 2 (GDS2). There are '\n '144 granules per 24hr interval, with a '\n 'total data volume of 27GB/day. In '\n 'addition to pixel-level earth locations, '\n 'Sun-sensor geometry, and ancillary data '\n 'from the NCEP global weather forecast, '\n 'ACSPO outputs include four brightness '\n 'temperatures (BTs) in M12 (3.7um), M14 '\n '(8.6um), M15 (11um), and M16 (12um) '\n 'bands, and two reflectances in M5 '\n '(0.67um) and M7 (0.87um) bands. The '\n 'reflectances are used for cloud '\n 'identification. Beginning with ACSPO '\n 'v2.60, all BTs and reflectances are '\n 'destriped (Bouali and Ignatov, 2014) and '\n 'resampled (Gladkova et al., 2016), to '\n 'minimize the effect of bow-tie '\n 'distortions and deletions. SSTs are '\n 'retrieved from destriped BTs.\\n'\n '\\n'\n 'SSTs are derived from BTs using the '\n 'Multi-Channel SST (MCSST; night) and '\n 'Non-Linear SST (NLSST; day) algorithms '\n '(Petrenko et al., 2014). An ACSPO '\n 'clear-sky mask (ACSM) is provided in each '\n 'pixel as part of variable l2p_flags, '\n 'which also includes day/night, land, ice, '\n 'twilight, and glint flags (Petrenko et '\n 'al., 2010). Fill values are reported in '\n 'all invalid pixels, including those with '\n '>5 km inland. For each valid water pixel '\n '(defined as ocean, sea, lake or river, '\n 'and up to 5 km inland), four BTs in '\n 'M12/14/15/16 (included for those users '\n 'interested in direct \"radiance '\n 'assimilation\", e.g., NOAA NCEP, NASA '\n 'GMAO, ECMWF) and two refelctances in M5/7 '\n 'are reported, along with derived SST. '\n 'Other variables include NCEP wind speed '\n 'and ACSPO SST minus reference SST '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). '\n 'Only ACSM confidently clear pixels are '\n 'recommended (equivalent to GDS2 quality '\n 'level=5). Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with QL=5. Note that users of ACSPO data '\n 'have the flexibility to ignore the ACSM '\n 'and derive their own clear-sky mask, and '\n 'apply it to BTs and SSTs. They may also '\n 'ignore ACSPO SSTs, and derive their own '\n 'SSTs from the original BTs.\\n'\n '\\n'\n 'The ACSPO VIIRS L2P product is monitored '\n 'and validated against quality controlled '\n 'in situ data provided by NOAA in situ SST '\n 'Quality Monitor system (iQuam; Xu and '\n 'Ignatov, 2014) using another NOAA system, '\n 'SST Quality Monitor (SQUAM; Dash et al, '\n '2010). Corresponding clear-sky BTs are '\n 'validated against RTM simulations in the '\n 'Monitoring IR Clear-sky Radiances over '\n 'Ocean for SST system (MICROS; Liang and '\n 'Ignatov, 2011). A reduced size (1GB/day), '\n 'equal-angle gridded (0.02-deg '\n 'resolution), ACSPO L3U product is also '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-OSPO-L3U-v2.61, '\n 'where gridded L2P SSTs with QL=5 only are '\n 'reported, and BT layers omitted.',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': '',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'Name': 'Office '\n 'of '\n 'Satellite '\n 'and '\n 'Product '\n 'Operations '\n 'website',\n 'Protocol': 'HTTP'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 2P '\n 'OSPO dataset '\n 'v2.61 from VIIRS '\n 'on S-NPP '\n 'Satellite (GDS '\n 'v2) (GDS version '\n '2)',\n 'Version': '2.61'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}],\n 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.star.nesdis.noaa.gov',\n 'URLContentType': 'DataContactURL'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'NonDataCenterAffiliation': 'Technical '\n 'Contact',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Kihai,',\n 'LastName': 'Yury',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Petrenko,',\n 'LastName': 'Boris',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Jonasson,',\n 'LastName': 'Olafur',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Pennybacker,',\n 'LastName': 'Matthew',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Gladkova,',\n 'LastName': 'Irina',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Asheville',\n 'Country': 'USA',\n 'PostalCode': '28801-5001',\n 'StateProvince': 'NC',\n 'StreetAddresses': [ 'Federal '\n 'Building '\n '151 '\n 'Patton '\n 'Avenue']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-713-3277'},\n { 'Type': 'Fax',\n 'Value': '301-713-3300'},\n { 'Type': 'Email',\n 'Value': 'NODC.DataOfficer@noaa.gov'}],\n 'RelatedUrls': [ { 'Description': 'Main '\n 'NCEI '\n 'website '\n 'providing '\n 'links '\n 'to '\n 'access '\n 'data '\n 'and '\n 'data '\n 'services.',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.ncei.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'National Centers for '\n 'Environmental '\n 'Information, NESDIS, '\n 'NOAA, U.S. Department '\n 'of Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-08-06T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P OSPO dataset v2.61 from '\n 'VIIRS on S-NPP Satellite (GDS v2) (GDS '\n 'version 2)',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'OCEANS',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'BIOTA',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL OCEAN'}],\n 'MetadataAssociations': [ { 'EntryId': 'GHRSST Level '\n '2P OSPO '\n 'dataset v2.61 '\n 'from VIIRS on '\n 'S-NPP '\n 'Satellite (GDS '\n 'v2)',\n 'Type': 'COLLECTION',\n 'Version': '2.61'}],\n 'Platforms': [ { 'LongName': 'Suomi National '\n 'Polar-orbiting '\n 'Partnership',\n 'ShortName': 'SUOMI-NPP'},\n { 'Instruments': [ { 'LongName': 'Visible '\n 'Infrared '\n 'Imaging '\n 'Radiometer '\n 'Suite',\n 'ShortName': 'VIIRS'}],\n 'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Purpose': 'This dataset is available to the public '\n 'for a wide variety of uses including '\n 'scientific research and analysis.',\n 'RelatedUrls': [ { 'Description': 'Navigate directly '\n 'to the URL for a '\n 'descriptive web '\n 'page with download '\n 'links.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://doi.org/10.7289/v5pr7sx5',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Search for data '\n 'granules belonging '\n 'to this collection '\n '(a granule is the '\n 'smallest '\n 'aggregation of data '\n 'that can be '\n 'independently '\n 'described and '\n 'retrieved).',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://www.nodc.noaa.gov/search/granule/rest/find/document?searchText=fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*&start=1&max=100&f=searchPage',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through a '\n 'variety of services '\n 'via a THREDDS '\n '(Thematic Real-time '\n 'Environmental '\n 'Distributed Data '\n 'Services) Data '\n 'Server (TDS). '\n 'Depending on the '\n 'dataset, the TDS '\n 'can provide WMS, '\n 'WCS, DAP, HTTP, and '\n 'other data access '\n 'and metadata '\n 'services as well. '\n 'For more '\n 'information on the '\n 'TDS, see '\n 'http://www.unidata.ucar.edu/software/thredds/current/tds/.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/thredds/catalog/ghrsst/L2P/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Navigate directly '\n 'to the URL for data '\n 'access and direct '\n 'download.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/L2P/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through '\n 'the File Transfer '\n 'Protocol (FTP). You '\n 'may use any FTP '\n 'client to download '\n 'these data.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/L2P/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Preview graphic',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://data.nodc.noaa.gov/cgi-bin/gfx?id=gov.noaa.nodc:GHRSST-VIIRS_NPP-OSPO-L2P',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/miscellaneous-documents/GHRSSTUserGuidev91.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/governance-documents/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dissemination '\n 'reports log',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.nodc.noaa.gov/SatelliteData/ghrsst/logs/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Journal Article',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://dx.doi.org/10.1002/2013JD020637',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'SST products '\n 'monitored',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Samples, Interface '\n 'Control Document '\n 'describing file '\n 'contents, '\n 'background ppt and '\n 'other info',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/ACSPO/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'and A. Heidinger, '\n '2010: Clear-Sky '\n 'Mask for ACSPO. '\n 'JTech, 27, '\n '1609-1623',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://journals.ametsoc.org/doi/abs/10.1175/2010JTECHA1413.1',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'J. Stroup, P. Dash, '\n '2014: Evaluation '\n 'and Selection of '\n 'SST Regression '\n 'Algorithms for JPSS '\n 'VIIRS. 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Kihai',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'oceanography',\n 'Term': 'Not provided',\n 'Topic': 'Not provided'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS',\n 'VariableLevel2': 'WIND SPEED'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SEA ICE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ICE EXTENT'}],\n 'ShortName': '10.7289/v5pr7sx5',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2014-05-19T00:00:00.000Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'Distribution '\n 'liability: '\n 'NOAA '\n 'and '\n 'NCEI '\n 'make '\n 'no '\n 'warranty, '\n 'expressed '\n 'or '\n 'implied, '\n 'regarding '\n 'these '\n 'data, '\n 'nor '\n 'does '\n 'the '\n 'fact '\n 'of '\n 'distribution '\n 'constitute '\n 'such '\n 'a '\n 'warranty. '\n 'NOAA '\n 'and '\n 'NCEI '\n 'cannot '\n 'assume '\n 'liability '\n 'for '\n 'any '\n 'damages '\n 'caused '\n 'by '\n 'any '\n 'errors '\n 'or '\n 'omissions '\n 'in '\n 'these '\n 'data. '\n 'If '\n 'appropriate, '\n 'NCEI '\n 'can '\n 'only '\n 'certify '\n 'that '\n 'the '\n 'data '\n 'it '\n 'distributes '\n 'are '\n 'an '\n 'authentic '\n 'copy '\n 'of '\n 'the '\n 'records '\n 'that '\n 'were '\n 'accepted '\n 'for '\n 'inclusion '\n 'in '\n 'the '\n 'NCEI '\n 'archives.'}},\n 'Version': '2.61'}},\n { 'meta': { 'concept-id': 'C1214622565-ISRO',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': '3DIMG_L2B_SST',\n 'provider-id': 'ISRO',\n 'revision-date': '2018-11-29T13:46:53Z',\n 'revision-id': 7,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'INSAT-3D Imager Level-2B Sea Surface '\n 'Temperature in HDF-5 Format',\n 'AccessConstraints': { 'Description': '\\n'\n ' '\n 'Registration '\n 'Required for '\n 'Download, '\n 'products '\n 'available at no '\n 'cost\\n'\n ' '},\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.uuid',\n 'Value': 'f1c77e0e-ab19-4d9d-807c-ff00e7788e35'},\n { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-01 '\n '16:24:23'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'AncillaryKeywords': ['Geophysical Parameter L2B SST'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 6.0,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'No '\n 'Fees',\n 'Format': 'HDF-5',\n 'FormatType': 'Native',\n 'Media': [ 'FTP']}]},\n 'CollectionCitations': [ { 'Title': 'INSAT-3D Imager '\n 'Level-2B Sea '\n 'Surface '\n 'Temperature'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Ahmedabad',\n 'Country': 'India',\n 'PostalCode': '380058',\n 'StateProvince': 'Gujarat',\n 'StreetAddresses': [ 'Room '\n 'No '\n '6244, '\n 'SAC '\n 'BOPAL '\n 'Campus, '\n 'ISRO, '\n 'BOPAL']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'nitant@sac.isro.gov.in'},\n { 'Type': 'Telephone',\n 'Value': '+91-79-26916244'}]},\n 'FirstName': 'NITANT',\n 'LastName': 'DUBE',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.mosdac.gov.in/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Ahmedabad',\n 'Country': 'India',\n 'PostalCode': '380058',\n 'StateProvince': 'Gujarat',\n 'StreetAddresses': [ 'Room '\n 'No '\n '6056, '\n 'SAC '\n 'BOPAL '\n 'Campus, '\n 'ISRO, '\n 'BOPAL']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'admin@mosdac.gov.in'},\n { 'Type': 'Telephone',\n 'Value': '+91-79-26916056'}]},\n 'FirstName': 'PUSHPA',\n 'LastName': 'SHAH',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Meteorological and '\n 'Oceanographic '\n 'Satellite Data '\n 'Archival Centre '\n '(MOSDAC)',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'IN/ISRO/MOSDAC',\n 'Uuid': '70726ec3-c6bd-4f58-b32f-ff78c3cc219b'}],\n 'DataLanguage': 'eng',\n 'DirectoryNames': [ {'ShortName': 'ISRO'},\n {'ShortName': 'CEOS'}],\n 'EntryTitle': 'INSAT-3D Imager Level-2B Sea Surface '\n 'Temperature',\n 'ISOTopicCategories': ['OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'Subregion1': 'EASTERN ASIA',\n 'Type': 'ASIA'},\n { 'Category': 'CONTINENT',\n 'Subregion1': 'SOUTHCENTRAL '\n 'ASIA',\n 'Type': 'ASIA'},\n { 'Category': 'CONTINENT',\n 'Subregion1': 'SOUTHEASTERN '\n 'ASIA',\n 'Type': 'ASIA'},\n { 'Category': 'CONTINENT',\n 'Subregion1': 'SOUTHERN ASIA',\n 'Type': 'ASIA'},\n { 'Category': 'CONTINENT',\n 'Subregion1': 'WESTERN ASIA',\n 'Type': 'ASIA'}],\n 'MetadataDates': [ { 'Date': '2014-07-22T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-11-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ShortName': '3D-IMAGER'}],\n 'ShortName': 'INSAT-3D',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'INSAT 3D Meteorological '\n 'Data Processing System '\n '(IMDPS)',\n 'ShortName': 'IMDPS'},\n { 'LongName': 'CEOS WGISS Integrated '\n 'Catalog',\n 'ShortName': 'CWIC'}],\n 'Purpose': 'Sea surface temperature is derived from '\n 'split thermal window channels (TIR1, TIR2) '\n 'during daytime \\n'\n ' and using additional mid IR window '\n 'channel (MIR) during nighttime over cloud '\n 'free oceanic regions. \\n'\n ' The most important part of the SST '\n 'retrieval from IR observations is the '\n 'atmospheric correction, \\n'\n ' especially over tropics.',\n 'Quality': '\\n Validation Completed\\n ',\n 'RelatedUrls': [ { 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.mosdac.gov.in/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': '3DIMG_L2B_SST',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 163.15671,\n 'NorthBoundingCoordinate': 81.04153,\n 'SouthBoundingCoordinate': -81.04153,\n 'WestBoundingCoordinate': 0.843296}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2013-10-01T00:00:00.000Z'}]}],\n 'TemporalKeywords': ['30min'],\n 'UseConstraints': { 'Description': { 'Description': '\\n'\n ' '\n 'Non-commercial, '\n 'scientific '\n 'use\\n'\n ' '}},\n 'Version': 'Not provided'}},\n { 'meta': { 'associations': { 'services': [ 'S1571647120-LANCEAMSR2']},\n 'concept-id': 'C1000000000-LANCEAMSR2',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': True,\n 'has-temporal-subsetting': False,\n 'has-transforms': True,\n 'has-variables': True,\n 'native-id': 'NRT AMSR2 L2B GLOBAL SWATH GSFC '\n 'PROFILING ALGORITHM 2010: SURFACE '\n 'PRECIPITATION, WIND SPEED OVER OCEAN, '\n 'WATER VAPOR OVER OCEAN AND CLOUD LIQUID '\n 'WATER OVER OCEAN V0',\n 'provider-id': 'LANCEAMSR2',\n 'revision-date': '2020-03-05T19:34:19Z',\n 'revision-id': 30,\n 'user-id': 'lance_amsr2'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer 2 (AMSR2) instrument on the '\n 'Global Change Observation Mission - Water '\n '1 (GCOM-W1) provides global passive '\n 'microwave measurements of terrestrial, '\n 'oceanic, and atmospheric parameters for '\n 'the investigation of global water and '\n 'energy cycles. Near real-time (NRT) '\n 'products are generated within 3 hours of '\n 'the last observations in the file, by the '\n 'Land Atmosphere Near real-time Capability '\n 'for EOS (LANCE) at the AMSR Science '\n 'Investigator-led Processing System (AMSR '\n 'SIPS), which is collocated with the '\n 'Global Hydrology Resource Center (GHRC) '\n 'DAAC. The GCOM-W1 AMSR2 Level-2B rain '\n 'and ocean products include global '\n 'precipitation and ocean parameters (not '\n 'including Sea Surface Temperatures), '\n 'calculated by the Goddard PROFiling '\n 'algorithm (GPROF) 2010 version using as '\n 'input the resampled brightness '\n 'temperature (Level-1R) data provided by '\n 'the Japanese Aerospace Exploration Agency '\n '(JAXA). Data are stored in HDF-EOS5 '\n 'format and are available via HTTP from '\n 'the EOSDIS LANCE system at '\n 'https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/. '\n 'If data latency is not a primary concern, '\n 'please consider using science quality '\n 'products. Science products are created '\n 'using the best available ancillary, '\n 'calibration and ephemeris information. '\n 'Science quality products are an '\n 'internally consistent, well-calibrated '\n \"record of the Earth's geophysical \"\n 'properties to support science. The AMSR '\n 'SIPS plans to start producing initial '\n 'AMSR2 standard science quality data '\n 'products in late 2015 and they will be '\n 'available from the NSIDC DAAC. Notice: '\n 'All LANCE AMSR2 data should be used with '\n 'the understanding that these are '\n 'preliminary products. Cross calibration '\n 'with AMSR-E products has not been '\n 'performed. As updates are made to the '\n 'L1R data set, those changes will be '\n 'reflected in this higher level product.',\n 'AccessConstraints': { 'Description': 'This product '\n 'has full public '\n 'access.',\n 'Value': 0.0},\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Product '\n 'DOI',\n 'Name': 'identifier_product_doi',\n 'Value': '10.5067/AMSR2/A2_RainOcn_NRT'},\n { 'DataType': 'STRING',\n 'Description': 'DOI '\n 'authority',\n 'Name': 'identifier_product_doi_authority',\n 'Value': 'http://dx.doi.org'},\n { 'DataType': 'STRING',\n 'Description': 'Flag to '\n 'indicate '\n 'ascending '\n 'or '\n 'descending',\n 'Name': 'AscendingDescendingFlg',\n 'Value': 'Ascending and '\n 'Descending'}],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Fees': '0.0',\n 'Format': 'Not '\n 'provided',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Kummerow, '\n 'Christian, '\n 'Ralph '\n 'Ferraro '\n 'and '\n 'David '\n 'Duncan.2015. '\n 'NRT '\n 'AMSR2 '\n 'L2B '\n 'GLOBAL '\n 'SWATH '\n 'GSFC '\n 'PROFILING '\n 'ALGORITHM '\n '2010: '\n 'SURFACE '\n 'PRECIPITATION, '\n 'WIND '\n 'SPEED '\n 'OVER '\n 'OCEAN, '\n 'WATER '\n 'VAPOR '\n 'OVER '\n 'OCEAN '\n 'AND '\n 'CLOUD '\n 'LIQUID '\n 'WATER '\n 'OVER '\n 'OCEAN '\n '[indicate '\n 'subset '\n 'used]. '\n 'Dataset '\n 'available '\n 'online '\n 'from '\n 'the '\n 'NASA '\n 'Global '\n 'Hydrology '\n 'Resource '\n 'Center '\n 'DAAC, '\n 'Huntsville, '\n 'Alabama, '\n 'U.S.A. '\n 'DOI: '\n 'http://dx.doi.org/10.5067/AMSR2/A2_RainOcn_NRT'}],\n 'CollectionDataType': 'NEAR_REAL_TIME',\n 'CollectionProgress': 'ACTIVE',\n 'DOI': {'DOI': '10.5067/AMSR2/A2_RainOcn_NRT'},\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Huntsville',\n 'Country': 'USA',\n 'PostalCode': '35805',\n 'StateProvince': 'Alabama',\n 'StreetAddresses': [ '320 '\n 'Sparkman '\n 'Drive']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '+1 '\n '256-961-7932'},\n { 'Type': 'Fax',\n 'Value': '+1 '\n '256-824-5149'},\n { 'Type': 'Email',\n 'Value': 'support-ghrc@earthdata.nasa.gov'}]},\n 'Roles': ['DISTRIBUTOR'],\n 'ShortName': 'NASA/MSFC/AMSR '\n 'SIPS/LANCE'}],\n 'DataDates': [ { 'Date': '2017-01-24T16:53:49.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2020-03-03T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'NRT AMSR2 L2B GLOBAL SWATH GSFC '\n 'PROFILING ALGORITHM 2010: SURFACE '\n 'PRECIPITATION, WIND SPEED OVER OCEAN, '\n 'WATER VAPOR OVER OCEAN AND CLOUD LIQUID '\n 'WATER OVER OCEAN V0',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'Platforms': [ { 'Instruments': [ { 'ComposedOf': [ { 'ShortName': 'AMSR2'}],\n 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2',\n 'ShortName': 'AMSR2'}],\n 'LongName': 'Global Change '\n 'Observation Mission '\n '1st-Water',\n 'ShortName': 'GCOM-W1',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Land, Atmosphere Near '\n 'real-time Capability for '\n 'EOS',\n 'ShortName': 'LANCE'}],\n 'RelatedUrls': [ { 'Description': 'Files may be '\n 'downloaded directly '\n 'to your workstation '\n 'from this link',\n 'Type': 'GET DATA',\n 'URL': 'https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/R00/hdfeos5/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_LW.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_WS.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_D_LW.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_D_SP.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_SP.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_SR.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_WV.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Files may be '\n 'downloaded directly '\n 'to your workstation '\n 'from this link',\n 'Subtype': 'LANCE',\n 'Type': 'GET DATA',\n 'URL': 'https://lance.itsc.uah.edu/amsr2-science/data/level2/rainocean/R00/hdfeos5/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The guide document '\n 'contains detailed '\n 'information about '\n 'the dataset',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://lance.nsstc.nasa.gov/amsr2-science/doc/LANCE_A2_RainOcn_NRT_dataset.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Interactively '\n 'browse imagery in '\n 'EOSDIS Worldview',\n 'Subtype': 'WORLDVIEW',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://worldview.earthdata.nasa.gov/?p=geographic&l=MODIS_Terra_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor,OrbitTracks_GCOM-W1_Ascending(hidden),OrbitTracks_GCOM-W1_Descending(hidden),AMSR2_Cloud_Liquid_Water_Day,AMSR2_Cloud_Liquid_Water_Night(hidden),AMSR2_Columnar_Water_Vapor_Day(hidden),AMSR2_Columnar_Water_Vapor_Night(hidden),AMSR2_Surface_Precipitation_Rate_Day(hidden),AMSR2_Surface_Precipitation_Rate_Night(hidden),AMSR2_Surface_Rain_Rate_Day(hidden),AMSR2_Surface_Rain_Rate_Night(hidden),AMSR2_Wind_Speed_Day(hidden),AMSR2_Wind_Speed_Night(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'The home page for '\n 'the project or '\n 'program which '\n 'sponsored the '\n 'dataset',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://earthdata.nasa.gov/earth-observation-data/near-real-time',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'LANCE NRT AMSR2 L2B '\n 'Global Swath Rain '\n 'Ocean Data '\n 'Quickview using '\n 'Python and GIS',\n 'Subtype': 'DATA RECIPE',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/home/data-recipes/lance-nrt-amsr2-l2b-global-swath-rain-ocean-data-quickview-using-python-and-gis',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Instructions for '\n 'citing GHRC data',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Water '\n 'Vapor',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Water Vapor '\n 'Profiles'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Water '\n 'Vapor',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Water Vapor '\n 'Indicators',\n 'VariableLevel2': 'Total '\n 'Precipitable '\n 'Water'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Winds',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Surface '\n 'Winds',\n 'VariableLevel2': 'Wind Speed'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Winds',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Surface '\n 'Winds',\n 'VariableLevel2': 'Wind '\n 'Direction'},\n { 'Category': 'Earth Science',\n 'Term': 'Precipitation',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Precipitation '\n 'Rate'},\n { 'Category': 'Earth Science',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Water '\n 'Vapor',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Water Vapor '\n 'Indicators',\n 'VariableLevel2': 'Water Vapor'},\n { 'Category': 'Earth Science',\n 'Term': 'Clouds',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Cloud '\n 'Microphysics',\n 'VariableLevel2': 'Cloud Liquid '\n 'Water/Ice'}],\n 'ShortName': 'A2_RainOcn_NRT',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 89.2,\n 'SouthBoundingCoordinate': -89.3,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2019-01-20T00:00:00.000Z'}]}],\n 'TemporalKeywords': ['1 minute - < 1 hour'],\n 'Version': '0'}},\n { 'meta': { 'concept-id': 'C1653649483-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+NOAA/STAR+GOES-16+ABI+L2P+America+Region+SST+v2.70+dataset+in+GDS2',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-18T18:33:43Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'GOES-16 (G16) is the first satellite in '\n 'the US NOAA third generation of '\n 'Geostationary Operational Environmental '\n 'Satellites (GOES), a.k.a. GOES-R series '\n '(which will also include -S, -T, and -U). '\n 'G16 was launched on 19 Nov 2016 and '\n 'initially placed in an interim position '\n 'at 89.5-deg W, between GOES-East and '\n '-West. Upon completion of Cal/Val in Dec '\n '2018, it was moved to its permanent '\n 'position at 75.2-deg W, and declared NOAA '\n 'operational GOES-East on 18 Dec 2018. \\n'\n 'NOAA is responsible for all GOES-R '\n 'products, including Sea Surface '\n 'Temperature (SST) from the Advanced '\n 'Baseline Imager (ABI). The ABI offers '\n 'vastly enhanced capabilities for SST '\n 'retrievals, over the heritage GOES-I/P '\n 'Imager, including five narrow bands '\n '(centered at 3.9, 8.4, 10.3, 11.2, and '\n '12.3 um) out of 16 that can be used for '\n 'SST, as well as accurate sensor '\n 'calibration, image navigation and '\n 'co-registration, spectral fidelity, and '\n 'sophisticated pre-processing '\n '(geo-rectification, radiance '\n 'equalization, and mapping). From altitude '\n '35,800 km, G16/ABI can accurately map SST '\n 'in a Full Disk (FD) area from 15-135-deg '\n 'W and 60S-60N, with spatial resolution '\n '2km at nadir (degrading to 15km at view '\n 'zenith angle, 67-deg) and temporal '\n 'sampling of 10min (15min prior to 2 Apr '\n '2019). \\n'\n 'The Level 2 Preprocessed (L2P) SST '\n 'product is derived at the native sensor '\n 'resolution using NOAA Advanced Clear-Sky '\n 'Processor for Ocean (ACSPO) system. ACSPO '\n 'first processes every 10min FD data SSTs '\n 'are derived from BTs using the ACSPO '\n 'clear-sky mask (ACSM; Petrenko et al., '\n '2010) and Non-Linear SST (NLSST) '\n 'algorithm (Petrenko et al., 2014). '\n 'Currently, only 4 longwave bands centered '\n 'at 8.4, 10.3, 11.2, and 12.3 um are used '\n '(the 3.9 microns was initially excluded, '\n 'to minimize possible discontinuities in '\n 'the diurnal cycle). The regression is '\n 'tuned against quality controlled in situ '\n 'SSTs from drifting and tropical mooring '\n 'buoys in the NOAA iQuam system (Xu and '\n 'Ignatov, 2014). The 10-min FD data are '\n 'subsequently collated in time, to produce '\n '1-hr L2P product, with improved coverage, '\n 'and reduced cloud leakages and image '\n 'noise, compared to each individual 10min '\n 'image. \\n'\n 'In the collated L2P, SSTs and BTs are '\n 'only reported in clear-sky water pixels '\n '(defined as ocean, sea, lake or river, '\n 'and up to 5 km inland) and fill values '\n 'elsewhere. The L2P is reported in netCDF4 '\n 'GHRSST Data Specification version 2 '\n '(GDS2) format, 24 granules per day, with '\n 'a total data volume of 0.6GB/day. In '\n 'addition to SST, ACSPO files also include '\n 'sun-sensor geometry, four BTs in ABI '\n 'bands 11 (8.4um), 13 (10.3um), 14 '\n '(11.2um), and 15 (12.3um) and two '\n 'reflectances in bands 2 and 3 (0.64um and '\n '0.86um; used for cloud identification). '\n 'The l2p_flags layer includes day/night, '\n 'land, ice, twilight, and glint flags. '\n 'Other variables include NCEP wind speed '\n 'and ACSPO SST minus reference SST '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0).\\n'\n 'Pixel-level earth locations are not '\n 'reported in the granules, as they remain '\n 'unchanged from granule to granule. To '\n 'obtain those, user has a choice of using '\n 'a flat lat-lon file, or a Python script, '\n 'both available at '\n 'ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. '\n 'Per GDS2 specifications, two additional '\n 'Sensor-Specific Error Statistics layers '\n '(SSES bias and standard deviation) are '\n 'reported in each pixel. \\n'\n 'The ACSPO VIIRS L2P product is monitored '\n 'and validated against in situ data (Xu '\n 'and Ignatov, 2014) using the Satellite '\n 'Quality Monitor SQUAM (Dash et al, 2010), '\n 'and BTs are validated against RTM '\n 'simulation in MICROS (Liang and Ignatov, '\n '2011). A reduced size (0.2GB/day), '\n 'equal-angle gridded (0.02-deg '\n 'resolution), ACSPO L3C product is also '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L3C-v2.70, '\n 'where gridded L2P SSTs are reported, and '\n 'BT layers omitted.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'NOAA/NESDIS '\n 'USA, '\n '5200 '\n 'Auth '\n 'Rd, '\n 'Camp '\n 'Springs, '\n 'MD, '\n '20746, '\n 'NOAA/NESDIS, '\n '2019-05-15, '\n 'GHRSST '\n 'NOAA/STAR '\n 'GOES-16 '\n 'ABI '\n 'L2P '\n 'America '\n 'Region '\n 'SST '\n 'v2.70 '\n 'dataset '\n 'in '\n 'GDS2, '\n '10.5067/GHG16-2PO27, '}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHG16-2PO27'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'NOAA/NESDIS'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '301-683-3379'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Center for Satellite '\n 'Applications and '\n 'Research'}],\n 'DataDates': [ { 'Date': '2019-03-28T21:34:17.610Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-03-28T21:34:17.610Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST NOAA/STAR GOES-16 ABI L2P '\n 'America Region SST v2.70 dataset in '\n 'GDS2',\n 'LocationKeywords': [ { 'Category': 'OTHER',\n 'Type': 'Western Atlantic'}],\n 'MetadataDates': [ { 'Date': '2019-11-18T18:33:40.123Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '7000.0'}],\n 'LongName': 'Advanced '\n 'Baseline '\n 'Imager '\n '(ABI)',\n 'ShortName': 'ABI'}],\n 'LongName': 'Geostationary '\n 'Operational '\n 'Environmental Satellite '\n '16',\n 'ShortName': 'GOES-16',\n 'Type': 'Geostationary'}],\n 'ProcessingLevel': {'Id': '2P'},\n 'Projects': [{'ShortName': 'GHRSST'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L2P/GOES16/STAR/v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHG16-2PO27',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/docs/geo_nav.py',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dash, P., A. '\n 'Ignatov, Y. Kihai',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Liang, X.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/iquam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Home Page of the '\n 'GHRSST Project',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/docs/G16_075_0_W.nc',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHG16-2PO27',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/v2.70/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'None',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABI_G16-STAR-L2P-v2.70',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -15.0,\n 'NorthBoundingCoordinate': 59.0,\n 'SouthBoundingCoordinate': -59.0,\n 'WestBoundingCoordinate': -135.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2017-12-15T00:00:00.000Z'}]}],\n 'Version': '2.70'}},\n { 'meta': { 'concept-id': 'C1666605372-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+NOAA/STAR+GOES-16+ABI++L3C+America+Region+SST+v2.70+dataset+in+GDS2',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-10T18:30:10Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The ACSPO G16/ABI L3C (Level 3 Collated) '\n 'product is a gridded version of the ACSPO '\n 'G16/ABI L2P product available at '\n 'https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L2P-v2.70. '\n 'The L3C output files are 1hr granules in '\n 'netCDF4 format, compliant with the GHRSST '\n 'Data Specification version 2 (GDS2). '\n 'There are 24 granules per 24hr interval, '\n 'with a total data volume of 0.2GB/day. '\n 'Fill values are reported at all invalid '\n 'pixels, including pixels with 5 km '\n 'inland. For each valid water pixel '\n '(defined as ocean, sea, lake or river, '\n 'and up to 5 km inland), the following '\n 'layers are reported: SSTs, ACSPO '\n 'clear-sky mask (ACSM; provided in each '\n 'grid as part of l2p_flags, which also '\n 'includes day/night, land, ice, twilight, '\n 'and glint flags), NCEP wind speed, and '\n 'ACSPO SST minus reference (Canadian Met '\n 'Centre 0.1deg L4 SST; available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). '\n 'All valid SSTs in L3C are recommended for '\n 'users. Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with valid SST. The ACSPO VIIRS L3U '\n 'product is monitored and validated '\n 'against iQuam in situ data (Xu and '\n 'Ignatov, 2014) in SQUAM (Dash et al, '\n '2010).',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'NOAA/NESDIS '\n 'USA, '\n '5200 '\n 'Auth '\n 'Rd, '\n 'Camp '\n 'Springs, '\n 'MD, '\n '20746, '\n 'NOAA/NESDIS, '\n '2019-05-30, '\n 'GHRSST '\n 'NOAA/STAR '\n 'GOES-16 '\n 'ABI '\n 'L3C '\n 'America '\n 'Region '\n 'SST '\n 'v2.70 '\n 'dataset '\n 'in '\n 'GDS2, '\n '10.5067/GHG16-3UO27, '}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHG16-3UO27'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'NOAA/NESDIS'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '301-683-3379'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Center for Satellite '\n 'Applications and '\n 'Research'}],\n 'DataDates': [ { 'Date': '2019-03-28T21:40:22.827Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-03-28T21:40:22.827Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST NOAA/STAR GOES-16 ABI L3C '\n 'America Region SST v2.70 dataset in '\n 'GDS2',\n 'LocationKeywords': [ { 'Category': 'OTHER',\n 'Type': 'Western Atlantic'}],\n 'MetadataDates': [ { 'Date': '2019-12-10T18:30:08.166Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '7000.0'}],\n 'LongName': 'Advanced '\n 'Baseline '\n 'Imager '\n '(ABI)',\n 'ShortName': 'ABI'}],\n 'LongName': 'Geostationary '\n 'Operational '\n 'Environmental Satellite '\n '16',\n 'ShortName': 'GOES-16',\n 'Type': 'Geostationary'}],\n 'ProcessingLevel': {'Id': '3C'},\n 'Projects': [{'ShortName': 'GHRSST'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L3C/AMERICAS/GOES16/STAR/v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Home Page of the '\n 'GHRSST Project',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/',\n 'URLContentType': 'DistributionURL'},\n { 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=ABI_G16-STAR-L3C-v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Dash, P., A. '\n 'Ignatov, Y. Kihai',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHG16-3UO27',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Xu, F.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/iquam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHG16-3UO27',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L3C/AMERICAS/GOES16/STAR/v2.70/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L3C/AMERICAS/GOES16/STAR/v2.70/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'None',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABI_G16-STAR-L3C-v2.70',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -15.0,\n 'NorthBoundingCoordinate': 59.0,\n 'SouthBoundingCoordinate': -59.0,\n 'WestBoundingCoordinate': -135.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2017-12-15T00:00:00.000Z'}]}],\n 'Version': '2.70'}},\n { 'meta': { 'concept-id': 'C1658476070-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+RAMSSA+Australian+Regional+Foundation+Sea+Surface+Temperature+Analysis',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:28Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis at the Australian '\n 'Bureau of Meteorology using optimal '\n 'interpolation (OI) on a regional 1/12 '\n 'degree grid over the Australian region '\n '(20N - 70S, 60E - 170W). This BLUELink '\n 'Regional Australian Multi-Sensor SST '\n 'Analysis (RAMSSA) v1.0 system blends '\n 'satellite SST observations from the '\n 'Advanced Very High Resolution Radiometer '\n '(AVHRR), the Advanced Along Track '\n 'Scanning Radiometer (AATSR), and, the '\n 'Advanced Microwave Scanning '\n 'Radiometer-EOS (AMSRE), and in situ data '\n 'from ships, and drifting and moored buoy '\n 'from the Global Telecommunications System '\n '(GTS). The processing results in daily '\n 'foundation SST estimates that are largely '\n 'free of nocturnal cooling and diurnal '\n 'warming effects.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n '2008-01-24, '\n 'GHRSST '\n 'Level '\n '4 '\n 'RAMSSA '\n 'Australian '\n 'Regional '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis, '\n '10.5067/GHRAM-4FA01, '\n 'http://www.bom.gov.au/jshess/docs/2011/beggs_hres.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHRAM-4FA01'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694394'},\n { 'Type': 'Email',\n 'Value': 'h.beggs@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Helen',\n 'LastName': 'Beggs',\n 'MiddleName': 'none',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694746'},\n { 'Type': 'Email',\n 'Value': 'ghrsst@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Leon',\n 'LastName': 'Majewski',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'}],\n 'DataDates': [ { 'Date': '2008-01-29T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:46.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 RAMSSA Australian '\n 'Regional Foundation Sea Surface '\n 'Temperature Analysis',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'OCEANIA'}],\n 'MetadataDates': [ { 'Date': '2019-11-20T20:39:25.914Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.12'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.74'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 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'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/L4/AUS/ABOM/RAMSSA_09km/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABOM-L4HRfnd-AUS-RAMSSA_09km',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': 60.0},\n { 'EastBoundingCoordinate': -170.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 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'Telephone',\n 'Value': '1 '\n '303 '\n '492 '\n '6199'},\n { 'Type': 'Email',\n 'Value': 'nsidc@nsidc.org'}],\n 'RelatedUrls': [ { 'Description': 'Archiving '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://nsidc.org/daac',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['DISTRIBUTOR'],\n 'ShortName': 'NASA NSIDC DAAC'},\n { 'ContactInformation': { 'RelatedUrls': [ { 'Description': 'Originating '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://weather.msfc.nasa.gov/AMSR/',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['PROCESSOR'],\n 'ShortName': 'NASA/MSFC/AMSR-E '\n 'SIPS'},\n { 'ContactInformation': { 'RelatedUrls': [ { 'Description': 'Originating '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://weather.msfc.nasa.gov/AMSR/',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['ORIGINATOR'],\n 'ShortName': 'NASA/MSFC/AMSR-E '\n 'SIPS'}],\n 'DataLanguage': 'eng; usa',\n 'EntryTitle': 'AMSR-E/Aqua Monthly L3 Global '\n 'Ascending/Descending .25x.25 deg Ocean '\n 'Grids V002',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'Geographic Region',\n 'Type': 'Global Ocean'}],\n 'MetadataDates': [ { 'Date': '2004-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-11-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ComposedOf': [ { 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer-EOS',\n 'ShortName': 'AMSR-E'}],\n 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer-EOS',\n 'NumberOfInstruments': 1,\n 'ShortName': 'AMSR-E',\n 'Technique': 'instrument'}],\n 'LongName': 'Earth Observing System, '\n 'AQUA',\n 'ShortName': 'AQUA',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': { 'Id': 'Level 3',\n 'ProcessingLevelDescription': 'Geophysical '\n 'variables '\n 'mapped '\n 'on '\n 'a '\n 'grid'},\n 'Purpose': 'Scientific Research',\n 'RelatedUrls': [ { 'Description': 'Direct download via '\n 'HTTPS protocol.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://n5eil01u.ecs.nsidc.org/AMSA/AE_MoOcn.002',\n 'URLContentType': 'DistributionURL'},\n { 'Description': \"NASA's newest \"\n 'search and order '\n 'tool for '\n 'subsetting, '\n 'reprojecting, and '\n 'reformatting data.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search/granules?p=C179014697-NSIDC_ECS&m=-31.078125!0.28125!1!1!0!0%2C2&q=AE_MoOcn',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Provides access to '\n 'data, '\n 'documentation, '\n 'tools, citation '\n 'information, '\n 'support, and other '\n 'resources.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_MOOCN.002',\n 'URLContentType': 'CollectionURL'},\n { 'Description': \"Includes a user's \"\n 'guide, supplemental '\n 'documents like '\n 'ATBDs and academic '\n 'papers, How Tos, '\n 'FAQs, etc.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_MOOCN.002',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'PRECIPITABLE '\n 'WATER '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN WINDS',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICES',\n 'VariableLevel2': 'WATER '\n 'VAPOR'}],\n 'ShortName': 'AE_MoOcn',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 89.24,\n 'SouthBoundingCoordinate': -89.24,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-19T00:00:00.000Z',\n 'EndingDateTime': '2011-10-01T23:59:59.999Z'}]}],\n 'Version': '2'}},\n { 'meta': { 'associations': { 'services': [ 'S1568899363-NSIDC_ECS',\n 'S1613645416-NSIDC_ECS',\n 'S1613689509-NSIDC_ECS']},\n 'concept-id': 'C130038008-NSIDC_ECS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': True,\n 'has-spatial-subsetting': True,\n 'has-temporal-subsetting': False,\n 'has-transforms': True,\n 'has-variables': True,\n 'native-id': 'AMSR-E/Aqua L2B Global Swath Ocean '\n 'Products derived from Wentz Algorithm '\n 'V002',\n 'provider-id': 'NSIDC_ECS',\n 'revision-date': '2020-03-10T19:26:43Z',\n 'revision-id': 105,\n 'user-id': 'cmr_nsidc_ops'},\n 'umm': { 'Abstract': 'This daily Level-2B swath data set '\n 'includes Sea Surface Temperature (SST), '\n 'Near-Surface Wind Speed, Columnar Water '\n 'Vapor, and Cloud liquid Water data '\n 'arrays, and was used as input to generate '\n 'the following daily, weekly, and monthly '\n 'Level-3 gridded ocean products; '\n 'AE_DyOcn, AE_WkOcn, and AE_MoOcn.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Satellite '\n 'Direction',\n 'Name': 'AscendingDescendingFlg'},\n { 'DataType': 'INT',\n 'Description': 'The index '\n 'number for '\n 'the last '\n 'polygon '\n 'associated '\n 'with the '\n 'nominal '\n 'pass '\n 'number in '\n 'the '\n 'granule',\n 'Name': 'EndingPolygonNumber'},\n { 'DataType': 'INT',\n 'Description': 'The '\n 'nominal '\n 'pass index '\n 'number for '\n 'the pass '\n 'that best '\n 'describes '\n 'the '\n 'spatial '\n 'location '\n 'of the '\n 'granule, '\n 'where the '\n 'pass is '\n 'either the '\n 'ascending '\n 'or '\n 'descending '\n 'portion of '\n 'an orbit',\n 'Name': 'NominalPassIndex'},\n { 'DataType': 'INT',\n 'Description': 'The index '\n 'number for '\n 'the first '\n 'polygon '\n 'associated '\n 'with the '\n 'nominal '\n 'pass '\n 'number in '\n 'the '\n 'granule.',\n 'Name': 'StartingPolygonNumber'},\n { 'DataType': 'STRING',\n 'Description': 'Digital '\n 'object '\n 'identifier '\n 'that '\n 'uniquely '\n 'identifies '\n 'this data '\n 'product',\n 'Name': 'identifier_product_doi'},\n { 'DataType': 'STRING',\n 'Description': 'URL of the '\n 'digital '\n 'object '\n 'identifier '\n 'resolving '\n 'authority',\n 'Name': 'identifier_product_doi_authority'}],\n 'CollectionCitations': [ { 'Publisher': 'NASA National '\n 'Snow and Ice '\n 'Data Center '\n 'Distributed '\n 'Active '\n 'Archive '\n 'Center',\n 'Title': 'AMSR-E/Aqua L2B '\n 'Global Swath '\n 'Ocean Products '\n 'derived from '\n 'Wentz Algorithm '\n 'V002',\n 'Version': '2'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'StateProvince': 'Colorado'}]},\n 'GroupName': 'NASA National Snow '\n 'and Ice Data Center '\n 'Distributed Active '\n 'Archive Center',\n 'Roles': ['User Services']}],\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'PostalCode': '80309-0449',\n 'StateProvince': 'CO',\n 'StreetAddresses': [ 'CIRES, '\n '449 '\n 'UCB',\n 'University '\n 'of '\n 'Colorado']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '303 '\n '492 '\n '6199'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '303 '\n '492 '\n '2468'},\n { 'Type': 'Email',\n 'Value': 'nsidc@nsidc.org'}]},\n 'FirstName': 'NSIDC',\n 'LastName': 'Services',\n 'MiddleName': 'User',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904 '\n 'x16'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'wentz@remss.com'}]},\n 'FirstName': 'Frank',\n 'LastName': 'Wentz',\n 'MiddleName': 'J.',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'thomas@remss.com'}]},\n 'FirstName': 'Thomas',\n 'LastName': 'Meissner',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904 '\n 'x16'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'wentz@remss.com'}]},\n 'FirstName': 'Frank',\n 'LastName': 'Wentz',\n 'MiddleName': 'J.',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'thomas@remss.com'}]},\n 'FirstName': 'Thomas',\n 'LastName': 'Meissner',\n 'Roles': ['Technical Contact']}],\n 'DOI': {'DOI': '10.5067/AMSR-E/AE_OCEAN.002'},\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'PostalCode': '80309-0449',\n 'StateProvince': 'CO',\n 'StreetAddresses': [ 'National '\n 'Snow '\n 'and '\n 'Ice '\n 'Data '\n 'Center',\n 'CIRES, '\n '449 '\n 'UCB',\n 'University '\n 'of '\n 'Colorado']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '303 '\n '492 '\n '6199'},\n { 'Type': 'Email',\n 'Value': 'nsidc@nsidc.org'}],\n 'RelatedUrls': [ { 'Description': 'Archiving '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://nsidc.org/daac',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA NSIDC DAAC'},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'PostalCode': '80309-0449',\n 'StateProvince': 'CO',\n 'StreetAddresses': [ 'National '\n 'Snow '\n 'and '\n 'Ice '\n 'Data '\n 'Center',\n 'CIRES, '\n '449 '\n 'UCB',\n 'University '\n 'of '\n 'Colorado']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '303 '\n '492 '\n '6199'},\n { 'Type': 'Email',\n 'Value': 'nsidc@nsidc.org'}],\n 'RelatedUrls': [ { 'Description': 'Archiving '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://nsidc.org/daac',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['DISTRIBUTOR'],\n 'ShortName': 'NASA NSIDC DAAC'},\n { 'ContactInformation': { 'RelatedUrls': [ { 'Description': 'Originating '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://weather.msfc.nasa.gov/AMSR/',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['PROCESSOR'],\n 'ShortName': 'NASA/MSFC/AMSR-E '\n 'SIPS'},\n { 'ContactInformation': { 'RelatedUrls': [ { 'Description': 'Originating '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://weather.msfc.nasa.gov/AMSR/',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['ORIGINATOR'],\n 'ShortName': 'NASA/MSFC/AMSR-E '\n 'SIPS'}],\n 'DataLanguage': 'eng; usa',\n 'EntryTitle': 'AMSR-E/Aqua L2B Global Swath Ocean '\n 'Products derived from Wentz Algorithm '\n 'V002',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'Geographic Region',\n 'Type': 'Global Ocean'}],\n 'MetadataDates': [ { 'Date': '2004-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2020-03-06T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ComposedOf': [ { 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer-EOS',\n 'ShortName': 'AMSR-E'}],\n 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer-EOS',\n 'NumberOfInstruments': 1,\n 'ShortName': 'AMSR-E',\n 'Technique': 'instrument'}],\n 'LongName': 'Earth Observing System, '\n 'AQUA',\n 'ShortName': 'AQUA',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': { 'Id': 'Level 2B',\n 'ProcessingLevelDescription': 'Derived '\n 'geophysical '\n 'variables'},\n 'Purpose': 'Scientific Research',\n 'RelatedUrls': [ { 'Description': 'Direct download via '\n 'HTTPS protocol.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://n5eil01u.ecs.nsidc.org/AMSA/AE_Ocean.002',\n 'URLContentType': 'DistributionURL'},\n { 'Description': \"NASA's newest \"\n 'search and order '\n 'tool for '\n 'subsetting, '\n 'reprojecting, and '\n 'reformatting data.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search/granules?p=C130038008-NSIDC_ECS&m=-30.515625!0.5625!1!1!0!0%2C2&q=AE_Ocean',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Provides access to '\n 'data, '\n 'documentation, '\n 'tools, citation '\n 'information, '\n 'support, and other '\n 'resources.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_OCEAN.002',\n 'URLContentType': 'CollectionURL'},\n { 'Description': \"Includes a user's \"\n 'guide, supplemental '\n 'documents like '\n 'ATBDs and academic '\n 'papers, How Tos, '\n 'FAQs, etc.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_OCEAN.002',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'PRECIPITABLE '\n 'WATER '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN WINDS',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER '\n 'VAPOR'}],\n 'ShortName': 'AE_Ocean',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'ORBIT',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 89.24,\n 'SouthBoundingCoordinate': -89.24,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'OrbitParameters': { 'InclinationAngle': 98.15,\n 'NumberOfOrbits': 0.5,\n 'Period': 98.88,\n 'StartCircularLatitude': -90.0,\n 'SwathWidth': 1450.0},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-19T00:00:00.000Z',\n 'EndingDateTime': '2011-10-03T23:59:59.999Z'}]}],\n 'Version': '2'}},\n { 'meta': { 'concept-id': 'C1243477376-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH2RET_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:00Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2RET. However, it contains '\n 'science retrievals that use the HSB. '\n 'Because the HSB instrument lived only '\n 'from September 2002 through January 2003 '\n 'when it terminally failed, the data set '\n 'covers these five months only. The AIRS '\n 'Standard Retrieval Product consists of '\n 'retrieved estimates of cloud and surface '\n 'properties, plus profiles of retrieved '\n 'temperature, water vapor, ozone, carbon '\n 'monoxide and methane. Estimates of the '\n 'errors associated with these quantities '\n 'is also part of the Standard Product. The '\n 'temperature profile vertical resolution '\n 'is 28 levels total between 1100 mb and '\n '0.1 mb, while moisture profile is '\n 'reported at 14 atmospheric layers between '\n '1100 mb and 50 mb. The horizontal '\n 'resolution is 50 km. An AIRS granule has '\n 'been set as 6 minutes of data, 30 '\n 'footprints cross track by 45 lines along '\n 'track. There are 240\\n'\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2RET_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH2RET',\n 'Title': 'AIRS/Aqua L2 '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA203'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'AIRS/Aqua L2 Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) V006 '\n '(AIRH2RET) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'},\n { 'LongName': 'Humidity '\n 'Sounder '\n 'for '\n 'Brazil',\n 'ShortName': 'HSB'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'}],\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH2RET_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2RET_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRH2RET.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRH2RET.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH2RET%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH2RET+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'}],\n 'ShortName': 'AIRH2RET',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2003-02-05T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477377-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH2SUP_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:01Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2SUP. However, it contains '\n 'science retrievals that use the HSB. '\n 'Because the HSB instrument lived only '\n 'from September 2002 through January 2003 '\n 'when it terminally failed, the data set '\n 'covers these five months only. The '\n 'Support Product includes higher vertical '\n 'resolution profiles of the quantities '\n 'found in the Standard Product, plus '\n 'intermediate outputs (e.g., '\n 'microwave-only retrieval), research '\n 'products such as the abundance of trace '\n 'gases, and detailed quality assessment '\n 'information. The Support Product profiles '\n 'contain 100 levels between 1100 and .016 '\n 'mb; this higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than that in the '\n 'Standard Product profiles. The intended '\n 'users of the Support Product are '\n 'researchers interested in generating '\n 'forward radiance or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data with 30 footprints '\n 'cross track by 45 scanlines of AMSU-A '\n 'data or 135 scanlines of AIRS and HSB '\n 'data. There are 240 granules per day, '\n 'with an orbit repeat cycle of '\n 'approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2SUP_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH2SUP',\n 'Title': 'AIRS/Aqua L2 '\n 'Support Retrieval '\n '(AIRS+AMSU+HSB) '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA209'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Support Retrieval '\n '(AIRS+AMSU+HSB) V006 (AIRH2SUP) at GES '\n 'DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'},\n { 'LongName': 'Humidity '\n 'Sounder '\n 'for '\n 'Brazil',\n 'ShortName': 'HSB'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'}],\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS+AMSU+HSB) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH2SUP_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2SUP_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRH2SUP.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRH2SUP.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH2SUP%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH2SUP+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MICROWAVE',\n 'Topic': 'SPECTRAL/ENGINEERING',\n 'VariableLevel1': 'BRIGHTNESS '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH2SUP',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2003-02-05T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517226-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3SP8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:04Z',\n 'revision-id': 15,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 563.3,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SP8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3SP8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Support '\n 'Multiday Product '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA314'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Support Multiday '\n 'Product (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3SP8) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical '\n 'retrieval\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3SP8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SP8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3SP8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3SP8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3SP8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3SP8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH3SP8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-02-08T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517230-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3SPD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:05Z',\n 'revision-id': 15,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 474.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3SPD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Support '\n 'Daily Product '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA305'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Support Daily '\n 'Product (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3SPD) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3SPD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3SPD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3SPD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3SPD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3SPD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH3SPD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2003-02-06T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517247-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3SPM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:07Z',\n 'revision-id': 15,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 554.5,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3SPM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Support '\n 'Monthly Product '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA323'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Support Product '\n '(AIRS+AMSU+HSB) 1 degree x 1 degree '\n 'V006 (AIRH3SPM) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (With '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3SPM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3SPM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3SPM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3SPM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3SPM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH3SPM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-03-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517250-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3ST8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:08Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX3ST8. However, it contains '\n 'science retrievals that use the Humidity '\n 'Sounder for Brazil (HSB). Because the HSB '\n 'instrument lived only from September 2002 '\n 'through January 2003 when it terminally '\n 'failed, the data set covers these five '\n 'months only. The AIRS Level 3 8-Day '\n 'Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers an 8-day period, or one-half of '\n 'the Aqua orbit repeat cycle. The mean '\n 'values are simply the arithmetic means of '\n 'the daily products, weighted by the '\n 'number of input counts for each day in '\n 'that grid box. The geophysical parameters '\n 'have been averaged and binned into 1 x 1 '\n 'deg grid cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 452.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3ST8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3ST8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA311'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3ST8) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical '\n 'retrieval\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3ST8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3ST8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3ST8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3ST8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3ST8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3ST8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRH3ST8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-02-08T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517253-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3STD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:09Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX3STD. However, it contains '\n 'science retrievals that use the Humidity '\n 'Sounder for Brazil (HSB). Because the HSB '\n 'instrument lived only from September 2002 '\n 'through January 2003 when it terminally '\n 'failed, the data set covers these five '\n 'months only. The AIRS Level 3 Daily '\n 'Gridded Product contains standard '\n 'retrieval means, standard deviations and '\n 'input counts. Each file covers a temporal '\n 'period of 24 hours for either the '\n 'descending (equatorial crossing North to '\n 'South @1:30 AM local time) or ascending '\n '(equatorial crossing South to North @1:30 '\n 'PM local time) orbit. The data starts at '\n 'the international dateline and progresses '\n 'westward (as do the subsequent orbits of '\n 'the satellite) so that neighboring '\n 'gridded cells of data are no more than a '\n 'swath of time apart (about 90 minutes). '\n 'The two parts of a scan line crossing the '\n 'dateline are included in separate L3 '\n 'files, according to the date, so that '\n 'data points in a grid box are always '\n 'coincident in time. The edge of the AIRS '\n 'Level 3 gridded cells is at the date line '\n '(the 180E/W longitude boundary). When '\n 'plotted, this produces a map with 0 '\n 'degrees longitude in the center of the '\n 'image unless the bins are reordered. This '\n 'method is preferred because the left '\n '(West) side of the image and the right '\n '(East) side of the image contain data '\n 'farthest apart in time. The gridding '\n 'scheme used by AIRS is the same as used '\n 'by TOVS Pathfinder to create Level 3 '\n 'products. The daily Level 3 products have '\n 'gores between satellite paths where there '\n 'is no coverage for that day. The '\n 'geophysical parameters have been averaged '\n 'and binned into 1 x 1 grid cells, from '\n '-180.0 to +180.0 deg longitude and from '\n '-90.0 to +90.0 deg latitude. For each '\n 'grid map of 4-byte floating-point mean '\n 'values there is a corresponding 4-byte '\n 'floating-point map of standard deviation '\n 'and a 2-byte integer grid map of counts. '\n 'The counts map provides the user with the '\n 'number of points per bin that were '\n 'included in the mean and can be used to '\n 'generate custom multi-day maps from the '\n 'daily gridded products. The thermodynamic '\n 'parameters are: Skin Temperature (land '\n 'and sea surface), Air Temperature at the '\n 'surface, Profiles of Air Temperature and '\n 'Water Vapor, Tropopause Characteristics, '\n 'Column Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 376.1,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3STD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA302'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3STD) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3STD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3STD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3STD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3STD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3STD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRH3STD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2003-02-06T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517238-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3STM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:11Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX3STM. However, it contains '\n 'science retrievals that use the Humidity '\n 'Sounder for Brazil (HSB). Because the HSB '\n 'instrument lived only from September 2002 '\n 'through January 2003 when it terminally '\n 'failed, the data set covers these five '\n 'months only. The AIRS Level 3 Monthly '\n 'Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers a calendar month. The mean values '\n 'are simply the arithmetic means of the '\n 'daily products, weighted by the number of '\n 'input counts for each day in that grid '\n 'box. The geophysical parameters have been '\n 'averaged and binned into 1 x 1 grid '\n 'cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 445.7,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3STM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA320'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3STM) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (With '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3STM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3STM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3STM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3STM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3STM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRH3STM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-03-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477381-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2RET_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:24Z',\n 'revision-id': 18,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2RET. It is a new product '\n 'produced using AIRS IR only because the '\n 'radiometric noise in AMSU channel 4 '\n 'started to increase significantly (since '\n 'June 2007). The AIRS Standard Retrieval '\n 'Product consists of retrieved estimates '\n 'of cloud and surface properties, plus '\n 'profiles of retrieved temperature, water '\n 'vapor, ozone, carbon monoxide and '\n 'methane. Estimates of the errors '\n 'associated with these quantities is also '\n 'part of the Standard Product. The '\n 'temperature profile vertical resolution '\n 'is 28 levels total between 1100 mb and '\n '0.1 mb, while moisture profile is '\n 'reported at 14 atmospheric layers between '\n '1100 mb and 50 mb. The horizontal '\n 'resolution is 50 km. An AIRS granule has '\n 'been set as 6 minutes of data, 30 '\n 'footprints cross track by 45 lines along '\n 'track. There are 240\\n'\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2RET',\n 'Title': 'AIRS/Aqua L2 '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA202'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'AIRS/Aqua L2 Standard Physical '\n 'Retrieval (AIRS-only) V006 (AIRS2RET) '\n 'at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong,',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Edition': '121',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '15'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Edition': '41',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '2'}],\n 'Quality': 'The product is similar to AIRX2RET except '\n 'the processing uses only the AIRS '\n 'channels. No AMSU information was used in '\n 'the generation of this product. The '\n 'quality of data products, described in the '\n 'associated references, provide information '\n 'about numerous validation studies '\n 'conducted and papers written documenting '\n 'the excellence of the products using '\n 'radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitation of '\n 'the AIRS-only Version-6 retrieval '\n 'products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias. Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2RET_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRS2RET.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRS2RET.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS2RET%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2RET+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS '\n 'instrument,algorithms, '\n 'and other '\n 'AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'}],\n 'ShortName': 'AIRS2RET',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1345119345-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2RET_NRT_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:25Z',\n 'revision-id': 22,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'Level 2 Near Real Time (NRT) Standard '\n 'Physical Retrieval (AIRS-only) product '\n '(AIRS2RET_NRT_006) differs from the '\n 'routine product (AIRS2RET_006) in four '\n 'ways to meet the three hour latency '\n 'requirements of the Land Atmosphere NRT '\n 'Capability Earth Observing System '\n '(LANCE): (1) The NRT granules are '\n 'produced without previous or subsequent '\n 'granules if those granules are not '\n 'available within 5 minutes, (2) the '\n 'predictive ephemeris/attitude data are '\n 'used rather than the definitive '\n 'ephemeris/attitude, (3) if the forecast '\n 'surface pressure is unavailable, a '\n 'surface climatology is used, and (4) no '\n 'ice cloud properties retrievals are '\n 'performed. The consequences of these '\n 'differences are described in the AIRS '\n 'Near Real Time (NRT) data products '\n 'document. The Atmospheric Infrared '\n 'Sounder (AIRS) is a grating spectrometer '\n '(R = 1200) aboard the second Earth '\n 'Observing System (EOS) polar-orbiting '\n 'platform, EOS Aqua. In combination with '\n 'the Advanced Microwave Sounding Unit '\n '(AMSU) and the Humidity Sounder for '\n 'Brazil (HSB), AIRS constitutes an '\n 'innovative atmospheric sounding group of '\n 'visible, infrared, and microwave sensors. '\n 'This product is produced using AIRS IR '\n 'only because the radiometric noise in '\n 'AMSU channel 4 started to increase '\n 'significantly (since June 2007). The AIRS '\n 'Standard Retrieval Product consists of '\n 'retrieved estimates of cloud and surface '\n 'properties, plus profiles of retrieved '\n 'temperature, water vapor, ozone, carbon '\n 'monoxide and methane. Estimates of the '\n 'errors associated with these quantities '\n 'is also part of the Standard Product. The '\n 'temperature profile vertical resolution '\n 'is 28 levels total between 1100 mb and '\n '0.1 mb, while moisture profile is '\n 'reported at 14 atmospheric layers between '\n '1100 mb and 50 mb. The horizontal '\n 'resolution is 50 km. An AIRS granule has '\n 'been set as 6 minutes of data, 30 '\n 'footprints cross track by 45 lines along '\n 'track. There are 240\\n'\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': { 'Description': 'You must '\n 'register using '\n 'the EOSDIS User '\n 'Registration '\n 'System in order '\n 'to access LANCE '\n 'NRT AIRS data. '\n 'You can '\n 'register at '\n 'https://urs.eosdis.nasa.gov/users/new'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'LANCE',\n 'NRT',\n 'RELATIVE_START_DATE: -7'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_NRT_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2016-10-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2RET_NRT',\n 'Title': 'AIRS/Aqua L2 Near '\n 'Real Time (NRT) '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionDataType': 'NEAR_REAL_TIME',\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Near Real Time (NRT) '\n 'Standard Physical Retrieval (AIRS-only) '\n 'V006 (AIRS2RET_NRT) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2016-09-26T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'},\n { 'LongName': 'Land, Atmosphere Near '\n 'real-time Capability for '\n 'EOS',\n 'ShortName': 'LANCE'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Edition': '8',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '1'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '2',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Series': 'EEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing,',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '41'},\n { 'Author': 'Thomas Hearty, '\n 'Feng Ding, Ed '\n 'Esfandiari, '\n 'Andrey '\n 'Savtchenko, '\n 'Michael '\n 'Theobald, '\n 'Bruce Vollmer, '\n 'Xin-Min Hua, '\n 'Evan Manning, '\n 'and Edward '\n 'Olsen',\n 'PublicationPlace': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'Title': 'AIRS Near Real '\n 'Time (NRT) data '\n 'products'}],\n 'Purpose': 'The Near Real Time (NRT) product is for '\n 'users whose primary interest is the low '\n 'latency for data availability. While '\n 'standard data products are available '\n 'within 3 days of observation, NRT data are '\n 'usually available within 3 hours of '\n 'observation.',\n 'Quality': 'The product is similar to AIRX2RET except '\n 'the processing uses only the AIRS '\n 'channels. No AMSU information was used in '\n 'the generation of this product. As a Near '\n 'Real Time (NRT) product this differs from '\n 'AIRS2RET.006 AIRS-only in four ways to '\n 'meet the three hour latency requirement of '\n 'the Land Atmosphere NRT Capability Earth '\n 'Observing System (LANCE). For additional '\n 'information about NRT processing see '\n 'either the Related URL section or the '\n 'Publication References section for the V6 '\n 'NRT memo. The quality of data products, '\n 'described in the associated references, '\n 'provide information about numerous '\n 'validation studies conducted and papers '\n 'written documenting the excellence of the '\n 'products using radiosondes, ground truth, '\n 'other satellites, and model analysis '\n 'products. There are however several '\n 'limitation of the AIRS-only Version-6 '\n 'retrieval products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias. Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2RET_NRT_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_NRT_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS. User '\n 'registration is '\n 'required. Register '\n 'for a username and '\n 'password at '\n 'https://urs.eosdis.nasa.gov/users/new',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_NRT/AIRS2RET_NRT.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_NRT/AIRS2RET_NRT.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'datacasting.',\n 'Subtype': 'DATACAST URL',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/datacasting/AIRS2RET_NRT.006.datacast-feed.xml',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2RET_NRT+005',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS '\n 'instrument,algorithms, '\n 'and other '\n 'AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Memo on NRT vs '\n 'Standard Product',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'LAYERED '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AIRS2RET_NRT',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2016-10-15T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477382-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2SUP_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:29Z',\n 'revision-id': 18,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2SUP. It is a new product '\n 'produced using AIRS IR only because the '\n 'radiometric noise in AMSU channel 4 '\n 'started to increase significantly (since '\n 'June 2007). The Support Product includes '\n 'higher vertical resolution profiles of '\n 'the quantities found in the Standard '\n 'Product, plus intermediate outputs (e.g., '\n 'microwave-only retrieval), research '\n 'products such as the abundance of trace '\n 'gases, and detailed quality assessment '\n 'information. The Support Product profiles '\n 'contain 100 levels between 1100 and .016 '\n 'mb; this higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than that in the '\n 'Standard Product profiles. The intended '\n 'users of the Support Product are '\n 'researchers interested in generating '\n 'forward radiance or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data, 30 footprints cross '\n 'track by 45 lines along track. There are '\n '240 granules per day, with an orbit '\n 'repeat cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2SUP',\n 'Title': 'AIRS/Aqua L2 '\n 'Support Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA208'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'AIRS/Aqua L2 Support Retrieval '\n '(AIRS-only) V006 (AIRS2SUP) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Edition': '41',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '2'}],\n 'Quality': 'The product is similar to AIRX2SUP except '\n 'the processing uses only the AIRS '\n 'channels. No AMSU information was used in '\n 'the generation of this product. The '\n 'quality of data products, described in the '\n 'associated references, provide information '\n 'about numerous validation studies '\n 'conducted and papers written documenting '\n 'the excellence of the products using '\n 'radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitation of '\n 'the AIRS-only Version-6 retrieval '\n 'products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also , the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS-only) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2SUP_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRS2SUP.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRS2SUP.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS2SUP%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2SUP+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS2SUP',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1345119372-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2SUP_NRT_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:30Z',\n 'revision-id': 22,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'Level 2 Near Real Time (NRT) Support '\n 'Retrieval (AIRS-only) product '\n '(AIRS2SUP_NRT_006) differs from the '\n 'routine product (AIRS2SUP_006) in four '\n 'ways to meet the three hour latency '\n 'requirements of the Land Atmosphere NRT '\n 'Capability Earth Observing System '\n '(LANCE): (1) The NRT granules are '\n 'produced without previous or subsequent '\n 'granules if those granules are not '\n 'available within 5 minutes, (2) the '\n 'predictive ephemeris/attitude data are '\n 'used rather than the definitive '\n 'ephemeris/attitude, (3) if the forecast '\n 'surface pressure is unavailable, a '\n 'surface climatology is used, and (4) no '\n 'ice cloud properties retrievals are '\n 'performed. The consequences of these '\n 'differences are described in the AIRS '\n 'Near Real Time (NRT) data products '\n 'document. The Atmospheric Infrared '\n 'Sounder (AIRS) is a grating spectrometer '\n '(R = 1200) aboard the second Earth '\n 'Observing System (EOS) polar-orbiting '\n 'platform, EOS Aqua. In combination with '\n 'the Advanced Microwave Sounding Unit '\n '(AMSU) and the Humidity Sounder for '\n 'Brazil (HSB), AIRS constitutes an '\n 'innovative atmospheric sounding group of '\n 'visible, infrared, and microwave sensors. '\n 'This product is product produced using '\n 'AIRS IR only because the radiometric '\n 'noise in AMSU channel 4 started to '\n 'increase significantly (since June 2007). '\n 'The Support Product includes higher '\n 'vertical resolution profiles of the '\n 'quantities found in the Standard Product, '\n 'plus intermediate outputs (e.g., '\n 'microwave-only retrieval), research '\n 'products such as the abundance of trace '\n 'gases, and detailed quality assessment '\n 'information. The Support Product profiles '\n 'contain 100 levels between 1100 and .016 '\n 'mb; this higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than that in the '\n 'Standard Product profiles. The intended '\n 'users of the Support Product are '\n 'researchers interested in generating '\n 'forward radiance or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data, 30 footprints cross '\n 'track by 45 scanlines. There are 240 '\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': { 'Description': 'You must '\n 'register using '\n 'the EOSDIS User '\n 'Registration '\n 'System in order '\n 'to access LANCE '\n 'NRT AIRS data. '\n 'You can '\n 'register at '\n 'https://urs.eosdis.nasa.gov/users/new'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone',\n 'LANCE',\n 'NRT',\n 'RELATIVE_START_DATE: -7'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_NRT_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2016-10-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2SUP_NRT',\n 'Title': 'AIRS/Aqua L2 Near '\n 'Real Time (NRT) '\n 'Support Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionDataType': 'NEAR_REAL_TIME',\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Near Real Time (NRT) '\n 'Support Retrieval (AIRS-only) V006 '\n '(AIRS2SUP_NRT) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2016-09-26T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'},\n { 'LongName': 'Land, Atmosphere Near '\n 'real-time Capability for '\n 'EOS',\n 'ShortName': 'LANCE'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres,',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': '. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '2',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Series': 'IEEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '41'},\n { 'Author': 'Thomas Hearty, '\n 'Feng Ding, Ed '\n 'Esfandiari, '\n 'Andrey '\n 'Savtchenko, '\n 'Michael '\n 'Theobald, '\n 'Bruce Vollmer, '\n 'Xin-Min Hua, '\n 'Evan Manning, '\n 'and Edward '\n 'Olsen',\n 'PublicationPlace': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'Title': 'AIRS Near Real '\n 'Time (NRT) data '\n 'products'}],\n 'Purpose': 'The Near Real Time (NRT) product is for '\n 'users whose primary interest is the low '\n 'latency for data availability. While '\n 'standard data products are available '\n 'within 3 days of observation, NRT data are '\n 'usually available within 3 hours of '\n 'observation.',\n 'Quality': 'The product is similar to AIRS2SUP except '\n 'the processing system that produced these '\n 'radiances is a Near Real Time (NRT) '\n 'system. This product this differs from '\n 'AIRS2SUP.006 AIRSonly in four ways to meet '\n 'the three hour latency requirement of the '\n 'Land Atmosphere NRT Capability Earth '\n 'Observing System (LANCE). For additional '\n 'information about NRT processing see '\n 'either the RelatedURL section or the '\n 'References section for the V6 NRT memo.\\n'\n '\\n'\n 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitation of '\n 'the AIRS-only Version-6 retrieval '\n 'products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias. Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS-only) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2SUP_NRT_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_NRT_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS. User '\n 'registration is '\n 'required. Register '\n 'for a username and '\n 'password at '\n 'https://urs.eosdis.nasa.gov/users/new',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_NRT/AIRS2SUP_NRT.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS Near Real Time '\n 'data products.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_NRT/AIRS2RET_NRT.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'datacasting.',\n 'Subtype': 'DATACAST URL',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/datacasting/AIRS2SUP_NRT.006.datacast-feed.xml',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2SUP_NRT+005',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Memo on NRT vs '\n 'Standard Product',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'AEROSOLS',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TRACE '\n 'GASES/TRACE '\n 'SPECIES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PLANETARY '\n 'BOUNDARY '\n 'LAYER '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'LAYERED '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AIRS2SUP_NRT',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2016-10-15T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517268-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3SP8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:41Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 563.3,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SP8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3SP8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Support '\n 'Product '\n '(AIRS-only) 1 '\n 'degree X 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA315'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Support Product '\n '(AIRS-only) 1 degree X 1 degree V006 '\n '(AIRS3SP8) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multi-day standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3SP8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SP8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3SP8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3SP8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3SP8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3SP8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS3SP8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517272-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3SPD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:42Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 474.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3SPD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Support '\n 'Product '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA306'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Support Product '\n '(AIRS-only) 1 degree x 1 degree V006 '\n '(AIRS3SPD) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3SPD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3SPD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3SPD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3SPD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3SPD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS3SPD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517285-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3SPM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:43Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 554.5,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3SPM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Support '\n 'Product '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA324'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Support Product '\n '(AIRS-only) 1 degree x 1 degree V006 '\n '(AIRS3SPM) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3SPM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3SPM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3SPM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3SPM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3SPM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS3SPM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006',\n 'VersionDescription': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1238517287-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3ST8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:44Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Only Level 3 '\n '8-Day Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers an 8-day period, or one-half of '\n 'the Aqua orbit repeat cycle. The mean '\n 'values are simply the arithmetic means of '\n 'the daily products, weighted by the '\n 'number of input counts for each day in '\n 'that grid box. The geophysical parameters '\n 'have been averaged and binned into 1 x 1 '\n 'deg grid cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 452.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3ST8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3ST8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS-only) 1 '\n 'degree X 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA312'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Standard Physical '\n 'Retrieval (AIRS-only) 1 degree X 1 '\n 'degree V006 (AIRS3ST8) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'mproved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics,',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multi-day standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3ST8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3ST8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3ST8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3ST8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3ST8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3ST8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRS3ST8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517289-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3STD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:46Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Only Level 3 '\n 'Daily Gridded Product contains standard '\n 'retrieval means, standard deviations and '\n 'input counts. Each file covers a temporal '\n 'period of 24 hours for either the '\n 'descending (equatorial crossing North to '\n 'South @1:30 AM local time) or ascending '\n '(equatorial crossing South to North @1:30 '\n 'PM local time) orbit. The data starts at '\n 'the international dateline and progresses '\n 'westward (as do the subsequent orbits of '\n 'the satellite) so that neighboring '\n 'gridded cells of data are no more than a '\n 'swath of time apart (about 90 minutes). '\n 'The two parts of a scan line crossing the '\n 'dateline are included in separate L3 '\n 'files, according to the date, so that '\n 'data points in a grid box are always '\n 'coincident in time. The edge of the AIRS '\n 'Level 3 gridded cells is at the date line '\n '(the 180E/W longitude boundary). When '\n 'plotted, this produces a map with 0 '\n 'degrees longitude in the center of the '\n 'image unless the bins are reordered. This '\n 'method is preferred because the left '\n '(West) side of the image and the right '\n '(East) side of the image contain data '\n 'farthest apart in time. The gridding '\n 'scheme used by AIRS is the same as used '\n 'by TOVS Pathfinder to create Level 3 '\n 'products. The daily Level 3 products have '\n 'gores between satellite paths where there '\n 'is no coverage for that day. The '\n 'geophysical parameters have been averaged '\n 'and binned into 1 x 1 deg grid cells, '\n 'from -180.0 to +180.0 deg longitude and '\n 'from -90.0 to +90.0 deg latitude. For '\n 'each grid map of 4-byte floating-point '\n 'mean values there is a corresponding '\n '4-byte floating-point map of standard '\n 'deviation and a 2-byte integer grid map '\n 'of counts. The counts map provides the '\n 'user with the number of points per bin '\n 'that were included in the mean and can be '\n 'used to generate custom multi-day maps '\n 'from the daily gridded products. The '\n 'thermodynamic parameters are: Skin '\n 'Temperature (land and sea surface), Air '\n 'Temperature at the surface, Profiles of '\n 'Air Temperature and Water Vapor, '\n 'Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 376.1,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3STD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA303'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Standard Physical '\n 'Retrieval (AIRS-only) 1 degree x 1 '\n 'degree V006 (AIRS3STD) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3STD_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3STD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3STD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3STD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3STD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRS3STD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517301-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3STM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:47Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Only Level 3 '\n 'Monthly Gridded Retrieval Product '\n 'contains standard retrieval means, '\n 'standard deviations and input counts. '\n 'Each file covers a calendar month. The '\n 'mean values are simply the arithmetic '\n 'means of the daily products, weighted by '\n 'the number of input counts for each day '\n 'in that grid box. The geophysical '\n 'parameters have been averaged and binned '\n 'into 1 x 1 deg grid cells, from -180.0 to '\n '+180.0 deg longitude and from -90.0 to '\n '+90.0 deg latitude. For each grid map of '\n '4-byte floating-point mean values there '\n 'is a corresponding 4-byte floating-point '\n 'map of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 445.7,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3STM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA321'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Standard Physical '\n 'Retrieval (AIRS-only) 1 degree x 1 '\n 'degree V006 (AIRS3STM) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3STM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3STM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3STM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3STM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3STM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRS3STM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477383-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX2RET_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2020-02-12T18:12:06Z',\n 'revision-id': 32,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Standard '\n 'Retrieval Product consists of retrieved '\n 'estimates of cloud and surface '\n 'properties, plus profiles of retrieved '\n 'temperature, water vapor, ozone, carbon '\n 'monoxide and methane. Estimates of the '\n 'errors associated with these quantities '\n 'are also be part of the Standard Product. '\n 'The temperature profile vertical '\n 'resolution is 28 levels total between '\n '1100 mb and 0.1 mb, while moisture '\n 'profile is reported at 14 atmospheric '\n 'layers between 1100 mb and 50 mb. The '\n 'horizontal resolution is 50 km. An AIRS '\n 'granule has been set as 6 minutes of '\n 'data, 30 footprints cross track by 45 '\n 'lines along track. There are 240 granules '\n 'per day, with an orbit repeat cycle of '\n 'approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2RET_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX2RET',\n 'Title': 'AIRS/Aqua L2 '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS+AMSU) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA201'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Standard Physical '\n 'Retrieval (AIRS+AMSU) V006 (AIRX2RET) '\n 'at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens',\n 'Title': 'mproved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde '\n 'data”',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Edition': '120',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '3'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'journal '\n 'Editiors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Special Issue, '\n 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '2',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Series': 'IEEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '41'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the Version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias, '\n 'another is that the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.\\n'\n '\\n'\n 'This product stopped after September 24, '\n '2016 as the power to the AMSU-A2 '\n 'instrument on Aqua was lost. For data '\n 'after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX2RET_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2RET_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRX2RET.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRX2RET.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX2RET%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX2RET+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'}],\n 'ShortName': 'AIRX2RET',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Not '\n 'provided'}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2016-09-24T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477317-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX2SUP_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:04Z',\n 'revision-id': 19,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The Support Product '\n 'includes higher vertical resolution '\n 'profiles of the quantities found in the '\n 'Standard Product plus intermediate output '\n '(e.g., microwave-only retrieval), '\n 'research products such as the abundance '\n 'of trace gases, and detailed quality '\n 'assessment information. The Support '\n 'Product profiles contain 100 pressure '\n 'levels between 1100 and .016 mb; this '\n 'higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than in the '\n 'Standard Product profiles. The horizontal '\n 'resolution is 50 km. The intended users '\n 'of the Support Product are researchers '\n 'interested in generating forward '\n 'radiance, or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data, 30 footprints cross '\n 'track by 45 lines along track. There are '\n '240 granules per day, with an orbit '\n 'repeat cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2SUP_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX2SUP',\n 'Title': 'AIRS/Aqua L2 '\n 'Support Retrieval '\n '(AIRS+AMSU) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA207'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Support Retrieval '\n '(AIRS+AMSU) V006 (AIRX2SUP) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres,',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Edition': '120',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the '\n 'Arctic”, '\n 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Volume': '3'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations”',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Edition': '121',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '4'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '41',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'ReportNumber': '2',\n 'Series': 'IEEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the Version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. '\n 'Another is that the thickness of the AIRS '\n 'atmospheric temperature layer structure '\n 'near the surface is not sensitive enough '\n 'for the determination of a consistently '\n 'accurate boundary layer. For trace gases, '\n 'the total column CO and total column '\n 'methane (CH4) are dominated by the initial '\n 'guess and should not be used for research '\n 'purposes. In addition, the AIRS retrieval '\n 'is not sensitive to either constituent '\n 'near the surface. Also, the total column '\n 'ozone is good, but the shape of the '\n 'profile can be incorrect in regions of '\n 'temperature inversion. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off '\n 'the African continent toward the Americas '\n 'for high levels of ozone. Each variable '\n 'contains a flag indicating the quality of '\n 'the value. The three options for this '\n 'quality flag are: 0 for best quality, 1 '\n 'for good quality, 2 for do not use. \\n'\n '\\n'\n 'This product stopped after September 24, '\n '2016 as the power to the AMSU-A2 '\n 'instrument on Aqua was lost. For data '\n 'after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS+AMSU) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX2SUP_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2SUP_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRX2SUP.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRX2SUP.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX2SUP%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX2SUP+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MICROWAVE',\n 'Topic': 'SPECTRAL/ENGINEERING',\n 'VariableLevel1': 'BRIGHTNESS '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX2SUP',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2016-09-24T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517314-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3SP8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:11Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 563.3,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SP8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3SP8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Support '\n 'Multiday Product '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA313'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Support Multiday '\n 'Product (AIRS+AMSU) 1 degree x 1 degree '\n 'V006 (AIRX3SP8) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3SP8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SP8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3SP8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3SP8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3SP8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3SP8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX3SP8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517317-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3SPD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:12Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 474.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3SPD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Support '\n 'Product '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA304'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Support Product '\n '(AIRS+AMSU) 1 degree x 1 degree V006 '\n '(AIRX3SPD) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics,',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3SPD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3SPD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3SPD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3SPD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3SPD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX3SPD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2016-09-25T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517340-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3SPM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:13Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 554.5,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3SPM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Support '\n 'Product '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA322'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Support Product '\n '(AIRS+AMSU) 1 degree x 1 degree V006 '\n '(AIRX3SPM) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'mproved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3SPM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3SPM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3SPM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3SPM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3SPM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX3SPM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517323-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3ST8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:14Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Level 3 8-Day '\n 'Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers an 8-day period, or one-half of '\n 'the Aqua orbit repeat cycle. The mean '\n 'values are simply the arithmetic means of '\n 'the daily products, weighted by the '\n 'number of input counts for each day in '\n 'that grid box. The geophysical parameters '\n 'have been averaged and binned into 1 x 1 '\n 'deg grid cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 452.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3ST8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3ST8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA310'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Standard Physical '\n 'Retrieval (AIRS+AMSU) 1 degree x 1 '\n 'degree V006 (AIRX3ST8) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3ST8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3ST8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3ST8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3ST8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3ST8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3ST8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRX3ST8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517344-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3STD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:16Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Level 3 Daily '\n 'Gridded Product contains standard '\n 'retrieval means, standard deviations and '\n 'input counts. Each file covers a temporal '\n 'period of 24 hours for either the '\n 'descending (equatorial crossing North to '\n 'South @1:30 AM local time) or ascending '\n '(equatorial crossing South to North @1:30 '\n 'PM local time) orbit. The data starts at '\n 'the international dateline and progresses '\n 'westward (as do the subsequent orbits of '\n 'the satellite) so that neighboring '\n 'gridded cells of data are no more than a '\n 'swath of time apart (about 90 minutes). '\n 'The two parts of a scan line crossing the '\n 'dateline are included in separate L3 '\n 'files, according to the date, so that '\n 'data points in a grid box are always '\n 'coincident in time. The edge of the AIRS '\n 'Level 3 gridded cells is at the date line '\n '(the 180E/W longitude boundary). When '\n 'plotted, this produces a map with 0 '\n 'degrees longitude in the center of the '\n 'image unless the bins are reordered. This '\n 'method is preferred because the left '\n '(West) side of the image and the right '\n '(East) side of the image contain data '\n 'farthest apart in time. The gridding '\n 'scheme used by AIRS is the same as used '\n 'by TOVS Pathfinder to create Level 3 '\n 'products. The daily Level 3 products have '\n 'gores between satellite paths where there '\n 'is no coverage for that day. The '\n 'geophysical parameters have been averaged '\n 'and binned into 1 x 1 deg grid cells, '\n 'from -180.0 to +180.0 deg longitude and '\n 'from -90.0 to +90.0 deg latitude. For '\n 'each grid map of 4-byte floating-point '\n 'mean values there is a corresponding '\n '4-byte floating-point map of standard '\n 'deviation and a 2-byte integer grid map '\n 'of counts. The counts map provides the '\n 'user with the number of points per bin '\n 'that were included in the mean and can be '\n 'used to generate custom multi-day maps '\n 'from the daily gridded products. The '\n 'thermodynamic parameters are: Skin '\n 'Temperature (land and sea surface), Air '\n 'Temperature at the surface, Profiles of '\n 'Air Temperature and Water Vapor, '\n 'Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 376.1,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3STD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA301'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Standard Physical '\n 'Retrieval (AIRS+AMSU) 1 degree x 1 '\n 'degree V006 (AIRX3STD) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3STD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3STD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3STD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3STD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3STD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&version=1.1.1&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Get map example for '\n 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&VERSION=1.1.1&REQUEST=GetMap&SRS=EPSG:4326&WIDTH=720&HEIGHT=360&LAYERS=AIRX3STD_TOTH2OVAP_A&TRANSPARENT=TRUE&FORMAT=image/png&bbox=-180,-90,180,90',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Coverage Service.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Get Coverage data '\n 'example for NASA '\n 'GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Coverage Service.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCoverage&CRS=EPSG:4326&format=netCDF&resx=1.0&resy=1.0&BBOX=-179.5,-89.5,179.5,89.5&Coverage=AIRX3STD:CO_VMR_A&Time=2013-08-11',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRX3STD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2016-09-25T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517346-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3STM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:17Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Level 3 '\n 'Monthly Gridded Retrieval Product '\n 'contains standard retrieval means, '\n 'standard deviations and input counts. '\n 'Each file covers a calendar month. The '\n 'mean values are simply the arithmetic '\n 'means of the daily products, weighted by '\n 'the number of input counts for each day '\n 'in that grid box. The geophysical '\n 'parameters have been averaged and binned '\n 'into 1 x1 deg grid cells, from -180.0 to '\n '+180.0 deg longitude and from -90.0 to '\n '+90.0 deg latitude. For each grid map of '\n '4-byte floating-point mean values there '\n 'is a corresponding 4-byte floating-point '\n 'map of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 445.7,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3STM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA319'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Standard Physical '\n 'Retrieval (AIRS+AMSU) 1 degree x 1 '\n 'degree V006 (AIRX3STM) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3STM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3STM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3STM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3STM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3STM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&version=1.1.1&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Get map example for '\n 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&VERSION=1.1.1&REQUEST=GetMap&SRS=EPSG:4326&WIDTH=720&HEIGHT=360&LAYERS=AIRX3STM_TOTH2OVAP_A&TRANSPARENT=TRUE&FORMAT=image/png&bbox=-180,-90,180,90',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Coverage Service.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Example of getting '\n 'coverage data from '\n 'the NASA GES DISC '\n 'AIRS Gridded L3 '\n 'data Web Coverage '\n 'Service. It '\n 'requests one of the '\n 'variables, '\n 'AIRX3STM:CO_VMR_A '\n 'as listed in the '\n 'GetCapabilities '\n 'response, at given '\n 'TIME and BBOX.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCoverage&CRS=EPSG:4326&format=netCDF&resx=1.0&resy=1.0&BBOX=-197.5,-89.5,179.5,89.5&Coverage=AIRX3STM:CO_VMR_A&Time=2013-07',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRX3STM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1658476139-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+2P+Global+Subskin+Sea+Surface+Temperature+version+8a+from+the+Advanced+Microwave+Scanning+Radiometer+2+on+the+GCOM-W+satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:56Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer 2 (AMSR2) was launched on 18 '\n 'May 2012, onboard the Global Change '\n 'Observation Mission - Water (GCOM-W) '\n 'satellite developed by the Japan '\n 'Aerospace Exploration Agency (JAXA). The '\n 'GCOM-W mission aims to establish the '\n 'global and long-term observation system '\n 'to collect data, which is needed to '\n 'understand mechanisms of climate and '\n 'water cycle variations, and demonstrate '\n 'its utilization. AMSR2 onboard the first '\n 'generation of the GCOM-W satellite will '\n 'continue Aqua/AMSR-E observations of '\n 'water vapor, cloud liquid water, '\n 'precipitation, SST, sea surface wind '\n 'speed, sea ice concentration, snow depth, '\n 'and soil moisture. AMSR2 is a remote '\n 'sensing instrument for measuring weak '\n 'microwave emission from the surface and '\n 'the atmosphere of the Earth. From about '\n '700 km above the Earth, AMSR2 will '\n 'provide us highly accurate measurements '\n 'of the intensity of microwave emission '\n 'and scattering. The antenna of AMSR2 '\n 'rotates once per 1.5 seconds and obtains '\n 'data over a 1450 km swath. This conical '\n 'scan mechanism enables AMSR2 to acquire a '\n 'set of daytime and nighttime data with '\n 'more than 99% coverage of the Earth every '\n '2 days. Remote Sensing Systems (RSS, or '\n 'REMSS), providers of these SST data for '\n 'the Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR2 instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"_rt_\" within the file '\n 'name) which is made as available as soon '\n 'as possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v7.2\" within the file '\n 'name) are processed when RSS receives the '\n 'atmospheric model National Center for '\n 'Environmental Prediction (NCEP) Final '\n 'Analysis (FNL) Operational Global '\n 'Analysis. The NCEP wind directions are '\n 'particularly useful for retrieving more '\n 'accurate SSTs and wind speeds. This '\n 'dataset adheres to the GHRSST Data '\n 'Processing Specification (GDS) version 2 '\n 'format specifications.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'Remote '\n 'Sensing '\n 'Systems, '\n '2017-10-31, '\n 'GHRSST '\n 'Level '\n '2P '\n 'Global '\n 'Subskin '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'version '\n '8a '\n 'from '\n 'the '\n 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2 '\n 'on '\n 'the '\n 'GCOM-W '\n 'satellite, '\n '10.5067/GHAM2-2PR8A, '\n 'http://www.remss.com'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHAM2-2PR8A'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Remote Sensing '\n 'Systems'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2017-09-18T17:57:41.242Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-09-18T17:57:41.242Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P Global Subskin Sea '\n 'Surface Temperature version 8a from the '\n 'Advanced Microwave Scanning Radiometer '\n '2 on the GCOM-W satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-20T20:39:54.386Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 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'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/R/',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/IDL/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AMSR2 SSTs: '\n 'algorithm '\n 'description, '\n 'browsing of data, '\n 'and ftp of data',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.remss.com/missions/amsr/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHAM2-2PR8A',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/AMSR2/REMSS/v8a/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Sub-skin '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'AMSR2-REMSS-L2P-v8a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2012-07-02T19:00:44.000Z'}]}],\n 'Version': '8a'}},\n { 'meta': { 'concept-id': 'C1658476016-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+3U+Global+Subskin+Sea+Surface+Temperature+version+8a+from+the+Advanced+Microwave+Scanning+Radiometer+2+on+the+GCOM-W+satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:08Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'GDS2 Version -The Advanced Microwave '\n 'Scanning Radiometer 2 (AMSR2) was '\n 'launched on 18 May 2012, onboard the '\n 'Golbal Change Observation Mission - Water '\n '(GCOM-W) satellite developed by the Japan '\n 'Aerospace Exploration Agency (JAXA). The '\n 'GCOM-W mission aims to establish the '\n 'global and long-term observation system '\n 'to collect data, which is needed to '\n 'understand mechanisms of climate and '\n 'water cycle variations, and demonstrate '\n 'its utilization. AMSR2 onboard the first '\n 'generation of the GCOM-W satellite will '\n 'continue Aqua/AMSR-E observations of '\n 'water vapor, cloud liquid water, '\n 'precipitation, SST, sea surface wind '\n 'speed, sea ice concentration, snow depth, '\n 'and soil moisture. AMSR2 is a remote '\n 'sensing instrument for measuring weak '\n 'microwave emission from the surface and '\n 'the atmosphere of the Earth. From about '\n '700 km above the Earth, AMSR2 will '\n 'provide us highly accurate measurements '\n 'of the intensity of microwave emission '\n 'and scattering. The antenna of AMSR2 '\n 'rotates once per 1.5 seconds and obtains '\n 'data over a 1450 km swath. This conical '\n 'scan mechanism enables AMSR2 to acquire a '\n 'set of daytime and nighttime data with '\n 'more than 99% coverage of the Earth every '\n '2 days. Remote Sensing Systems (RSS, or '\n 'REMSS), providers of these SST data for '\n 'the Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR-E instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"rt\" within the file name) '\n 'which is made as available as soon as '\n 'possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v8\" within the file name) '\n 'are processed when RSS receives the '\n 'atmospheric mode NCEP FNL analysis. The '\n 'NCEP wind directions are particularly '\n 'useful for retrieving more accurate SSTs '\n 'and wind speeds. The final \"v8\" products '\n 'will continue to accumulate new swaths '\n '(half orbits) until the maps are full, '\n 'generally within 2 days.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'Remote '\n 'Sensing '\n 'Systems, '\n '2017-11-30, '\n 'GHRSST '\n 'Level '\n '3U '\n 'Global '\n 'Subskin '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'version '\n '8a '\n 'from '\n 'the '\n 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2 '\n 'on '\n 'the '\n 'GCOM-W '\n 'satellite, '\n '10.5067/GHAM2-3UR8A, '\n 'http://www.remss.com'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHAM2-3UR8A'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Remote Sensing '\n 'Systems'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2017-09-08T22:29:02.939Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-09-08T22:29:02.939Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 3U Global Subskin Sea '\n 'Surface Temperature version 8a from the '\n 'Advanced Microwave Scanning Radiometer '\n '2 on the GCOM-W satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-20T20:39:06.338Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 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'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac.jpl.nasa.gov/SeaSurfaceTemperature',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Full details of the '\n 'AMSR2',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://suzaku.eorc.jaxa.jp/GCOM_W/w_amsr2/whats_amsr2.html',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 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'https://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=AMSR2-REMSS-L3U-v8a',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Samples, Interface '\n 'Control Document '\n 'describing file '\n 'contents, '\n 'background ppt and '\n 'other info',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.remss.com/missions/amsr/',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHAM2-3UR8A',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L3U/AMSR2/REMSS/v8a/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SUB-SKIN '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AMSR2-REMSS-L3U-v8a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -179.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2012-07-02T23:24:00.000Z'}]}],\n 'Version': '8a'}},\n { 'meta': { 'concept-id': 'C1666605425-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+2P+Global+Subskin+Sea+Surface+Temperature+from+the+Advanced+Scanning+Microwave+Radiometer+-+Earth++Observing+System+(AMSR-E)+on+the+NASA+Aqua+Satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-10T18:30:18Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer (AMSR-E) was launched on 4 May '\n \"2002, aboard NASA's Aqua spacecraft. The \"\n 'National Space Development Agency of '\n 'Japan (NASDA) provided AMSR-E to NASA as '\n \"an indispensable part of Aqua's global \"\n 'hydrology mission. Over the oceans, '\n 'AMSR-E is measuring a number of important '\n 'geophysical parameters, including sea '\n 'surface temperature (SST), wind speed, '\n 'atmospheric water vapor, cloud water, and '\n 'rain rate. A key feature of AMSR-E is its '\n 'capability to see through clouds, thereby '\n 'providing an uninterrupted view of global '\n 'SST and surface wind fields. Remote '\n 'Sensing Systems (RSS, or REMSS) is the '\n 'provider of these SST data for the Group '\n 'for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR-E instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"_rt_\" within the file '\n 'name) which is made as available as soon '\n 'as possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v7\" within the file name) '\n 'are processed when RSS receives the '\n 'atmospheric model National Center for '\n 'Environmental Prediction (NCEP) Final '\n 'Analysis (FNL) Operational Global '\n 'Analysis. The NCEP wind directions are '\n 'particularly useful for retrieving more '\n 'accurate SSTs and wind speeds. This '\n 'dataset adheres to the GHRSST Data '\n 'Processing Specification (GDS) version 2 '\n 'format specifications.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'Remote '\n 'Sensing '\n 'Systems, '\n '2014-12-01, '\n 'GHRSST '\n 'Level '\n '2P '\n 'Global '\n 'Subskin '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'from '\n 'the '\n 'Advanced '\n 'Scanning '\n 'Microwave '\n 'Radiometer '\n '- '\n 'Earth '\n 'Observing '\n 'System '\n '(AMSR-E) '\n 'on '\n 'the '\n 'NASA '\n 'Aqua '\n 'Satellite, '\n '10.5067/GHAMS-2GR07, '\n 'http://www.remss.com'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHAMS-2GR07'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Remote Sensing '\n 'Systems'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2014-10-07T22:41:20.867Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:45.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P Global Subskin Sea '\n 'Surface Temperature from the Advanced '\n 'Scanning Microwave Radiometer - Earth '\n 'Observing System (AMSR-E) on the NASA '\n 'Aqua Satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-12-10T18:30:15.958Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s 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'http://thredds.jpl.nasa.gov/thredds/catalog/ncml_aggregation/OceanTemperature/ghrsst/catalog.html?dataset=ncml_aggregation/OceanTemperature/ghrsst/aggregate__ghrsst_REMSS-L2P_GRIDDED_25-AMSRE.ncml',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AMSR-E calibration '\n 'description',\n 'Subtype': 'INSTRUMENT/SENSOR '\n 'CALIBRATION '\n 'DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.remss.com',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'GET DATA',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AMSR-E SSTs: '\n 'algorithm '\n 'description, '\n 'browsing of data, '\n 'and ftp of 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INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/AMSRE/REMSS/v7/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SUB-SKIN '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AMSRE-REMSS-L2P-v7a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-01T12:06:00.000Z',\n 'EndingDateTime': '2011-10-04T06:51:45.000Z'}]}],\n 'Version': '7.0'}},\n { 'meta': { 'concept-id': 'C1657548613-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+3U+Global+Subskin+Sea+Surface+Temperature+from+the+Advanced+Scanning+Microwave+Radiometer+-+Earth++Observing+System+(AMSR-E)+on+the+NASA+Aqua+Satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-18T21:27:48Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer (AMSR-E) was launched on 4 May '\n \"2002, aboard NASA's Aqua spacecraft. The \"\n 'National Space Development Agency of '\n 'Japan (NASDA) provided AMSR-E to NASA as '\n \"an indispensable part of Aqua's global \"\n 'hydrology mission. Over the oceans, '\n 'AMSR-E is measuring a number of important '\n 'geophysical parameters, including sea '\n 'surface temperature (SST), wind speed, '\n 'atmospheric water vapor, cloud water, and '\n 'rain rate. A key feature of AMSR-E is its '\n 'capability to see through clouds, thereby '\n 'providing an uninterrupted view of global '\n 'SST and surface wind fields. Remote '\n 'Sensing Systems (RSS, or REMSS) is the '\n 'provider of these SST data for the Group '\n 'for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR-E instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"_rt_\" within the file '\n 'name) which is made as available as soon '\n 'as possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v7\" within the file name) '\n 'are processed when RSS receives the '\n 'atmospheric model National Center for '\n 'Environmental Prediction (NCEP) Final '\n 'Analysis (FNL) Operational Global '\n 'Analysis. The NCEP wind directions are '\n 'particularly useful for retrieving more '\n 'accurate SSTs and wind speeds. 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'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2017-06-13T22:12:37.481Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-06-13T22:12:37.481Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 3U Global Subskin Sea '\n 'Surface Temperature from the Advanced '\n 'Scanning Microwave Radiometer - Earth '\n 'Observing System (AMSR-E) on the NASA '\n 'Aqua Satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-18T21:27:45.408Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s 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collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L3U/AMSRE/REMSS/v7a',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L3U/AMSRE/REMSS/v7a',\n 'URLContentType': 'DistributionURL'},\n { 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://aqua.nasa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 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'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHAMS-3GR7A',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHAMS-3GR7A',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L3U/AMSRE/REMSS/v7a/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SUB-SKIN '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AMSRE-REMSS-L3U-v7a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-01T16:12:00.000Z',\n 'EndingDateTime': '2011-10-04T06:54:00.000Z'}]}],\n 'Version': '7a'}},\n { 'meta': { 'concept-id': 'C1652977738-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AMSR-E+Level+3+Sea+Surface+Temperature+for+Climate+Model+Comparison',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-06T18:12:48Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'This data set contains sea surface '\n 'temperature (SST) data on a monthly 1 '\n 'degree grid from the Advanced Microwave '\n 'Scanning Radiometer (AMSR-E) aboard '\n \"NASA's Aqua spacecraft. The data were \"\n 'produced by Remote Sensing Systems in '\n 'support of the CMIP5 (Coupled Model '\n 'Intercomparison Project Phase 5) under '\n 'the World Climate Research Program '\n '(WCRP). Along with this dataset, two '\n 'additional ancillary data files are '\n 'included in the same directory which '\n 'contain the number of observations and '\n 'standard error co-located on the same 1 '\n 'degree grids. AMSR-E, a '\n 'passive-microwave radiometer launched on '\n 'the Aqua platform on 4 May 2002, was '\n 'provided by the National Space '\n 'Development Agency (NASDA) of Japan to '\n \"NASA as an indispensable part of Aqua's \"\n 'global hydrology mission. Over the '\n 'oceans, AMSR-E is measuring a number of '\n 'important geophysical parameters, '\n 'including SST, wind speed, atmospheric '\n 'water vapor, cloud water, and rain rate. '\n 'A key feature of AMSR-E is its capability '\n 'to see through clouds, thereby providing '\n 'an uninterrupted view of global SST and '\n 'surface wind fields.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'JPL, '\n '2011-03-01, '\n 'AMSR-E '\n 'Level '\n '3 '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'for '\n 'Climate '\n 'Model '\n 'Comparison, '\n '10.5067/SST00-1D1M1, '\n 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/amsre/L3/sst_1deg_1mo/docs/tosTechNote_AMSRE_L3_v7_200206-201012.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/SST00-1D1M1'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'REMSS'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '818-393-6710'},\n { 'Type': 'Email',\n 'Value': 'edward.m.armstrong@jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Edward',\n 'LastName': 'Armstrong',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2012-01-26T22:15:23.132Z',\n 'Type': 'CREATE'},\n { 'Date': 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'NASA/OBPG, Reynolds & '\n 'Smith NOAA/NCDC'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '(818) '\n '393-7165'},\n { 'Type': 'Email',\n 'Value': 'podaac@podaac.jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'User',\n 'LastName': 'Services',\n 'MiddleName': 'null',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'}],\n 'DataDates': [ { 'Date': '2017-10-21T00:17:01.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-12-07T07:31:31.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'Aquarius Official Release Level 3 '\n 'Ancillary Reynolds Sea Surface '\n 'Temperature Standard Mapped Image '\n 'Ascending Seasonal Data V5.0',\n 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'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-19',\n 'ShortName': 'NOAA-19',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting '\n 'Buoys',\n 'ShortName': 'InSitu'}],\n 'LongName': 'InSitu',\n 'ShortName': 'InSitu',\n 'Type': 'instrument'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-7',\n 'ShortName': 'NOAA-7',\n 'Type': 'SPACECRAFT'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [{'ShortName': 'AQUARIUS SAC-D'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/L3/mapped/V5/3month/SCIA',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA Aquarius/SAC-D '\n 'mission website',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://aquarius.nasa.gov/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'ATBD, Validation '\n 'Analysis, Product '\n 'Specifications, etc',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/docs/v5/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Aquarius Data '\n \"User's Guide\",\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/docs/v5/AQ-010-UG-0008_AquariusUserGuide_DatasetV5.0.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'IDL Reader and '\n 'calling routines',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/sw/idl/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Mission and '\n 'Instrument Overview',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://podaac.jpl.nasa.gov/aquarius',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Information on '\n 'observatory '\n 'maneuvers, '\n 'anomalies and other '\n 'events',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://oceancolor.gsfc.nasa.gov/sdpscgi/public/aquarius_report.cgi',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'MATLAB Reader and '\n 'calling routines',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/sw/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/AQR50-3R3AS',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/3month/SCIA/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Blended '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2011-08-25T01:55:23.000Z',\n 'EndingDateTime': '2015-06-07T11:41:38.000Z'}]}],\n 'Version': '5.0'}},\n { 'meta': { 'concept-id': 'C1649544930-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'Aquarius+Official+Release+Level+3+Ancillary+Reynolds+Sea+Surface+Temperature+Standard+Mapped+Image+Ascending+7-Day+Data+V5.0',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-10-30T17:01:05Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'Aquarius Level 3 ancillary sea surface '\n 'temperature (SST) standard mapped image '\n 'data are the ancillary SST data used in '\n 'the Aquarius calibration for salinity '\n 'retrieval. They are simply the daily '\n 'SSTs from the Reynolds National Climatic '\n 'Data Center (NCDC) 0.25 degree GHRSST '\n 'dataset, gridded and averaged using the '\n 'Aquarius processing L2-L3 processing '\n 'scheme to the same 1 degree spatial '\n 'resolution and daily, 7 day, monthly, '\n '7-Day, and annual time intervals as '\n 'Aquarius L3 standard salinity and wind '\n 'speed products. This particular data set '\n 'is the 7-Day, ascending ancillary sea '\n 'surface temperature product associated '\n 'with version 5.0 of the Aquarius data '\n 'set, which is the official end of mission '\n 'public data release from the '\n 'AQUARIUS/SAC-D mission.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'HDF5',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'NASA '\n 'Aquarius '\n 'project, '\n 'NASA/GSFC '\n 'OBPG, '\n '2017-12-07, '\n 'Aquarius '\n 'Official '\n 'Release '\n 'Level '\n '3 '\n 'Ancillary '\n 'Reynolds '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Standard '\n 'Mapped '\n 'Image '\n 'Ascending '\n '7-Day '\n 'Data '\n 'V5.0, '\n '10.5067/AQR50-3R7AS, '\n 'http://podaac.jpl.nasa.gov/SeaSurfaceSalinity/Aquarius'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/AQR50-3R7AS'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Aquarius Project '\n 'NASA/OBPG, Reynolds & '\n 'Smith NOAA/NCDC'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '(818) '\n '393-7165'},\n { 'Type': 'Email',\n 'Value': 'podaac@podaac.jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'User',\n 'LastName': 'Services',\n 'MiddleName': 'null',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'}],\n 'DataDates': [ { 'Date': '2017-10-21T00:17:01.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-12-07T07:31:31.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'Aquarius Official Release Level 3 '\n 'Ancillary Reynolds Sea Surface '\n 'Temperature Standard Mapped Image '\n 'Ascending 7-Day Data V5.0',\n 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'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-11',\n 'ShortName': 'NOAA-11',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-14',\n 'ShortName': 'NOAA-14',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 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This particular data set '\n 'is the Daily, ascending ancillary sea '\n 'surface temperature product associated '\n 'with version 5.0 of the Aquarius data '\n 'set, which is the official end of mission '\n 'public data release from the '\n 'AQUARIUS/SAC-D mission.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'HDF5',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'NASA '\n 'Aquarius '\n 'project, '\n 'NASA/GSFC '\n 'OBPG, '\n '2017-12-07, '\n 'Aquarius '\n 'Official '\n 'Release '\n 'Level '\n '3 '\n 'Ancillary '\n 'Reynolds '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Standard '\n 'Mapped '\n 'Image '\n 'Ascending '\n 'Daily '\n 'Data '\n 'V5.0, '\n '10.5067/AQR50-3R1AS, '\n 'http://podaac.jpl.nasa.gov/SeaSurfaceSalinity/Aquarius'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/AQR50-3R1AS'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Aquarius Project '\n 'NASA/OBPG, 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'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-10-30T17:04:15.753Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-11',\n 'ShortName': 'NOAA-11',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-14',\n 'ShortName': 'NOAA-14',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-9',\n 'ShortName': 'NOAA-9',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.1'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-16',\n 'ShortName': 'NOAA-16',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '101.2'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.7'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-17',\n 'ShortName': 'NOAA-17',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.12'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.74'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-19',\n 'ShortName': 'NOAA-19',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting '\n 'Buoys',\n 'ShortName': 'InSitu'}],\n 'LongName': 'InSitu',\n 'ShortName': 'InSitu',\n 'Type': 'instrument'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-7',\n 'ShortName': 'NOAA-7',\n 'Type': 'SPACECRAFT'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [{'ShortName': 'AQUARIUS SAC-D'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/L3/mapped/V5/daily/SCIA',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Information on '\n 'observatory '\n 'maneuvers, '\n 'anomalies and other '\n 'events',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://oceancolor.gsfc.nasa.gov/sdpscgi/public/aquarius_report.cgi',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Mission and '\n 'Instrument Overview',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://podaac.jpl.nasa.gov/aquarius',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'NASA Aquarius/SAC-D '\n 'mission website',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://aquarius.nasa.gov/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'ATBD, Validation '\n 'Analysis, Product '\n 'Specifications, etc',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/docs/v5/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Aquarius Data '\n \"User's Guide\",\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/docs/v5/AQ-010-UG-0008_AquariusUserGuide_DatasetV5.0.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'IDL Reader and '\n 'calling routines',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/sw/idl/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'MATLAB Reader and '\n 'calling routines',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/sw/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/AQR50-3R1AS',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/daily/SCIA/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Blended '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2011-08-25T01:45:23.000Z',\n 'EndingDateTime': '2015-06-07T11:41:38.000Z'}]}],\n 'Version': '5.0'}}],\n 'took': 90}\n\n\nHere we’re now limiting the search to those with the Oceans topic as well as the ‘Ocean Temperature’ term. To limit this further, we are only searching for collections that contain granules (data). We do this by specifying\nhas_granules_or_cwic=true\nSo we are closing in on this. Let’s add a time range and find some PO.DAAC data:\n\nwith request.urlopen(cmr_url+\"collections.umm_json?science_keywords[0][topic]=OCEANS&science_keywords[0][term]=Ocean%20Temperature&has_granules_or_cwic=true&temporal=2018-01-01T10:00:00Z,2019-01-01T10:00:00Z&provider_short_name=PODAAC&processing_level_id=4&page_size=50\") as response:\n data = response.read()\n encoding = response.info().get_content_charset('utf-8')\n JSON_object = json.loads(data.decode(encoding))\n pp = pprint.PrettyPrinter(indent=2)\n pp.pprint(JSON_object)\n\n{ 'hits': 29,\n 'items': [ { 'meta': { 'concept-id': 'C1658476070-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+RAMSSA+Australian+Regional+Foundation+Sea+Surface+Temperature+Analysis',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:28Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis at the Australian '\n 'Bureau of Meteorology using optimal '\n 'interpolation (OI) on a regional 1/12 '\n 'degree grid over the Australian region '\n '(20N - 70S, 60E - 170W). This BLUELink '\n 'Regional Australian Multi-Sensor SST '\n 'Analysis (RAMSSA) v1.0 system blends '\n 'satellite SST observations from the '\n 'Advanced Very High Resolution Radiometer '\n '(AVHRR), the Advanced Along Track '\n 'Scanning Radiometer (AATSR), and, the '\n 'Advanced Microwave Scanning '\n 'Radiometer-EOS (AMSRE), and in situ data '\n 'from ships, and drifting and moored buoy '\n 'from the Global Telecommunications System '\n '(GTS). The processing results in daily '\n 'foundation SST estimates that are largely '\n 'free of nocturnal cooling and diurnal '\n 'warming effects.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n '2008-01-24, '\n 'GHRSST '\n 'Level '\n '4 '\n 'RAMSSA '\n 'Australian '\n 'Regional '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis, '\n '10.5067/GHRAM-4FA01, '\n 'http://www.bom.gov.au/jshess/docs/2011/beggs_hres.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHRAM-4FA01'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694394'},\n { 'Type': 'Email',\n 'Value': 'h.beggs@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Helen',\n 'LastName': 'Beggs',\n 'MiddleName': 'none',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694746'},\n { 'Type': 'Email',\n 'Value': 'ghrsst@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Leon',\n 'LastName': 'Majewski',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'}],\n 'DataDates': [ { 'Date': '2008-01-29T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:46.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 RAMSSA Australian '\n 'Regional Foundation Sea Surface '\n 'Temperature Analysis',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'OCEANIA'}],\n 'MetadataDates': [ { 'Date': '2019-11-20T20:39:25.914Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.12'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.74'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-19',\n 'ShortName': 'NOAA-19',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-20',\n 'ShortName': 'NOAA-20',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting '\n 'Buoys',\n 'ShortName': 'InSitu'}],\n 'LongName': 'InSitu',\n 'ShortName': 'InSitu',\n 'Type': 'instrument'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '100.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.19'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '1450.0'}],\n 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2',\n 'ShortName': 'AMSR2'}],\n 'LongName': 'Global Change '\n 'Observation Mission 1st '\n '- Water 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'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/L4/AUS/ABOM/RAMSSA_09km/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABOM-L4HRfnd-AUS-RAMSSA_09km',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': 60.0},\n { 'EastBoundingCoordinate': -170.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 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Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis at the Australian '\n 'Bureau of Meteorology using optimal '\n 'interpolation (OI) on a global 0.25 '\n 'degree grid. This BLUELink Global '\n 'Australian Multi-Sensor SST Analysis '\n '(GAMSSA) v1.0 system blends satellite SST '\n 'observations from the Advanced Very High '\n 'Resolution Radiometer (AVHRR), the '\n 'Advanced Along Track Scanning Radiometer '\n '(AATSR), and, the Advanced Microwave '\n 'Scanning Radiometer-EOS (AMSRE), and in '\n 'situ data from ships, and drifting and '\n 'moored buoy from the Global '\n 'Telecommunications System (GTS). In order '\n 'to produce a foundation SST estimate, the '\n 'AATSR skin SST data stream is converted '\n 'to foundation SST using the Donlon et al. '\n '(2002) skin to foundation temperature '\n 'conversion algorithms. These '\n 'empirically-derived algorithms apply a '\n 'small correction for the cool-skin effect '\n 'depending on surface wind speed, and '\n 'filter out SST values suspected to be '\n 'affected by diurnal warming by excluding '\n 'cases which have experienced recent '\n 'surface wind speeds of below 6 ms-1 '\n 'during the day and less than 2 ms-1 '\n 'during the night.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n '2008-07-28, '\n 'GHRSST '\n 'Level '\n '4 '\n 'GAMSSA '\n 'Global '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis, '\n '10.5067/GHGAM-4FA01, '\n 'http://www.bom.gov.au/australia/charts/bulletins/apob77.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHGAM-4FA01'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694394'},\n { 'Type': 'Email',\n 'Value': 'h.beggs@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Helen',\n 'LastName': 'Beggs',\n 'MiddleName': 'none',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694746'},\n { 'Type': 'Email',\n 'Value': 'ghrsst@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Leon',\n 'LastName': 'Majewski',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'}],\n 'DataDates': [ { 'Date': '2008-08-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': 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'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/L4/GLOB/ABOM/GAMSSA_28km/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABOM-L4LRfnd-GLOB-GAMSSA_28km',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2008-08-24T00:00:00.000Z'}]}],\n 'Version': '1.0'}},\n { 'meta': { 'concept-id': 'C1652971997-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+AVHRR_OI+Global+Blended+Sea+Surface+Temperature+Analysis+(GDS+version+2)+from+NCEI',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-06T18:11:02Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) global Level 4 sea '\n 'surface temperature analysis produced '\n 'daily on a 0.25 degree grid at the NOAA '\n 'National Centers for Environmental '\n 'Information. This product uses optimal '\n 'interpolation (OI) by interpolating and '\n 'extrapolating SST observations from '\n 'different sources, resulting in a '\n 'smoothed complete field. The sources of '\n 'data are satellite (AVHRR) and in situ '\n 'platforms (i.e., ships and buoys), and '\n 'the specific datasets employed may change '\n 'over. At the marginal ice zone, sea ice '\n 'concentrations are used to generate proxy '\n 'SSTs. A preliminary version of this file '\n 'is produced in near-real time (1-day '\n 'latency), and then replaced with a final '\n 'version after 2 weeks. 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'\n '2 (2013)',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Sea_Surface_Temperature_Optimum_Interpolation/AlgorithmDescription.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Web site with '\n 'general description',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.ncdc.noaa.gov/oa/climate/research/sst/description.php',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/R/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Viva Banzon, Thomas '\n 'M. Smith, Toshio '\n 'Mike Chin, Chunying '\n 'Liu, and William '\n 'Hankins, A '\n 'long-term record of '\n 'blended satellite '\n 'and in situ '\n 'sea-surface '\n 'temperature for '\n 'climate monitoring, '\n 'modeling and '\n 'environmental '\n 'studies, Earth '\n 'Syst. Sci. Data, 8, '\n '165-176, 2016',\n 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.earth-syst-sci-data.net/8/165/2016/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Reynolds, et '\n 'al.(2009)',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.ncdc.noaa.gov/sites/default/files/attachments/Reynolds2009_oisst_daily_v02r00_version2-features.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/idl/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Live Access Server',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/las',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'GET DATA',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/python/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHAAO-4BC02',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/NCEI/AVHRR_OI/v2/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Blended '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'AVHRR_OI-NCEI-L4-GLOB-v2.0',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1981-09-01T00:00:00.000Z'}]}],\n 'Version': '2.0'}},\n { 'meta': { 'concept-id': 'C1652972273-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+CMC0.1deg+Global+Foundation+Sea+Surface+Temperature+Analysis+(GDS+version+2)',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-06T18:11:06Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature (SST) analysis produced daily '\n 'on an operational basis at the Canadian '\n 'Meteorological Center. This dataset '\n 'merges infrared satellite SST at varying '\n 'points in the time series from the '\n 'Advanced Very High Resolution Radiometer '\n '(AVHRR) from NOAA-18,19, the European '\n 'Meteorological Operational-A (METOP-A) '\n 'and Operational-B (METOP-B), and '\n 'microwave data from the Advanced '\n 'Microwave Scanning Radiometer 2 (AMSR2) '\n 'onboard the GCOM-W satellite in '\n 'conjunction with in situ observations of '\n 'SST from drifting buoys and ships from '\n 'the ICOADS program. It uses the previous '\n 'days analysis as the background field for '\n 'the statistical interpolation used to '\n 'assimilate the satellite and in situ '\n 'observations. This dataset adheres to the '\n 'GHRSST Data Processing Specification '\n '(GDS) version 2 format specifications.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Canada '\n 'Meteorological '\n 'Center, '\n 'Canada '\n 'Meteorological '\n 'Center, '\n '2016-07-29, '\n 'GHRSST '\n 'Level '\n '4 '\n 'CMC0.1deg '\n 'Global '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis '\n '(GDS '\n 'version '\n '2), '\n '10.5067/GHCMC-4FM03, '\n 'none'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHCMC-4FM03'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'CMC'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '514-421-5002'},\n { 'Type': 'Email',\n 'Value': 'dorina.surcel-colan@canada.ca'}]},\n 'ContactPersons': [ { 'FirstName': 'Dorina',\n 'LastName': 'Colan',\n 'MiddleName': 'Surcel',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Canada Meteorological '\n 'Center'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '818-393-6710'},\n { 'Type': 'Email',\n 'Value': 'edward.m.armstrong@jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Edward',\n 'LastName': 'Armstrong',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'}],\n 'DataDates': [ { 'Date': '2015-12-21T18:32:39.515Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:46.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 CMC0.1deg Global '\n 'Foundation Sea Surface 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'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHCMC-4FM03',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/CMC/CMC0.1deg/v3/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'CMC0.1deg-CMC-L4-GLOB-v3.0',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 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'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis by the Danish '\n 'Meteorological Institute (DMI) using an '\n 'optimal interpolation (OI) approach on a '\n 'global 0.05 degree grid. 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HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'user services',\n 'Type': 'USE SERVICE API',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHGDM-4FD02',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/DMI/DMI_OI/v1/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 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'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+GAMSSA_28km+Global+Foundation+Sea+Surface+Temperature+Analysis+v1.0+dataset+(GDS2)',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-07T16:08:53Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis, produced daily on '\n 'an operational basis at the Australian '\n 'Bureau of Meteorology (BoM) using optimal '\n 'interpolation (OI) on a global 0.25 '\n 'degree grid. This Global Australian '\n 'Multi-Sensor SST Analysis (GAMSSA) v1.0 '\n 'system blends satellite SST observations '\n 'from passive infrared and passive '\n 'microwave radiometers with in situ data '\n 'from ships, drifting buoys and moorings '\n 'from the Global Telecommunications System '\n '(GTS). SST observations that have '\n 'experienced recent surface wind speeds '\n 'less than 6 m/s during the day or less '\n 'than 2 m/s during night are rejected from '\n 'the analysis. The processing results in '\n 'daily foundation SST estimates that are '\n 'largely free of nocturnal cooling and '\n 'diurnal warming effects. Sea ice '\n 'concentrations are supplied by the '\n 'NOAA/NCEP 12.7 km sea ice analysis. In '\n 'the absence of observations, the analysis '\n 'relaxes to the Reynolds and Smith (1994) '\n 'Monthly 1 degree SST climatology for 1961 '\n '- 1990.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF '\n '4.3',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n '2019-09-18, '\n 'GHRSST '\n 'Level '\n '4 '\n 'GAMSSA_28km '\n 'Global '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis '\n 'v1.0 '\n 'dataset '\n '(GDS2), '\n '10.5067/GHGAM-4FA1A, '\n 'http://www.bom.gov.au/australia/charts/bulletins/apob77.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHGAM-4FA1A'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694394'},\n { 'Type': 'Email',\n 'Value': 'h.beggs@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Helen',\n 'LastName': 'Beggs',\n 'MiddleName': 'none',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694746'},\n { 'Type': 'Email',\n 'Value': 'ghrsst@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Leon',\n 'LastName': 'Majewski',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'}],\n 'DataDates': [ { 'Date': '2019-08-23T23:59:16.644Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-08-23T23:59:16.644Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 GAMSSA_28km Global '\n 'Foundation Sea Surface Temperature '\n 'Analysis v1.0 dataset (GDS2)',\n 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'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis at the Office of '\n 'Satellite and Product Operations (OSPO) '\n 'using optimal interpolation (OI) on a '\n 'global 0.054 degree grid. \\n'\n 'The Geo-Polar Blended Sea Surface '\n 'Temperature (SST) Analysis combines '\n 'multi-satellite retrievals of sea surface '\n 'temperature into a single analysis of '\n 'SST. This analysis uses both daytime and '\n 'nighttime data from sensors that include '\n 'the Advanced Very High Resolution '\n 'Radiometer (AVHRR), the Visible Infrared '\n 'Imager Radiometer Suite (VIIRS), the '\n 'Geostationary Operational Environmental '\n 'Satellite (GOES) imager, the Japanese '\n 'Advanced Meteorological Imager (JAMI) and '\n 'in situ data from ships, drifting and '\n 'moored buoys. This analysis was '\n 'specifically produced to be used as a '\n 'lower boundary condition in Numerical '\n 'Weather Prediction (NWP) models. This '\n 'dataset adheres to the GHRSST Data '\n 'Processing Specification (GDS) version 2 '\n 'format specifications.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Office '\n 'of '\n 'Satellite '\n 'Products '\n 'and '\n 'Operations, '\n 'The '\n 'GHRSST '\n 'Project '\n 'Office, '\n '2015-03-11, '\n 'GHRSST '\n 'Level '\n '4 '\n 'OSPO '\n 'Global '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis '\n '(GDS '\n 'version '\n '2), '\n '10.5067/GHGPB-4FO02, '\n 'www.osdpd.nesdis.noaa.gov'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHGPB-4FO02'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'OSPO'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '818-393-6710'},\n { 'Type': 'Email',\n 'Value': 'edward.m.armstrong@jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Edward',\n 'LastName': 'Armstrong',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '301-763-8102'},\n { 'Type': 'Email',\n 'Value': 'Eileen.maturi@noaa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Eileen',\n 'LastName': 'Maturi',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NOAA Office of '\n 'Satellite and Product '\n 'Operations'}],\n 'DataDates': [ { 'Date': '2014-08-05T02:38:36.840Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:46.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 OSPO Global Foundation '\n 'Sea Surface Temperature Analysis (GDS '\n 'version 2)',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-18T21:27:25.672Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '7000.0'}],\n 'LongName': 'Advanced '\n 'Baseline '\n 'Imager '\n '(ABI)',\n 'ShortName': 'ABI'}],\n 'LongName': 'Geostationary '\n 'Operational '\n 'Environmental Satellite '\n '16',\n 'ShortName': 'GOES-16',\n 'Type': 'Geostationary'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': 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'ShortName': 'SEVIRI'}],\n 'LongName': 'Meteosat-10',\n 'ShortName': 'METEOSAT-10',\n 'Type': 'Geostationary'}],\n 'ProcessingLevel': {'Id': '4'},\n 'Projects': [{'ShortName': 'GHRSST'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L4/GLOB/OSPO/Geo_Polar_Blended/v1',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/OSPO/Geo_Polar_Blended/v1',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'GET DATA',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHGPB-4FO02',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'GHRSST Project '\n 'homepage',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Fieguth, P. '\n 'Multiply-Rooted '\n 'Multiscale Models '\n 'for Large-Scale '\n 'Estimation, IEEE '\n 'Image Processing, '\n '10(11), 1676-1686, '\n '2001',\n 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=967396',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Khellah, F., P.W. '\n 'Fieguth, M.J. '\n 'Murray and M.R. '\n 'Allen, Statistical '\n 'Processing of Large '\n 'Image Sequences, '\n 'IEEE Transactions '\n 'on Geoscience and '\n 'Remote Sensing, 12 '\n '(1), 80-93, 2005',\n 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1369331',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Fieguth,P.W. et al. '\n 'Mapping '\n 'Mediterranean '\n 'altimeter data with '\n 'a multiresolution '\n 'optimal '\n 'interpolation '\n 'algorithm, J. '\n 'Atmos. Ocean Tech, '\n '15 (2): 535-546, '\n '1998',\n 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://journals.ametsoc.org/doi/full/10.1175/1520-0426%281998%29015%3C0535%3AMMADWA%3E2.0.CO%3B2',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Live Access Server',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/las',\n 'URLContentType': 'DistributionURL'},\n { 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=Geo_Polar_Blended-OSPO-L4-GLOB-v1.0',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHGPB-4FO02',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/OSPO/Geo_Polar_Blended/v1/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'Geo_Polar_Blended-OSPO-L4-GLOB-v1.0',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2014-06-02T00:00:00.000Z'}]}],\n 'Version': '1.0'}},\n { 'meta': { 'concept-id': 'C1657544629-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+OSPO+Global+Nighttime+Foundation+Sea+Surface+Temperature+Analysis+(GDS+version+2)',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-18T21:27:24Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis at the Office of '\n 'Satellite and Product Operations (OSPO) '\n 'using optimal interpolation (OI) on a '\n 'global 0.054 degree grid. The Geo-Polar '\n 'Blended Sea Surface Temperature (SST) '\n 'Analysis combines multi-satellite '\n 'retrievals of sea surface temperature '\n 'into a single analysis of SST. This '\n 'analysis includes only nighttime data '\n 'from sensors that include the Advanced '\n 'Very High Resolution Radiometer (AVHRR), '\n 'the Visible Infrared Imager Radiometer '\n 'Suite (VIIRS), the Geostationary '\n 'Operational Environmental Satellite '\n '(GOES) imager, the Japanese Advanced '\n 'Meteorological Imager (JAMI) and in situ '\n 'data from ships, drifting and moored '\n 'buoys. This analysis was specifically '\n 'produced to be used as a lower boundary '\n 'condition in Numerical Weather Prediction '\n '(NWP) models. This dataset adheres to the '\n 'GHRSST Data Processing Specification '\n '(GDS) version 2 format specifications.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Office '\n 'of '\n 'Satellite '\n 'Products '\n 'and '\n 'Operations, '\n 'The '\n 'GHRSST '\n 'Project '\n 'Office, '\n '2015-03-11, '\n 'GHRSST '\n 'Level '\n '4 '\n 'OSPO '\n 'Global '\n 'Nighttime '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis '\n '(GDS '\n 'version '\n '2), '\n '10.5067/GHGPN-4FO02, '\n 'www.osdpd.nesdis.noaa.gov'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHGPN-4FO02'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'OSPO'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '818-393-6710'},\n { 'Type': 'Email',\n 'Value': 'edward.m.armstrong@jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Edward',\n 'LastName': 'Armstrong',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '301-763-8102'},\n { 'Type': 'Email',\n 'Value': 'Eileen.maturi@noaa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Eileen',\n 'LastName': 'Maturi',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NOAA Office of '\n 'Satellite and Product '\n 'Operations'}],\n 'DataDates': [ { 'Date': '2014-08-05T02:44:36.854Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:45.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 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'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '7000.0'}],\n 'LongName': 'Advanced '\n 'Baseline '\n 'Imager '\n '(ABI)',\n 'ShortName': 'ABI'}],\n 'LongName': 'Geostationary '\n 'Operational '\n 'Environmental Satellite '\n '16',\n 'ShortName': 'GOES-16',\n 'Type': 'Geostationary'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 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'\n '(1998). '\n 'Basin-scale, '\n 'high-wavenumber sea '\n 'surface wind fields '\n 'from a '\n 'multiresolution '\n 'analysis of '\n 'scatterometer data. 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Remote '\n 'sensing of '\n 'environment 200 '\n '(2017): 154-169.',\n 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://doi.org/10.1016/j.rse.2017.07.029',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'GHRSST Project '\n 'website',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/idl/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/R/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'GET DATA',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/python/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHGMR-4FJ04',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'FOUNDATION '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-01T09:00:00.000Z'}]}],\n 'Version': '4.1'}},\n { 'meta': { 'concept-id': 'C1646568487-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+MUR+0.25deg+Global+Foundation+Sea+Surface+Temperature+Analysis+(v4.2)',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-10-17T15:10:57Z',\n 'revision-id': 3,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis produced as a '\n 'retrospective dataset (four day latency) '\n 'and near-real-time dataset (one day '\n 'latency) at the JPL Physical Oceanography '\n 'DAAC using wavelets as basis functions in '\n 'an optimal interpolation approach on a '\n 'global 0.25 degree grid. The version 4 '\n 'Multiscale Ultrahigh Resolution (MUR) L4 '\n 'analysis is based upon nighttime GHRSST '\n 'L2P skin and subskin SST observations '\n 'from several instruments including the '\n 'NASA Advanced Microwave Scanning '\n 'Radiometer-EOS (AMSR-E), the JAXA '\n 'Advanced Microwave Scanning Radiometer 2 '\n 'on GCOM-W1, the Moderate Resolution '\n 'Imaging Spectroradiometers (MODIS) on the '\n 'NASA Aqua and Terra platforms, the US '\n 'Navy microwave WindSat radiometer, the '\n 'Advanced Very High Resolution Radiometer '\n '(AVHRR) on several NOAA satellites, and '\n 'in situ SST observations from the NOAA '\n 'iQuam project. The ice concentration data '\n 'are from the archives at the EUMETSAT '\n 'Ocean and Sea Ice Satellite Application '\n 'Facility (OSI SAF) High Latitude '\n 'Processing Center and are also used for '\n 'an improved SST parameterization for the '\n 'high-latitudes. The dataset also '\n 'contains an additional SST anomaly '\n 'variable derived from a MUR climatology.\\n'\n '\\n'\n '\\n'\n 'This dataset is funded by the NASA '\n 'MEaSUREs '\n 'program(http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), '\n 'and created by a team led by Dr. Toshio '\n 'M. Chin from JPL. It adheres to the '\n 'GHRSST Data Processing Specification '\n '(GDS) version 2 format specifications. '\n 'Use the file global metadata \"history:\" '\n 'attribute to determine if a granule is '\n 'near-realtime or retrospective.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'JPL '\n 'MUR '\n 'MEaSUREs '\n 'Project, '\n 'JPL '\n 'NASA, '\n '2018-10-17, '\n 'GHRSST '\n 'Level '\n '4 '\n 'MUR '\n '0.25deg '\n 'Global '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis '\n '(v4.2), '\n '10.5067/GHM25-4FJ42, '\n 'http://mur.jpl.nasa.gov'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHM25-4FJ42'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Jet Propulsion '\n 'Laboratory'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': ' '\n '1-818-393-2510'},\n { 'Type': 'Email',\n 'Value': 'mike.chin@jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Mike',\n 'LastName': 'Chin',\n 'MiddleName': 'noen',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Jet Propulsion '\n 'Laboratory'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '818-393-6710'},\n { 'Type': 'Email',\n 'Value': 'edward.m.armstrong@jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Edward',\n 'LastName': 'Armstrong',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'}],\n 'DataDates': [ { 'Date': '2019-08-09T19:47:48.185Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-08-09T19:47:48.185Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 MUR 0.25deg Global '\n 'Foundation Sea Surface 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'Degrees',\n 'Value': '98.7'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '1200.0'}],\n 'LongName': 'WindSat',\n 'ShortName': 'WINDSAT'}],\n 'LongName': 'Coriolis',\n 'ShortName': 'CORIOLIS',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '98.8'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.2'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2330.0'}],\n 'LongName': 'Moderate-Resolution '\n 'Imaging '\n 'Spectroradiometer',\n 'ShortName': 'MODIS'}],\n 'LongName': 'Earth Observing System, '\n 'TERRA',\n 'ShortName': 'Terra',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.12'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.74'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 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'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '1450.0'}],\n 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2',\n 'ShortName': 'AMSR2'}],\n 'LongName': 'Global Change '\n 'Observation Mission 1st '\n '- Water (SHIZUKU)',\n 'ShortName': 'GCOM-W1',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting '\n 'Buoys',\n 'ShortName': 'InSitu'}],\n 'LongName': 'SPURS-I SVP Drifters',\n 'ShortName': 'Drifter',\n 'Type': 'BUOY'}],\n 'ProcessingLevel': {'Id': '4'},\n 'Projects': [{'ShortName': 'GHRSST'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR25/v4.2',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L4/GLOB/JPL/MUR25/v4.2',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'R language read '\n 'software',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/R/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'GET DATA',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'MUR Project '\n 'homepage',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://mur.rsmas.miami.edu',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'GHRSST Project '\n 'website',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Live Access Server',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/las',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'IDL read software',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/idl/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'MATLAB read '\n 'software',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Homepage for the '\n 'NASA MEaSUREs '\n 'projects',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://science.nasa.gov/earth-science/earth-science-data/Earth-Science-Data-Records-Programs/MEaSUREs-Projects/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Algorithm '\n 'Theoretical Basis '\n 'Document',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/docs/Chin2017.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'THREDDS access URL',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=MUR25-JPL-L4-GLOB-v04.2',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Python read '\n 'software',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/python/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Chin T.M., Milliff '\n 'R.F., Large W.G. '\n '(1998). '\n 'Basin-scale, '\n 'high-wavenumber sea '\n 'surface wind fields '\n 'from a '\n 'multiresolution '\n 'analysis of '\n 'scatterometer data. '\n 'Journal of '\n 'Atmospheric and '\n 'Oceanic Technology, '\n '15: 741-763',\n 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://journals.ametsoc.org/doi/abs/10.1175/1520-0426%281998%29015%3C0741:BSHWSS%3E2.0.CO;2',\n 'URLContentType': 'PublicationURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHM25-4FJ42',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Python Subsetting '\n 'Script',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac.jpl.nasa.gov/forum/viewtopic.php?f=5&t=219',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHM25-4FJ42',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR25/v4.2/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'FOUNDATION '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'MUR25-JPL-L4-GLOB-v04.2',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-01T09:00:00.000Z'}]}],\n 'Version': '4.2'}},\n { 'meta': { 'concept-id': 'C1658476085-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+MW_IR_OI+Global+Foundation+Sea+Surface+Temperature+analysis+version+5.0+from+REMSS',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:32Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) global Level 4 sea '\n 'surface temperature analysis produced '\n 'daily on a 0.09-degree grid at Remote '\n 'Sensing Systems. This product uses '\n 'optimal interpolation (OI) from both '\n 'microwave (MW) sensors including the '\n 'Global Precipitation Measurement (GPM) '\n 'Microwave Imager (GMI), the Tropical '\n 'Rainfall Measuring Mission (TRMM) '\n 'Microwave Imager (TMI), the NASA Advanced '\n 'Microwave Scanning Radiometer-EOS '\n '(AMSRE), the Advanced Microwave Scanning '\n 'Radiometer 2 (AMSR2) onboard the GCOM-W1 '\n 'satellite, and WindSat operates on the '\n 'Coriolis satellite, and infrared (IR) '\n 'sensors such as the Moderate Resolution '\n 'Imaging Spectroradiometer (MODIS) on the '\n 'NASA Aqua and Terra platform and the '\n 'Visible Infrared Imaging Radiometer Suite '\n '(VIIRS) on board the Suomi-NPP '\n 'satellite. The through-cloud '\n 'capabilities of microwave radiometers '\n 'provide a valuable picture of global sea '\n 'surface temperature (SST) while infrared '\n 'radiometers (i.e., MODIS) have a higher '\n 'spatial resolution. This analysis does '\n 'not use any in situ SST data such as '\n 'drifting buoy SST. Comparing with '\n 'previous version 4.0 dataset, the version '\n '5.0 has made the updates in several '\n 'areas, including the diurnal warming '\n 'model, the sensor-specific error '\n 'statistics (SSES) for each microwave '\n 'sensor, the sensor correlation model, and '\n 'the quality mask.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'Remote '\n 'Sensing '\n 'Systems, '\n '2017-12-29, '\n 'GHRSST '\n 'Level '\n '4 '\n 'MW_IR_OI '\n 'Global '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'analysis '\n 'version '\n '5.0 '\n 'from '\n 'REMSS, '\n '10.5067/GHMWI-4FR05, '\n 'http://www.remss.com'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHMWI-4FR05'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Remote Sensing '\n 'Systems'},\n { 'Roles': ['ARCHIVER'],\n 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This product uses '\n 'optimal interpolation (OI) from microwave '\n '(MW) sensors including the Global '\n 'Precipitation Measurement (GPM) Microwave '\n 'Imager (GMI), the Tropical Rainfall '\n 'Measuring Mission (TRMM) Microwave Imager '\n '(TMI), the NASA Advanced Microwave '\n 'Scanning Radiometer-EOS (AMSRE), the '\n 'Advanced Microwave Scanning Radiometer 2 '\n '(AMSR2) onboard the GCOM-W1 satellite, '\n 'and WindSat operates on the Coriolis '\n 'satellite. The through-cloud capabilities '\n 'of microwave radiometers provide a '\n 'valuable picture of global sea surface '\n 'temperature (SST). This analysis does not '\n 'use any in situ SST data such as drifting '\n 'buoy SST. 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'https://doi.org/10.5067/GHOST-4FK02',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/UKMO/OSTIA/v2/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'},\n { 'Category': 'Earth Science',\n 'DetailedVariable': 'Sea Ice '\n 'Fraction',\n 'Term': 'Sea Ice',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Ice '\n 'Concentration'}],\n 'ShortName': 'OSTIA-UKMO-L4-GLOB-v2.0',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 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'GHRSST+Level+4+RAMSSA_9km+Australian+Regional+Foundation+Sea+Surface+Temperature+Analysis+v1.0+dataset+(GDS2)',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-06T22:47:03Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis, produced daily on '\n 'an operational basis at the Australian '\n 'Bureau of Meteorology (BoM) using optimal '\n 'interpolation (OI) on a regional 1/12 '\n 'degree grid over the Australian region '\n '(20N - 70S, 60E - 170W). This Regional '\n 'Australian Multi-Sensor SST Analysis '\n '(RAMSSA) v1.0 system blends satellite SST '\n 'observations from passive infrared and '\n 'passive microwave radiometers, with in '\n 'situ data from ships, Argo floats, XBTs, '\n 'CTDs, drifting buoys and moorings from '\n 'the Global Telecommunications System '\n '(GTS). SST observations that have '\n 'experienced recent surface wind speeds '\n 'less than 6 m/s during the day or less '\n 'than 2 m/s during night are rejected from '\n 'the analysis. The processing results in '\n 'daily foundation SST estimates that are '\n 'largely free of nocturnal cooling and '\n 'diurnal warming effects. Sea ice '\n 'concentrations are supplied by the '\n 'NOAA/NCEP 12.7 km sea ice analysis. In '\n 'the absence of observations, the analysis '\n 'relaxes to the BoM Global Weekly 1 degree '\n 'OI SST analysis, which relaxes to the '\n 'Reynolds and Smith (1994) Monthly 1 '\n 'degree SST climatology for 1961 - 1990.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF '\n '4.3',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n '2019-11-14, '\n 'GHRSST '\n 'Level '\n '4 '\n 'RAMSSA_9km '\n 'Australian '\n 'Regional '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis '\n 'v1.0 '\n 'dataset '\n '(GDS2), '\n '10.5067/GHRAM-4FA1A, '\n 'http://www.bom.gov.au/jshess/docs/2011/beggs_hres.pdf, '\n 'DOI: '\n '10.22499/2.6101.001'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHRAM-4FA1A'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694746'},\n { 'Type': 'Email',\n 'Value': 'ghrsst@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Leon',\n 'LastName': 'Majewski',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694394'},\n { 'Type': 'Email',\n 'Value': 'h.beggs@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Helen',\n 'LastName': 'Beggs',\n 'MiddleName': 'none',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'}],\n 'DataDates': [ { 'Date': '2019-09-03T21:16:56.600Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-03T21:16:56.600Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 RAMSSA_9km Australian '\n 'Regional Foundation Sea Surface '\n 'Temperature Analysis v1.0 dataset '\n '(GDS2)',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'OCEANIA'}],\n 'MetadataDates': [ { 'Date': '2019-11-06T22:47:00.185Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting '\n 'Buoys',\n 'ShortName': 'InSitu'}],\n 'LongName': 'InSitu',\n 'ShortName': 'InSitu',\n 'Type': 'instrument'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '100.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.19'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': 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'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHRAM-4FA1A',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/AUS/ABOM/RAMSSA/v1.0/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'FOUNDATION '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'RAMSSA_09km-ABOM-L4-AUS-v01',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': 60.0},\n { 'EastBoundingCoordinate': -170.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2006-06-12T00:00:00.000Z'}]}],\n 'Version': '1.0'}},\n { 'meta': { 'concept-id': 'C1655116781-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'NOAA+Smith+and+Reynolds+Extended+Reconstructed+Sea+Surface+Temperature+(ERSST)+Level+4+Monthly+Version+4+Dataset+in+netCDF',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-10T18:29:41Z',\n 'revision-id': 3,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Smith & Reynolds Extended '\n 'Reconstructed Sea Surface Temperature '\n '(ERSST) Level 4 dataset provides a '\n 'historical reconstruction of monthly '\n 'global ocean surface temperatures and '\n 'temperature anomalies over a 2 degree '\n 'spatial grid since 1854 from in-situ '\n 'observations based on a consistent '\n 'statistical methodology that accounts for '\n 'uneven sampling distributions over time '\n 'and related observational biases. Version '\n '4 of this dataset implements release 2.5 '\n 'of ICOADS (International Comprehensive '\n 'Ocean-Atmosphere Data Set) and is '\n 'supplemented by monthly GTS (Global '\n 'Telecommunications Ship and buoy) system '\n 'data. As for the prior ERSST version, v4 '\n 'implements Empirical Orthogonal '\n 'Teleconnection analysis (EOT) but with an '\n 'improved tuning method for sparsely '\n 'sampled regions and periods. ERSST '\n 'anomalies are computed with respect to a '\n '1971-2000 monthly climatology. The '\n 'version 4 has been improved from previous '\n 'version 3b. Major revisions include '\n 'updated and substantially more complete '\n 'input data from the ICOADS release 2.5, '\n 'revised EOTs and EOT acceptance '\n 'criterion, updated SST quality control '\n 'procedures, revised SST anomaly '\n 'evaluation methods, updated bias '\n 'adjustments of ship SSTs using the Hadley '\n 'Centre Nighttime Marine Air Temperature '\n 'dataset version 2 (HadNMAT2), and buoy '\n 'SST bias adjustment not previously made '\n 'in v3b. The ERSST v4 in netCDF format '\n 'contains extended reconstructed sea '\n 'surface temperature, SST anomaly, and '\n 'associated estimated SST error standard '\n 'deviation fields, incompliance with CF1.6 '\n 'standard metadata.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Smith, '\n 'T., '\n 'Reynolds, '\n 'R., '\n \"NOAA's \"\n 'National '\n 'Centers '\n 'for '\n 'Environmental '\n 'Information '\n '(NCEI), '\n '1981-11-07, '\n 'NOAA '\n 'Smith '\n 'and '\n 'Reynolds '\n 'Extended '\n 'Reconstructed '\n 'Sea '\n 'Surface '\n 'Temperature '\n '(ERSST) '\n 'Level '\n '4 '\n 'Monthly '\n 'Version '\n '4 '\n 'Dataset '\n 'in '\n 'netCDF, '\n '10.5067/ERSST-L4N40, '\n 'https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/ERSST-L4N40'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 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'\n 'J. Climate',\n 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/docs/ERSST5_Huang-2017.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/idl/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/ERSST-L4N50',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature '\n 'Reconstruction'}],\n 'ShortName': 'REYNOLDS_NCDC_L4_MONTHLY_V5',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1854-01-01T00:00:00.000Z'}]}],\n 'Version': '5'}},\n { 'meta': { 'concept-id': 'C1652973820-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'Smith+and+Reynolds+NCDC+Level+4+Historical+Reconstructed+SST+Monthly+Version+2',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-06T18:11:36Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Smith & Reynolds Extended '\n 'Reconstructed Sea Surface Temperature '\n '(ERSST) Level 4 dataset provides a '\n 'historical reconstruction of monthly '\n 'global ocean surface temperatures and '\n 'temperature anomalies on a 2 degree '\n 'spatial grid from 1854-2009 from in-situ '\n 'observations based on a consistent '\n 'statistical methodology that accounts for '\n 'uneven sampling distributions over time '\n 'and related observational biases. Version '\n '2 of this dataset implements release 2 of '\n 'ICOADS (International Comprehensive '\n 'Ocean-Atmosphere Data Set) and utilizes '\n 'Empirical Orthogonal Teleconnections '\n '(EOT) in reconstruction analyses to '\n 'estimate both surface temperature and '\n 'associated standard deviation error '\n 'fields. 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'Type': 'UPDATE'}],\n 'EntryTitle': 'Smith and Reynolds NCDC Level 4 '\n 'Historical Reconstructed SST Monthly '\n 'Version 2',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-06T18:11:33.606Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting '\n 'Buoys',\n 'ShortName': 'InSitu'}],\n 'LongName': 'Global Telecommunication '\n 'System Ship and Buoy In '\n 'Situ Observations',\n 'ShortName': 'NCEP GTS',\n 'Type': 'InSitu'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 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False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1854-01-01T00:00:00.000Z'}]}],\n 'Version': '2'}},\n { 'meta': { 'concept-id': 'C1652973478-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'Smith+and+Reynolds+NCDC+Level+4+Historical+Reconstructed+SST+Monthly+Version+3b+Ascii',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-06T18:11:28Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Smith & Reynolds Extended '\n 'Reconstructed Sea Surface Temperature '\n '(ERSST) Level 4 dataset provides a '\n 'historical reconstruction of monthly '\n 'global ocean surface temperatures and '\n 'temperature anomalies on a 2 degree '\n 'spatial grid since 1854 from in-situ '\n 'observations based on a consistent '\n 'statistical 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'Type': 'CREATE'},\n { 'Date': '2011-01-21T17:10:13.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'Smith and Reynolds NCDC Level 4 '\n 'Historical Reconstructed SST Monthly '\n 'Version 3b Ascii',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-06T18:11:25.754Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 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'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/L4/GLOB/UKMO/OSTIA',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'GET DATA',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHOST-4FK01&apidoc',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'GHRSST Project '\n 'homepage',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Live Access Server',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/las',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHOST-4FK01',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/L4/GLOB/UKMO/OSTIA/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'UKMO-L4HRfnd-GLOB-OSTIA',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2006-04-01T00:00:00.000Z'}]}],\n 'Version': '1.0'}},\n { 'meta': { 'concept-id': 'C1653649472-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+K10_SST+Global+10+km+Analyzed+Sea+Surface+Temperature+from+Naval+Oceanographic+Office+(NAVO)+in+GDS2.0',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-07T19:03:09Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'This is a Group for High Resolution Sea '\n 'Surface Temperature (GHRSST) Level 4 sea '\n 'surface temperature (SST) analysis '\n 'dataset produced daily on an operational '\n 'basis by the Naval Oceanographic Office '\n '(NAVO) on a global 0.1x0.1 degree grid. '\n 'The K10 (NAVO 10-km gridded SST analyzed '\n 'product) L4 analysis uses SST '\n 'observations from the following '\n 'instruments: Advanced Very High '\n 'Resolution Radiometer (AVHRR), Visible '\n 'Infrared Imaging Radiometer Suite '\n '(VIIRS), and Spinning Enhanced Visible '\n 'and InfraRed Imager (SEVIRI). The AVHRR '\n 'data for this comes from the MetOp-A, '\n 'MetOp-B, and NOAA-19 satellites; VIIRS '\n 'data is sourced from the Suomi_NPP '\n 'satellite; SEVIRI data comes from the '\n 'Meteosat-8 and -11 satellites. The age '\n '(time-lag), reliability, and resolution '\n 'of the data are used in the weighted '\n 'average with the analysis tuned to '\n 'represent SST at a reference depth of '\n '1-meter. Input data from the AVHRR '\n 'Pathfinder 9km climatology dataset '\n '(1985-1999) is used when no new satellite '\n 'SST retrievals are available after 34 '\n 'days. Comparing with its predecessor '\n '(DOI: https://doi.org/10.5067/GHK10-L4N01 '\n '), this updated dataset has no major '\n 'changes in Level-4 interpolated K10 '\n 'algorithm, except for using different '\n 'satellite instrument data, and updating '\n 'metadata and file format. The major '\n 'updates include: (a) updated and enhanced '\n 'the granule-level metadata information, '\n '(b) converted the SST file from GHRSST '\n 'Data Specification (GDS) v1.0 to v2.0, '\n '(c) added the sea_ice_fraction variable '\n 'to the product, and (d) updated the '\n 'filename convention to reflect compliance '\n 'with GDS v2.0.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Naval '\n 'Oceanographic '\n 'Office, '\n 'Naval '\n 'Oceanographic '\n 'Office, '\n '2018-12-29, '\n 'GHRSST '\n 'Level '\n '4 '\n 'K10_SST '\n 'Global '\n '10 '\n 'km '\n 'Analyzed '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'from '\n 'Naval '\n 'Oceanographic '\n 'Office '\n '(NAVO) '\n 'in '\n 'GDS2.0, '\n '10.5067/GHK10-L4N01, '\n 'none'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHK10-L4N01'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Naval Oceanographic '\n 'Office'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': 'null'},\n { 'Type': 'Email',\n 'Value': 'daniel.olszewski@navy.mil'}]},\n 'ContactPersons': [ { 'FirstName': 'Daniel',\n 'LastName': 'Olszewski',\n 'MiddleName': 'null',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Naval Oceanographic '\n 'Office (NAVOCEANO)'}],\n 'DataDates': [ { 'Date': '2018-09-13T00:58:04.861Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-09-13T00:58:04.861Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 K10_SST Global 10 km '\n 'Analyzed Sea Surface Temperature from '\n 'Naval Oceanographic Office (NAVO) in '\n 'GDS2.0',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-07T19:03:07.263Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '11140.0'}],\n 'LongName': 'Spinning '\n 'Enhanced '\n 'Visible '\n 'and '\n 'Infrared '\n 'Imager',\n 'ShortName': 'SEVIRI'}],\n 'LongName': 'Meteosat Second '\n 'Generation 2',\n 'ShortName': 'MSG2',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '11140.0'}],\n 'LongName': 'Spinning '\n 'Enhanced '\n 'Visible '\n 'and '\n 'Infrared '\n 'Imager',\n 'ShortName': 'SEVIRI'}],\n 'LongName': 'Meteosat Second '\n 'Generation 4',\n 'ShortName': 'MSG4',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '101.3'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.7'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer',\n 'ShortName': 'AVHRR'}],\n 'LongName': 'Meteorological '\n 'Operational Satellite - '\n 'A',\n 'ShortName': 'METOP-A',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '101.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '97.1'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '3040.0'}],\n 'LongName': 'Visible '\n 'Infrared '\n 'Imaging '\n 'Radiometer '\n 'Suite',\n 'ShortName': 'VIIRS'}],\n 'LongName': 'Suomi National '\n 'Polar-orbiting '\n 'Partnership',\n 'ShortName': 'SUOMI-NPP',\n 'Type': 'spacecraft'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '101.3'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.7'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer',\n 'ShortName': 'AVHRR'}],\n 'LongName': 'Meteorological '\n 'Operational Satellite - '\n 'B',\n 'ShortName': 'METOP-B',\n 'Type': 'SPACECRAFT'}],\n 'ProcessingLevel': {'Id': '4'},\n 'Projects': [{'ShortName': 'GHRSST'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L4/GLOB/NAVO/K10_SST/v1',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/NAVO/K10_SST/v1',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Live Access Server',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/las',\n 'URLContentType': 'DistributionURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHK10-L4N01',\n 'URLContentType': 'DistributionURL'},\n { 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=K10_SST-NAVO-L4-GLOB-v01',\n 'URLContentType': 'DistributionURL'},\n { 'Subtype': 'PUBLICATIONS',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/NAVO/K10_SST/docs/GMPE_Matthew_2012.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/NAVO/K10_SST/docs/contents.html',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'netCDF generic '\n 'readers',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHK10-L4N01',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/NAVO/K10_SST/v1/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Sea '\n 'Surface '\n 'Temperature '\n '1m',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'K10_SST-NAVO-L4-GLOB-v01',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2018-10-01T00:00:00.000Z'}]}],\n 'Version': '1.0'}}],\n 'took': 92}\n\n\ntemporal=2018-01-01T10:00:00Z,2019-01-01T10:00:00Z&provider_short_name=PODAAC&processing_level_id=4\nwe sepcified a temporal range for all of 2018, PODAAC as the provider, and level 4 data, since it’s a bit easier for us to work with.\nok, that got us down to ~29 collections. Let’s use python to get some information we’re interested in.\n\nfor i in JSON_object[\"items\"]:\n print(i['meta']['concept-id'] + \" \" + i['meta']['native-id'].replace('+',' '))\n #print(\"\\t\"+i['meta']['native-id'].replace('+',' '))\n print(\"\\tBeginning Data Time: \"+str(i['umm']['TemporalExtents'][0]['RangeDateTimes'][0]['BeginningDateTime']))\n \n # Bounding Box Info:\n br_array = i['umm']['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['BoundingRectangles']\n for br in br_array:\n print(\"\\tBounding Rectangle: West: {}, North: {}, East: {}, South: {}\".format(br['WestBoundingCoordinate'], br['NorthBoundingCoordinate'], br['EastBoundingCoordinate'], br['SouthBoundingCoordinate']))\n\n \n \n \n \n\nC1658476070-PODAAC GHRSST Level 4 RAMSSA Australian Regional Foundation Sea Surface Temperature Analysis\n Beginning Data Time: 2008-04-01T00:00:00.000Z\n Bounding Rectangle: West: 60.0, North: 20.0, East: 180.0, South: -70.0\n Bounding Rectangle: West: -180.0, North: 20.0, East: -170.0, South: -70.0\nC1657548743-PODAAC GHRSST Level 4 GAMSSA Global Foundation Sea Surface Temperature Analysis\n Beginning Data Time: 2008-08-24T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1652971997-PODAAC GHRSST Level 4 AVHRR_OI Global Blended Sea Surface Temperature Analysis (GDS version 2) from NCEI\n Beginning Data Time: 1981-09-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1652972273-PODAAC GHRSST Level 4 CMC0.1deg Global Foundation Sea Surface Temperature Analysis (GDS version 2)\n Beginning Data Time: 2016-01-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1653649473-PODAAC GHRSST Level 4 DMI_OI Global Foundation Sea Surface Temperature Analysis (GDS version 2)\n Beginning Data Time: 2013-04-30T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1653205971-PODAAC GHRSST Level 4 GAMSSA_28km Global Foundation Sea Surface Temperature Analysis v1.0 dataset (GDS2)\n Beginning Data Time: 2008-07-23T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1657544973-PODAAC GHRSST Level 4 OSPO Global Foundation Sea Surface Temperature Analysis (GDS version 2)\n Beginning Data Time: 2014-06-02T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1657544629-PODAAC GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)\n Beginning Data Time: 2014-06-02T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1650311564-PODAAC GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis\n Beginning Data Time: 2010-06-09T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 80.0, East: 180.0, South: -80.0\nC1664741463-PODAAC GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)\n Beginning Data Time: 2002-06-01T09:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1646568487-PODAAC GHRSST Level 4 MUR 0.25deg Global Foundation Sea Surface Temperature Analysis (v4.2)\n Beginning Data Time: 2002-06-01T09:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1658476085-PODAAC GHRSST Level 4 MW_IR_OI Global Foundation Sea Surface Temperature analysis version 5.0 from REMSS\n Beginning Data Time: 2002-06-01T00:00:00.000Z\n Bounding Rectangle: West: -179.0, North: 90.0, East: 180.0, South: -90.0\nC1658476097-PODAAC GHRSST Level 4 MW_OI Global Foundation Sea Surface Temperature analysis version 5.0 from REMSS\n Beginning Data Time: 1997-12-31T16:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1652972817-PODAAC GHRSST Level 4 K10_SST Global 1 meter Sea Surface Temperature Analysis\n Beginning Data Time: 2008-04-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1658476026-PODAAC Black Sea High Resolution SST L4 Analysis 0.0625 deg Resolution\n Beginning Data Time: 2007-12-31T19:00:00.000Z\n Bounding Rectangle: West: 26.375, North: 48.812, East: 42.375, South: 38.75\nC1658476036-PODAAC Mediterranean Sea High Resolution SST L4 Analysis 1/16deg Resolution\n Beginning Data Time: 2007-12-31T19:00:00.000Z\n Bounding Rectangle: West: -18.125, North: 46.0, East: 36.25, South: 30.25\nC1658476046-PODAAC Black Sea Ultra High Resolution SST L4 Analysis 0.01 deg Resolution\n Beginning Data Time: 2007-12-31T19:00:00.000Z\n Bounding Rectangle: West: 26.375, North: 48.812, East: 42.375, South: 38.75\nC1658476058-PODAAC Mediterranean Sea Ultra High Resolution SST L4 Analysis 0.01 deg Resolution\n Beginning Data Time: 2007-12-31T19:00:00.000Z\n Bounding Rectangle: West: -18.125, North: 46.0, East: 36.25, South: 30.25\nC1652972902-PODAAC GHRSST Level 4 OSTIA Global Foundation Sea Surface Temperature Analysis (GDS version 2)\n Beginning Data Time: 2013-04-25T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1653205927-PODAAC GHRSST Level 4 RAMSSA_9km Australian Regional Foundation Sea Surface Temperature Analysis v1.0 dataset (GDS2)\n Beginning Data Time: 2006-06-12T00:00:00.000Z\n Bounding Rectangle: West: 60.0, North: 20.0, East: 180.0, South: -70.0\n Bounding Rectangle: West: -180.0, North: 20.0, East: -170.0, South: -70.0\nC1655116781-PODAAC NOAA Smith and Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 Monthly Version 4 Dataset in netCDF\n Beginning Data Time: 1854-01-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1655116806-PODAAC NOAA Smith and Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 Monthly Version 5 Dataset in netCDF\n Beginning Data Time: 1854-01-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1652973820-PODAAC Smith and Reynolds NCDC Level 4 Historical Reconstructed SST Monthly Version 2\n Beginning Data Time: 1854-01-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1652973478-PODAAC Smith and Reynolds NCDC Level 4 Historical Reconstructed SST Monthly Version 3b Ascii\n Beginning Data Time: 1854-01-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1652973774-PODAAC Smith and Reynolds NCDC Level 4 Historical Reconstructed SST Monthly Version 3b netCDF\n Beginning Data Time: 1854-01-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1645281597-PODAAC Reynolds NCEP Level 4 Optimally Interpolated SST Monthly Version 2\n Beginning Data Time: 1981-11-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1652973931-PODAAC Reynolds NCEP Level 4 Optimally Interpolated SST Weekly Version 2\n Beginning Data Time: 1981-01-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1650311566-PODAAC GHRSST Level 4 OSTIA Global Foundation Sea Surface Temperature Analysis\n Beginning Data Time: 2006-04-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\nC1653649472-PODAAC GHRSST Level 4 K10_SST Global 10 km Analyzed Sea Surface Temperature from Naval Oceanographic Office (NAVO) in GDS2.0\n Beginning Data Time: 2018-10-01T00:00:00.000Z\n Bounding Rectangle: West: -180.0, North: 90.0, East: 180.0, South: -90.0\n\n\nWe now have the start times, CMR Concept-ID (the unique collection identifier), title, and Bounding rectangles for spatial coverage. This is a lot of information we can use to decide on a dataset, and we can keep adding more information.\nFor now, lets choose “C1664741463-PODAAC”: GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)"
+ "objectID": "external/DownloadDopplerScattData.html#install-the-podaac-download-ustility-using-pip",
+ "href": "external/DownloadDopplerScattData.html#install-the-podaac-download-ustility-using-pip",
+ "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
+ "section": "Install the PODAAC download ustility using pip",
+ "text": "Install the PODAAC download ustility using pip\n\n!pip install podaac-data-subscriber > pip.log"
},
{
- "objectID": "notebooks/podaac_cmr_tutorial.html#granule-search",
- "href": "notebooks/podaac_cmr_tutorial.html#granule-search",
- "title": "Introduction to Programmatic Common Metadata Repository Search",
- "section": "Granule Search",
- "text": "Granule Search\nUsing this collection, and more specifically, its concept-ID, we can now search for data we’re interested in.\n\nwith request.urlopen(cmr_url+\"granules.umm_json?concept-id=C1664741463-PODAAC\") as response:\n data = response.read()\n encoding = response.info().get_content_charset('utf-8')\n JSON_object = json.loads(data.decode(encoding))\n pp = pprint.PrettyPrinter(indent=2)\n pp.pprint(JSON_object)\n\n{ 'hits': 6497,\n 'items': [ { 'meta': { 'concept-id': 'G1664772388-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:29:51Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 332.35974979400635,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:39:29.000Z'},\n 'GranuleUR': '20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:33:18.462Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:29:51.182Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/152/20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/152/20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-01T09:00:00.000Z',\n 'EndingDateTime': '2002-06-01T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664777267-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020602090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:40:20Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 331.33482456207275,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:35:04.000Z'},\n 'GranuleUR': '20020602090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:31:03.105Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:40:19.803Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/153/20020602090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/153/20020602090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-02T09:00:00.000Z',\n 'EndingDateTime': '2002-06-02T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664777275-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020603090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:40:21Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 329.771671295166,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:30:42.000Z'},\n 'GranuleUR': '20020603090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:32:32.948Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:40:21.251Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/154/20020603090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/154/20020603090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-03T09:00:00.000Z',\n 'EndingDateTime': '2002-06-03T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664779141-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020604090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:44:55Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 327.5539598464966,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:26:22.000Z'},\n 'GranuleUR': '20020604090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:35:14.196Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:44:54.615Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/155/20020604090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/155/20020604090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-04T09:00:00.000Z',\n 'EndingDateTime': '2002-06-04T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664777247-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020605090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:40:17Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 318.64849376678467,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:21:59.000Z'},\n 'GranuleUR': '20020605090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:30:38.111Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:40:17.171Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/156/20020605090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/156/20020605090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-05T09:00:00.000Z',\n 'EndingDateTime': '2002-06-05T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664773829-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020606090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:33:11Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 310.25645446777344,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:17:42.000Z'},\n 'GranuleUR': '20020606090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:34:35.618Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:33:10.374Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/157/20020606090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/157/20020606090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-06T09:00:00.000Z',\n 'EndingDateTime': '2002-06-06T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664774358-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020607090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:34:16Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 316.9076223373413,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:13:25.000Z'},\n 'GranuleUR': '20020607090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:26:43.980Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:34:15.270Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/158/20020607090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/158/20020607090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-07T09:00:00.000Z',\n 'EndingDateTime': '2002-06-07T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664779133-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020608090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:44:54Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 327.881872177124,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:09:05.000Z'},\n 'GranuleUR': '20020608090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:33:29.406Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:44:53.293Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/159/20020608090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/159/20020608090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-08T09:00:00.000Z',\n 'EndingDateTime': '2002-06-08T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664773751-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020609090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:33:07Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 328.56553649902344,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:04:43.000Z'},\n 'GranuleUR': '20020609090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:27:35.415Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:33:06.277Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/160/20020609090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/160/20020609090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-09T09:00:00.000Z',\n 'EndingDateTime': '2002-06-09T09:00:00.000Z'}}}},\n { 'meta': { 'concept-id': 'G1664774368-PODAAC',\n 'concept-type': 'granule',\n 'format': 'application/echo10+xml',\n 'native-id': '20020610090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-06T16:34:17Z',\n 'revision-id': 1},\n 'umm': { 'CollectionReference': { 'ShortName': 'MUR-JPL-L4-GLOB-v4.1',\n 'Version': '4.1'},\n 'DataGranule': { 'ArchiveAndDistributionInformation': [ { 'Name': 'Not '\n 'provided',\n 'Size': 327.50969791412354,\n 'SizeUnit': 'MB'}],\n 'DayNightFlag': 'Unspecified',\n 'ProductionDateTime': '2015-08-19T10:00:23.000Z'},\n 'GranuleUR': '20020610090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'MetadataSpecification': { 'Name': 'UMM-G',\n 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6',\n 'Version': '1.6'},\n 'OrbitCalculatedSpatialDomains': [{}],\n 'ProviderDates': [ { 'Date': '2015-08-19T19:28:50.109Z',\n 'Type': 'Insert'},\n { 'Date': '2019-12-06T16:34:16.609Z',\n 'Type': 'Update'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the granule.',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/161/20020610090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'},\n { 'Description': 'The OPENDAP '\n 'location for the '\n 'granule.',\n 'MimeType': 'text/html',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/161/20020610090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html'}],\n 'SpatialExtent': { 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 58.885,\n 'NorthBoundingCoordinate': 53.855,\n 'SouthBoundingCoordinate': -87.3,\n 'WestBoundingCoordinate': -179.641}]}}},\n 'TemporalExtent': { 'RangeDateTime': { 'BeginningDateTime': '2002-06-10T09:00:00.000Z',\n 'EndingDateTime': '2002-06-10T09:00:00.000Z'}}}}],\n 'took': 36}\n\n\nAlright, 6497 hits (at time of this writing). Let’s once again use some parsing magic to get some information on these data granules.\n\nfor i in JSON_object[\"items\"]:\n print(i['meta']['concept-id'] + \" \" + i['meta']['native-id'].replace('+',' '))\n\n\n dist_info = i['umm']['DataGranule']['ArchiveAndDistributionInformation'][0]\n print(\"\\tGranule Size: \"+\"{:.3f}\".format(dist_info['Size']) + \" \" + str(dist_info['SizeUnit']))\n print(\"\\tBeginning Data Time: \"+str(i['umm']['TemporalExtent']['RangeDateTime']['BeginningDateTime']))\n \n # Bounding Box Info:\n br_array = i['umm']['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['BoundingRectangles']\n for br in br_array:\n print(\"\\tBounding Rectangle: West: {}, North: {}, East: {}, South: {}\".format(br['WestBoundingCoordinate'], br['NorthBoundingCoordinate'], br['EastBoundingCoordinate'], br['SouthBoundingCoordinate']))\n\n related_urls = i['umm']['RelatedUrls']\n for url in related_urls:\n print(\"\\t{} ({})\".format(url[\"URL\"], url['Description']))\n \n \n\nG1664772388-PODAAC 20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 332.360 MB\n Beginning Data Time: 2002-06-01T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/152/20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/152/20020601090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664777267-PODAAC 20020602090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 331.335 MB\n Beginning Data Time: 2002-06-02T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/153/20020602090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/153/20020602090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664777275-PODAAC 20020603090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 329.772 MB\n Beginning Data Time: 2002-06-03T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/154/20020603090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/154/20020603090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664779141-PODAAC 20020604090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 327.554 MB\n Beginning Data Time: 2002-06-04T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/155/20020604090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/155/20020604090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664777247-PODAAC 20020605090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 318.648 MB\n Beginning Data Time: 2002-06-05T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/156/20020605090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/156/20020605090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664773829-PODAAC 20020606090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 310.256 MB\n Beginning Data Time: 2002-06-06T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/157/20020606090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/157/20020606090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664774358-PODAAC 20020607090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 316.908 MB\n Beginning Data Time: 2002-06-07T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/158/20020607090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/158/20020607090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664779133-PODAAC 20020608090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 327.882 MB\n Beginning Data Time: 2002-06-08T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/159/20020608090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/159/20020608090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664773751-PODAAC 20020609090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 328.566 MB\n Beginning Data Time: 2002-06-09T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/160/20020609090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/160/20020609090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\nG1664774368-PODAAC 20020610090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc\n Granule Size: 327.510 MB\n Beginning Data Time: 2002-06-10T09:00:00.000Z\n Bounding Rectangle: West: -179.641, North: 53.855, East: 58.885, South: -87.3\n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/161/20020610090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc (The HTTP location for the granule.)\n https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L4/GLOB/JPL/MUR/v4.1/2002/161/20020610090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc.html (The OPENDAP location for the granule.)\n\n\nUsing the above information, we can find the size and location (both whole file and OPeNDAP) URLs."
+ "objectID": "external/DownloadDopplerScattData.html#use-the-dopplerscatt-utility-program-to-download-the-data",
+ "href": "external/DownloadDopplerScattData.html#use-the-dopplerscatt-utility-program-to-download-the-data",
+ "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
+ "section": "Use the DopplerScatt utility program to download the data",
+ "text": "Use the DopplerScatt utility program to download the data\nModify the destination directory, starting and ending dates (using the same format shown), and the download utility path (although this should not need to be modified).\n\ndownload_dopplerscatt_data(data_dir = '../data/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1',\n start_date = '2021-11-03T00:00:00Z',\n end_date = '2021-11-04T00:00:00Z',\n downloader='podaac-data-downloader')\n\n[2022-11-27 10:44:34,150] {podaac_data_downloader.py:155} INFO - NOTE: Making new data directory at ../data/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1(This is the first run.)\n[2022-11-27 10:44:35,376] {podaac_data_downloader.py:243} INFO - Found 1 total files to download\n[2022-11-27 10:44:58,149] {podaac_data_downloader.py:276} INFO - 2022-11-27 10:44:58.149797 SUCCESS: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1/dopplerscatt_20211103_125259.tomoL2CF.nc\n[2022-11-27 10:44:58,150] {podaac_data_downloader.py:287} INFO - Downloaded Files: 1\n[2022-11-27 10:44:58,150] {podaac_data_downloader.py:288} INFO - Failed Files: 0\n[2022-11-27 10:44:58,150] {podaac_data_downloader.py:289} INFO - Skipped Files: 0\n[2022-11-27 10:44:59,026] {podaac_access.py:122} INFO - CMR token successfully deleted\n[2022-11-27 10:44:59,026] {podaac_data_downloader.py:299} INFO - END\n\n\nSuccesfully downloaded desired DopplerScatt data."
},
{
- "objectID": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html",
- "href": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html",
- "title": "Shapefile Search in the Common Metadata Repository (CMR)",
+ "objectID": "external/DownloadDopplerScattData.html#cleanup-auxiliary-files-if-desired",
+ "href": "external/DownloadDopplerScattData.html#cleanup-auxiliary-files-if-desired",
+ "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
+ "section": "Cleanup auxiliary files, if desired",
+ "text": "Cleanup auxiliary files, if desired\nThe netrc file is not secure, so you may want to remove it if you are concerned about the security of your Earthdata credentials. WARNING This will remove your existing netrc file, which may already contain other information in addition to your Earthdata credentials.\n\n!rm pip.log $netrc_file"
+ },
+ {
+ "objectID": "external/Direct_S3_Access_NetCDF.html",
+ "href": "external/Direct_S3_Access_NetCDF.html",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
"section": "",
- "text": "CMR allows the upload of ESRI Shapefiles via command line for granule and collection level search. To use this functionality from the command line, we can use python or curl to do our search. This example will run through a python request and a curl command line program for doing shapefile search.\nPrerequisites:\nWe will use a shapefile located in the github/podaac source repository for this search: https://github.com/podaac/tutorials/blob/master/notebooks/resources/gulf_shapefile.zip\nFor more information on collections, granules, and concept-ids, please refer to the following tutorial:\nhttps://github.com/podaac/tutorials/blob/master/notebooks/podaac_cmr_tutorial.ipynb\nThis collection is the MODIS_A-JPL-L2P-v2019.0 Level 2 collection from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra satellite. In the CMR environment it has the collection id:\nC1940473819-POCLOUD"
+ "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository."
},
{
- "objectID": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#python-tutorial-shapefile-search",
- "href": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#python-tutorial-shapefile-search",
- "title": "Shapefile Search in the Common Metadata Repository (CMR)",
- "section": "Python tutorial shapefile search",
- "text": "Python tutorial shapefile search\nThe following snippet will use the ‘requests’ library along with the shapefile available at github to perform a shapefile search on the CMR. It will return values that overlap or intersect the shapefile provided.\n\nimport requests\nimport json\nimport pprint\n\n# the URL of the CMR searvice\nurl = 'https://cmr.earthdata.nasa.gov/search/granules.json'\n\n#The shapefile we want to use in our search\nshp_file = open('resources/gulf_shapefile.zip', 'rb')\n\n#need to declare the file and the type we are uploading\nfiles = {'shapefile':('gulf_shapefile.zip',shp_file, 'application/shapefile+zip')}\n\n#used to define parameters such as the concept-id and things like temporal searches\nparameters = {'echo_collection_id':'C1940473819-POCLOUD'}\n\nresponse = requests.post(url, files=files, params=parameters)\npp = pprint.PrettyPrinter(indent=2)\npp.pprint(response.json())\n\n{ 'feed': { 'entry': [ { 'boxes': ['28.481 -83.616 49.941 -51.077'],\n 'browse_flag': True,\n 'collection_concept_id': 'C1940473819-POCLOUD',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 2P Global Sea Surface '\n 'Skin Temperature from the Moderate '\n 'Resolution Imaging Spectroradiometer '\n '(MODIS) on the NASA Aqua satellite '\n '(GDS2)',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '9.34600830078125E-5',\n 'id': 'G1966128926-POCLOUD',\n 'links': [ { 'href': 's3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/s3#',\n 'title': 'This link provides direct '\n 'download access via S3 to the '\n 'granule.'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc.md5',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc.md5'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'Download '\n '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.cmr.json',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.cmr.json'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'api endpoint to retrieve '\n 'temporary credentials valid '\n 'for same-region direct s3 '\n 'access'},\n { 'href': 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%202P%20Global%20Sea%20Surface%20Skin%20Temperature%20from%20the%20Moderate%20Resolution%20Imaging%20Spectroradiometer%20(MODIS)%20on%20the%20NASA%20Aqua%20satellite%20(GDS2)/granules/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'OPeNDAP request URL'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sea_surface_temperature.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.quality_level.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_bias.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_standard_deviation.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/flag/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/reprocessing/r2019/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst4/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/forum/viewforum.php?f=18&sid=e2d67e5a01815fc6e39fcd2087ed8bc8',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '75.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/MODIS_L2P_SST_DataCube.ipynb',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '49.94093 -81.73856 31.93185 -83.6126 '\n '30.8223 -71.67589 28.48104 -59.75119 '\n '37.68658 -55.70623 45.51698 '\n '-51.07733 48.7527 -66.02296 49.94093 '\n '-81.73856']],\n 'time_end': '2002-07-04T06:39:59.000Z',\n 'time_start': '2002-07-04T06:35:00.000Z',\n 'title': '20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0',\n 'updated': '2020-11-12T11:02:35.998Z'},\n { 'boxes': ['10.927 -86.378 32.007 -59.736'],\n 'browse_flag': True,\n 'collection_concept_id': 'C1940473819-POCLOUD',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 2P Global Sea Surface '\n 'Skin Temperature from the Moderate '\n 'Resolution Imaging Spectroradiometer '\n '(MODIS) on the NASA Aqua satellite '\n '(GDS2)',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '9.34600830078125E-5',\n 'id': 'G1965906207-POCLOUD',\n 'links': [ { 'href': 's3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/s3#',\n 'title': 'This link provides direct '\n 'download access via S3 to the '\n 'granule.'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'Download '\n '20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc.md5',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc.md5'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.cmr.json',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'Download '\n '20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.cmr.json'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#',\n 'title': 'api endpoint to retrieve '\n 'temporary credentials valid '\n 'for same-region direct s3 '\n 'access'},\n { 'href': 'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%202P%20Global%20Sea%20Surface%20Skin%20Temperature%20from%20the%20Moderate%20Resolution%20Imaging%20Spectroradiometer%20(MODIS)%20on%20the%20NASA%20Aqua%20satellite%20(GDS2)/granules/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'OPeNDAP request URL'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sea_surface_temperature.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.quality_level.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_bias.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020704064005-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.sses_standard_deviation.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/flag/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/reprocessing/r2019/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst4/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/',\n 'hreflang': 'en-US',\n 'inherited': 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'https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/GHRSST%20Level%202P%20Global%20Sea%20Surface%20Skin%20Temperature%20from%20the%20Moderate%20Resolution%20Imaging%20Spectroradiometer%20(MODIS)%20on%20the%20NASA%20Aqua%20satellite%20(GDS2)/granules/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#',\n 'title': 'OPeNDAP request URL'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sea_surface_temperature.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.quality_level.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_bias.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_standard_deviation.png',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#',\n 'type': 'image/png'},\n { 'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/flag/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/reprocessing/r2019/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst4/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/forum/viewforum.php?f=18&sid=e2d67e5a01815fc6e39fcd2087ed8bc8',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'length': '75.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/MODIS_L2P_SST_DataCube.ipynb',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '21.10151 -63.24635 39.12532 '\n '-65.68633 37.97627 -78.84272 35.4481 '\n '-91.57713 17.99084 -85.09664 '\n '21.10151 -63.24635']],\n 'time_end': '2002-07-05T18:25:00.000Z',\n 'time_start': '2002-07-05T18:20:01.000Z',\n 'title': '20020705182006-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0',\n 'updated': '2020-11-12T08:49:28.019Z'}],\n 'id': 'https://cmr.earthdata.nasa.gov:443/search/granules.json',\n 'title': 'ECHO granule metadata',\n 'updated': '2022-10-24T23:43:37.549Z'}}"
+ "objectID": "external/Direct_S3_Access_NetCDF.html#summary",
+ "href": "external/Direct_S3_Access_NetCDF.html#summary",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Summary",
+ "text": "Summary\nIn this notebook, we will access monthly sea surface height from ECCO V4r4 (10.5067/ECG5D-SSH44). The data are provided as a time series of monthly netCDFs on a 0.5-degree latitude/longitude grid.\nWe will access the data from inside the AWS cloud (us-west-2 region, specifically) and load a time series made of multiple netCDF datasets into an xarray dataset. This approach leverages S3 native protocols for efficient access to the data."
},
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- "objectID": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#curl-command-line-syntax",
- "href": "notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html#curl-command-line-syntax",
- "title": "Shapefile Search in the Common Metadata Repository (CMR)",
- "section": "Curl command line syntax",
- "text": "Curl command line syntax\nThis command submits the same request as the Python example above, returning search results in JSON format for Granules that share spatial coverage with the input shapefile (resources/gulf_shapefile.zip) and belong to the target Collection (echo_collection_id=C1940473819-POCLOUD):\ncurl -XPOST \"https://cmr.earthdata.nasa.gov/search/granules.json\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1940473819-POCLOUD\" -F \"pretty=true\"\nThe (truncated) results:\n{\n \"feed\" : {\n \"updated\" : \"2020-05-18T22:09:58.452Z\",\n \"id\" : \"https://cmr.earthdata.nasa.gov:443/search/granules.json\",\n \"title\" : \"ECHO granule metadata\",\n \"entry\" : [ {\n \"time_start\" : \"2002-07-04T06:35:00.000Z\",\n \"granule_size\" : \"9.34600830078125E5\",\n \"online_access_flag\" : true,\n \"id\" : \"G1966128926-POCLOUD\",\n \"day_night_flag\" : \"UNSPECIFIED\",\n \"browse_flag\" : true,\n \"time_end\" : \"2002-07-04T06:39:59.000Z\",\n \"coordinate_system\" : \"CARTESIAN\",\n \"polygons\" : [ [ \"-74.21 -163.74 -72.95 -180 -75.43 -180 -74.21 -163.74\" ], [ \"-72.95 180 -69.22 161.56 -63.56 148.24 -59.74 142.63 -54.76 137.29 -43.19 129.21 -28.81 123 -9.39 117.38 70.28 101.89 78.71 98.84 83.8 94.5 85.75 90.5 87.14 84.43 88.59 63.9 89.12 2.26 88.28 -48.29 87.12 -61.14 85.38 -68.1 82.54 -72.63 77.71 -75.93 63.62 -80.05 9.8 -89.82 -15.54 -95.55 -35.84 -102.3 -50.51 -110.4 -56.3 -115.37 -60.99 -120.88 -64.95 -127.37 -68.22 -135.08 -70.76 -143.97 -72.76 -155.11 -74.35 -180 -89.345 -180 -88.32 -113.33 -86.69 -101.61 -83.86 -95.95 -79.32 -92.73 -70.2 -89.86 -10.6 -79.24 16.19 -73.27 33.1 -67.88 46.23 -61.6 52.76 -57.05 57.86 -52.26 62.64 -46.04 66.5 -38.81 69.5 -30.55 71.71 -21.38 74.26 1.26 73.75 29.71 72.1 42.61 69.64 53.42 66.25 62.72 62.36 69.82 57.32 76.19 51.2 81.62 37.2 89.55 18.32 96.04 -10.4 102.56 -70.39 113.28 -79.49 116.19 -83.94 119.43 -86.33 123.83 -87.89 131.86 -86.97 131.68 -85.71 133.93 -81.4 147.95 -77.84 164.04 -75.43 180 -72.95 180\" ], [ \"-74.35 180 -74.81 162.65 -77.45 142.63 -82.89 118.89 -85.78 110.18 -87.46 107.84 -88.13 109.51 -88.85 123.7 -89.345 180 -74.35 180\" ] ],\n \"original_format\" : \"UMM_JSON\",\n \"collection_concept_id\" : \"C1940473819-POCLOUD\",\n \"data_center\" : \"POCLOUD\",\n \"links\" : [ {\n \"rel\" : \"http://esipfed.org/ns/fedsearch/1.1/s3#\",\n \"hreflang\" : \"en-US\",\n \"href\" : \"s3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc\"\n },\n ...\nThis command gets the same listing again with curl, this time returning the search results in their native xml format:\ncurl -XPOST \"https://cmr.earthdata.nasa.gov/search/granules\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1940473819-POCLOUD\" -F \"pretty=true\"\nThe (truncated) results:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<results>\n <hits>1912</hits>\n <took>970</took>\n <references>\n <reference>\n <name>20020704063505-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0.nc</name>\n <id>G1966128926-POCLOUD</id>\n <location>https://cmr.earthdata.nasa.gov:443/search/concepts/G1226019017-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n ..."
+ "objectID": "external/Direct_S3_Access_NetCDF.html#requirements",
+ "href": "external/Direct_S3_Access_NetCDF.html#requirements",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Requirements",
+ "text": "Requirements\n\n1. AWS instance running in us-west-2\nNASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n3. netrc File\nYou will need a netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
},
{
- "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html",
- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis Jupyter Notebook contains examples related to geospatial search using the PO.DAAC HUC Feature Translation Service (FTS), previewing (viusualizing) the queried region of interest, and using FTS results to query data through NASA’s Common Metadata Repository (CMR).\nExample Use Case: Check if data is available over my region of interest using HUCs. In this example we are using the FTS-HUC API (https://fts.podaac.earthdata.nasa.gov/) to geospatially define our region of interest, namely the Upper Tuolumne River Basin in the San Joaquin River Basin in California’s Sierra Nevada Mountains, searching by HUC or region name, and then using those geospatial bounds (coordinates) to query Sentinel-1 data in CMR. 1. use FTS to define geographic region of interest (query by partial or exact HUC or HUC region name) 2. preview query 3. use coordinates returned by FTS to query Sentinel-1 data in CMR, by polygon or bounding box.\nResources\nUSGS Hydrologic unit map to help identifiy region of interest (e.g. HUC value or name) can be found here: https://water.usgs.gov/GIS/regions.html"
+ "objectID": "external/Direct_S3_Access_NetCDF.html#learning-objectives",
+ "href": "external/Direct_S3_Access_NetCDF.html#learning-objectives",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Learning Objectives",
+ "text": "Learning Objectives\n\nhow to retrieve temporary S3 credentials for in-region direct S3 bucket access\nhow to define a dataset of interest and find netCDF files in S3 bucket\nhow to perform in-region direct access of ECCO_L4_SSH_05DEG_MONTHLY_V4R4 data in S3\nhow to plot the data"
},
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- "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#install-libraries",
- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#install-libraries",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Install libraries",
- "text": "Install libraries\n\n%%capture\n\nimport sys\n!{sys.executable} -m pip install bs4 requests\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport json"
+ "objectID": "external/Direct_S3_Access_NetCDF.html#import-packages",
+ "href": "external/Direct_S3_Access_NetCDF.html#import-packages",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Import Packages",
+ "text": "Import Packages\n\nimport os\nimport requests\nimport s3fs\nimport xarray as xr\nimport hvplot.xarray"
},
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- "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-partial-region-matches",
- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-partial-region-matches",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Search Feature Translation Service for Partial Region Matches",
- "text": "Search Feature Translation Service for Partial Region Matches\nIf you are unsure what the corresponding HUC is for your region of interest, you can query the FTS for partial region matches, by setting EXACT = FALSE.\n\n###################\n\n# Querying partial matches with region \"San Joaquin\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"San Jo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with region \"San Jo\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"11.754 ms.\",\n \"hits\": 11,\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"San Joaquin\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/180400.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.93679916804501,36.36688239563472,-118.65438684397327,38.757297326299295\",\n \"Convex Hull Polygon\": 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\"HUC\": \"180500030306\"\n }\n }\n}"
+ "objectID": "external/Direct_S3_Access_NetCDF.html#get-temporary-aws-credentials",
+ "href": "external/Direct_S3_Access_NetCDF.html#get-temporary-aws-credentials",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Get Temporary AWS Credentials",
+ "text": "Get Temporary AWS Credentials\nDirect S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. For now, each NASA DAAC has different AWS credentials endpoints. Below are some of the credential endpoints to various DAACs:\n\ns3_cred_endpoint = {\n 'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'gesdisc': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',\n 'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\n 'ornldaac': 'https://data.ornldaac.earthdata.nasa.gov/s3credentials',\n 'ghrcdaac': 'https://data.ghrc.earthdata.nasa.gov/s3credentials'\n}\n\nCreate a function to make a request to an endpoint for temporary credentials. Remember, each DAAC has their own endpoint and credentials are not usable for cloud data from other DAACs.\n\ndef get_temp_creds(provider):\n return requests.get(s3_cred_endpoint[provider]).json()\n\n\ntemp_creds_req = get_temp_creds('podaac')\n#temp_creds_req"
},
{
- "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-matches",
- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-matches",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Search Feature Translation Service for Exact HUC Matches",
- "text": "Search Feature Translation Service for Exact HUC Matches\nHere we can set a HUC ID, or hydrologic unit code, and use this to query the HUC FTS. By defining the parameter EXACT = True, we tell the query to not search for partial matches.\nBased on the partial name response in the previous step, we can now do an exact search for San Joaquin River Basin, using its HUC ID (1804).\n\n###################\n\n# Querying exact matches for HUC \"1804\" = San Joaquin River Basin\n\nHUC = \"1804\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.791 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"1804\": {\n \"USGS Polygon\": {\n \"Object URL\": 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\"Region Name\": \"San Joaquin\"\n }\n }\n}"
+ "objectID": "external/Direct_S3_Access_NetCDF.html#set-up-an-s3fs-session-for-direct-access",
+ "href": "external/Direct_S3_Access_NetCDF.html#set-up-an-s3fs-session-for-direct-access",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Set up an s3fs session for Direct Access",
+ "text": "Set up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint.\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})\n\nIn this example we’re interested in the ECCO data collection from NASA’s PO.DAAC in Earthdata Cloud. In this case it’s the following string that unique identifies the collection of monthly, 0.5-degree sea surface height data (ECCO_L4_SSH_05DEG_MONTHLY_V4R4).\n\nshort_name = 'ECCO_L4_SSH_05DEG_MONTHLY_V4R4'\n\n\nbucket = os.path.join('podaac-ops-cumulus-protected/', short_name, '*2015*.nc')\nbucket\n\nGet a list of netCDF files located at the S3 path corresponding to the ECCO V4r4 monthly sea surface height dataset on the 0.5-degree latitude/longitude grid, for year 2015.\n\nssh_files = fs_s3.glob(bucket)\nssh_files"
},
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- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-partial-region-matches-1",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Search Feature Translation Service for Partial Region Matches",
- "text": "Search Feature Translation Service for Partial Region Matches\nBut in this case we are specifically interested in Tuolumne River Basin within the San Joaquin main basin, so let’s do a partial search for “Upper Tuo”, given we may not know the exact region name.\n\n###################\n\n# Querying partial matches with region \"Upper Tuo\"\n# This \"partial\" match is anything that BEGINS with the region specified.\n\nREGION = \"Upper Tuo\"\nEXACT = False\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that partially matches with region \"Upper Tuo\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"4.244 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": false,\n \"polygon_format\": 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\"HUC\": \"18040009\"\n }\n }\n}"
+ "objectID": "external/Direct_S3_Access_NetCDF.html#direct-in-region-access",
+ "href": "external/Direct_S3_Access_NetCDF.html#direct-in-region-access",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Direct In-region Access",
+ "text": "Direct In-region Access\nOpen with the netCDF files using the s3fs package, then load them all at once into a concatenated xarray dataset.\n\nfileset = [fs_s3.open(file) for file in ssh_files]\n\nCreate an xarray dataset using the open_mfdataset() function to “read in” all of the netCDF4 files in one call.\n\nssh_ds = xr.open_mfdataset(fileset,\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks='auto')\nssh_ds\n\nGet the SSH variable as an xarray dataarray\n\nssh_da = ssh_ds.SSH\nssh_da\n\nPlot the SSH time series using hvplot\n\nssh_da.hvplot.image(y='latitude', x='longitude', cmap='Viridis',).opts(clim=(ssh_da.attrs['valid_min'][0],ssh_da.attrs['valid_max'][0]))"
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- "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-and-named-region-matches",
- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#search-feature-translation-service-for-exact-huc-and-named-region-matches",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Search Feature Translation Service for Exact HUC and Named Region Matches",
- "text": "Search Feature Translation Service for Exact HUC and Named Region Matches\nGiven the above response, or that we already know an exact region name or HUC in USGS’s Watershed Boundary Dataset (WBD), we can use this instead of a partial search. Below is an example of searching by exact match using HUC ID (e.g. 18040009), and then by region name (“Upper Tuolumne”).\n\n###################\n\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"18040009\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.582 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"18040009\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": 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\"Region Name\": \"Upper Tuolumne\"\n }\n }\n}\n\n\n\n###################\n\n# Querying exact matches with region \"Upper Tuolumne\"\n\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\n# Note the change in endpoint from \"/huc\" to \"/region\"\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exact matches with region \"Upper Tuolumne\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.312 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"region\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"Upper Tuolumne\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": 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\"HUC\": \"18040009\"\n }\n }\n}"
+ "objectID": "external/Direct_S3_Access_NetCDF.html#resources",
+ "href": "external/Direct_S3_Access_NetCDF.html#resources",
+ "title": "How to Access Data Directly in Cloud (netCDF)",
+ "section": "Resources",
+ "text": "Resources\nDirect access to ECCO data in S3 (from us-west-2)\nData_Access__Direct_S3_Access__PODAAC_ECCO_SSH using CMR-STAC API to retrieve S3 links"
},
{
- "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#function-for-visualization",
- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#function-for-visualization",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Function for Visualization",
- "text": "Function for Visualization\nBelow is a function created specifically to visualize the output of the HUC Feature Translation Service.\n\ndef visualize(fts_response):\n \n regions = []\n bounding_boxes = []\n convex_hull_polygons = []\n visvalingam_polygons = []\n for element in fts_response['results']:\n for heading in fts_response['results'][element]:\n if heading == \"Bounding Box\":\n bounding_boxes.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Convex Hull Polygon\":\n convex_hull_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"Visvalingam Polygon\":\n visvalingam_polygons.append([float(e) for e in fts_response['results'][element][heading].split(\",\")])\n elif heading == \"HUC\":\n regions.append(\"Region Name: \" + element + \"\\n\" + \"HUC: \" + fts_response['results'][element][heading])\n elif heading == \"Region Name\":\n regions.append(\"Region Name: \" + fts_response['results'][element][heading] + \"\\n\" + \"HUC: \" + element)\n else:\n continue\n\n bounding_boxes = [box(e[0], e[1], e[2], e[3]) for e in bounding_boxes]\n convex_hull_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in convex_hull_polygons]\n visvalingam_polygons = [Polygon(np.asarray(polygon).reshape(-1,2)) for polygon in visvalingam_polygons]\n \n for i in range(len(bounding_boxes)):\n ax = gpd.GeoSeries(bounding_boxes[i]).plot(alpha=0.2, cmap='Pastel1', figsize=(10,10))\n gpd.GeoSeries(convex_hull_polygons[i]).plot(ax = ax, cmap='Pastel2')\n gpd.GeoSeries(visvalingam_polygons[i]).plot(alpha=0.5, ax=ax, cmap='viridis')\n\n plt.title(regions[i])\n plt.show()"
+ "objectID": "external/July_2022_Earthdata_Webinar.html",
+ "href": "external/July_2022_Earthdata_Webinar.html",
+ "title": "Earthdata Webinar",
+ "section": "",
+ "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club"
},
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- "objectID": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#visualization",
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- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Visualization",
- "text": "Visualization\nWe can take that response and pass it to the visualize() function created above. The pink polygon is Bounding Box, the green is Convex Hull Polygon and the purple color is Visvalingam Polygon\n\n#visualize FTS response\nvisualize(response)\n\n\n\n\nHere we can visualize the FTS response using HUC ID search (18040009) instead of Region search of “Upper Tuolumne”.\n\n###################\n# Querying exact matches with HUC \"18040009\" = Upper Tuolumne\n\nHUC = \"18040009\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/huc/{}?exact={}\".format(HUC, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Print all elements in HUC database that exactly match HUC \"1804\"\nprint(json.dumps(response, indent = 4))\n\n{\n \"status\": \"200 OK\",\n \"time\": \"2.61 ms.\",\n \"hits\": 1,\n \"search on\": {\n \"parameter\": \"HUC\",\n \"exact\": true,\n \"polygon_format\": \"\",\n \"page_number\": 1,\n \"page_size\": 100\n },\n \"results\": {\n \"18040009\": {\n \"USGS Polygon\": {\n \"Object URL\": \"https://podaac-feature-translation-service.s3-us-west-2.amazonaws.com/18040009.zip\",\n \"Source\": \"ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/WBD_18_HU2_Shape.zip\"\n },\n \"Bounding Box\": \"-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182\",\n \"Convex Hull Polygon\": \"-121.105517801627,37.57291785522102,-120.51777999837259,37.58160878749919,-119.26845687218679,37.73942430183757,-119.26095827844847,37.741190162251485,-119.26079495969867,37.74128122475133,-119.25581474616479,37.7450598684955,-119.25563206491506,37.74520087891193,-119.25521361804067,37.745555179953044,-119.20452512020273,37.79316755800414,-119.20311483687158,37.794898117376476,-119.20297581291345,37.79511513091779,-119.20108320354137,37.801137019450096,-119.20096521291657,37.803876760070864,-119.19927543166921,37.88483115890352,-119.19931234937746,37.885001276611604,-119.20064394937538,37.88738135160793,-119.31090541587093,38.044980644071586,-119.3277000731365,38.0651666159153,-119.32796109605277,38.06544024091488,-119.34908448143665,38.08655395234041,-119.62508146642494,38.22905559795254,-119.65624842470987,38.22952896670182,-119.65829346949835,38.22947615316025,-119.79473757241158,38.21799358859471,-119.99491475022586,38.196920114669126,-120.38613654232694,38.056378609678916,-121.15444382863438,37.62884831659255,-121.15500076925849,37.6284224540932,-121.15993039529252,37.62332076451776,-121.16822139007132,37.61386883849076,-121.17452907235321,37.605445134337174,-121.17462853797804,37.60522817287921,-121.17469632131127,37.60502320725453,-121.17471004943627,37.60496802808791,-121.17476593797784,37.604743358296616,-121.17472602131124,37.60443736142207,-121.1743974786034,37.603737121839856,-121.17385444318757,37.603213931215635,-121.12495024430518,37.575249448967384,-121.1206057318119,37.57340581772024,-121.1184699109819,37.573299354178744,-121.105517801627,37.57291785522102\",\n 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8332,-119.31574230232172,37.96621302648555,-119.30533157629623,38.02416955035392,-119.34927245018639,38.08565116171684,-119.35845034913046,38.08266815651314,-119.39810467927725,38.1068175096006,-119.43127595110076,38.11332130542388,-119.4403859021283,38.09636985024184,-119.46399499479998,38.09838383773871,-119.4692413104168,38.12798441894279,-119.48819819267908,38.132729004352086,-119.50246159786525,38.159339980352456,-119.50459633952858,38.140964939755975,-119.54763344883679,38.14419101891764,-119.54624260196397,38.15397065015242,-119.5773162810824,38.15780512931315,-119.57980050712018,38.17791634178195,-119.62908996641869,38.196015076128845,-119.62508146642494,38.22905559795254,-119.65612154137676,38.229472830243594\",\n \"Region Name\": \"Upper Tuolumne\"\n }\n }\n}\n\n\n\n#visualize FTS response\nvisualize(response)"
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+ "href": "external/July_2022_Earthdata_Webinar.html#abstract",
+ "title": "Earthdata Webinar",
+ "section": "Abstract",
+ "text": "Abstract\n\nNearly a petabyte of NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) data products have been moved to NASA’s Earthdata Cloud—hosted in the Amazon Web Services (AWS) cloud. To maximize the full potential of cloud computing on the big Data, one needs to be familiar with not only the data products and their access methods, but also a new set of knowledge for working in a cloud environment. This can be a daunting task for the majority of the science community, who may be familiar with high-performance computing, but not with AWS services. To aid end users in learning and to be successful during this paradigm shift, the PO.DAAC team has been exploring pathways toward practical solutions to help research groups migrate their workflow into cloud.\nDuring this webinar we will explain basic concepts of working in the cloud and use a simple science use case to demonstrate the workflow. Participants do not need prior knowledge of AWS services and the Earthdata Cloud. This is a step-by-step walkthrough of exploring and discovering PO.DAAC data and applying AWS cloud computing to analyze global sea level rise from altimetry data and Estimating the Circulation and Climate of the Ocean (ECCO) products.\nWe hope that you can start to practice cloud computing using AWS and PODAAC/Earthdata cloud products by following the 6 steps in this tutorial without investing a large amount of time."
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- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#query-cmr-by-bounding-box",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Query CMR by Bounding Box",
- "text": "Query CMR by Bounding Box\nHere is a more useful example of the Feature Translation Service. We can use results obtained from the FTS to then directly and automatically query on data using CMR. We’re extracting the bounding box representing Upper Tuolumne River Basin within the San Joaquin River Basin, and using it to search for granules available from the SMAP/Sentinel-1 missions, as an example.\n\n###################\n\nCOLLECTION_ID = \"C1931663473-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\nREGION = \"Upper Tuolumne\"\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain bounding box from response\nbbox = response['results'][REGION]['Bounding Box']\n\n# Query CMR by bounding box\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?bounding_box={}&echo_collection_id={}&pretty=True\".format(bbox, COLLECTION_ID))\n\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2021-08-04T19:04:43.269Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?bounding_box=-121.17476593797784,37.57291785522102,-119.19927543166921,38.22952896670182&echo_collection_id=C1931663473-NSIDC_ECS&pretty=True\",\n \"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\",\n \"time_start\": \"2015-04-01T01:20:04.000Z\",\n \"updated\": \"2020-12-11T20:16:06.973Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197590772\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:44.000Z\",\n \"id\": \"G1978082559-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8861885071\",\n \"browse_flag\": 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+ "objectID": "external/July_2022_Earthdata_Webinar.html#motivation",
+ "href": "external/July_2022_Earthdata_Webinar.html#motivation",
+ "title": "Earthdata Webinar",
+ "section": "Motivation",
+ "text": "Motivation\n\nIt is expected the NASA Earthdata will grow to >250 PB in 2025,\nCloud computing has a big potential\nThe path to the cloud computing is unclear for majority of the science and application community\nThe science community are often perplexed at the start line by a new language related to cloud computing and the large amount of different AWS tools and services, such as CloudFront, EC2, VPC, AMI, IAM, Bucket, Glacier, Snowcone, Snowball, Snowmobile, data lakes, just to name a few.\nWe aim to share our experience of passing the start line and start to run cloud computing, demonstrate a use case assuming zero knowledge of AWS cloud\nThe global mean sea level used here is an important climate indicator and relatively easy to calculate from the PODAAC data in the cloud."
},
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- "href": "notebooks/HUC Feature Translation Service Examples-updated-20210804.html#query-cmr-by-polygon",
- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Query CMR by Polygon",
- "text": "Query CMR by Polygon\nInstead of querying via bounding box from the FTS response, we can extract the polygon of the region and use this to query CMR. Similarly to above, we’re extracting information from the Upper Tuolumne River Basin and using this to search for granules available from the Sentinel-1 mission.\nHere we query by region in these two examples, however it would be equally valid to query by HUC ID.\n\n\n###################\n\nCOLLECTION_ID = \"C1931663473-NSIDC_ECS\" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\nREGION = \"Upper Tuolumne\"\nEXACT = True\n\n###################\n\n# Query Feature Translation Service and parse JSON response\nr = requests.get(\"https://fts.podaac.earthdata.nasa.gov/region/{}?exact={}\".format(REGION, EXACT))\n\n# Load response from FTS\nresponse = r.json()\n\n# Obtain Visvalingam polygon from response\n#polygon = response['results'][REGION]['Convex Hull Polygon']\npolygon = response['results'][REGION]['Visvalingam Polygon']\n\n# Query CMR by polygon\n# --------- #\n\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?polygon={}&echo_collection_id={}&pretty=True\".format(polygon, COLLECTION_ID))\n# --------- #\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2021-08-04T19:06:13.741Z\",\n \"id\": \"https://cmr.earthdata.nasa.gov:443/search/granules.json?polygon=-119.65612154137676,38.229472830243594,-119.65887392887248,38.21611789588928,-119.68748882882807,38.20072575945488,-119.74423195478164,38.21583016359807,-119.79473757241158,38.21799358859471,-119.80777226405803,38.20387888444998,-119.83634931818034,38.19900379279085,-119.8794751608217,38.205164957364616,-119.92926093053609,38.1903575073876,-119.95874486382365,38.194300821964816,-119.9915583491894,38.18745202718378,-119.99491475022586,38.196920114669126,-120.05974666158357,38.15445730952666,-120.10528053234623,38.13442047414111,-120.12843912918527,38.10262073148215,-120.20521266552441,38.056065841971076,-120.26427926855774,38.061807551337154,-120.34283367885251,38.04442165553081,-120.36616220485797,38.05864021280041,-120.38138515275097,38.04379106074015,-120.39399451523144,38.00852514516987,-120.41557226103123,38.00413815246833,-120.4432863495299,37.97081393481176,-120.45616958284324,37.92842260675252,-120.46878266719864,37.921885701554345,-120.4624829120001,37.891430354726594,-120.479954646348,37.871879250590325,-120.48053337030541,37.82415609128935,-120.5431307066666,37.84913992354228,-120.55227857644405,37.86382160997783,-120.57047251079081,37.845455260006304,-120.5664340514221,37.82240133087544,-120.59755003054045,37.81161053401718,-120.64595511379866,37.78181727677179,-120.67580313979397,37.7973594465393,-120.7399643115694,37.77691864136273,-120.75337800946522,37.73604807163446,-120.78855502295232,37.75464512473059,-120.82306428227372,37.739964848711736,-120.82298294894053,37.72367104352867,-120.84114918537068,37.706087340431,-120.8673865186633,37.69917305085835,-120.87598703427494,37.68470263317249,-120.91724289983591,37.65975447800287,-120.94840845082916,37.657548763422994,-121.0014800642885,37.642007069697115,-121.06141474752877,37.632796530128076,-121.15500076925849,37.6284224540932,-121.17472602131124,37.60443736142207,-121.15475178384224,37.60555392496201,-121.12649717971942,37.5761612791743,-121.105517801627,37.57291785522102,-121.06195388086127,37.58432554999496,-121.051833277752,37.59549621664428,-120.9623811789325,37.616090631195675,-120.93801052688701,37.61182627286894,-120.86057590200721,37.62129079368759,-120.85730386034561,37.613155902033554,-120.78087282192257,37.61414406661538,-120.73817309594716,37.63053679471494,-120.70606299078867,37.6253131207647,-120.65312547628753,37.630845649922776,-120.64372085026048,37.617530275985075,-120.59897548366325,37.61838293640045,-120.58566304618392,37.6280897895104,-120.55437374519079,37.61949203744041,-120.53035751501977,37.62416281034979,-120.52267756711501,37.58373637916253,-120.47482846302262,37.59087335206817,-120.45815308909016,37.620518593688814,-120.4371221932895,37.63718915616295,-120.3849402642038,37.635048797832894,-120.3755883360933,37.65339462697108,-120.39417890064777,37.66818361028146,-120.392313883984,37.68359100504921,-120.3559603048738,37.67552716860342,-120.32610607262848,37.648966585311314,-120.30639988828403,37.66573735924362,-120.3455439350983,37.72512544352645,-120.31539807993676,37.733894229971156,-120.28665821956469,37.72927809560332,-120.28245683519623,37.74541342682829,-120.26069274460497,37.73358365601331,-120.25387803732389,37.749232223697334,-120.200135472824,37.76372567054983,-120.17357968640687,37.79608365070794,-120.12741327606187,37.78170633927192,-120.08906888341306,37.81273366005712,-120.07891344384547,37.82866939649068,-120.05533048450707,37.812757440265386,-120.02518823247055,37.81132716005931,-119.96359235131615,37.78073240906514,-119.94507216905322,37.76542058617224,-119.90693944932076,37.75781682576735,-119.86549272230178,37.77222938512,-119.85359450461186,37.758767389307536,-119.83160174943771,37.76963435595735,-119.80589665676928,37.75608133722835,-119.75060675164673,37.77341942574316,-119.7359645225028,37.78635874968137,-119.6978660861036,37.78960109342637,-119.64907657576265,37.81516770484501,-119.65722697262504,37.83230492565173,-119.59708603105173,37.86135878810666,-119.58892104668939,37.88872669535584,-119.57247042275657,37.8998297015886,-119.53527651760601,37.90190377762701,-119.49696578120711,37.86409281310239,-119.47533168332404,37.85892923602705,-119.45447817606475,37.871394138091034,-119.44539026566218,37.858937875610366,-119.4045319969756,37.85021429541558,-119.40293857510306,37.833821247524384,-119.37314428244099,37.83849664855876,-119.35155096789117,37.82452854545545,-119.3552763335104,37.812805713182,-119.32340220126821,37.79368655279501,-119.29229722319144,37.762878678884476,-119.2876598648653,37.74535544245333,-119.26052065344913,37.74159433725089,-119.24428567430766,37.76834668616766,-119.22055414726117,37.77924966531742,-119.20108320354137,37.801137019450096,-119.21738830351609,37.8183151860901,-119.20449054936944,37.82981189961396,-119.21625690560114,37.847411426669964,-119.21512584414461,37.87042564434256,-119.19927543166921,37.88483115890352,-119.23787860140095,37.911280908862466,-119.26486444510903,37.91263911719369,-119.26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\"title\": \"ECHO granule metadata\",\n \"entry\": [\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\",\n \"time_start\": \"2015-04-01T01:20:04.000Z\",\n \"updated\": \"2020-12-11T20:16:06.973Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197590772\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T01:20:44.000Z\",\n \"id\": \"G1978082559-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"3.8861885071\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"38.5845451 -121.9242706 36.5554848 -121.9242706 36.5554848 -118.6670151 38.5845451 -118.6670151 38.5845451 -121.9242706\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/opendap/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": \"text/plain\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP1/AMSA/QA.001/2020.12.11/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.qa\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"text/xml\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T011959_20150401T015854_120W37N_R17000_001.h5.iso.xml\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/SMAP/SPL2SMAP_S.003/\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://search.earthdata.nasa.gov/search/granules?p=C1931663473-NSIDC_ECS&pg[0][gsk]=-start_date&tl=1583080558!4!!\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/data/data-access-tool/SPL2SMAP_S/versions/3/\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R17000_001.h5\",\n \"time_start\": \"2015-04-01T14:55:15.000Z\",\n \"updated\": \"2020-12-11T20:16:06.985Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197590771\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-04-01T14:55:53.000Z\",\n \"id\": \"G1978082510-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.2115306854\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"40.1287079 -122.2977142 38.0572586 -122.2977142 38.0572586 -118.9470978 40.1287079 -118.9470978 40.1287079 -122.2977142\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/opendap/DP4/SMAP/SPL2SMAP_S.003/2015.04.01/SMAP_L2_SM_SP_1AIWDV_20150401T145527_20150401T015919_120W39N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"type\": 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\"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/daac/subscriptions.html\"\n },\n {\n \"inherited\": true,\n \"length\": \"0.0KB\",\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://nsidc.org/data/data-access-tool/SPL2SMAP_S/versions/3/\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n },\n {\n \"inherited\": true,\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/documentation#\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://doi.org/10.5067/ASB0EQO2LYJV\"\n }\n ]\n },\n {\n \"producer_granule_id\": \"SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R17000_001.h5\",\n \"time_start\": \"2015-05-24T14:43:34.000Z\",\n \"updated\": \"2020-12-12T23:08:12.173Z\",\n \"dataset_id\": \"SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003\",\n \"data_center\": \"NSIDC_ECS\",\n \"title\": \"SC:SPL2SMAP_S.003:197692314\",\n \"coordinate_system\": \"GEODETIC\",\n \"time_end\": \"2015-05-24T14:44:19.000Z\",\n \"id\": \"G1978421514-NSIDC_ECS\",\n \"original_format\": \"ISO-SMAP\",\n \"granule_size\": \"5.2886819839\",\n \"browse_flag\": false,\n \"polygons\": [\n [\n \"37.9581909 -122.8578873 35.6854019 -122.8578873 35.6854019 -119.6006241 37.9581909 -119.6006241 37.9581909 -122.8578873\"\n ]\n ],\n \"collection_concept_id\": \"C1931663473-NSIDC_ECS\",\n \"online_access_flag\": true,\n \"links\": [\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"type\": \"application/x-hdfeos\",\n \"hreflang\": \"en-US\",\n \"href\": \"https://n5eil01u.ecs.nsidc.org/DP4/SMAP/SPL2SMAP_S.003/2015.05.24/SMAP_L2_SM_SP_1AIWDV_20150524T144343_20150524T140736_121W36N_R17000_001.h5\"\n },\n {\n \"rel\": \"http://esipfed.org/ns/fedsearch/1.1/metadata#\",\n \"type\": 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+ "title": "Earthdata Webinar",
+ "section": "Objectives",
+ "text": "Objectives\n\n\nSet up a cloud computing environment from scrath\nRun the global mean sea level code in the cloud\nBuild a webpage server using the same cloud computer\n\n\nTarget audience\n\nScience- and application-oriented group who\n\nhas interest in cloud computing;\nis familiar with python, conda, and jupyter-notebook;\nbut with limited knowledge of NASA Earthdata and IT;\nand zero knowledge of AWS cloud. *** ## Outline: the steps toward running in-cloud analysis\n\n\n\nGet an AWS account\nStart an AWS cloud computer (Elastic Computer Cloud, EC2)\n\nexplain AWS console, EC2 instance\n\nConfigure the EC2 with the necessary software\n\nWhile waiting, explain the global mean sea level analysis code\n\nConfigure a jupyter-lab on the EC2 and connect to it from browser\nDemo the code (this notebook) in the cloud and save the figure\nSet up an apache server (hosting website)\n\nCreate a static html webpage to show the result\n\n\n\nThere are many ways to achieve this goal. Many alternatives are much smarter but they usually involves a set of new knowledge related to cloud and/or AWS that steepens the learning curve and sometimes makes the process intimidating. The following steps are suggested here because it is believed to involve a minimum amount of specilized knowledge beyond our common practice on our own computer.\n\n\n\nImportant terms\n\n\n\n\n\n\n\n\nAWS terminology\nLong name\nMeaning\n\n\n\n\nAWS Region\n\nAWS facility. There are many of them. NASA Earth Data are in US-WEST-2, somewhere in Oregon.\n\n\nEC2\nElastic Computer Cloud\nA computer in one of the AWS regions. It is a common practice that you should use an EC2 in the region where you data is hosted.\n\n\nAWS console\n\nA web-based control panel for all AWS tools and services. You can start an EC2, create a storage disk (S3 bucket) and much more.\n\n\nKey Pair\n\nAn SSH key generated for accessing the EC2, e.g., through SSH. Anyone who has your key can connect to your EC2. It means that you can share the same EC2 with others just through sharing a Key Pair file."
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- "title": "HUC Feature Translation Service (FTS) Examples",
- "section": "Check with Earthdata Search",
- "text": "Check with Earthdata Search\nHere we show how to check the CMR response with Earth Data Search. First, let’s get the number of granules from CMR response.\nNote that Earthdata Search query data granules (files) by Visvalingam polygon. So make sure to use Visvalingam polygon when query CMR with polygon\n\nnumber_of_granules=cmr_response.headers['CMR-Hits']\nprint(number_of_granules)\n\n1325\n\n\nTo find granules in Earthdata Search, we need to first search for the collection. You can search for SMAP/Sentinel in the top left corner to find the Soil Mositure dataset.Earthdata allows you to do an Advanced seach over a HUC region. You can search by HUC ID or HUC region. In our case, let’s search for “HUC Region” and “Upper Tuolumne” .\n\n\n\nAdvanced Search\n\n\nFinally, we can locate the total number of granules from the search which matches with the one we identified from CMR.\nAlso, our search in Earh Data has a unique url with a project ID. This url corresponds to SMAP/Sentinel Soil Mositure granules within the Upper Tuolumne:\nhttps://search.earthdata.nasa.gov/search/granules?projectId=3965468611\n\n\n\nEarthdata Granules"
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+ "href": "external/July_2022_Earthdata_Webinar.html#step-1-get-an-aws-account",
+ "title": "Earthdata Webinar",
+ "section": "Step 1 – Get an AWS account",
+ "text": "Step 1 – Get an AWS account\n\nIf you already have an AWS account, skip to Step 2. ### Choices: 1. Look for institutional support (recommended) 1. Engage in NASA-funded programs (e.g., openscapes) 1. Apply a free AWS account (today’s focus) 1. It is free for a year but only offers small computers (1 CPU, 1GB memory) 1. With the offer of 750 hours per month, a free-tier EC2 can be on all time for a year. 1. Need your personal information including credit card\n(https://aws.amazon.com)\nThis page explains the five steps to create an AWS account. > https://progressivecoder.com/creating-an-aws-account-a-step-by-step-process-guide/. ***"
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- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
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+ "title": "Earthdata Webinar",
+ "section": "Step 2 - Start an EC2",
+ "text": "Step 2 - Start an EC2\n\nLog in through aws console https://aws.amazon.com/console/\nStart an EC2 (AWS jargon: launch an instance, layman’s interpretation: start a remote server hosted by AWS)\n\nName and Tags: earthdata_webinar\nApplication and OS Images (Amazon Machine Image): >Red Hat Enterprise Linux 8 (HVM) SSD Volume type (free-tier elegible)\nInstance type: t2.micro (1CPU, 1Gb memory) (If you have a institution- or project-supported AWS account, try to use a bigger computer with >4G memory.)\nKey pair (login): “Create new key pair”\n\nenter a name, e.g., “aws_ec2_jupyter” -> create key pair\nlook for the .pem file in the Download folder, move it to .ssh folder. > mv ~/Downloads/aws_ec2_jupyter.pem .ssh/\nchange permission to 400 using > chmod 400 aws_ec2_jupyter.pem\n\ncheck the two boxes for HTTP and HTTPS for the webserver\nAdd storage: 10 Gb should be fine for prototyping and testing. You have total 30Gb free storage, which can be split among three EC2s for example.\nClick “Launch Instance” button\n\n\n\nReference\n\nAWS get set up for amazon EC2: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/get-set-up-for-amazon-ec2.html\nAWS Get started with AWS EC2: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EC2_GetStarted.html ***"
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- "title": "Use Case: Co-locate satellite and in-situ data for cross-validation",
- "section": "Access temperature profiles from ArgoVis API",
- "text": "Access temperature profiles from ArgoVis API\nArgoVis is an API and visualization service that provides access to Argo float profiles. The endpoint for requesting profile data is given in the cell below:\n\nargo_api_endpoint = 'https://argovis.colorado.edu/selection/profiles/?'\n\nprint(argo_api_endpoint)\n\nhttps://argovis.colorado.edu/selection/profiles/?\n\n\nCreate the AOI polygon in required XY format, make it a string, and collect the dictionary of API parameters:\n\nargo_api_aoi = [[[aoi_minlon, aoi_minlat], \n [aoi_minlon, aoi_maxlat], \n [aoi_maxlon, aoi_maxlat],\n [aoi_maxlon, aoi_minlat],\n [aoi_minlon, aoi_minlat]]]\n\nargo_api_params = {\n 'startDate': start_date.replace(\"-0\",\"-\"), # 1.\n 'endDate': end_date.replace(\"-0\",\"-\"), # 1. No leading zeros in start/end dates\n 'shape': str(argo_api_aoi).replace(\" \",\"\"), # 2. Array of XY vertices for AOI polygon\n #'presRange': \"[0,30]\" # 3. We wont limit by pressure range\n}\n\nargo_api_params\n\n{'startDate': '2019-1-1',\n 'endDate': '2019-1-31',\n 'shape': '[[[-26.0,30.0],[-26.0,40.0],[-12.0,40.0],[-12.0,30.0],[-26.0,30.0]]]'}\n\n\nSubmit the request parameters to the Argovis API. You should receive a JSON response back. Print the number of profiles inside our AOI:\n\nargo_api_response = requests.get(url=argo_api_endpoint, params=argo_api_params)\n\n# Load the response from JSON if the response status is 200:\nif argo_api_response.status_code == 200:\n argo_profiles = argo_api_response.json()\n print(len(argo_profiles))\nelse:\n # Otherwise dump the text for more clues:\n print(argo_api_response.text)\n\n41\n\n\n\nPrepare profile data for further analysis\nConcatenate the list of metadata dictionaries returned for the argos into a table and update a few of its columns with Pythonic types:\n\nargo_df = pd.DataFrame(argo_profiles).sort_values(\"date\")\n\n# Add a column with pandas datetime objects for easier indexing\nargo_df['datetime'] = pd.to_datetime(argo_df['date'])\n# And then replace the original date column with Python dates\nargo_df['date'] = argo_df.datetime.apply(lambda x: x.date).tolist()\n\n# Add two columns of sanitized lats/lons to the data frame\nargo_df['lat'] = argo_df['roundLat'].astype(float).tolist()\nargo_df['lon'] = argo_df['roundLon'].astype(float).tolist()\n\nargo_df.info()\n\n<class 'pandas.core.frame.DataFrame'>\nInt64Index: 41 entries, 40 to 0\nData columns (total 36 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 _id 41 non-null object \n 1 POSITIONING_SYSTEM 41 non-null object \n 2 DATA_CENTRE 41 non-null object \n 3 PI_NAME 41 non-null object \n 4 WMO_INST_TYPE 41 non-null object \n 5 VERTICAL_SAMPLING_SCHEME 41 non-null object \n 6 DATA_MODE 41 non-null object \n 7 PLATFORM_TYPE 41 non-null object \n 8 measurements 41 non-null object \n 9 station_parameters 41 non-null object \n 10 pres_max_for_TEMP 41 non-null float64 \n 11 pres_min_for_TEMP 41 non-null float64 \n 12 pres_max_for_PSAL 41 non-null float64 \n 13 pres_min_for_PSAL 41 non-null float64 \n 14 max_pres 41 non-null float64 \n 15 date 41 non-null object \n 16 date_added 41 non-null object \n 17 date_qc 41 non-null int64 \n 18 lat 41 non-null float64 \n 19 lon 41 non-null float64 \n 20 geoLocation 41 non-null object \n 21 position_qc 41 non-null int64 \n 22 cycle_number 41 non-null int64 \n 23 dac 41 non-null object \n 24 platform_number 41 non-null int64 \n 25 station_parameters_in_nc 41 non-null object \n 26 nc_url 41 non-null object \n 27 DIRECTION 41 non-null object \n 28 BASIN 41 non-null int64 \n 29 core_data_mode 41 non-null object \n 30 roundLat 41 non-null object \n 31 roundLon 41 non-null object \n 32 strLat 41 non-null object \n 33 strLon 41 non-null object \n 34 formatted_station_parameters 41 non-null object \n 35 datetime 41 non-null datetime64[ns, UTC]\ndtypes: datetime64[ns, UTC](1), float64(7), int64(5), object(23)\nmemory usage: 11.9+ KB\n\n\nYou can download profiles in netCDF format from the FTP link stored in the nc_url fields of the response. Here’s the URL for the first of the profiles:\n\nprint(argo_df.iloc[0].nc_url)\n\nftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis/6902663/profiles/R6902663_124.nc\n\n\nDisplay a table summarizing the space/time characteristics of eaach profile:\n\nargo_df[['platform_number', 'cycle_number', 'datetime', 'lon', 'lat']] #, 'measurements']]\n\n\n\n\n\n\n\n\nplatform_number\ncycle_number\ndatetime\nlon\nlat\n\n\n\n\n40\n6902663\n124\n2019-01-01 20:14:00+00:00\n-17.383\n35.601\n\n\n39\n6901260\n49\n2019-01-02 05:43:00+00:00\n-12.812\n37.707\n\n\n38\n6901143\n228\n2019-01-02 09:22:20+00:00\n-21.083\n32.254\n\n\n37\n6902664\n124\n2019-01-02 20:28:00+00:00\n-18.411\n34.985\n\n\n36\n3901643\n43\n2019-01-04 06:13:00+00:00\n-22.429\n37.556\n\n\n35\n3901942\n48\n2019-01-05 20:23:30+00:00\n-15.286\n35.316\n\n\n34\n6901262\n22\n2019-01-06 05:42:59.999000+00:00\n-17.967\n34.228\n\n\n33\n3901932\n22\n2019-01-06 05:49:00+00:00\n-19.904\n33.428\n\n\n32\n1901688\n288\n2019-01-07 03:11:53+00:00\n-23.134\n34.258\n\n\n31\n6901260\n50\n2019-01-07 05:26:00+00:00\n-12.887\n37.905\n\n\n30\n1901688\n289\n2019-01-08 03:15:16+00:00\n-23.137\n34.248\n\n\n29\n6902552\n167\n2019-01-08 12:50:00+00:00\n-23.798\n33.144\n\n\n28\n1901688\n290\n2019-01-09 03:19:48+00:00\n-23.148\n34.233\n\n\n27\n1901688\n291\n2019-01-10 03:27:35+00:00\n-23.158\n34.218\n\n\n26\n6901273\n11\n2019-01-10 05:25:00+00:00\n-12.473\n32.219\n\n\n25\n6902663\n125\n2019-01-11 20:15:00+00:00\n-17.071\n35.830\n\n\n24\n6901260\n51\n2019-01-12 05:18:00+00:00\n-12.982\n38.047\n\n\n23\n6901143\n229\n2019-01-12 07:05:12+00:00\n-21.206\n32.474\n\n\n22\n6902664\n125\n2019-01-12 20:16:00+00:00\n-18.198\n35.057\n\n\n21\n3901643\n44\n2019-01-14 05:58:00+00:00\n-22.706\n37.542\n\n\n20\n3901942\n49\n2019-01-15 20:37:30+00:00\n-15.739\n34.976\n\n\n19\n6901262\n23\n2019-01-16 05:39:00+00:00\n-17.725\n34.223\n\n\n18\n6902785\n54\n2019-01-16 05:56:00+00:00\n-25.507\n38.293\n\n\n17\n3901932\n23\n2019-01-16 06:04:00+00:00\n-19.081\n34.101\n\n\n16\n6901260\n52\n2019-01-17 05:28:00+00:00\n-13.092\n38.266\n\n\n15\n6902552\n168\n2019-01-18 13:02:00+00:00\n-23.575\n33.225\n\n\n14\n1901688\n292\n2019-01-19 20:13:54.002000+00:00\n-23.272\n34.136\n\n\n13\n6901273\n12\n2019-01-20 05:31:00+00:00\n-12.447\n32.181\n\n\n12\n6902663\n126\n2019-01-21 20:21:00+00:00\n-16.892\n35.982\n\n\n11\n6901260\n53\n2019-01-22 05:33:00+00:00\n-13.146\n38.378\n\n\n10\n6901143\n230\n2019-01-22 09:01:03+00:00\n-21.136\n32.880\n\n\n9\n6902664\n126\n2019-01-22 20:23:00+00:00\n-18.099\n35.114\n\n\n8\n3901643\n45\n2019-01-24 06:11:00+00:00\n-23.115\n37.358\n\n\n7\n3901942\n50\n2019-01-25 20:21:30+00:00\n-14.963\n35.402\n\n\n6\n6901262\n24\n2019-01-26 05:47:59.999000+00:00\n-17.474\n34.302\n\n\n5\n3901932\n24\n2019-01-26 06:00:00+00:00\n-18.151\n34.375\n\n\n4\n6902785\n55\n2019-01-26 06:10:00+00:00\n-25.212\n38.213\n\n\n3\n6901260\n54\n2019-01-27 05:38:00+00:00\n-13.265\n38.484\n\n\n2\n6902552\n169\n2019-01-28 12:45:00+00:00\n-23.267\n33.294\n\n\n1\n1901688\n293\n2019-01-29 13:03:27.001000+00:00\n-23.403\n34.206\n\n\n0\n6901273\n13\n2019-01-30 05:27:00+00:00\n-12.737\n32.602\n\n\n\n\n\n\n\nNow plot argo profile locations on an interactive map.\nThis plot uses folium/leaflet. Hover/click the clusters (which correspond to specific Argo float platforms) to zoom to the groups of individual profiles and display metadata about them:\n\ndef _get_tooltip(profile: dict):\n return \"\"\"<b>Date</b>: {date}<br>\n <b>Profile ID</b>: {_id}<br>\n <b>Platform ID</b>: {platform_number}<br>\n <b>Latitude</b>: {lat}<br>\n <b>Longitude</b>: {lon}<br>\"\"\".format(**profile)\n\n\nm = folium.Map(location=[argo_df['lat'].mean(), argo_df['lon'].mean()], \n tiles=\"Stamen Terrain\",\n zoom_start=5, )\n\n# Loop over list of unique platform_numbers (floats)\nunique_argo_platform_numbers = argo_df.platform_number.unique().tolist()\n\nfor i, platform in enumerate(unique_argo_platform_numbers):\n # Get row(s) for the current platform\n p = argo_df[argo_df['platform_number']==platform]\n # Make an empty marker cluster to add to the map widget\n cluster = MarkerCluster(name=p['platform_number'])\n # Make markers in a loop and add to the cluster:\n for c in p['cycle_number'].tolist():\n # Select the row for the current profile ('cycle')\n profile = p[p['cycle_number']==c].iloc[0]\n # Create a new marker and add it to the cluster\n cluster.add_child(folium.Marker(\n location=[profile['lat'], profile['lon']],\n tooltip=_get_tooltip(profile.to_dict())))\n m.add_child(cluster)\n\ndisplay(m)\n\nMake this Notebook Trusted to load map: File -> Trust Notebook\n\n\n\nReformat profile data into data frames\nThe in situ measurements temperature, pressure, and salinity readings collected during each profile are returned inside the JSON response.\nThe format of the measurements field is perfect for conversion to pandas data frames. Apply pandas.DataFrame over the entire measurements column to make a pandas.Series of data frames, and replace the existing content in the measurements column:\n\nargo_df['measurements'] = argo_df['measurements'].apply(pd.DataFrame).tolist()\n\n# Print statistical summary of the table content:\nargo_df.iloc[0].measurements.describe()\n\n\n\n\n\n\n\n\ntemp\npres\npsal\n\n\n\n\ncount\n105.000000\n105.000000\n105.000000\n\n\nmean\n11.579429\n794.390476\n35.832990\n\n\nstd\n4.726514\n655.512828\n0.433002\n\n\nmin\n4.053000\n6.000000\n35.073000\n\n\n25%\n8.096000\n146.000000\n35.597000\n\n\n50%\n10.885000\n713.000000\n35.765000\n\n\n75%\n15.750000\n1363.000000\n36.128000\n\n\nmax\n18.418000\n2010.000000\n36.504000\n\n\n\n\n\n\n\nPlot temperature at the minimum pressure for each profile\nThis cell applies a lambda over the measurements column to slice the row corresponding to the minimum pressure bin for each profile and returns the corresponding temperature measurement:\n\ndef _get_prof_temp_at_pres_min(x):\n return x[x['pres']==x['pres'].min()]['temp'].item()\n\n# Apply the fuunction over the column of measurements tables\nargo_df['temp_at_pres_min'] = argo_df['measurements'].apply(_get_prof_temp_at_pres_min).tolist()\n\n# Plot temperature measured nearest to the sea surface for each profile \nargo_df.plot.scatter(x=\"datetime\", y=\"temp_at_pres_min\", figsize=(16, 4))\nplt.title(\"~Daily temperature at minimum pressure across ~40 argo profiles\")\nplt.xlabel(None)\nplt.ylabel(\"Temperature (degrees C)\")\nplt.ylim(15.5, 20.5)\nplt.grid(alpha=0.25)\n\n\n\n\n\n\nSelect an Argo of Interest and its platform_number\nSee which floats had the most profiles within our timeframe/area of interest:\n\nargo_df.groupby(\"platform_number\").count()['cycle_number']\n\nplatform_number\n1901688 6\n3901643 3\n3901932 3\n3901942 3\n6901143 3\n6901260 6\n6901262 3\n6901273 3\n6902552 3\n6902663 3\n6902664 3\n6902785 2\nName: cycle_number, dtype: int64\n\n\nChoose a float with six profiles to study further during the remainder of the notebook.\n\ntarget_argo = 6901260\n\n# Select rows (profiles) for the desired platform:\nargo_skinny = argo_df[argo_df.platform_number==target_argo].copy()\n\nargo_skinny.describe()\n\n\n\n\n\n\n\n\npres_max_for_TEMP\npres_min_for_TEMP\npres_max_for_PSAL\npres_min_for_PSAL\nmax_pres\ndate_qc\nlat\nlon\nposition_qc\ncycle_number\nplatform_number\nBASIN\ntemp_at_pres_min\n\n\n\n\ncount\n6.000000\n6.0\n6.000000\n6.0\n6.000000\n6.0\n6.000000\n6.000000\n6.0\n6.000000\n6.0\n6.0\n6.000000\n\n\nmean\n1992.666667\n6.0\n1992.666667\n6.0\n1992.666667\n1.0\n38.131167\n-13.030667\n1.0\n51.500000\n6901260.0\n1.0\n16.921000\n\n\nstd\n21.500388\n0.0\n21.500388\n0.0\n21.500388\n0.0\n0.297238\n0.168997\n0.0\n1.870829\n0.0\n0.0\n0.495337\n\n\nmin\n1961.000000\n6.0\n1961.000000\n6.0\n1961.000000\n1.0\n37.707000\n-13.265000\n1.0\n49.000000\n6901260.0\n1.0\n16.153000\n\n\n25%\n1980.500000\n6.0\n1980.500000\n6.0\n1980.500000\n1.0\n37.940500\n-13.132500\n1.0\n50.250000\n6901260.0\n1.0\n16.643250\n\n\n50%\n1994.500000\n6.0\n1994.500000\n6.0\n1994.500000\n1.0\n38.156500\n-13.037000\n1.0\n51.500000\n6901260.0\n1.0\n17.014000\n\n\n75%\n2010.750000\n6.0\n2010.750000\n6.0\n2010.750000\n1.0\n38.350000\n-12.910750\n1.0\n52.750000\n6901260.0\n1.0\n17.263250\n\n\nmax\n2014.000000\n6.0\n2014.000000\n6.0\n2014.000000\n1.0\n38.484000\n-12.812000\n1.0\n54.000000\n6901260.0\n1.0\n17.479000"
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+ "href": "external/July_2022_Earthdata_Webinar.html#step-3---login-and-install-the-necessary-software-and-prepare-earthdata-login-.netrc",
+ "title": "Earthdata Webinar",
+ "section": "Step 3 - Login and install the necessary software and prepare EarthData Login (.netrc)",
+ "text": "Step 3 - Login and install the necessary software and prepare EarthData Login (.netrc)\n\nFind the public IP from your EC2 dashboard. (The EC2 dashboard example: link)\nFirst connect to the instance via ssh.\n\n ssh -i \"~/.ssh/aws_ec2_jupyter.pem\" ec2-user@The_public_ip_address -L 9889:localhost:9889\n\nRemember to set the following parameters appropriately: * -i points the ssh client on your local machine at your pem key to authenticate * -L tunnels traffic on port 9889 between the ec2 instance and your local machine. This port number can be any value between 1024 and 32767. 1. Update packages. Optionally install wget, git etc. for downloading this notebook from github.com\n\n > ```sudo yum update -y && sudo yum install wget -y```\n\nDownload miniconda install script into tmp/ and execute it with bash. Then, activate the base environment.\n\n mkdir -p tmp\n wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O tmp/miniconda.sh && \\\n bash tmp/miniconda.sh -b -p $HOME/conda && \\\n source ~/conda/bin/activate\n\nCreate a new environment called jupyter running Python 3.7; activate it; install JupyterLab and other required packages.\n\n conda create -n jupyter python=3.7 -y && \\\n conda activate jupyter && \\\n conda install requests tqdm numpy pandas -y && \\\n conda install matplotlib netCDF4 -y &&\\\n conda install xarray jupyterlab s3fs hdf5 scipy -y &&\\\n conda install pyproj -y\n\nWarning: the free-tier EC2 has only 1Gb memory. Make sure to monitor the installation to avoid memory errors. If the installation is aborted due to lack of memory, repeat the installation again to pass the problem or considering install the packages one by one.\n\n\nAn EarthData Login (EDL) account is needed for accessing NASA Eartdata regardless the location of the data, either in the Earthdata cloud or on-premise from DAACs.\n\nRun the following line in the EC2 terminal: >bash echo \"machine urs.earthdata.nasa.gov\\n login your_earthdata_username\\n password your_earthdata_account_password\" > ~/.netrc\nUse a text editor to replace your_earthdata_username with your EDL username and your_earthdata_account_password with your EDL password. > shell vi ~/.netrc\nChange .netrc file permission: >shell chmod 400 ~/.netrc *** ### Advanced approach using “User data” box to install softwares while launching the EC2 (replacing step 3.3)\n\n\nThe following replaces step 3.3, but is not required in this tutorial.\nThe system software updates can be done by inserting the following bash script into the “User data” box during the Launch Instance step (Step 2). It replaces Steps 3.3.\n#!/bin/bash\n sudo yum update -y\n sudo yum install wget -y\n sudo yum install httpd -y\n sudo service httpd start"
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- "title": "Use Case: Co-locate satellite and in-situ data for cross-validation",
- "section": "Access sea surface temperature from MODIS",
- "text": "Access sea surface temperature from MODIS\nThe user guide for MODIS Level 2 Sea Surface Temperature (SST) from GHRSST is available on the PO.DAAC Drive: https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf\nWe will access L2 SST data for our AOI and time period of interest by submitting two subset requests to the Harmony API.\nRedefine the AOI to the minimum XY bounds of selected profiles\nSimply replace the aoi_* Python variables with min/max of the lat and lon columns in the new argo_skinny data frame:\n\naoi_minlon = argo_skinny.lon.min()\naoi_maxlon = argo_skinny.lon.max()\naoi_minlat = argo_skinny.lat.min()\naoi_maxlat = argo_skinny.lat.max()\n\naoi_minlon, aoi_minlat, aoi_maxlon, aoi_maxlat\n\n(-13.265, 37.707, -12.812, 38.484)\n\n\nSearch the Common Metadata Repository (CMR) for its unique concept-id\nThe API requires a dataset identifier that we must obtain from CMR. In the next cell, submit a request to the CMR API to grab the metadata for to the dataset/collection.\n\nmodis_results = requests.get(\n url='https://cmr.earthdata.nasa.gov/search/collections.umm_json', \n params={'provider': \"POCLOUD\",\n 'ShortName': \"MODIS_A-JPL-L2P-v2019.0\",\n 'token': _token}\n).json()\n\n# Select the first/only record in the JSON response:\nmodis_coll = modis_results['items'][0]\n\n# Select the 'concept-id' from the 'meta' dictionary:\nmodis_ccid = modis_coll['meta']['concept-id']\n\nmodis_ccid\n\n'C1940473819-POCLOUD'\n\n\n\nRequest subsets from the Harmony API\nWe will submit two requests to the Harmony API. The API is under active development, and it’s therefore recommended that you test your input parameters in the Swagger API interface.\nThe next cell joins the base url for the API to the concept-id obtained above. Run the cell and print the complete url to confirm:\n\nharmony_url = \"https://harmony.earthdata.nasa.gov\"\nharmony_url_modis = f\"{harmony_url}/{modis_ccid}/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?\"\n\nprint(harmony_url_modis)\n\nhttps://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?\n\n\nMake a dictionary of subset parameters and format the values to meet requirements of the Harmony API. (See the Swagger UI linked above for more information about those requirements.)\nNote how I’ve commented out the time parameter for the second half of January. I requested the first 15 days and then the second 15 days in two requests to get the whole month.\nHere we print the parameters for the first request:\n\nharmony_params_modis1 = {\n 'time': f'(\"{start_date}T00:00:00.000Z\":\"2019-01-15T23:59:59.999Z\")',\n 'lat': f'({aoi_minlat}:{aoi_maxlat})',\n 'lon': f'({aoi_minlon}:{aoi_maxlon})',\n}\n\nharmony_params_modis2 = {\n 'time': f'(\"2019-01-16T00:00:00.000Z\":\"{end_date}T23:59:59.999Z\")',\n 'lat': f'({aoi_minlat}:{aoi_maxlat})',\n 'lon': f'({aoi_minlon}:{aoi_maxlon})',\n}\n\nharmony_params_modis1\n\n{'time': '(\"2019-01-01T00:00:00.000Z\":\"2019-01-15T23:59:59.999Z\")',\n 'lat': '(37.707:38.484)',\n 'lon': '(-13.265:-12.812)'}\n\n\nComplete the url by formatting the query portion using the parameters dictionary:\n\nrequest_url_modis1 = harmony_url_modis+\"subset=time{time}&subset=lat{lat}&subset=lon{lon}\".format(**harmony_params_modis1)\nrequest_url_modis2 = harmony_url_modis+\"subset=time{time}&subset=lat{lat}&subset=lon{lon}\".format(**harmony_params_modis2)\n\nprint(request_url_modis1)\n\nhttps://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?subset=time(\"2019-01-01T00:00:00.000Z\":\"2019-01-15T23:59:59.999Z\")&subset=lat(37.707:38.484)&subset=lon(-13.265:-12.812)\n\n\n\n\nSubmit the request parameters to the Harmony API endpoint\nI’ve already submitted the two requests required to obtain full coverage for our region and timeframe of interest (the two urls in the job_status list below). To submit a new request, or to submit these two MODIS requests again, comment out the two items in the list like this:\njob_status = [\n #'https://...'\n #'https://...\n]\nIt should trigger new requests in the subsequent cells.\n\njob_status = [ \n# \"https://harmony.earthdata.nasa.gov/jobs/512ca343-3bfe-48c5-a480-9281b7348761\", # First time slice\n# \"https://harmony.earthdata.nasa.gov/jobs/5b29414d-3856-4e94-9568-01b32b02a951\", # Second time slice\n]\n\nThe next cell should download a JSON for your new request or from the first request that I submitted while I developed this notebook.\nPrint the message field of the JSON response:\n\nrequest_urls_for_modis = [request_url_modis1, request_url_modis2]\n\nif len(job_status)==0:\n # Loop over the list of request urls:\n for r in request_urls_for_modis:\n # Submit the request and decode the response from json string to dict:\n response_modis = requests.get(r)\n # If the response came back with something other than '2xx', raise an error:\n if not response_modis.status_code // 100 == 2: \n raise Exception(response_modis.text)\n else:\n response_data = response_modis.json()\n # Append the status endpoint to the list of 'job_status' urls:\n job_status.append(response_data['links'][0]['href'])\nelse:\n response_data = requests.get(job_status[0]).json()\n\nresponse_data['message']\n\n'The job is being processed'\n\n\nSuccessful requests to the API will respond with a JSON that starts like this:\n{\n \"username\": \"jmcnelis\",\n \"status\": \"running\",\n \"message\": \"The job is being processed\",\n \"progress\": 0,\n \"createdAt\": \"2021-02-25T02:09:35.972Z\",\n \"updatedAt\": \"2021-02-25T02:09:35.972Z\",\n ...\nThe example above is truncated to the first several lines for the sake of space.\nMonitor the status of an in-progress job\nSelect the status URL(s) from the list(s) of links:\n\nif len(job_status)==0:\n try:\n job_status = [l['href'] for l in response_data['links'] if l['title']==\"Job Status\"]\n except (KeyError, IndexError) as e:\n raise e\n\nprint(job_status)\n\n['https://harmony.earthdata.nasa.gov/jobs/558426d1-3df4-4cc2-80dc-943d03ac5810', 'https://harmony.earthdata.nasa.gov/jobs/dafd8c06-89b5-4dd6-af1d-cacb12512101']\n\n\nRun the next cell to monitor the status of as many requests as you need.\nIt will loop over the job_status list and wait for all the requests to finish processing. (It terminates when the status field of the JSON response does not contain the string \"running\".)\n\nwait = 10 # The number of seconds to wait between each status check\ncompleted = {} # A dict of JSON responses for completed jobs\n\n# Loop repeatedly to check job status. Wait before retrying.\nwhile True:\n for j in job_status: # Iterate over list of job urls\n if j in completed: # Skip if completed.\n continue\n # Get the current job's status as a JSON object.\n job_data = requests.get(j).json()\n if job_data['status']!='running':\n completed[j] = job_data # Add to 'completed' if finished\n # Break loop if 'completed' dictionary contains all jobs.\n if len(completed)==2:\n break\n # If still processing, print a status update and wait ten seconds.\n print(f\"# Job(s) in progress ({len(completed)+1}/{len(job_status)})\")\n time.sleep(wait)\n \nprint(f\"\\n{'&'*40}\\n%\\t\\tDONE!\\n{'&'*40}\\n\")\n\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (1/2)\n# Job(s) in progress (2/2)\n\n&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n% DONE!\n&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n\n\n\nThe final response(s) are massive whenever your subset results in a large number of output granules. Print everything but the links here:\n\nprint(json.dumps({k:v for k, v in job_data.items() if k!=\"links\"}, indent=2))\n\n{\n \"username\": \"jmcnelis\",\n \"status\": \"successful\",\n \"message\": \"The job has completed successfully\",\n \"progress\": 100,\n \"createdAt\": \"2021-03-15T21:08:45.844Z\",\n \"updatedAt\": \"2021-03-15T21:10:51.310Z\",\n \"request\": \"https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?subset=time(%222019-01-16T00%3A00%3A00.000Z%22%3A%222019-01-31T23%3A59%3A59.999Z%22)&subset=lat(37.707%3A38.484)&subset=lon(-13.265%3A-12.812)\",\n \"numInputGranules\": 55,\n \"jobID\": \"dafd8c06-89b5-4dd6-af1d-cacb12512101\"\n}\n\n\nNow look at the first url that points to a subset file (skip the first two because they point to other stuff about the order):\n\nprint(json.dumps(job_data['links'][2], indent=2))\n\n{\n \"href\": \"https://harmony.earthdata.nasa.gov/service-results/harmony-prod-staging/public/podaac/l2-subsetter/80c8503e-c958-4825-b072-ccdee3f7863b/20190116023001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4\",\n \"title\": \"20190116023001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4\",\n \"type\": \"application/x-netcdf4\",\n \"rel\": \"data\",\n \"bbox\": [\n -13.3,\n 37.7,\n -12.8,\n 38.5\n ],\n \"temporal\": {\n \"start\": \"2019-01-16T02:30:01.000Z\",\n \"end\": \"2019-01-16T02:34:59.000Z\"\n }\n}\n\n\nThis cell collects all the output links (Python dicts) from our requests in a list and prints the total number of outputs:\n\njob_links = []\n\nfor j in list(completed.values()):\n for l in j['links']:\n if l['href'].endswith(\"subsetted.nc4\"):\n job_links.append(l)\n\nprint(len(job_links))\n\n74\n\n\n\nPrepare subset data for further analysis\nGet the subset metadata as pandas.DataFrame. We can use apply logic to calculate stats over the time series in subsequent steps. Print the number of rows to confirm. (Should match above)\n\nsubsets_df = pd.DataFrame(data=[{**l, **l['temporal']} for l in job_links])\n\nprint(subsets_df.index.size)\n\n74\n\n\nSelect day/drop night observations\nAdd a day/night flag column to the table. Apply a function over the href column to check the source filename for a string indicating day/night for the swath:\n\nsubsets_df['daytime'] = subsets_df['href'].apply(lambda x: 'MODIS_A-N' not in x)\n\nsubsets_df.info()\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 74 entries, 0 to 73\nData columns (total 9 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 href 74 non-null object\n 1 title 74 non-null object\n 2 type 74 non-null object\n 3 rel 74 non-null object\n 4 bbox 74 non-null object\n 5 temporal 74 non-null object\n 6 start 74 non-null object\n 7 end 74 non-null object\n 8 daytime 74 non-null bool \ndtypes: bool(1), object(8)\nmemory usage: 4.8+ KB\n\n\nAnd finally, reformat the start timestamps as a new column containing pandas datetime objects instead of strings. Then, add one more column containing a date object (rather than the full datetime timestamp) which we’ll use to aggregate the data before plotting.\n\n# Add new 'datetime' column so that we aren't working with strings:\nsubsets_df['datetime'] = pd.to_datetime(subsets_df['start'])\n\n# Add new 'date' column for aggregation during the final steps of the workflow:\nsubsets_df['date'] = subsets_df.datetime.apply(lambda x: x.date()).tolist()\n\nsubsets_df.date.iloc[0]\n\ndatetime.date(2019, 1, 1)\n\n\n\n\n\nAccessing outputs from your subset request\nNow we will download all the netCDF subsets to the local workspace. (I’m inside AWS as I develop this ipynb.) Set a target directory and create it if needed:\n\ntarget_dir = f\"resources/data/\"\n\n!mkdir -p $target_dir\n\nThis function should handle downloads reliably–test by downloading the first netCDF subset from our table (subsets_df):\n\ndef download_target_file(url: str, force: bool=False):\n # Determine the target path for the download\n target_file = join(target_dir, basename(url))\n if isfile(target_file) and force is False:\n print(f\"# File already exists. Skipping...\\n({basename(url)})\\n\")\n return\n print(f\"# File downloading...\\n({basename(url)})\\n\")\n # Open a remote connection for download stream/write to disk:\n with requests.get(url) as r:\n # Raise exception if response has status other than '2xx':\n if not r.status_code // 100 == 2: \n raise Exception(r.text)\n else:\n # Otherwise write the file to disk:\n with open(target_file, 'wb') as f:\n for chunk in r.iter_content(chunk_size=1024):\n if chunk:\n f.write(chunk)\n \n\n# Test the download function by passing the URL for the first subset in the `subsets` table:\ndownload_target_file(url=subsets_df['href'].iloc[0])\n\n# Join the string path to the target file that should have just downloaded.\ntest_nc4 = join(target_dir, basename(subsets_df['href'].iloc[0]))\n\nprint(\"The first file downloaded successfully:\", isfile(test_nc4))\n\n# File already exists. Skipping...\n(20190101031001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\nThe first file downloaded successfully: True\n\n\nMake sure you can dump the header of that file with ncdump. (The output below is truncated.)\n\n!ncdump -h $test_nc4 | head -20\n\nnetcdf \\20190101031001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted {\ndimensions:\n nj = 92 ;\n ni = 42 ;\n time = 1 ;\nvariables:\n float lat(nj, ni) ;\n lat:_FillValue = -999.f ;\n lat:long_name = \"latitude\" ;\n lat:standard_name = \"latitude\" ;\n lat:units = \"degrees_north\" ;\n lat:valid_min = -90.f ;\n lat:valid_max = 90.f ;\n lat:comment = \"geographical coordinates, WGS84 projection\" ;\n lat:coverage_content_type = \"coordinate\" ;\n float lon(nj, ni) ;\n lon:_FillValue = -999.f ;\n lon:long_name = \"longitude\" ;\n lon:standard_name = \"longitude\" ;\n lon:units = \"degrees_east\" ;\n\n\nNetCDF file format errors indicate that the download was not successful. cat the file for more clues. Read and plot the sea_surface_temperature variable:\n\nwith xr.open_dataset(test_nc4) as ds:\n ds.sea_surface_temperature[0].plot()\n\n\n\n\n\nDownload all the netCDF subsets\nGet the links in the href column in a loop:\n\nfor u in subsets_df['href'].tolist():\n download_target_file(u)\n\n# File already exists. Skipping...\n(20190101031001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190101141501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190102021501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190102132001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190103030000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190103140501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104020501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104034001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104034501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104131001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190104144501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190105025001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190105135000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190106033000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190106143501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190107023501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190107134001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190108032000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190108142000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190109022501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190109132501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190110030501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190110141001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190111021000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190111131500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190111145500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190112025501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190112140001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113020001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113033501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113130500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190113144001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190114024000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190114134500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190115032501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190115143001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190116023001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190116133500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190117031000-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190117141501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190118021501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190118132001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190119030001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190119140500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190120020501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. 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Skipping...\n(20190129144001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190130024001-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190130134500-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190131032501-JPL-L2P_GHRSST-SSTskin-MODIS_A-N-v02.0-fv01.0_subsetted.nc4)\n\n# File already exists. Skipping...\n(20190131143001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4)\n\n\n\nThe next cell adds a column of absolute paths to the netCDF files to the data frame subsets_df:\n\nsubsets_df['path'] = subsets_df['href'].apply(lambda x: abspath(join(target_dir, basename(x))))\n\nisfile(subsets_df['path'].iloc[0])\n\nTrue\n\n\n\n\nLimit to daytime MODIS observations\nSelect just the daytime observations into a new data frame. (Remember we added a daytime column during a previous step.)\n\nsubsets_day = subsets_df[subsets_df.daytime==True].copy()\n\nprint(subsets_day.index.size, \"of\", subsets_df.index.size, \"MODIS acquisitions were collected during daytime\")\n\n37 of 74 MODIS acquisitions were collected during daytime\n\n\n\n\nData quality\nThe quality_level variable describes the observation quality for each pixel in the L2 swaths. Values are assigned between 1 and 6 corresponding to these quality levels:\n\nno_data\nbad_data\nworst_quality\nlow_quality\nacceptable_quality\nbest_quality\n\nThe next cell plots the masked SST grid for the first daytime observations:\n\nwith xr.open_dataset(subsets_day.iloc[0].path) as ds:\n\n # Create a mask for pixels that are \n quality_mask = ds.quality_level[0]==5\n\n # Fill pixels where ###### with np.nan:\n masked_ds = ds.where(quality_mask)\n\n # Plot the resulting array of sea surface temperature:\n masked_ds.sea_surface_temperature[0].plot()\n\n\n\n\n\n\n\nPlot time series from multiple data sources\nRoll the logic above into a few map-able functions that group the SST data by day to produce (up to) one daily mean.\n\nApply filter and mean in two functions\nget_user_stat reads the input netCDF and applies some user-specified function to the dataset to render the desired output, then closes the file.\nThe second function _masked_mean filters and calculates the XY mean of the sea_surface_temperature variable. (You could replace this function with your own to do something different.)\n\nTest the combined routine against the first file in the daytime MODIS table:\n\nsubsets_day['path'].iloc[0]\n\n'/Users/jmcnelis/tmp/appscitmp/tutorials/notebooks/SWOT-EA-2021/resources/data/20190101141501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4'\n\n\n\nimport warnings\n\ndef get_user_stat(netcdf, function):\n with xr.open_dataset(netcdf) as ds: \n output = function(ds)\n return output\n\n\ndef _masked_mean(ds):\n '''Produce any output stat/object you want in this function'''\n # Create a mask for pixels that are \n quality_mask = ds.quality_level[0]>=5\n # Fill pixels with np.nan where quality_level is less than 4:\n masked_ds = ds.where(quality_mask)\n # Ignore warnings about calculating mean over an empty array:\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n # Calculate mean over the XY dimensions (nj, ni in this case)\n value = masked_ds['sea_surface_temperature'].mean(['nj', 'ni']).data.item()\n #value = np.nanmean(masked_sst)\n return value\n\nval = get_user_stat(subsets_day['path'].iloc[0], function=_masked_mean)\n\nval-273.15 # subtract 273.15 to convert Kelvin to Celsius\n\n16.743005371093773\n\n\nThat should give a reasonable value in degrees Celsius.\n\nGet means for the filtered MODIS SST time series in a new column\nApply the _masked_mean function over the column of subsets (i.e. netCDF4 files) to get the time series in a new column sst_mean:\n\nsubsets_day['sst_mean'] = subsets_day['path'].apply(get_user_stat, args=(_masked_mean,))-273.15\n\nsubsets_day['sst_mean'].describe()\n\ncount 15.000000\nmean 16.404915\nstd 0.566561\nmin 15.284357\n25% 15.921838\n50% 16.546533\n75% 16.833688\nmax 17.222162\nName: sst_mean, dtype: float64\n\n\nWe may need to group by the date:\n\nsubsets_day_means = subsets_day.groupby(\"date\", as_index=False).mean()\n\nsubsets_day_means.describe()\n\n\n\n\n\n\n\n\nsst_mean\n\n\n\n\ncount\n15.000000\n\n\nmean\n16.404915\n\n\nstd\n0.566561\n\n\nmin\n15.284357\n\n\n25%\n15.921838\n\n\n50%\n16.546533\n\n\n75%\n16.833688\n\n\nmax\n17.222162\n\n\n\n\n\n\n\nNow plot the two time series along the same date axis for visual comparison:\n\nfig, ax = plt.subplots(figsize=(16, 4))\n\n# Plot mean sea surface temperature from MODIS SST from GHRSST\nsubsets_day_means.plot.scatter(\n x=\"date\",\n y=\"sst_mean\", \n label=\"SST observed by MODIS\",\n s=100,\n ax=ax\n)\n\n# Plot mean sea surface temperature from the Argo floats\nargo_skinny.plot.scatter(\n x=\"date\",\n y=\"temp_at_pres_min\",\n s=100,\n color=\"orange\",\n marker=\"v\",\n label=\"SST measured by Argo floats\",\n ax=ax\n)\n\n# Matplotlib aesthetic treatments starting from here -->\nax.set_ylabel(\"Temperature (deg C)\")\nax.set_ylim(15.0, 18.0)\nax.grid(alpha=0.25)\n\n\n\n\n\n\n\nMUR Level 4 SST from AWS Open Registry\nTry plotting the summarized time series for the two datasets against MUR L4 SST from AWS Open Registry: https://registry.opendata.aws/mur/\n\nimport fsspec\nimport xarray as xr\nfrom dask.distributed import Client\n\n# Reference the MUR L4 SST data on the AWS Open Registry\nurl = 's3://mur-sst/zarr'\n\n# Open the remote dataset from its S3 endpoint (pre-consolidated)\nds = xr.open_zarr(fsspec.get_mapper(url, anon=True), consolidated=True)\n\n# Slice the dataset along its X, Y, and T dimensions:\nmur_L4_subset = ds['analysed_sst'].sel(\n time=slice('2019-01-01','2019-01-31'),\n lat=slice(aoi_minlat, aoi_maxlat), \n lon=slice(aoi_minlon, aoi_maxlon),\n).persist()\n\n# Aggregate the spatial dimensions to compute the one-dimensional time series of means:\nmur_L4_subset_means = mur_L4_subset.groupby(\"time\").mean([\"lon\", \"lat\"])-273.15\n\nprint(mur_L4_subset_means)\n\n<xarray.DataArray 'analysed_sst' (time: 31)>\ndask.array<sub, shape=(31,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2019-01-01T09:00:00 ... 2019-01-31T09:00:00\n\n\nAdd the MUR time series to the subsets table so that they share the same time axis with the L2 time series:\n\nsubsets_day_means['L4_MUR_SST'] = mur_L4_subset_means.compute().data\n\nPlot the result alongside our data processed throughout the notebook:\n\nfig, ax = plt.subplots(figsize=(16, 5))\n\n# Plot the L4 SST from MUR (hosted by AWS Open Registry)\nsubsets_day_means.plot.line(\n x=\"date\",\n y=\"L4_MUR_SST\",\n color=\"red\",\n label=\"L4 MUR SST (AWS Open Registry)\",\n ax=ax,\n)\n\n# Plot the L2 SST from GHRSST (subset through Harmony API)\nsubsets_day_means.plot.scatter(\n x=\"date\",\n y=\"sst_mean\", \n label=\"L2 MODIS SST (EOSDIS Cloud)\",\n s=100,\n ax=ax\n)\n\n# Plot the in situ temps measured at the surface during Argo profiles (accessed from ArgoVis)\nargo_skinny.plot.scatter(\n x=\"date\",\n y=\"temp_at_pres_min\",\n s=100,\n color=\"orange\",\n marker=\"v\",\n label=\"In situ measurements (ArgoVis API)\",\n ax=ax\n)\n\n# Matplotlib aesthetic treatments starting from here -->\nplt.xticks(rotation=15)\nax.set_xlabel(None)\nax.set_xlim(subsets_day_means.date.iloc[0], subsets_day_means.date.iloc[-1])\nax.set_ylabel(\"Temperature (deg C)\")\nax.set_ylim(15.0, 18.0)\nax.grid(alpha=0.25)\nax.set_title(\"Daily SST from L2 MODIS, L4 MUR, and in situ measurements (January 2019)\")\n\nText(0.5, 1.0, 'Daily SST from L2 MODIS, L4 MUR, and in situ measurements (January 2019)')"
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+ "title": "Earthdata Webinar",
+ "section": "Step 4 - Set up a jupyter-lab",
+ "text": "Step 4 - Set up a jupyter-lab\nJupyterlab is a web-based interactive development environment for python and other languages. It is a perfect tool for accessing the computing resources on an EC2 through SSH tunneling. Jupyterlab and the associated software are install in Step 3. Here is two steps to start and connect to a jupyterlab server on the EC2.\n\nUse Python to generate and store a hashed password as a shell variable: >\n\nPW=\"$(python3 -c 'from notebook.auth import passwd; import getpass; print(passwd(getpass.getpass(), algorithm=\"sha256\"))')\"\n\nStart jupyter lab instance with the following parameters: >\n\njupyter lab --port=9889 --ip='127.0.0.1' --NotebookApp.token='' --NotebookApp.password=\"$PW\" --notebook-dir=\"$HOME\" --no-browser```\n\n1. Access the server through your web browser: http://127.0.0.1:9889/\n\n<div class=\"alert alert-block alert-success\">\n<b>Optional for convinence</b> </br> \nYou can use tmux to start a screen to keep the jupyterlab running on the EC2 even after logging.</br> \n\nDetach the screen by pressing CTRL + b -> d. \n</div>\n\n\n#### Reference\n\n* https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html#conda\n* https://requests.readthedocs.io/en/master/user/install/\n* https://matplotlib.org/stable/#installation\n* https://shapely.readthedocs.io/en/latest/\n***\n\n## Step 5 - Run the code (this notebook) in the cloud and save the figure\n\n## Data products\n\n1. MEaSURES-SSH version JPL1812\n - short name: ```SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812```\n - [landing page](https://podaac.jpl.nasa.gov/dataset/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812) (Newer version is available)\n1. GMSL\n - short name: ```JPL_RECON_GMSL```\n - [landing page](https://podaac.jpl.nasa.gov/dataset/JPL_RECON_GMSL)\n1. ECCO global mean sea level (used in the reader's exercise)\n - short name: `ECCO_L4_GMSL_TIME_SERIES_MONTHLY_V4R4`\n - [landing page](https://doi.org/10.5067/ECTSM-MSL44)\n\n::: {.cell execution_count=28}\n``` {.python .cell-code}\n#load python modules\n\nimport xarray as xr\nimport numpy as np\nimport pylab as plt\nimport pandas as pd\n#Short_name is used to identify a specific dataset in NASA Earthdata. \nshort_name='SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812'\n:::"
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- "text": "CMR allows the upload of ESRI Shapefiles via command line for granule and collection level search. To use this functionality from the command line, we can use curl or python to do our search. This example will run through the curl command line program and a python request for doing shapefile search.\nPrerequisites:\n\na valid ESRI Shapefile\n(optional) Collection identifier (concept-id) for granule level search\n\nWe will use a shapefile located in the github/podaac source repository for this search: https://github.com/podaac/tutorials/blob/master/notebooks/resources/gulf_shapefile.zip\nFor more information on collections, granules, and concept-ids, please refer to the following tutorial:\nhttps://github.com/podaac/tutorials/blob/master/notebooks/podaac_cmr_tutorial.ipynb\nFor our tutorial, we will be using a TEST collection available at the cmr uat environment. This is an environment, open to the public, where new functionality is released and users can submit feedback on it before it makes its way to the operational system.\nThis collection is an L2 collection titled AMSR2-REMSS-L2P-v8a, from the Advanced Microwave Scanning Radiometer 2. It has the concept id:\nC1225996408-POCUMULUS"
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+ "title": "Earthdata Webinar",
+ "section": "The interface to the AWS Simple Storage Service (S3) file system (stores all NASA Earthdata)",
+ "text": "The interface to the AWS Simple Storage Service (S3) file system (stores all NASA Earthdata)\nPO.DAAC cloud (POCLOUD) is a part of Earthdata Cloud. The data are hosted in a S3 bucket on AWS US-West-2. “US-West-2” is a term that refers to the AWS center in Oregon. In this case, the so-called ‘Direct-S3 access’ only works on the machines hosted in the US-West-2.\ns3fs is a pythonic file interface to S3 built on top of botocore. s3fs allows typical file-system style operations like cp, mv, ls, du, glob, and put/get of local files to/from S3. Details can be find on its website https://s3fs.readthedocs.io/en/latest/.\nIt is important that you set up the .netrc file correctly in order to enable the following init_S3FileSystem module. The .netrc file should be placed in your home folder. A typical .netrc file has the following content:\nmachine urs.earthdata.nasa.gov\n login your_earthdata_username\n password your_earthdata_account_password\nMake sure the permission of the .netrc file is set to 400: chmod 400 ~/.netrc\nIf you do not have or do not remember your Earthdata Login information, go here to register or here to reset password.\n\nAWS credentials with EDL\nAWS requires security credentials to access AWS S3.\nWith your EDL, you can obtain a temporay S3 credential through https://archive.podaac.earthdata.nasa.gov/s3credentials. It is a ‘digital key’ to access the Earthdata in AWS cloud. Here is an example:\n\n{“accessKeyId”: “ASIATNGJQBXBOPDTNBBD”, “secretAccessKey”: “odLdojElxfKDU5nw49+hPawe9oKUkR+ZXQqBcs5g”, “sessionToken”: “FwoGZXIvYXdzECgaDB4IzakIEQUrg/N3MiLdASJm6nrFYJ6SCZN5jPlfO4X3NBQTTSwIetjIU1BO0l863AmtL4D/4q8g2HQwgV351qpN3kp1v6yifKRfZ6T1oDtauSTizxnjQ7LislVVaxmwFqqH1oEbu4HKvi+0AmEUSzz2IwcJPgY5L9D8P2N8ccevIwgKLcvkWcIM0zMtp0TRsvdBE0W+NTDxc6RZlCQdclKtvf3jPqreJtigSH/MSePzORwR7FaFxXZYQpXLP+MRNmDMdrDzwFpaZKd9pgCBfnUkAL8w/ub+9WfVjh4lCfNuNUiGNLi2cS9VBeYtKKL16pYGMi17j1gp08JS6p9nD2egc3LyIL2vSIZouhNrJzisZqbLH8yZTq3rCG2pPsPcrFk=”, “expiration”: “2022-07-22 15:56:34+00:00”}\n\nFurther reading: https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html\n\ndef init_S3FileSystem():\n \"\"\"\n This routine automatically pull your EDL crediential from .netrc file and use it to obtain an AWS S3 credential through a podaac service accessable at https://archive.podaac.earthdata.nasa.gov/s3credentials\n \n Return:\n =======\n \n s3: an AWS S3 filesystem\n \"\"\"\n import requests,s3fs\n creds = requests.get('https://archive.podaac.earthdata.nasa.gov/s3credentials').json()\n s3 = s3fs.S3FileSystem(anon=False,\n key=creds['accessKeyId'],\n secret=creds['secretAccessKey'], \n token=creds['sessionToken'])\n return s3"
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- "title": "This Notebook is no longer up to date, a newer version exists here",
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- "text": "CMR allows the upload of ESRI Shapefiles via command line for granule and collection level search. To use this functionality from the command line, we can use curl or python to do our search. This example will run through the curl command line program and a python request for doing shapefile search.\nPrerequisites:\n\na valid ESRI Shapefile\n(optional) Collection identifier (concept-id) for granule level search\n\nWe will use a shapefile located in the github/podaac source repository for this search: https://github.com/podaac/tutorials/blob/master/notebooks/resources/gulf_shapefile.zip\nFor more information on collections, granules, and concept-ids, please refer to the following tutorial:\nhttps://github.com/podaac/tutorials/blob/master/notebooks/podaac_cmr_tutorial.ipynb\nFor our tutorial, we will be using a TEST collection available at the cmr uat environment. This is an environment, open to the public, where new functionality is released and users can submit feedback on it before it makes its way to the operational system.\nThis collection is an L2 collection titled AMSR2-REMSS-L2P-v8a, from the Advanced Microwave Scanning Radiometer 2. It has the concept id:\nC1225996408-POCUMULUS"
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+ "title": "Earthdata Webinar",
+ "section": "Use s3fs.glob to get all file names",
+ "text": "Use s3fs.glob to get all file names\nThe S3FileSystem allows typical file-system style operations like cp, mv, ls, du, glob. Once the s3fs file system is established, we can use ‘glob’ to get all file names from a collection. In this case, the collection S3 path is\ns3://podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/\nUsing the following will get a list netcdf filenames:\nfns=s3sys.glob(\"s3://podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/*.nc\")\n\ns3sys=init_S3FileSystem()\n\ns3path=\"s3://podaac-ops-cumulus-protected/%s/\"%short_name\nfns=s3sys.glob(s3path+\"*.nc\")\nprint(fns[0])\n#Set the time stamps associated with the files\ntime=pd.date_range(start='1992-10-02',periods=len(fns),freq='5D') \n\npodaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/ssh_grids_v1812_1992100212.nc\n\n\n\nprint('There are %i files.'%len(fns))\n\nThere are 1922 files.\n\n\nHere is an example file.\n\nd=xr.open_dataset(s3sys.open(fns[0]))\nd\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (Longitude: 2160, nv: 2, Latitude: 960, Time: 1)\nCoordinates:\n * Longitude (Longitude) float32 0.08333 0.25 0.4167 ... 359.6 359.8 359.9\n * Latitude (Latitude) float32 -79.92 -79.75 -79.58 ... 79.58 79.75 79.92\n * Time (Time) datetime64[ns] 1992-10-02T12:00:00\nDimensions without coordinates: nv\nData variables:\n Lon_bounds (Longitude, nv) float32 -1.388e-17 0.1667 ... 359.8 360.0\n Lat_bounds (Latitude, nv) float32 -80.0 -79.83 -79.83 ... 79.83 79.83 80.0\n Time_bounds (Time, nv) datetime64[ns] 1992-09-02T12:00:00 1992-11-01T12:...\n SLA (Time, Longitude, Latitude) float32 ...\n SLA_ERR (Time, Longitude, Latitude) float32 ...\nAttributes: (12/13)\n Conventions: CF-1.6\n ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n Institution: Jet Propulsion Laboratory\n geospatial_lat_min: -79.916664\n geospatial_lat_max: 79.916664\n geospatial_lon_min: 0.083333336\n ... ...\n time_coverage_start: 1992-10-02\n time_coverage_end: 1992-10-02\n date_created: 2019-02-11T20:19:57.736094\n version_number: 1812\n summary: Sea level anomaly grids from altimeter data using...\n title: Sea Level Anormaly Estimate based on Altimeter Dataxarray.DatasetDimensions:Longitude: 2160nv: 2Latitude: 960Time: 1Coordinates: (3)Longitude(Longitude)float320.08333 0.25 0.4167 ... 359.8 359.9standard_name :longitudeunits :degrees_eastpoint_spacing :evenlong_name :longitudeaxis :Xbounds :Lon_boundsarray([8.333334e-02, 2.500000e-01, 4.166667e-01, ..., 3.595833e+02,\n 3.597500e+02, 3.599167e+02], dtype=float32)Latitude(Latitude)float32-79.92 -79.75 ... 79.75 79.92standard_name :latitudeunits :degrees_northpoint_spacing :evenlong_name :latitudeaxis :Ybounds :Lat_boundsarray([-79.916664, -79.75 , -79.583336, ..., 79.583336, 79.75 ,\n 79.916664], dtype=float32)Time(Time)datetime64[ns]1992-10-02T12:00:00standard_name :timelong_name :Timebounds :Time_boundsaxis :Tarray(['1992-10-02T12:00:00.000000000'], dtype='datetime64[ns]')Data variables: (5)Lon_bounds(Longitude, nv)float32...units :degrees_eastcomment :longitude values at the west and east bounds of each pixel.array([[-1.387779e-17, 1.666667e-01],\n [ 1.666667e-01, 3.333333e-01],\n [ 3.333333e-01, 5.000000e-01],\n ...,\n [ 3.595000e+02, 3.596667e+02],\n [ 3.596667e+02, 3.598333e+02],\n [ 3.598333e+02, 3.600000e+02]], dtype=float32)Lat_bounds(Latitude, nv)float32...units :degrees_northcomment :latitude values at the north and south bounds of each pixel.array([[-80. , -79.833336],\n [-79.833336, -79.666664],\n [-79.666664, -79.5 ],\n ...,\n [ 79.5 , 79.666664],\n [ 79.666664, 79.833336],\n [ 79.833336, 80. ]], dtype=float32)Time_bounds(Time, nv)datetime64[ns]...comment :Time bounds for each time value, same value as time variable. The time variable is defined on points instead of on bounding boxes.array([['1992-09-02T12:00:00.000000000', '1992-11-01T12:00:00.000000000']],\n dtype='datetime64[ns]')SLA(Time, Longitude, Latitude)float32...units :mlong_name :Sea Level Anomaly Estimatestandard_name :sea_surface_height_above_sea_levelalias :sea_surface_height_above_sea_level[2073600 values with dtype=float32]SLA_ERR(Time, Longitude, Latitude)float32...units :mlong_name :Sea Level Anomaly Error Estimate[2073600 values with dtype=float32]Attributes: (13)Conventions :CF-1.6ncei_template_version :NCEI_NetCDF_Grid_Template_v2.0Institution :Jet Propulsion Laboratorygeospatial_lat_min :-79.916664geospatial_lat_max :79.916664geospatial_lon_min :0.083333336geospatial_lon_max :359.91666time_coverage_start :1992-10-02time_coverage_end :1992-10-02date_created :2019-02-11T20:19:57.736094version_number :1812summary :Sea level anomaly grids from altimeter data using Kriging interpolation, which gives best linear prediction based upon prior knowledge of covariance. title :Sea Level Anormaly Estimate based on Altimeter Data\n\n\n\nPlot an example\n\nplt.figure(figsize=(15,7))\nplt.contourf(d['Longitude'],d['Latitude'],d['SLA'][0,...].T,levels=np.arange(-0.5,0.6,0.05))\nplt.ylabel('Latitude')\nplt.xlabel('Longitude')\nplt.title('Sea Level Anomaly %s'%d.time_coverage_start)\n\nText(0.5, 1.0, 'Sea Level Anomaly 1992-10-02')"
},
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- "href": "notebooks/Podaac_CMR_Shapefile_Search.html#python-tutorial-shapefile-search",
- "title": "This Notebook is no longer up to date, a newer version exists here",
- "section": "Python tutorial shapefile search",
- "text": "Python tutorial shapefile search\nThe following snippet will use the ‘requests’ library along with the shapefile available at github to perform a shapefile search on the CMR. It will return values that overlap or intersect the shapefile provided.\n\nimport requests\nimport json\nimport pprint\n\n# the URL of the CMR searvice\nurl = 'https://cmr.uat.earthdata.nasa.gov/search/granules.json'\n\n#The shapefile we want to use in our search\nshp_file = open('resources/gulf_shapefile.zip', 'rb')\n\n#need to declare the file and the type we are uploading\nfiles = {'shapefile':('gulf_shapefile.zip',shp_file, 'application/shapefile+zip')}\n\n#used to define parameters such as the concept-id and things like temporal searches\nparameters = {'echo_collection_id':'C1225996408-POCUMULUS'}\n\nresponse = requests.post(url, files=files, params=parameters)\npp = pprint.PrettyPrinter(indent=2)\npp.pprint(response.json())\n\n{ 'feed': { 'entry': [ { 'browse_flag': False,\n 'collection_concept_id': 'C1225996408-POCUMULUS',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCUMULUS',\n 'dataset_id': 'PODAAC-GHAM2-2PR8A',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': 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-87.37 -51.91 -86.7 -51.35 '\n '-84.71 -46.76 -79.14 -25.78 -75.63 '\n '-5.06 -74.22 12.67'],\n [ '89.12 180 88.7 139.61 87.61 118.89 '\n '85.45 108.25 81.36 102.44 76.26 '\n '99.59 67.2 96.89 2.66 84.87 -26.48 '\n '77.33 -38.2 72.8 -48.19 67.35 -55.84 '\n '61.23 -61.53 54.49 -67.4 43.27 '\n '-71.52 28.58 -73.78 11.11 -74.43 '\n '-16.08 -76.59 -36.18 -82.49 -63.69 '\n '-85.68 -73.58 -87.61 -76.1 -88.52 '\n '-68.49 -89.11 -47.72 -89.02 43.7 '\n '-87.75 68.92 -86.53 75.08 -84.68 '\n '79.14 -81.03 82.51 -73.53 85.44 9.28 '\n '101.12 29.92 107.08 45.19 113.93 '\n '52.33 118.74 58.08 124.12 62.55 '\n '129.96 66.35 136.99 69.31 144.96 '\n '71.84 155.45 74.365 180 89.12 180']],\n 'time_end': '2018-01-02T19:48:16.000Z',\n 'time_start': '2018-01-02T18:10:08.000Z',\n 'title': '20180102181008-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29943-v02.0-fv01.0.nc'},\n { 'browse_flag': False,\n 'collection_concept_id': 'C1225996408-POCUMULUS',\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCUMULUS',\n 'dataset_id': 'PODAAC-GHAM2-2PR8A',\n 'day_night_flag': 'UNSPECIFIED',\n 'granule_size': '1.0242048E7',\n 'id': 'G1226019419-POCUMULUS',\n 'links': [ { 'href': 's3://podaac-dev-l2ss-samples/20180102181008-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_v08_r29943-v02.0-fv01.0.nc',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'http://data.nodc.noaa.gov/cgi-bin/nph-dods/ghrsst/GDS2/L2P/AMSR2/REMSS/v8a/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/AMSR2/REMSS/v8a/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/GDS2/L2P/AMSR2/REMSS/v8a',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://podaac-ftp.jpl.nasa.gov/allData/ghrsst/data/GDS2/L2P/AMSR2/REMSS/v8a',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/IDL/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://suzaku.eorc.jaxa.jp/GCOM_W/w_amsr2/whats_amsr2.html',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'https://podaac.jpl.nasa.gov/SeaSurfaceTemperature',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'http://podaac-tools.jpl.nasa.gov/hitide/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/R/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHAM2-2PR8A',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'},\n { 'href': 'http://www.remss.com/missions/amsr/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n { 'href': 'ftp://podaac.jpl.nasa.gov/allData/ghrsst/sw/generic_nc_readers/python/',\n 'hreflang': 'en-US',\n 'inherited': True,\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/metadata#'}],\n 'online_access_flag': True,\n 'original_format': 'UMM_JSON',\n 'polygons': [ [ '-74.22 12.67 -72.07 -9.88 -69.9 '\n '-19.92 -67.08 -28.4 -63.4 -35.9 '\n '-58.95 -42.2 -53.53 -47.66 -47.65 '\n '-52.02 -34.56 -58.69 -17.31 -64.43 '\n '8.06 -70.3 67.96 -81.39 77.65 -84.51 '\n '83.02 -88.37 86.38 -95.57 88.07 '\n '-108.72 89.12 -180 74.365 -180 74.38 '\n '-164.66 73.43 -150.84 71.69 -139.02 '\n '68.87 -127.94 65.36 -119.27 60.86 '\n '-111.89 55.65 -106 48.87 -100.59 '\n '35.47 -93.58 18.07 -87.75 -9.48 '\n '-81.48 -70.19 -70.63 -79.47 -67.68 '\n '-84.1 -64.24 -86.49 -59.54 -87.96 '\n '-51.37 -87.37 -51.91 -86.7 -51.35 '\n '-84.71 -46.76 -79.14 -25.78 -75.63 '\n '-5.06 -74.22 12.67'],\n [ '89.12 180 88.7 139.61 87.61 118.89 '\n '85.45 108.25 81.36 102.44 76.26 '\n '99.59 67.2 96.89 2.66 84.87 -26.48 '\n '77.33 -38.2 72.8 -48.19 67.35 -55.84 '\n '61.23 -61.53 54.49 -67.4 43.27 '\n '-71.52 28.58 -73.78 11.11 -74.43 '\n '-16.08 -76.59 -36.18 -82.49 -63.69 '\n '-85.68 -73.58 -87.61 -76.1 -88.52 '\n '-68.49 -89.11 -47.72 -89.02 43.7 '\n '-87.75 68.92 -86.53 75.08 -84.68 '\n '79.14 -81.03 82.51 -73.53 85.44 9.28 '\n '101.12 29.92 107.08 45.19 113.93 '\n '52.33 118.74 58.08 124.12 62.55 '\n '129.96 66.35 136.99 69.31 144.96 '\n '71.84 155.45 74.365 180 89.12 180']],\n 'time_end': '2018-01-02T19:48:16.000Z',\n 'time_start': '2018-01-02T18:10:08.000Z',\n 'title': '20180102181008-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_v08_r29943-v02.0-fv01.0.nc'}],\n 'id': 'https://cmr.uat.earthdata.nasa.gov:443/search/granules.json',\n 'title': 'ECHO granule metadata',\n 'updated': '2020-05-18T21:41:56.223Z'}}"
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+ "title": "Earthdata Webinar",
+ "section": "Calculate the global mean SSHA",
+ "text": "Calculate the global mean SSHA\nThe global mean SSH is calculated as follows.\n\\(SSH_{mean} = \\sum \\eta(\\phi,\\lambda)*A(\\phi)\\), where \\(\\phi\\) is latitude, \\(\\lambda\\) is longitude, \\(A\\) is the area of the grid at latitude \\(\\phi\\), and \\(\\eta(\\phi,\\lambda)*A(\\phi)\\) is the weighted SLA at \\((\\phi,\\lambda)\\).\nThe following routine area pre-calculates the area as a function of latitude for the 1/6-degree resolution grids.\n\ndef area(lats):\n \"\"\"\n Calculate the area associated with a 1/6 by 1/6 degree box at latitude specified in 'lats'. \n \n Parameter\n ==========\n lats: a list or numpy array of size N\n the latitudes of interest. \n \n Return\n =======\n out: Array (N)\n area values (unit: m^2)\n \"\"\"\n # Modules:\n from pyproj import Geod\n # Define WGS84 as CRS:\n geod = Geod(ellps='WGS84')\n dx=1/12.0\n c_area=lambda lat: geod.polygon_area_perimeter(np.r_[-dx,dx,dx,-dx], lat+np.r_[-dx,-dx,dx,dx])[0]\n out=[]\n for lat in lats:\n out.append(c_area(lat))\n return np.array(out)\n\ndef global_mean(fn_s3,s3sys,ssh_area):\n \"\"\"\n Calculate the global mean given an s3 file of SSH, a s3fs.S3FileSystem, \n and the ssh_area, which is precalculated to save computing time. \n Parameter:\n ===========\n fn_s3: S3 file name, e.g., s3://podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/ssh_grids_v1812_1992100212.nc\n s3sys: generated by s3fs.S3FileSystem\n ssh_area: the area size associated with MEaSURES-SSH 1/6-degree resolution product. \n \n Return\n =======\n dout: scalar\n The global mean sea level (default unit from MEaSURES-SSH: meter)\n \"\"\"\n with xr.open_dataset(s3sys.open(fn_s3))['SLA'] as d:\n dout=((d*ssh_area).sum()/(d/d*ssh_area).sum()).values\n return dout\n\n\nd=xr.open_dataset(s3sys.open(fns[0]))\n#pre-calculate the area for reuse\nssh_area=area(d.Latitude.data).reshape(1,-1)\n\n\nprint('The global mean sea level from %s is %7.5f meters.'%(fns[0],global_mean(fns[0],s3sys,ssh_area) ) )\n\nThe global mean sea level from podaac-ops-cumulus-protected/SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1812/ssh_grids_v1812_1992100212.nc is -0.00632 meters."
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- "title": "This Notebook is no longer up to date, a newer version exists here",
- "section": "Curl command line syntax",
- "text": "Curl command line syntax\nThis command submits the same request as the Python example above, returning search results in JSON format for Granules that share spatial coverage with the input shapefile (resources/gulf_shapefile.zip) and belong to the target Collection (echo_collection_id=C1225996408-POCUMULUS):\ncurl -XPOST \"https://cmr.uat.earthdata.nasa.gov/search/granules.json\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1225996408-POCUMULUS\" -F \"pretty=true\"\nThe (truncated) results:\n{\n \"feed\" : {\n \"updated\" : \"2020-05-18T22:09:58.452Z\",\n \"id\" : \"https://cmr.uat.earthdata.nasa.gov:443/search/granules.json\",\n \"title\" : \"ECHO granule metadata\",\n \"entry\" : [ {\n \"time_start\" : \"2018-01-01T05:56:16.000Z\",\n \"granule_size\" : \"1.0242048E7\",\n \"online_access_flag\" : true,\n \"id\" : \"G1226019017-POCUMULUS\",\n \"day_night_flag\" : \"UNSPECIFIED\",\n \"browse_flag\" : false,\n \"time_end\" : \"2018-01-01T07:34:24.000Z\",\n \"coordinate_system\" : \"CARTESIAN\",\n \"polygons\" : [ [ \"-74.21 -163.74 -72.95 -180 -75.43 -180 -74.21 -163.74\" ], [ \"-72.95 180 -69.22 161.56 -63.56 148.24 -59.74 142.63 -54.76 137.29 -43.19 129.21 -28.81 123 -9.39 117.38 70.28 101.89 78.71 98.84 83.8 94.5 85.75 90.5 87.14 84.43 88.59 63.9 89.12 2.26 88.28 -48.29 87.12 -61.14 85.38 -68.1 82.54 -72.63 77.71 -75.93 63.62 -80.05 9.8 -89.82 -15.54 -95.55 -35.84 -102.3 -50.51 -110.4 -56.3 -115.37 -60.99 -120.88 -64.95 -127.37 -68.22 -135.08 -70.76 -143.97 -72.76 -155.11 -74.35 -180 -89.345 -180 -88.32 -113.33 -86.69 -101.61 -83.86 -95.95 -79.32 -92.73 -70.2 -89.86 -10.6 -79.24 16.19 -73.27 33.1 -67.88 46.23 -61.6 52.76 -57.05 57.86 -52.26 62.64 -46.04 66.5 -38.81 69.5 -30.55 71.71 -21.38 74.26 1.26 73.75 29.71 72.1 42.61 69.64 53.42 66.25 62.72 62.36 69.82 57.32 76.19 51.2 81.62 37.2 89.55 18.32 96.04 -10.4 102.56 -70.39 113.28 -79.49 116.19 -83.94 119.43 -86.33 123.83 -87.89 131.86 -86.97 131.68 -85.71 133.93 -81.4 147.95 -77.84 164.04 -75.43 180 -72.95 180\" ], [ \"-74.35 180 -74.81 162.65 -77.45 142.63 -82.89 118.89 -85.78 110.18 -87.46 107.84 -88.13 109.51 -88.85 123.7 -89.345 180 -74.35 180\" ] ],\n \"original_format\" : \"UMM_JSON\",\n \"collection_concept_id\" : \"C1225996408-POCUMULUS\",\n \"data_center\" : \"POCUMULUS\",\n \"links\" : [ {\n \"rel\" : \"http://esipfed.org/ns/fedsearch/1.1/data#\",\n \"hreflang\" : \"en-US\",\n \"href\" : \"s3://podaac-dev-l2ss-samples/20180101055616-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29921-v02.0-fv01.0.nc\"\n },\n ...\nThis command gets the same listing again with curl, this time returning the search results in their native xml format:\ncurl -XPOST \"https://cmr.uat.earthdata.nasa.gov/search/granules\" -F \"shapefile=@resources/gulf_shapefile.zip;type=application/shapefile+zip\" -F \"echo_collection_id=C1225996408-POCUMULUS\" -F \"pretty=true\"\nThe (truncated) results:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<results>\n <hits>1912</hits>\n <took>970</took>\n <references>\n <reference>\n <name>20180101055616-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29921-v02.0-fv01.0.nc</name>\n <id>G1226019017-POCUMULUS</id>\n <location>https://cmr.uat.earthdata.nasa.gov:443/search/concepts/G1226019017-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n <reference>\n <name>20180101055616-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_v08_r29921-v02.0-fv01.0.nc</name>\n <id>G1226019025-POCUMULUS</id>\n <location>https://cmr.uat.earthdata.nasa.gov:443/search/concepts/G1226019025-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n <reference>\n <name>20180101073424-REMSS-L2P_GHRSST-SSTsubskin-AMSR2-L2B_rt_r29922-v02.0-fv01.0.nc</name>\n <id>G1226019035-POCUMULUS</id>\n <location>https://cmr.uat.earthdata.nasa.gov:443/search/concepts/G1226019035-POCUMULUS/1</location>\n <revision-id>1</revision-id>\n </reference>\n ..."
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+ "href": "external/July_2022_Earthdata_Webinar.html#demonstrate-using-a-single-thread",
+ "title": "Earthdata Webinar",
+ "section": "Demonstrate using a single thread",
+ "text": "Demonstrate using a single thread\n\nBenchmark: using a single thread takes about 17 min to calculate all 1922 files. Here the program is sped up by skipping every 360 days (72 steps). The small EC2 can handle the computation because it involves one file per step.\n\n\n%%time\n\n#Loop 26-year 5-daily SSH fields (1922 files)\n#Skip every 72 files to speed up\n\nresult=[]\nt_local=time[::72]\nfor fn in fns[::72]:\n result.append(global_mean(fn,s3sys,ssh_area)*1e3 )\nresult=np.array(result)\n\nCPU times: user 4.35 s, sys: 1.26 s, total: 5.61 s\nWall time: 45 s\n\n\n\nfrom scipy.stats import linregress\n\nplt.figure(figsize=(14,5))\nplt.plot(t_local,result-10,'r-o')\ntyr=(t_local-t_local[0])/np.timedelta64(1,'Y') #convert the number of years\nmsk=np.isnan(result)\ntyr=tyr[~msk]\nresult=result[~msk]\n\n#Calculate the linear trend using linear regression `linregress`\nrate=linregress(tyr[1:],result[1:]) \nprint('The estimated sea level rise rate between 1993 and 2018: %5.1fmm/year.'%(rate[0]) )\nplt.text(t_local[0],10, 'Linear trend: %5.1fmm/year'%(rate[0]),fontsize=16)\nplt.xlabel('Time (year)',fontsize=16)\nplt.ylabel('Global Mean SLA (mm)',fontsize=16)\n\nplt.grid(True)\nplt.show()\n\nThe estimated sea level rise rate between 1993 and 2018: 2.5mm/year.\n\n\n\n\n\n\nQuiz The global sea level trend from altimetry should be 3.0mm/year. Why did we get 2.5mm/year from the above analysis? Can you get 3.0mm/year by modifying the above code?\nHint: The above analysis is aliased.\n\n\nAdd the GMSL from Frederikse et al. https://podaac.jpl.nasa.gov/dataset/JPL_RECON_GMSL\n\nfrom scipy.stats import linregress\n\nplt.figure(figsize=(14,5))\nplt.plot(t_local,result-10,'r-o',label='altimetry')\n\nplt.xlabel('Time (year)',fontsize=16)\nplt.ylabel('Global Mean SLA (meter)',fontsize=16)\nplt.grid(True)\n\n# Add GMSL from \n\nd1=xr.open_dataset('https://opendap.jpl.nasa.gov/opendap/allData/homage/L4/gmsl/global_timeseries_measures.nc')\nprint(d1)\nd1['global_average_sea_level_change'].plot(label='in-situ')\nplt.legend()\n\nplt.savefig('gmsl.png')\n\n<xarray.Dataset>\nDimensions: (time: 119)\nCoordinates:\n * time (time) datetime64[ns] ...\nData variables: (12/21)\n global_average_sea_level_change (time) float32 ...\n global_average_sea_level_change_upper (time) float32 ...\n global_average_sea_level_change_lower (time) float32 ...\n glac_mean (time) float32 ...\n glac_upper (time) float32 ...\n glac_lower (time) float32 ...\n ... ...\n global_average_thermosteric_sea_level_change (time) float32 ...\n global_average_thermosteric_sea_level_change_upper (time) float32 ...\n global_average_thermosteric_sea_level_change_lower (time) float32 ...\n sum_of_contrib_processes_mean (time) float32 ...\n sum_of_contrib_processes_upper (time) float32 ...\n sum_of_contrib_processes_lower (time) float32 ...\nAttributes: (12/42)\n title: Global sea-level changes and contributing proc...\n summary: This file contains reconstructed global-mean s...\n id: 10.5067/GMSLT-FJPL1\n naming_authority: gov.nasa.jpl\n source: Frederikse et al. The causes of sea-level rise...\n project: NASA sea-level change science team (N-SLCT)\n ... ...\n time_coverage_start: 1900-01-01\n time_coverage_end: 2018-12-31\n time_coverage_duration: P119Y\n time_coverage_resolution: P1Y\n date_created: 2020-07-28\n date_modified: 2020-09-14\n\n\n\n\n\n\nQuiz The global sea level trend from tide-gauge reconstruction (3.5mm/year) is steeper than altimetry-based analysis (3.0mm/year). Why is that?\nHint: Altimetry-based analysis does not consider vertical land motion."
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- "title": "Harmony Concise + L2SS-Py Demo",
- "section": "",
- "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
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+ "href": "external/July_2022_Earthdata_Webinar.html#step-6-run-an-apache-web-server-to-show-the-result",
+ "title": "Earthdata Webinar",
+ "section": "Step 6 – Run an apache web server to show the result",
+ "text": "Step 6 – Run an apache web server to show the result\nBecause the EC2 was set up to allow HTTPS traffic, we can setup a simple website to host the GMSL result. This simple website should be an illustration using an EC2 to streamline a web application. You can set up a cron job to establish auto-update of the result.\nFrom your EC2 command line, install apache webserver:\n\n\n\nsudo yum install httpd -y\nStart the server and auto-start when stopped\n\n\n\nsudo service httpd start\nCopy and paste the following code to make a webpage index.html\n<html>\n<head>\n <center>\n <h1 style=\"font-size:30px\">The global mean sea level </h1>\n <h2 style=\"font-size:20px\">Hosted on my personal AWS EC2</h2>\n <img src=\"gmsl.png\" alt=\"Global Mean Sea Level\" width=\"700\">\n <h1 style=\"font-size:20px\">Diagnosed from MEaSURES-SSH (red) and JPL_RECON_GMSL (blue)</h1>\n <h1 style=\"font-size:20px\">Earthdata webinar, 07/27/2022</h1>\n <h1 style=\"font-size:20px;color=purple\">Cloud-based analysis is fun!</h1>\n <img src=\"https://chucktownfloods.cofc.edu/wp-content/uploads/2019/07/Earthdata-Logo.jpg\" width=\"200\">\n </center>\n</head>\n</html>\nMove index.html to the default location /var/www/html/ using >\nsudo cp index.html /var/www/html/\nMake sure to use cp not mv to change the ownership to root. Note, it is the default but not the good practice to use root for this. We use it to simplify the tutorial.\nCopy gmsl.png into /var/www/html/.\n\n```shell sudo cp gmsl.png /var/www/html/\n\nAccess the webpage through the EC2 IP address from browser."
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- "title": "Harmony Concise + L2SS-Py Demo",
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- "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
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+ "text": "Summary\n\nOnline materials for using AWS cloud and Eararthdata are abundant but often oranized by topics.\nHere we focus on building a simple workflow from scratch to show how in-cloud analysis can be achieved with minimal knowledge of AWS cloud\nBy repeating these steps, one is anticipated to learn the basic concepts of AWS and in-cloud analysis as well as PODAAC/Earthdata cloud.\n\n\nConclusions\n\nApply cloud-computing is not difficult, but finding the right path is.\nRelying on free AWS account linked to personal finance is not sustainable. The community needs a clear official instruction on the channels of getting supported.\nSupport for small-size proposals are needed to advance cloud computing from early adopters to mainstream.\n\n\n\nLesson Learned\n\nLearn as a group\n\nSmall-size ‘coding-clubs’ with scienists and engineers is helpful to solve problems faster.\n\nStart from basics\nLearn cloud by solving a practical problem, for example:\n\nI would like to analyze global mean sea level in the cloud\nI would like to build a regional sea level rise indictor in the cloud and host the result realtime through a website\nI would like to build a notebook to show diverse satellite and in-situ data to support a field campaign in realtime.\n\nRestricted cyber environment needs more attention to the Virtual Private Cloud (VPC) configuration. (We wasted many months on this item.)\n\n\n\nFuture development (stay tuned)\n\nScale-up analysis in the cloud\n\nAWS Lambda\nAWS Batch\nAWS HPC"
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- "title": "Harmony Concise + L2SS-Py Demo",
- "section": "What is L2SS-Py + Concise?",
- "text": "What is L2SS-Py + Concise?\nHarmony supports chaining multiple services together. The L2SS-Py + Concise chain allows users to combine spatial, temporal, and variable subsetting with granule concatenation."
+ "objectID": "external/July_2022_Earthdata_Webinar.html#further-reading",
+ "href": "external/July_2022_Earthdata_Webinar.html#further-reading",
+ "title": "Earthdata Webinar",
+ "section": "Further reading",
+ "text": "Further reading\n\nUse Dask to speed up the computation\nCalculate the global mean sea level from ECCO"
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- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
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+ "title": "Data Subscriber: Continual, Scripted Access to PODAAC data",
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+ "text": "The PO.DAAC Data subscriber is a python-based tool for continuously downloading data from the PO.DAAC archive. Use this script if you want to constantly download the newest data from PO.DAAC as it comes in.\nFor installation and dependency information, please see the top-level README.\n$> podaac-data-subscriber -h\n-h, --help show this help message and exit\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\n-f, --force Flag to force downloading files that are listed in CMR query, even if the file exists and checksum matches\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time after which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time before which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from the command line.\n Default: \"-180,-90,180,90\".\n-dc Flag to use cycle number for directory where data products will be downloaded.\n-dydoy Flag to use start time (Year/DOY) of downloaded data for directory where data products will be downloaded.\n-dymd Flag to use start time (Year/Month/Day) of downloaded data for directory where data products will be downloaded.\n-dy Flag to use start time (Year) of downloaded data for directory where data products will be downloaded.\n--offset OFFSET Flag used to shift timestamp. Units are in hours, e.g. 10 or -10.\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs.\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\n--process PROCESS_CMD\n Processing command to run on each downloaded file (e.g., compression). Can be specified multiple times.\n--version Display script version information and exit.\n--verbose Verbose mode.\n-p PROVIDER, --provider PROVIDER\n Specify a provider for collection search. Default is POCLOUD.\n--dry-run Search and identify files to download, but do not actually download them\n\n\nUsage:\nusage: PO.DAAC data subscriber [-h] -c COLLECTION -d OUTPUTDIRECTORY [-f] [-sd STARTDATE] [-ed ENDDATE] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-m MINUTES] [-e EXTENSIONS] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs. \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --minutes must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the subscriber.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nEvery time the script runs successfully (that is, no errors), a .update__<collectionname> file is created in your download directory with the last run timestamp. This timestamp will be used the next time the script is run. It will look for data between the timestamp in that file and the current time to determine new files to download.\n\n\n\nThere are numerous ‘times’ available to query on in CMR. For the default subscriber, we look at the ‘created at’ field, which will look for when a granule file was ingested into the archive. This means as PO.DAAC gets data, your subscriber will also get data, regardless of the time range within the granule itself.\n\n\n\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token\n\n\n\n\n\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the subscriber is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the subscriber will skip downloading that file.\nThis can drastically reduce the time for the subscriber to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the subscriber to always download matching files, by using –force/-f.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -f\n\n\n\nTo automatically run and update a local file system with data files from a collection, one can use a syntax like the following:\n10 * * * * podaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d /path/to/data/VIIRS_N20-OSPO-L2P-v2.61 -e .nc -e .h5 -m 60 -b=\"-180,-90,180,90\" --verbose >> ~/.subscriber.log\n\nThis will run every hour at ten minutes passed, and output will be appended to a local file called ~/.subscriber.log\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\"\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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- "text": "Running the Demo\nThe remaining notebook walks through constructing a request that first subsets multiple files from a collection and then concatenates the results together into a single output file. This is accomplished using the Harmony coverages API through the use of the harmony-py python library.\nThe collection being used in the demonstration is the ASCATB-L2-25km collection which contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 25 km sampling resolution.\nThe first step is to import the libraries needed to run the demo.\n\nimport xarray as xr\nimport tempfile\nfrom IPython.display import display, JSON\nfrom datetime import datetime, timedelta, time\nfrom harmony import BBox, Client, Collection, Request, Environment, LinkType\n\nfrom mpl_toolkits.basemap import Basemap\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport s3fs\n\nimport warnings\nwarnings.filterwarnings('ignore')\n%matplotlib inline\n\nCreate Harmony-py client.\n\nharmony_client = Client(env=Environment.PROD)\n\nWith the client created, we can contruct and validate the request. As this is a subsetting + concatenation request, we specify options on the request that define spatial bounds, variables we are interested in, temporal bounds, and indicated the result should be concatenated. Since this is a near real time dataset, we will request the data from yesterday.\n\ncollection = Collection(id='C2075141559-POCLOUD')\n\nyesterday = datetime.today() - timedelta(days=1)\n\nrequest = Request(\n collection=collection,\n spatial=BBox(-180, -30, 180, 30),\n variables=[\n 'wind_speed', \n 'wind_dir'\n ],\n temporal={\n 'start': datetime.combine(yesterday, time.min),\n 'stop': datetime.combine(yesterday, time.max)\n },\n concatenate=True\n)\n\nrequest.is_valid()\n\nTrue\n\n\nNow that we have a valid request we simply need to call the submit function using the client we created earlier and pass in the request as a parameter.\nTip: if you want to see the request before submitting it, use the request_as_curl function on the client to get an equivalent curl command for the request that will be submitted.\n\nprint(harmony_client.request_as_curl(request))\njob_id = harmony_client.submit(request)\nprint(f'Job ID: {job_id}')\n\ncurl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=046f3430c6ca2f9e3e00d94c0bee2f70' -H 'User-Agent: Windows/10 harmony-py/0.4.2 CPython/3.8.12 python-requests/2.25.1' 'https://harmony.earthdata.nasa.gov/C2075141559-POCLOUD/ogc-api-coverages/1.0.0/collections/wind_speed,wind_dir/coverage/rangeset?forceAsync=true&subset=lat%28-30%3A30%29&subset=lon%28-180%3A180%29&subset=time%28%222022-10-19T00%3A00%3A00%22%3A%222022-10-19T23%3A59%3A59.999999%22%29&concatenate=true'\nJob ID: 87ec4775-7949-482c-96b2-11f5e6941d15\n\n\nAfter submitting the request it is possible to retrieve the current processing status by using the job ID returned from the submission.\n\nharmony_client.status(job_id)\n\n{'status': 'running',\n 'message': 'The job is being processed',\n 'progress': 0,\n 'created_at': datetime.datetime(2022, 10, 20, 22, 45, 28, 721000, tzinfo=tzutc()),\n 'updated_at': datetime.datetime(2022, 10, 20, 22, 45, 29, 72000, tzinfo=tzutc()),\n 'created_at_local': '2022-10-20T15:45:28-07:00',\n 'updated_at_local': '2022-10-20T15:45:29-07:00',\n 'data_expiration': datetime.datetime(2022, 11, 19, 22, 45, 28, 721000, tzinfo=tzutc()),\n 'data_expiration_local': '2022-11-19T14:45:28-08:00',\n 'request': 'https://harmony.earthdata.nasa.gov/C2075141559-POCLOUD/ogc-api-coverages/1.0.0/collections/wind_speed,wind_dir/coverage/rangeset?forceAsync=true&subset=lat(-30%3A30)&subset=lon(-180%3A180)&subset=time(%222022-10-19T00%3A00%3A00%22%3A%222022-10-19T23%3A59%3A59.999999%22)&concatenate=true',\n 'num_input_granules': 16}\n\n\nIf the request is still running, we can wait until the Harmony request has finished processing. This cell will wait until the request has finised.\n\nharmony_client.wait_for_processing(job_id, show_progress=True)\n\n [ Processing: 100% ] |###################################################| [|]\n\n\nNow that the request has completed we can inspect the results using xarray and matplotlib.\nFirst, let’s download the result into a temporary directory\n\ntemp_dir = tempfile.mkdtemp()\nfutures = harmony_client.download_all(job_id, directory=temp_dir, overwrite=True)\nfile_names = [f.result() for f in futures]\nfile_names\n\n['C:\\\\Users\\\\nickles\\\\AppData\\\\Local\\\\Temp\\\\tmpqzco2nld\\\\C2075141559-POCLOUD_merged.nc4']\n\n\nWith the output file downloaded, now we can open concatenated granule using xarray to inspect some of the metadata.\nNotice the variable subset has been successfully executed – only wind_dir and wind_speed vars are present. In addition, there is a new dimension subset_index added to each variable in the dataset. The index of this dimension corresponds to the original file named in the subset_files variable that contained the data at that index.\n\nds = xr.open_dataset(file_names[0], decode_times=False)\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (subset_index: 16, NUMROWS: 596, NUMCELLS: 42)\nCoordinates:\n lat (subset_index, NUMROWS, NUMCELLS) float32 ...\n lon (subset_index, NUMROWS, NUMCELLS) float32 ...\nDimensions without coordinates: subset_index, NUMROWS, NUMCELLS\nData variables:\n subset_files (subset_index) object 'ascat_20221018_222700_metopb_52328_e...\n time (subset_index, NUMROWS, NUMCELLS) float64 ...\n wind_speed (subset_index, NUMROWS, NUMCELLS) float32 ...\n wind_dir (subset_index, NUMROWS, NUMCELLS) float32 ...\nAttributes: (12/18)\n title: MetOp-B ASCAT Level 2 25.0 km Ocean Sur...\n title_short_name: ASCATB-L2-25km\n Conventions: CF-1.6\n institution: EUMETSAT/OSI SAF/KNMI\n source: MetOp-B ASCAT\n software_identification_level_1: 1000\n ... ...\n processing_level: L2\n rev_orbit_period: 6081.7\n orbit_inclination: 98.7\n references: ASCAT Wind Product User Manual, https:/...\n comment: Orbit period and inclination are consta...\n history_json: [{\"date_time\": \"2022-10-20T22:45:37.904...xarray.DatasetDimensions:subset_index: 16NUMROWS: 596NUMCELLS: 42Coordinates: (2)lat(subset_index, NUMROWS, NUMCELLS)float32...valid_min :-9000000valid_max :9000000standard_name :latitudelong_name :latitudeunits :degrees_north[400512 values with dtype=float32]lon(subset_index, NUMROWS, NUMCELLS)float32...valid_min :0valid_max :36000000standard_name :longitudelong_name :longitudeunits :degrees_east[400512 values with dtype=float32]Data variables: (4)subset_files(subset_index)object...long_name :List of subsetted files used to create this merge product.array(['ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_170300_metopb_52339_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_000900_metopb_52329_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_184500_metopb_52340_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_220600_metopb_52342_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_202700_metopb_52341_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_234800_metopb_52343_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_015100_metopb_52330_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_051200_metopb_52332_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_033300_metopb_52331_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_065400_metopb_52333_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_083600_metopb_52334_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_101800_metopb_52335_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_133900_metopb_52337_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_120000_metopb_52336_eps_o_250_3301_ovw.l2_subsetted.nc4'],\n dtype=object)time(subset_index, NUMROWS, NUMCELLS)float64...valid_min :0valid_max :2147483647standard_name :timelong_name :timeunits :seconds since 1990-01-01calendar :proleptic_gregorian[400512 values with dtype=float64]wind_speed(subset_index, NUMROWS, NUMCELLS)float32...valid_min :0valid_max :5000standard_name :wind_speedlong_name :wind speed at 10 munits :m s-1[400512 values with dtype=float32]wind_dir(subset_index, NUMROWS, NUMCELLS)float32...valid_min :0valid_max :3600standard_name :wind_to_directionlong_name :wind direction at 10 munits :degree[400512 values with dtype=float32]Attributes: (18)title :MetOp-B ASCAT Level 2 25.0 km Ocean Surface Wind Vector Producttitle_short_name :ASCATB-L2-25kmConventions :CF-1.6institution :EUMETSAT/OSI SAF/KNMIsource :MetOp-B ASCATsoftware_identification_level_1 :1000instrument_calibration_version :0software_identification_wind :3301pixel_size_on_horizontal :25.0 kmservice_type :epsprocessing_type :Ocontents :ovwprocessing_level :L2rev_orbit_period :6081.7orbit_inclination :98.7references :ASCAT Wind Product User Manual, https://osi-saf.eumetsat.int/, https://scatterometer.knmi.nl/comment :Orbit period and inclination are constant values. All wind directions in oceanographic convention (0 deg. flowing North)history_json :[{\"date_time\": \"2022-10-20T22:45:37.904685+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:40.891502+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:37.825551+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_184500_metopb_52340_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:38.951797+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_220600_metopb_52342_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:39.479597+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_015100_metopb_52330_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:40.201629+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_065400_metopb_52333_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:39.958642+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_101800_metopb_52335_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:37.611733+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_170300_metopb_52339_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:40.394288+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_202700_metopb_52341_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:39.465600+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_051200_metopb_52332_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:39.632834+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_083600_metopb_52334_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:43.428456+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_120000_metopb_52336_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:37.471227+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_000900_metopb_52329_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:39.335118+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_234800_metopb_52343_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:40.743323+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_033300_metopb_52331_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:43.732829+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_133900_metopb_52337_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:48.424799+00:00\", \"derived_from\": [\"ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_170300_metopb_52339_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_000900_metopb_52329_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_184500_metopb_52340_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_220600_metopb_52342_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_202700_metopb_52341_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_234800_metopb_52343_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_015100_metopb_52330_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_051200_metopb_52332_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_033300_metopb_52331_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_065400_metopb_52333_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_083600_metopb_52334_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_101800_metopb_52335_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_133900_metopb_52337_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_120000_metopb_52336_eps_o_250_3301_ovw.l2_subsetted.nc4\"], \"program\": \"concise\", \"version\": \"0.5.0\", \"parameters\": \"input_files=[PosixPath('/tmp/tmp6qevy37z/ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_170300_metopb_52339_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_000900_metopb_52329_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_184500_metopb_52340_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_220600_metopb_52342_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_202700_metopb_52341_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_234800_metopb_52343_eps_o_250_3301_ovw.l2_subsetted.nc4'), 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\"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}]\n\n\nUsing matplotlib, we can genearte a plot for each granule that makes up this concatenated granule. Plot wind_speed for each granule using subset_index dimension.\n\nfig = plt.figure(figsize=(20, 40))\n\nfor index in range(0, len(ds.subset_index)): \n ax = fig.add_subplot((len(ds.subset_index)+1)//2, 2, index+1, projection=ccrs.PlateCarree())\n\n p = ds.isel(subset_index=index).plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=\"wind_speed\",\n s=1,\n levels=9,\n cmap=\"jet\",\n ax=ax\n )\n \n ax.set_global()\n ax.coastlines()\n\nplt.show()\n\n\n\n\nPlot wind_speed for all data in this concatenated granule on a single map. Notice that the data is within the spatial bounds we provided earlier.\n\nplt.figure(figsize=(12, 6))\nax = plt.axes(projection=ccrs.PlateCarree())\n\np = ds.plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=\"wind_speed\",\n s=1,\n levels=9,\n cmap=\"jet\",\n ax=ax\n)\n\nax.set_global()\nax.coastlines()\nplt.show()"
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+ "text": "Usage:\nusage: PO.DAAC data subscriber [-h] -c COLLECTION -d OUTPUTDIRECTORY [-f] [-sd STARTDATE] [-ed ENDDATE] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-m MINUTES] [-e EXTENSIONS] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs. \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --minutes must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the subscriber.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nEvery time the script runs successfully (that is, no errors), a .update__<collectionname> file is created in your download directory with the last run timestamp. This timestamp will be used the next time the script is run. It will look for data between the timestamp in that file and the current time to determine new files to download."
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- "text": "Internal Note:"
+ "text": "There are numerous ‘times’ available to query on in CMR. For the default subscriber, we look at the ‘created at’ field, which will look for when a granule file was ingested into the archive. This means as PO.DAAC gets data, your subscriber will also get data, regardless of the time range within the granule itself."
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+ "text": "The netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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- "text": "Requirements\n\n1. Compute environment\ninternal note (remove this note in final tutorial): keep one or both of these Req 1 depending on environment required to run the noteook\nThis tutorial can be run in the following environments: - AWS instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. - Local compute environment e.g. laptop, server: this tutorial can be run on your local machine\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n3. netrc File\nYou will need a .netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A .netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata tutorial.\ninternal note (delete in final tutorial) You can use the netrc approach in the notebook or leverage the earthaccess package.\n\n\n4. Additional Requirements\nAny other requirements needed for reproducing this tutorial."
+ "objectID": "external/Subscriber.html#advanced-usage",
+ "href": "external/Subscriber.html#advanced-usage",
+ "title": "Data Subscriber: Continual, Scripted Access to PODAAC data",
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+ "text": "Use the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the subscriber is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the subscriber will skip downloading that file.\nThis can drastically reduce the time for the subscriber to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the subscriber to always download matching files, by using –force/-f.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -f\n\n\n\nTo automatically run and update a local file system with data files from a collection, one can use a syntax like the following:\n10 * * * * podaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d /path/to/data/VIIRS_N20-OSPO-L2P-v2.61 -e .nc -e .h5 -m 60 -b=\"-180,-90,180,90\" --verbose >> ~/.subscriber.log\n\nThis will run every hour at ten minutes passed, and output will be appended to a local file called ~/.subscriber.log\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\"\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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- "section": "Learning Objectives",
- "text": "Learning Objectives\n\nenter objective\nenter objective\n…"
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+ "href": "external/NASA_Earthdata_Authentication.html",
+ "title": "How to Authenticate for NASA Earthdata Programmatically",
+ "section": "",
+ "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository."
},
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- "text": "Import Packages\ninternal note (delete in final tutorial): update the cell below for specific tutorial\n\n# e.g. \nimport os\nimport requests\nimport s3fs\nimport xarray as xr\nimport hvplot.xarray\n\nInternal Note (delete in final tutorial): The following section is optional. Keep if working in the cloud, remove if tutorial is for local workflow."
+ "objectID": "external/NASA_Earthdata_Authentication.html#summary",
+ "href": "external/NASA_Earthdata_Authentication.html#summary",
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+ "text": "Summary\nThis notebook creates a hidden .netrc file (_netrc for Window OS) with Earthdata login credentials in your home directory. This file is needed to access NASA Earthdata assets from a scripting environment like Python.\n\nEarthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nAuthentication via netrc File\nYou will need a netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A netrc file can be created manually within text editor and saved to your home directory. An example of the required content is below.\nmachine urs.earthdata.nasa.gov\nlogin <USERNAME>\npassword <PASSWORD>\n<USERNAME> and <PASSWORD> would be replaced by your actual Earthdata Login username and password respectively."
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- "title": "Tutorial Title",
- "section": "Get Temporary AWS Credentials (Optional section for cloud use)",
- "text": "Get Temporary AWS Credentials (Optional section for cloud use)\nDirect S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. For now, each NASA DAAC has different AWS credentials endpoints. Below are some of the credential endpoints to various DAACs:\n\ns3_cred_endpoint = {\n 'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'gesdisc': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',\n 'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\n 'ornldaac': 'https://data.ornldaac.earthdata.nasa.gov/s3credentials',\n 'ghrcdaac': 'https://data.ghrc.earthdata.nasa.gov/s3credentials'\n}\n\nCreate a function to make a request to an endpoint for temporary credentials. Remember, each DAAC has their own endpoint and credentials are not usable for cloud data from other DAACs.\n\ndef get_temp_creds(provider):\n return requests.get(s3_cred_endpoint[provider]).json()\n\n\ntemp_creds_req = get_temp_creds('podaac')\n#temp_creds_req"
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+ "href": "external/NASA_Earthdata_Authentication.html#import-required-packages",
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+ "section": "Import Required Packages",
+ "text": "Import Required Packages\n\nfrom netrc import netrc\nfrom subprocess import Popen\nfrom platform import system\nfrom getpass import getpass\nimport os\n\nThe code below will:\n\ncheck what operating system (OS) is being used to determine which netrc file to check for/create (.netrc or _netrc)\ncheck if you have an netrc file, and if so, varify if those credentials are for the Earthdata endpoint\ncreate a netrc file if a netrc file is not present.\n\n\nurs = 'urs.earthdata.nasa.gov' # Earthdata URL endpoint for authentication\nprompts = ['Enter NASA Earthdata Login Username: ',\n 'Enter NASA Earthdata Login Password: ']\n\n# Determine the OS (Windows machines usually use an '_netrc' file)\nnetrc_name = \"_netrc\" if system()==\"Windows\" else \".netrc\"\n\n# Determine if netrc file exists, and if so, if it includes NASA Earthdata Login Credentials\ntry:\n netrcDir = os.path.expanduser(f\"~/{netrc_name}\")\n netrc(netrcDir).authenticators(urs)[0]\n\n# Below, create a netrc file and prompt user for NASA Earthdata Login Username and Password\nexcept FileNotFoundError:\n homeDir = os.path.expanduser(\"~\")\n Popen('touch {0}{2} | echo machine {1} >> {0}{2}'.format(homeDir + os.sep, urs, netrc_name), shell=True)\n Popen('echo login {} >> {}{}'.format(getpass(prompt=prompts[0]), homeDir + os.sep, netrc_name), shell=True)\n Popen('echo \\'password {} \\'>> {}{}'.format(getpass(prompt=prompts[1]), homeDir + os.sep, netrc_name), shell=True)\n # Set restrictive permissions\n Popen('chmod 0600 {0}{1}'.format(homeDir + os.sep, netrc_name), shell=True)\n\n # Determine OS and edit netrc file if it exists but is not set up for NASA Earthdata Login\nexcept TypeError:\n homeDir = os.path.expanduser(\"~\")\n Popen('echo machine {1} >> {0}{2}'.format(homeDir + os.sep, urs, netrc_name), shell=True)\n Popen('echo login {} >> {}{}'.format(getpass(prompt=prompts[0]), homeDir + os.sep, netrc_name), shell=True)\n Popen('echo \\'password {} \\'>> {}{}'.format(getpass(prompt=prompts[1]), homeDir + os.sep, netrc_name), shell=True)\n\n\nSee if the file was created\nIf the file was created, we’ll see a .netrc file (_netrc for Window OS) in the list printed below. To view the contents from a Jupyter environment, click File on the top toolbar, select Open from Path…, type .netrc, and click Open. The .netrc file will open within the text editor.\n\n!!! Beware, your password will be visible if the .netrc file is opened in the text editor.\n\n\n!ls -al ~/"
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- "title": "SWOT Simulated North American Continent Hydrology Dataset",
+ "objectID": "external/Introduction_to_xarray.html",
+ "href": "external/Introduction_to_xarray.html",
+ "title": "Xarray",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
+ "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
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- "title": "SWOT Simulated North American Continent Hydrology Dataset",
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- "text": "Finding ways to visualize SWOT Simulated Shapefile Dataset\n\nLearning Objectives:\n\nAccessing SWOT shapefile hydrology dataset and visualizing it locally.\nAccessing & visualizing dataset through the use of Geopandas & Matplotlib.\n\nThis tutorial is looking to explore geospatial libraries and visualizing vector datasets without the use of a GIS desktop software.\n\n\nDataset:\nSWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1:\n\nDOI: https://doi.org/10.5067/KARIN-2RSP1\n\nThis SWOT simulated river data includes shapefiles of River Nodes and River Reaches. Shapefiles of SWOT sample data can be downloaded here. The single file this notebook will perform most analysis on can be downloaded here.\n\n\nSetting up Environments\n\nPrior to running the notebook, the environments must be set correctly.\nThis notebook can be ran using both Python 3.9 and 3.10 as long as the libraries are correctly installed.\nUtilizing Anaconda Navigator to create your enviroments. Accessing the Conda-Forge channel to install geopsatial libraries.\nGDAL and GeoPandas will direct and install majority of the libraries you will need, but some libraries will need to be installed by searching them individually.\n\n\n\nLibraries Needed\n\nimport os\nimport pandas as pd\nimport numpy as np\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport contextily as cx\n\n\n\nOpening a Single Shapefile\nUsing Geopandas to open & read a single shapefile. (Change the path to your pre-downloaded shapefile)\n\nRiver = gpd.read_file('C:\\SWOT\\SWOT_River_Reaches\\SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.shp')\nRiver\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n0\n74225000301\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0806Z\n33.916062\n-95.560044\nno_data\n3.495055e+01\n-1.000000e+12\n2.515300e-01\n...\n156.0\n3097.993819\n54\n1461998.230\n10753.251601\n-1.000000e+12\n0\n2\n1\nLINESTRING (-95.53934 33.88031, -95.53966 33.8...\n\n\n1\n74225000311\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0806Z\n33.906781\n-95.654646\nno_data\n3.405308e+01\n-1.000000e+12\n5.495000e-02\n...\n175.0\n3701.568090\n79\n1477859.906\n15861.676389\n-1.000000e+12\n0\n2\n1\nLINESTRING (-95.59665 33.94170, -95.59665 33.9...\n\n\n2\n74225000321\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0807Z\n33.870673\n-95.762974\nno_data\n3.364557e+01\n-1.000000e+12\n2.726000e-02\n...\n183.0\n3908.298409\n68\n1491552.334\n13692.428266\n-1.000000e+12\n0\n4\n1\nLINESTRING (-95.72430 33.89448, -95.72463 33.8...\n\n\n3\n74225000331\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0807Z\n33.851562\n-95.845656\nno_data\n3.345162e+01\n-1.000000e+12\n3.449000e-02\n...\n148.0\n3991.344178\n61\n1503740.939\n12188.604492\n-1.000000e+12\n0\n2\n1\nLINESTRING (-95.80786 33.86129, -95.80818 33.8...\n\n\n4\n74225000341\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0806Z\n33.866701\n-95.946894\nno_data\n3.316602e+01\n-1.000000e+12\n2.511000e-02\n...\n175.0\n4193.042490\n61\n1515895.262\n12154.323029\n-1.000000e+12\n0\n3\n1\nLINESTRING (-95.89671 33.87151, -95.89704 33.8...\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n480\n75165000211\n-1.000000e+12\n-1.000000e+12\nno_data\n30.433862\n-96.288294\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n80.0\n370.935908\n53\n418067.216\n10666.156902\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.27241 30.40543, -96.27215 30.4...\n\n\n481\n75165000221\n-1.000000e+12\n-1.000000e+12\nno_data\n30.479698\n-96.336497\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n84.0\n415.828931\n53\n428691.064\n10623.847758\n-1.000000e+12\n0\n2\n1\nLINESTRING (-96.31719 30.45052, -96.31751 30.4...\n\n\n482\n75165000231\n-1.000000e+12\n-1.000000e+12\nno_data\n30.531514\n-96.369330\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n67.0\n475.710190\n65\n441697.761\n13006.697340\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.35467 30.51265, -96.35491 30.5...\n\n\n483\n75165000241\n-1.000000e+12\n-1.000000e+12\nno_data\n30.560974\n-96.417506\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n80.0\n373.408224\n49\n451539.432\n9841.670412\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.39119 30.53292, -96.39150 30.5...\n\n\n484\n75165000251\n-1.000000e+12\n-1.000000e+12\nno_data\n30.591646\n-96.453558\nno_data\n-1.000000e+12\n-1.000000e+12\n-1.000000e+12\n...\n84.0\n551.788513\n49\n461358.159\n9818.727520\n-1.000000e+12\n0\n2\n1\nLINESTRING (-96.42988 30.58532, -96.42977 30.5...\n\n\n\n\n485 rows × 111 columns\n\n\n\n\n\nConverting a Shapefile\nIf you want a more in depth view of the datasets attributes you can convert it to a CSV.\nAlso if you would like to open up the dataset as a GeoJSON, Geopandas can help transform the dataset.\n\nRiver.to_csv(\"C:\\SWOT\\SWOT_Attributes.csv\")\n\n\nRiver.to_file(\"C:\\SWOT\\SWOT.json\", driver='GeoJSON')\n\n\n\nPlotting a Shapefile\nUsing Matplotlib to plot the shapefiles, then adding a basemap for context using the library Contextily.\nContextily offers a list of basemap providers that can be easily visualized.\nhttps://contextily.readthedocs.io/en/latest/intro_guide.html\n\nfig, ax = plt.subplots(figsize=(25,15))\nRiver.plot(ax=ax, color='black')\ncx.add_basemap(ax, crs=River.crs, source=cx.providers.OpenTopoMap)\n\n\n\n\n\n\nShapefile Attribute Visualization\nShapefiles have various attributes or variables with each column signifiying individual data values.\nPreviously we plotted by showcasing all the river reaches of that shapefile on the map.\nYou can also plot a shapefile based on a specific variable.\nWithin Matplotlib you can specifiy the column parameter based on the column within the data’s attributes.\nFor the example below, we will look at the column ‘wse’ which stands for water surface elevation.\n\n#First, we set all -999999999999 values to nan so that the color variation shows for the simulated values\nRiver[\"wse\"] = River.wse.apply(lambda x: x if x > -10 else np.nan)\n\n\nfig, ax = plt.subplots(figsize=(15,25))\nRiver.plot(column='wse', ax=ax, legend=True, cmap='viridis')\n\n<AxesSubplot:>\n\n\n\n\n\n\n\nYou can also specifiy which row of attributes you would like to plot using Pandas ‘.loc’ or ‘.iloc’.\n\nfig, ax = plt.subplots(figsize=(25,15))\nRiver.loc[1:5].plot(column='wse',ax=ax, legend=True)\n\n<AxesSubplot:>\n\n\n\n\n\n\n\nQuerying a Shapefile\nIf you want to search for a specific reach id or a specific length of river reach that is possible through a spatial query using Geopandas.\nUtilizing comparison operators (>, <, ==, >=, <=).\nYou can zoom into a particular river reach by specifying it’s row of attributes. Here we specify reach id# ‘75165000221’ which is a section of the Brazos River in Texas.\n\nQuery = River.query(\"reach_id == '75165000221'\")\nQuery\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n481\n75165000221\n-1.000000e+12\n-1.000000e+12\nno_data\n30.479698\n-96.336497\nno_data\nNaN\n-1.000000e+12\n-1.000000e+12\n...\n84.0\n415.828931\n53\n428691.064\n10623.847758\n-1.000000e+12\n0\n2\n1\nLINESTRING (-96.31719 30.45052, -96.31751 30.4...\n\n\n\n\n1 rows × 111 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(25,15))\nQuery.plot(ax=ax, legend=True)\ncx.add_basemap(ax, crs=River.crs, source=cx.providers.Esri.NatGeoWorldMap)\n\n\n\n\n\n\nYou can specify river reaches that have a water surface elevation greater than 35 meters.\n\nWSE = River.query('wse > 35')\nWSE\n\n\n\n\n\n\n\n\nreach_id\ntime\ntime_tai\ntime_str\np_lat\np_lon\nriver_name\nwse\nwse_u\nwse_r_u\n...\np_width\np_wid_var\np_n_nodes\np_dist_out\np_length\np_maf\np_dam_id\np_n_ch_max\np_n_ch_mod\ngeometry\n\n\n\n\n47\n74242200111\n7.138265e+08\n7.138264e+08\n2022-08-14T21:0741Z\n35.317087\n-96.025428\nno_data\n35.15211\n-1.000000e+12\n0.37004\n...\n75.0\n2698.101164\n66\n1825209.708\n13152.428425\n-1.000000e+12\n0\n2\n1\nLINESTRING (-95.97846 35.30313, -95.97879 35.3...\n\n\n49\n74242200131\n7.138265e+08\n7.138264e+08\n2022-08-14T21:0741Z\n35.272401\n-96.149649\nno_data\n35.44462\n-1.000000e+12\n0.55727\n...\n80.0\n571.014323\n46\n1843850.674\n9289.513641\n-1.000000e+12\n0\n2\n1\nLINESTRING (-96.14746 35.30464, -96.14721 35.3...\n\n\n50\n74242200141\n7.138265e+08\n7.138264e+08\n2022-08-14T21:0741Z\n35.275389\n-96.208073\nno_data\n35.21224\n-1.000000e+12\n0.16871\n...\n80.0\n402.586845\n68\n1857401.869\n13551.195121\n-1.000000e+12\n0\n2\n1\nLINESTRING (-96.17403 35.26340, 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42.7...\n\n\n386\n74295800141\n7.138263e+08\n7.138263e+08\n2022-08-14T21:0507Z\n43.699405\n-100.216124\nno_data\n38.30095\n-1.000000e+12\n10.35277\n...\n127.0\n1214.735618\n47\n3695005.134\n9362.384395\n-1.000000e+12\n0\n2\n1\nLINESTRING (-100.18767 43.70568, -100.18804 43...\n\n\n441\n75140100401\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0849Z\n31.378090\n-95.684602\nno_data\n44.96841\n-1.000000e+12\n0.00208\n...\n80.0\n375.277777\n82\n399697.389\n16375.887635\n-1.000000e+12\n0\n1\n1\nLINESTRING (-95.65686 31.34060, -95.65687 31.3...\n\n\n455\n75140300011\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0839Z\n31.899540\n-96.012439\nno_data\n35.29524\n-1.000000e+12\n0.00000\n...\n67.0\n511.629791\n79\n538860.706\n15891.038850\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.00043 31.86864, -96.00043 31.8...\n\n\n473\n75165000141\n7.138265e+08\n7.138265e+08\n2022-08-14T21:0907Z\n30.226423\n-96.104246\nno_data\n52.37545\n-1.000000e+12\n5.08629\n...\n92.0\n858.963903\n48\n351257.312\n9672.898906\n-1.000000e+12\n0\n1\n1\nLINESTRING (-96.12550 30.21370, -96.12531 30.2...\n\n\n\n\n19 rows × 111 columns\n\n\n\n\nfig, ax = plt.subplots(figsize=(25,15))\nWSE.plot(ax=ax, color='black')\ncx.add_basemap(ax, crs=River.crs, source=cx.providers.Esri.NatGeoWorldMap)"
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+ "section": "Why do we need xarray?",
+ "text": "Why do we need xarray?\nAs Geoscientists, we often work with time series of data with two or more dimensions: a time series of calibrated, orthorectified satellite images; two-dimensional grids of surface air temperature from an atmospheric reanalysis; or three-dimensional (level, x, y) cubes of ocean salinity from an ocean model. These data are often provided in GeoTIFF, NetCDF or HDF format with rich and useful metadata that we want to retain, or even use in our analysis. Common analyses include calculating means, standard deviations and anomalies over time or one or more spatial dimensions (e.g. zonal means). Model output often includes multiple variables that you want to apply similar analyses to.\n\n\n\nA schematic of multi-dimensional data\n\n\nThe schematic above shows a typical data structure for multi-dimensional data. There are two data cubes, one for temperature and one for precipitation. Common coordinate variables, in this case latitude, longitude and time are associated with each variable. Each variable, including coordinate variables, will have a set of attributes: name, units, missing value, etc. The file containing the data may also have attributes: source of the data, model name coordinate reference system if the data are projected. Writing code using low-level packages such as netcdf4 and numpy to read the data, then perform analysis, and write the results to file is time consuming and prone to errors."
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- "text": "Bonus\nOpening a folder with multiple shapefiles\n\nIf you have multiple River Reaches or Nodes in a folder, it is possible to visualize all on one map.\nUtilizing both Glob and Pathlib libraries to read the folder, then using Pandas concat to merge the reaches to its own variable.\nMatplotlib Basemap offers the customization ability to create your own basemap.\n\nhttps://matplotlib.org/basemap/users/geography.html\n\nimport glob\nfrom pathlib import Path\nimport pandas as pd\n\n# Direct folder path of shapefiles\nfolder = Path(\"C:\\\\SWOT\\\\SWOT_River_Reaches\")\n\n# State filename extension to look for within the folder, in this case .shp which is the shapefile\nshapefiles = folder.glob(\"*.shp\")\n\n# Merge/Combine multiple shapefiles in folder into one\ngdf = pd.concat([\n gpd.read_file(shp)\n for shp in shapefiles\n]).pipe(gpd.GeoDataFrame)\n\n\nfrom mpl_toolkits.basemap import Basemap \n\nfig, ax = plt.subplots(figsize=(25,15))\ngdf.plot(ax=ax, legend=True, color = 'black')\nmap = Basemap(llcrnrlon=-130, llcrnrlat=20, urcrnrlon=-65.,urcrnrlat=52., lat_0 = 40., lon_0 = -80)\nmap.drawmapboundary(fill_color='lightblue', color=\"black\")\nmap.fillcontinents(color='tan',lake_color='lightblue')\nmap.drawcountries(color='grey', linewidth=1)\nmap.drawstates(color='lightgrey', linewidth=1)\n\n<matplotlib.collections.LineCollection at 0x251e1a4ce80>"
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+ "title": "Xarray",
+ "section": "What is xarray",
+ "text": "What is xarray\nxarray is an open-source project and python package to work with labelled multi-dimensional arrays. It is leverages numpy, pandas, matplotlib and dask to build Dataset and DataArray objects with built-in methods to subset, analyze, interpolate, and plot multi-dimensional data. It makes working with multi-dimensional data cubes efficient and fun. It will change your life for the better. You’ll be more attractive, more interesting, and better equiped to take on lifes challenges."
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- "text": "Next Steps\n\nThis notebook has helped showcase how to visualize shapefile data without the use of a GIS desktop software.\nShowcasing different ways of plotting based on variables and adding context to the map.\nLocal visualization was the first step, but the next goal is to move towards utilizing the cloud."
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+ "title": "Xarray",
+ "section": "What you will learn from this tutorial",
+ "text": "What you will learn from this tutorial\nIn this tutorial you will learn how to:\n\nload a netcdf file into xarray\ninterrogate the Dataset and understand the difference between DataArray and Dataset\nsubset a Dataset\ncalculate annual and monthly mean fields\ncalculate a time series of zonal means\nplot these results\n\nAs always, we’ll start by importing xarray. We’ll follow convention by giving the module the shortname xr\n\nimport xarray as xr\nxr.set_options(keep_attrs=True)\nimport hvplot.xarray\n\n\n\n\n\n\n\n\n\n\n\nI’m going to use one of xarray’s tutorial datasets. In this case, air temperature from the NCEP reanalysis. I’ll assign the result of the open_dataset to ds. I may change this to access a dataset directly\n\nds = xr.tutorial.open_dataset(\"air_temperature\")\n\nAs we are in an interactive environment, we can just type ds to see what we have.\n\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lat: 25, time: 2920, lon: 53)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nData variables:\n air (time, lat, lon) float32 ...\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:lat: 25time: 2920lon: 53Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (1)air(time, lat, lon)float32...long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ][3869000 values with dtype=float32]Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html\n\n\nFirst thing to notice is that ds is an xarray.Dataset object. It has dimensions, lat, lon, and time. It also has coordinate variables with the same names as these dimensions. These coordinate variables are 1-dimensional. This is a NetCDF convention. The Dataset contains one data variable, air. This has dimensions (time, lat, lon).\nClicking on the document icon reveals attributes for each variable. Clicking on the disk icon reveals a representation of the data.\nEach of the data and coordinate variables can be accessed and examined using the variable name as a key.\n\nds.air\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n[3869000 values with dtype=float32]\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degK\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920lat: 25lon: 53...[3869000 values with dtype=float32]Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\n\nds['air']\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n[3869000 values with dtype=float32]\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degK\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920lat: 25lon: 53...[3869000 values with dtype=float32]Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nThese are xarray.DataArray objects. This is the basic building block for xarray.\nVariables can also be accessed as attributes of ds.\n\nds.time\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'time' (time: 2920)>\narray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n standard_name: time\n long_name: Timexarray.DataArray'time'time: 29202013-01-01 2013-01-01T06:00:00 ... 2014-12-31T18:00:00array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Coordinates: (1)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (2)standard_name :timelong_name :Time\n\n\nA major difference between accessing a variable as an attribute versus using a key is that the attribute is read-only but the key method can be used to update the variable. For example, if I want to convert the units of air from Kelvin to degrees Celsius.\n\nds['air'] = ds.air - 273.15\n\nThis approach can also be used to add new variables\n\nds['air_kelvin'] = ds.air + 273.15\n\nIt is helpful to update attributes such as units, this saves time, confusion and mistakes, especially when you save the dataset.\n\nds['air'].attrs['units'] = 'degC'\n\n\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lat: 25, time: 2920, lon: 53)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nData variables:\n air (time, lat, lon) float32 -31.95 -30.65 -29.65 ... 23.04 22.54\n air_kelvin (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:lat: 25time: 2920lon: 53Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (2)air(time, lat, lon)float32-31.95 -30.65 ... 23.04 22.54long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n -37.649994, -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n -37.850006, -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n -36.759995, -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 ,\n 21.950012, 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 ,\n 22.75 , 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 ,\n 23.640015, 23.450012]],\n\n [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n -39.550003, -37.350006],\n [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n -40.649994, -37.449997],\n [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n -39.759995, -34.649994],\n...\n [ 20.540009, 20.73999 , 22.23999 , ..., 21.940002,\n 21.540009, 21.140015],\n [ 23.140015, 24.040009, 24.440002, ..., 22.140015,\n 21.940002, 21.23999 ],\n [ 24.640015, 25.23999 , 25.339996, ..., 22.540009,\n 22.339996, 22.040009]],\n\n [[-28.059998, -28.86 , -29.86 , ..., -31.460007,\n -31.660004, -31.36 ],\n [-23.259995, -23.86 , -24.759995, ..., -33.559998,\n -32.86 , -31.460007],\n [-10.160004, -10.959991, -11.76001 , ..., -33.259995,\n -30.559998, -26.86 ],\n ...,\n [ 20.640015, 20.540009, 21.940002, ..., 22.140015,\n 21.940002, 21.540009],\n [ 22.940002, 23.73999 , 24.040009, ..., 22.540009,\n 22.540009, 22.040009],\n [ 24.540009, 24.940002, 24.940002, ..., 23.339996,\n 23.040009, 22.540009]]], dtype=float32)air_kelvin(time, lat, lon)float32241.2 242.5 243.5 ... 296.2 295.7long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 ,\n 238.59999],\n [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999,\n 239.29999],\n [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 ,\n 241.7 ],\n ...,\n [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 ,\n 294.69998],\n [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 ,\n 295.19998],\n [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 ,\n 296.6 ]],\n\n [[242.09999, 242.7 , 243.09999, ..., 232. , 233.59999,\n 235.79999],\n [243.59999, 244.09999, 244.2 , ..., 231. , 232.5 ,\n 235.7 ],\n [253.2 , 252.89 , 252.09999, ..., 230.79999, 233.39 ,\n 238.5 ],\n...\n [293.69 , 293.88998, 295.38998, ..., 295.09 , 294.69 ,\n 294.29 ],\n [296.29 , 297.19 , 297.59 , ..., 295.29 , 295.09 ,\n 294.38998],\n [297.79 , 298.38998, 298.49 , ..., 295.69 , 295.49 ,\n 295.19 ]],\n\n [[245.09 , 244.29 , 243.29 , ..., 241.68999, 241.48999,\n 241.79 ],\n [249.89 , 249.29 , 248.39 , ..., 239.59 , 240.29 ,\n 241.68999],\n [262.99 , 262.19 , 261.38998, ..., 239.89 , 242.59 ,\n 246.29 ],\n ...,\n [293.79 , 293.69 , 295.09 , ..., 295.29 , 295.09 ,\n 294.69 ],\n [296.09 , 296.88998, 297.19 , ..., 295.69 , 295.69 ,\n 295.19 ],\n [297.69 , 298.09 , 298.09 , ..., 296.49 , 296.19 ,\n 295.69 ]]], dtype=float32)Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"
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- "title": "The following notebook is no longer supported, see this related notebook highlighting Zarr Data Cubes",
- "section": "",
- "text": "Table of Contents"
+ "objectID": "external/Introduction_to_xarray.html#subsetting-and-indexing",
+ "href": "external/Introduction_to_xarray.html#subsetting-and-indexing",
+ "title": "Xarray",
+ "section": "Subsetting and Indexing",
+ "text": "Subsetting and Indexing\nSubsetting and indexing methods depend on whether you are working with a Dataset or DataArray. A DataArray can be accessed using positional indexing just like a numpy array. To access the temperature field for the first time step, you do the following.\n\nds['air'][0,:,:]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (lat: 25, lon: 53)>\narray([[-31.949997, -30.649994, -29.649994, ..., -40.350006, -37.649994,\n -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006, -37.850006,\n -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997, -36.759995,\n -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 , 21.950012,\n 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 , 22.75 ,\n 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 , 23.640015,\n 23.450012]], dtype=float32)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n time datetime64[ns] 2013-01-01\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'lat: 25lon: 53-31.95 -30.65 -29.65 -29.15 -29.05 ... 24.64 24.45 23.75 23.64 23.45array([[-31.949997, -30.649994, -29.649994, ..., -40.350006, -37.649994,\n -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006, -37.850006,\n -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997, -36.759995,\n -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 , 21.950012,\n 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 , 22.75 ,\n 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 , 23.640015,\n 23.450012]], dtype=float32)Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time()datetime64[ns]2013-01-01standard_name :timelong_name :Timearray('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nNote this returns a DataArray with coordinates but not attributes.\nHowever, the real power is being able to access variables using coordinate variables. I can get the same subset using the following. (It’s also more explicit about what is being selected and robust in case I modify the DataArray and expect the same output.)\n\nds['air'].sel(time='2013-01-01').time\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'time' (time: 4)>\narray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\nAttributes:\n standard_name: time\n long_name: Timexarray.DataArray'time'time: 42013-01-01 2013-01-01T06:00:00 2013-01-01T12:00:00 2013-01-01T18:00:00array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')Coordinates: (1)time(time)datetime64[ns]2013-01-01 ... 2013-01-01T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (2)standard_name :timelong_name :Time\n\n\n\nds.air.sel(time='2013-01-01')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 4, lat: 25, lon: 53)>\narray([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n -37.649994, -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n -37.850006, -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n -36.759995, -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 ,\n 21.950012, 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 ,\n 22.75 , 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 ,\n 23.640015, 23.450012]],\n\n [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n -39.550003, -37.350006],\n [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n -40.649994, -37.449997],\n [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n -39.759995, -34.649994],\n...\n [ 22.450012, 22.25 , 22.25 , ..., 23.140015,\n 22.140015, 21.850006],\n [ 23.049988, 23.350006, 23.140015, ..., 23.25 ,\n 22.850006, 22.450012],\n [ 23.25 , 23.140015, 23.25 , ..., 23.850006,\n 23.850006, 23.640015]],\n\n [[-31.259995, -31.350006, -31.350006, ..., -38.759995,\n -37.649994, -35.550003],\n [-26.850006, -27.850006, -28.949997, ..., -42.259995,\n -41.649994, -38.649994],\n [-16.549988, -18.449997, -21.050003, ..., -42.449997,\n -41.350006, -37.050003],\n ...,\n [ 23.450012, 23.25 , 22.850006, ..., 23.350006,\n 22.640015, 22.140015],\n [ 23.850006, 24.350006, 23.950012, ..., 23.640015,\n 23.450012, 23.140015],\n [ 24.350006, 24.549988, 24.350006, ..., 24.640015,\n 24.850006, 24.75 ]]], dtype=float32)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 4lat: 25lon: 53-31.95 -30.65 -29.65 -29.15 -29.05 ... 25.45 25.05 24.64 24.85 24.75array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n -37.649994, -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n -37.850006, -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n -36.759995, -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 ,\n 21.950012, 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 ,\n 22.75 , 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 ,\n 23.640015, 23.450012]],\n\n [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n -39.550003, -37.350006],\n [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n -40.649994, -37.449997],\n [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n -39.759995, -34.649994],\n...\n [ 22.450012, 22.25 , 22.25 , ..., 23.140015,\n 22.140015, 21.850006],\n [ 23.049988, 23.350006, 23.140015, ..., 23.25 ,\n 22.850006, 22.450012],\n [ 23.25 , 23.140015, 23.25 , ..., 23.850006,\n 23.850006, 23.640015]],\n\n [[-31.259995, -31.350006, -31.350006, ..., -38.759995,\n -37.649994, -35.550003],\n [-26.850006, -27.850006, -28.949997, ..., -42.259995,\n -41.649994, -38.649994],\n [-16.549988, -18.449997, -21.050003, ..., -42.449997,\n -41.350006, -37.050003],\n ...,\n [ 23.450012, 23.25 , 22.850006, ..., 23.350006,\n 22.640015, 22.140015],\n [ 23.850006, 24.350006, 23.950012, ..., 23.640015,\n 23.450012, 23.140015],\n [ 24.350006, 24.549988, 24.350006, ..., 24.640015,\n 24.850006, 24.75 ]]], dtype=float32)Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2013-01-01T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nI can also do slices. I’ll extract temperatures for the state of Colorado. The bounding box for the state is [-109 E, -102 E, 37 N, 41 N].\nIn the code below, pay attention to both the order of the coordinates and the range of values. The first value of the lat coordinate variable is 41 N, the second value is 37 N. Unfortunately, xarray expects slices of coordinates to be in the same order as the coordinates. Note lon is 0 to 360 not -180 to 180, and I let python calculate it for me within the slice.\n\nds.air.sel(lat=slice(41.,37.), lon=slice(360-109,360-102))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920, lat: 2, lon: 3)>\narray([[[-10.049988 , -9.25 , -8.75 ],\n [ -6.25 , -6.549988 , -6.3599854]],\n\n [[-18.149994 , -14.950012 , -9.950012 ],\n [-13.649994 , -11.049988 , -7.25 ]],\n\n [[-20.449997 , -18.649994 , -13.359985 ],\n [-19.350006 , -16.950012 , -11.25 ]],\n\n ...,\n\n [[-24.460007 , -28.259995 , -25.759995 ],\n [-16.959991 , -24.059998 , -24.059998 ]],\n\n [[-24.36 , -26.160004 , -23.460007 ],\n [-15.959991 , -22.86 , -22.960007 ]],\n\n [[-17.559998 , -15.359985 , -13.660004 ],\n [-13.76001 , -15.959991 , -14.459991 ]]], dtype=float32)\nCoordinates:\n * lat (lat) float32 40.0 37.5\n * lon (lon) float32 252.5 255.0 257.5\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920lat: 2lon: 3-10.05 -9.25 -8.75 -6.25 -6.55 ... -15.36 -13.66 -13.76 -15.96 -14.46array([[[-10.049988 , -9.25 , -8.75 ],\n [ -6.25 , -6.549988 , -6.3599854]],\n\n [[-18.149994 , -14.950012 , -9.950012 ],\n [-13.649994 , -11.049988 , -7.25 ]],\n\n [[-20.449997 , -18.649994 , -13.359985 ],\n [-19.350006 , -16.950012 , -11.25 ]],\n\n ...,\n\n [[-24.460007 , -28.259995 , -25.759995 ],\n [-16.959991 , -24.059998 , -24.059998 ]],\n\n [[-24.36 , -26.160004 , -23.460007 ],\n [-15.959991 , -22.86 , -22.960007 ]],\n\n [[-17.559998 , -15.359985 , -13.660004 ],\n [-13.76001 , -15.959991 , -14.459991 ]]], dtype=float32)Coordinates: (3)lat(lat)float3240.0 37.5standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([40. , 37.5], dtype=float32)lon(lon)float32252.5 255.0 257.5standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([252.5, 255. , 257.5], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nWhat if we want temperature for a point, for example Denver, CO (39.72510678889283 N, -104.98785545855408 E). xarray can handle this! If we just want data from the nearest grid point, we can use sel and specify the method as “nearest”.\n\ndenver_lat, denver_lon = 39.72510678889283, -104.98785545855408\n\n\nds.air.sel(lat=denver_lat, lon=360+denver_lon, method='nearest').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nIf we want to interpolate, we can use interp(). In this case I use linear or bilinear interpolation.\ninterp() can also be used to resample data to a new grid and even reproject data\n\nds.air.interp(lat=denver_lat, lon=360+denver_lon, method='linear')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920)>\narray([ -8.95085077, -14.49752791, -18.43715163, ..., -27.78736503,\n -25.78552388, -15.41780902])\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n lat float64 39.73\n lon float64 255.0\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920-8.951 -14.5 -18.44 -11.33 -8.942 ... -22.4 -27.79 -25.79 -15.42array([ -8.95085077, -14.49752791, -18.43715163, ..., -27.78736503,\n -25.78552388, -15.41780902])Coordinates: (3)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')lat()float6439.73standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray(39.72510679)lon()float64255.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray(255.01214454)Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nsel() and interp() can also be used on Dataset objects.\n\nds.sel(lat=slice(41,37), lon=slice(360-109,360-102))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lat: 2, time: 2920, lon: 3)\nCoordinates:\n * lat (lat) float32 40.0 37.5\n * lon (lon) float32 252.5 255.0 257.5\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nData variables:\n air (time, lat, lon) float32 -10.05 -9.25 -8.75 ... -15.96 -14.46\n air_kelvin (time, lat, lon) float32 263.1 263.9 264.4 ... 259.4 257.2 258.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:lat: 2time: 2920lon: 3Coordinates: (3)lat(lat)float3240.0 37.5standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([40. , 37.5], dtype=float32)lon(lon)float32252.5 255.0 257.5standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([252.5, 255. , 257.5], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (2)air(time, lat, lon)float32-10.05 -9.25 ... -15.96 -14.46long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[-10.049988 , -9.25 , -8.75 ],\n [ -6.25 , -6.549988 , -6.3599854]],\n\n [[-18.149994 , -14.950012 , -9.950012 ],\n [-13.649994 , -11.049988 , -7.25 ]],\n\n [[-20.449997 , -18.649994 , -13.359985 ],\n [-19.350006 , -16.950012 , -11.25 ]],\n\n ...,\n\n [[-24.460007 , -28.259995 , -25.759995 ],\n [-16.959991 , -24.059998 , -24.059998 ]],\n\n [[-24.36 , -26.160004 , -23.460007 ],\n [-15.959991 , -22.86 , -22.960007 ]],\n\n [[-17.559998 , -15.359985 , -13.660004 ],\n [-13.76001 , -15.959991 , -14.459991 ]]], dtype=float32)air_kelvin(time, lat, lon)float32263.1 263.9 264.4 ... 257.2 258.7long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[263.1 , 263.9 , 264.4 ],\n [266.9 , 266.6 , 266.79 ]],\n\n [[255. , 258.19998, 263.19998],\n [259.5 , 262.1 , 265.9 ]],\n\n [[252.7 , 254.5 , 259.79 ],\n [253.79999, 256.19998, 261.9 ]],\n\n ...,\n\n [[248.68999, 244.89 , 247.39 ],\n [256.19 , 249.09 , 249.09 ]],\n\n [[248.79 , 246.98999, 249.68999],\n [257.19 , 250.29 , 250.18999]],\n\n [[255.59 , 257.79 , 259.49 ],\n [259.38998, 257.19 , 258.69 ]]], dtype=float32)Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html\n\n\n\nds.interp(lat=denver_lat, lon=360+denver_lon, method='linear')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 2920)\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n lat float64 39.73\n lon float64 255.0\nData variables:\n air (time) float64 -8.951 -14.5 -18.44 ... -27.79 -25.79 -15.42\n air_kelvin (time) float64 264.2 258.7 254.7 261.8 ... 245.4 247.4 257.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:time: 2920Coordinates: (3)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')lat()float6439.73standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray(39.72510679)lon()float64255.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray(255.01214454)Data variables: (2)air(time)float64-8.951 -14.5 ... -25.79 -15.42long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([ -8.95085077, -14.49752791, -18.43715163, ..., -27.78736503,\n -25.78552388, -15.41780902])air_kelvin(time)float64264.2 258.7 254.7 ... 247.4 257.7long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([264.19914312, 258.65246598, 254.71284227, ..., 245.36262886,\n 247.36447002, 257.73218487])Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"
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- "title": "The following notebook is no longer supported, see this related notebook highlighting Zarr Data Cubes",
- "section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\n\nfrom urllib import request, parse\nfrom http.cookiejar import CookieJar\nimport getpass\nimport netrc\nimport requests\nimport urllib\nimport json\nimport pprint\nimport time\nimport os\nfrom os import makedirs, path\nfrom os.path import isdir, basename\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen, urlretrieve\nfrom datetime import datetime, timedelta\nfrom json import dumps, loads\nimport shutil\nfrom osgeo import gdal, gdalconst\n#from nco import Nco\nimport glob\n#import shutil\nimport xarray as xr\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\n%matplotlib inline\n\n\n# Constants\n\n# the local download directory\ndownload_dir = \"./modis-datacube-output\"\n\n# URS, CMR, Harmony roots\nedl = \"urs.earthdata.nasa.gov\"\ncmr = \"cmr.earthdata.nasa.gov\"\nharmony_root = 'https://harmony.earthdata.nasa.gov'\n\ndownload_dir = os.path.abspath(download_dir) + os.path.sep\nPath(download_dir).mkdir(parents=True, exist_ok=True)"
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+ "text": "Analysis\nAs a simple example, let’s try to calculate a mean field for the whole time range.\n\nds.mean(dim='time').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nWe can also calculate a zonal mean (averaging over longitude)\n\nds.mean(dim='lon').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nOther aggregation methods include min(), max(), std(), along with others.\n\nds.std(dim='time').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nThe data we have are in 6h timesteps. This can be resampled to daily or monthly. If you are familiar with pandas, xarray uses the same methods.\n\nds.resample(time='M').mean().hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\nds_mon = ds.resample(time='M').mean()\nds_mon\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 24, lat: 25, lon: 53)\nCoordinates:\n * time (time) datetime64[ns] 2013-01-31 2013-02-28 ... 2014-12-31\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\nData variables:\n air (time, lat, lon) float32 -28.68 -28.49 -28.48 ... 24.57 24.56\n air_kelvin (time, lat, lon) float32 244.5 244.7 244.7 ... 297.7 297.7 297.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:time: 24lat: 25lon: 53Coordinates: (3)time(time)datetime64[ns]2013-01-31 ... 2014-12-31array(['2013-01-31T00:00:00.000000000', '2013-02-28T00:00:00.000000000',\n '2013-03-31T00:00:00.000000000', '2013-04-30T00:00:00.000000000',\n '2013-05-31T00:00:00.000000000', '2013-06-30T00:00:00.000000000',\n '2013-07-31T00:00:00.000000000', '2013-08-31T00:00:00.000000000',\n '2013-09-30T00:00:00.000000000', '2013-10-31T00:00:00.000000000',\n '2013-11-30T00:00:00.000000000', '2013-12-31T00:00:00.000000000',\n '2014-01-31T00:00:00.000000000', '2014-02-28T00:00:00.000000000',\n '2014-03-31T00:00:00.000000000', '2014-04-30T00:00:00.000000000',\n '2014-05-31T00:00:00.000000000', '2014-06-30T00:00:00.000000000',\n '2014-07-31T00:00:00.000000000', '2014-08-31T00:00:00.000000000',\n '2014-09-30T00:00:00.000000000', '2014-10-31T00:00:00.000000000',\n '2014-11-30T00:00:00.000000000', '2014-12-31T00:00:00.000000000'],\n dtype='datetime64[ns]')lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)Data variables: (2)air(time, lat, lon)float32-28.68 -28.49 ... 24.57 24.56long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[-28.68323 , -28.486452 , -28.479755 , ..., -30.658554 ,\n -29.743628 , -28.474194 ],\n [-26.076784 , -26.127504 , -26.4225 , ..., -32.5679 ,\n -31.105167 , -28.442825 ],\n [-22.770565 , -23.31516 , -24.042498 , ..., -31.165657 ,\n -28.38291 , -24.144924 ],\n ...,\n [ 22.688152 , 22.00097 , 21.773153 , ..., 22.218397 ,\n 21.734531 , 21.118395 ],\n [ 23.31952 , 23.16702 , 22.698233 , ..., 22.43775 ,\n 22.190727 , 21.715578 ],\n [ 23.903486 , 23.89203 , 23.585333 , ..., 23.154608 ,\n 22.947426 , 22.889124 ]],\n\n [[-32.41607 , -32.44866 , -32.738483 , ..., -31.54482 ,\n -30.430185 , -29.205448 ],\n [-31.216885 , -31.08063 , -31.236965 , ..., -32.135708 ,\n -30.825186 , -28.42241 ],\n [-27.826433 , -28.123934 , -28.78045 , ..., -29.734114 ,\n -27.383936 , -23.491434 ],\n...\n [ 24.899088 , 24.200085 , 24.072004 , ..., 24.861843 ,\n 24.510258 , 23.995668 ],\n [ 25.815008 , 25.661922 , 25.121607 , ..., 24.954088 ,\n 25.071083 , 24.735588 ],\n [ 26.023424 , 26.06767 , 25.74576 , ..., 25.566338 ,\n 25.591848 , 25.630259 ]],\n\n [[-26.348473 , -26.260897 , -26.380894 , ..., -33.07903 ,\n -32.067986 , -30.868315 ],\n [-25.419994 , -24.849277 , -24.405483 , ..., -34.531376 ,\n -32.82783 , -30.179682 ],\n [-23.181051 , -23.56476 , -23.574757 , ..., -35.446938 ,\n -31.91259 , -26.923311 ],\n ...,\n [ 23.299198 , 22.541454 , 22.60839 , ..., 23.378307 ,\n 23.067505 , 22.662996 ],\n [ 24.295895 , 24.286139 , 24.031782 , ..., 23.80259 ,\n 23.908312 , 23.579037 ],\n [ 24.897346 , 25.076134 , 24.909689 , ..., 24.547583 ,\n 24.573233 , 24.560413 ]]], dtype=float32)air_kelvin(time, lat, lon)float32244.5 244.7 244.7 ... 297.7 297.7long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[244.4667 , 244.66354, 244.67027, ..., 242.49142, 243.40633,\n 244.67577],\n [247.07323, 247.02248, 246.7275 , ..., 240.58205, 242.04489,\n 244.70726],\n [250.37941, 249.83484, 249.10748, ..., 241.98434, 244.76712,\n 249.00505],\n ...,\n [295.83795, 295.15085, 294.9229 , ..., 295.36826, 294.88437,\n 294.26828],\n [296.46942, 296.31686, 295.84802, ..., 295.5876 , 295.34058,\n 294.86536],\n [297.05316, 297.0418 , 296.73517, ..., 296.30438, 296.09732,\n 296.0389 ]],\n\n [[240.73384, 240.7013 , 240.4115 , ..., 241.60518, 242.71988,\n 243.94455],\n [241.93309, 242.06935, 241.913 , ..., 241.01428, 242.32481,\n 244.72758],\n [245.32361, 245.0261 , 244.36955, ..., 243.41588, 245.7661 ,\n 249.65858],\n...\n [298.04895, 297.35007, 297.22195, ..., 298.01172, 297.66013,\n 297.14554],\n [298.96484, 298.81186, 298.27136, ..., 298.10403, 298.22104,\n 297.88547],\n [299.17334, 299.2175 , 298.89566, ..., 298.71625, 298.74167,\n 298.7802 ]],\n\n [[246.80156, 246.88907, 246.76907, ..., 240.07089, 241.08206,\n 242.2817 ],\n [247.72998, 248.30064, 248.74443, ..., 238.61859, 240.3222 ,\n 242.97026],\n [249.96893, 249.58516, 249.57521, ..., 237.70308, 241.23743,\n 246.22667],\n ...,\n [296.4491 , 295.6914 , 295.75824, ..., 296.52817, 296.21747,\n 295.8128 ],\n [297.44586, 297.43613, 297.1817 , ..., 296.95242, 297.05823,\n 296.72897],\n [298.0472 , 298.22598, 298.0595 , ..., 297.6975 , 297.72318,\n 297.71024]]], dtype=float32)Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html\n\n\nThis is a really short time series but as an example, let’s calculate a monthly climatology (at least for 2 months). For this we can use groupby()\n\nds_clim = ds_mon.groupby(ds_mon.time.dt.month).mean()"
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- "title": "The following notebook is no longer supported, see this related notebook highlighting Zarr Data Cubes",
- "section": "Perform the subsetting operation using the Earthdata Harmony service",
- "text": "Perform the subsetting operation using the Earthdata Harmony service\nHarmony allows us to use a CMR style query to find and subset all matching granules for a collection, datetime, and spatial bounding box.\n\ntry: \n harmonyConfig = {\n 'collection_id': ccid, \n 'ogc-api-coverages_version': '1.0.0',\n 'variable': 'all',\n 'lat': '(' + str(south) + \":\" + str(north) + ')',\n 'lon': '(' + str(west) + \":\" + str(east) + ')', \n 'start': start_time,\n 'stop': stop_time\n }\n\n # subset granule\n async_url = harmony_root + '/{collection_id}/ogc-api-coverages/{ogc-api-coverages_version}/collections/{variable}/coverage/rangeset?subset=lat{lat}&subset=lon{lon}&subset=time(\"{start}\":\"{stop}\")'.format(**harmonyConfig)\n\n print('Request URL', async_url)\n async_response = request.urlopen(async_url)\n async_results = async_response.read()\n async_json = json.loads(async_results)\n pprint.pprint(async_json)\n\n\nexcept urllib.error.HTTPError as e:\n print(f\" [{datetime.now()}] FAILURE: {f}\\n\\n{e}\\n{e.read()}\\n\")\n raise e \nexcept Exception as e:\n print(f\" [{datetime.now()}] FAILURE: {f}\\n\\n{e}\\n\")\n raise e\n \n\nMonitor the Harmony job\n\njobConfig = {\n 'jobID': async_json['jobID']\n}\n\njob_url = harmony_root+'/jobs/{jobID}'.format(**jobConfig)\nprint('Job URL', job_url)\n\n\n\njob_response = request.urlopen(job_url)\njob_results = job_response.read()\njob_json = json.loads(job_results)\n\nprint('Job response:')\nprint()\npprint.pprint(job_json)\nwhile job_json['status'] == 'running' and job_json['progress'] < 100:\n print('Job status is running. Progress is ', job_json['progress'], '%. Trying again.')\n time.sleep(10)\n loop_response = request.urlopen(job_url)\n loop_results = loop_response.read()\n job_json = json.loads(loop_results)\n if job_json['status'] == 'running':\n continue\n\nDownload subsets to local computer\n\n# Download the data\nfile_urls = []\n\nfor job_result in job_json['links']:\n\n download_url = job_result['href']\n file_name = job_result['title']\n if file_name == 'Job Status':\n continue\n \n if file_name == 'STAC catalog':\n continue\n \n print(\"downloading \" + file_name)\n \n #store output in a defined directory with a meaninful filename based on orginal name\n out_file = download_dir + file_name\n print(out_file)\n file_urls.append(out_file)\n with request.urlopen(download_url) as response, open(out_file, 'wb') as output:\n shutil.copyfileobj(response, output) \n \n \n\nPerform resampling/reprojection on subsets using the GDAL module and gdal.Warp(). GDAL will only work on one variable (“layer”) at a time and also strip out important CF metadata and coordinate variables. Therefore we will use NCO tricks to correct these artifacts here and in the next steps. Use the ‘pynco’ module for NCO python bindings.\n\nnco = Nco()\n\n# Keyword args for gdal.Warp():\n# Set the output size in decimal degrees close to 1 km native resolution.\n# Use 'bilinear' interpolation (another option is 'cubicspline')\nkwargs = {'format': 'netCDF', 'copyMetadata': True, \n 'outputBounds': [west, south, east, north], \n 'xRes': 0.01,\n 'yRes' : 0.01,\n 'dstSRS':'+proj=longlat +datum=WGS84 +no_defs',\n 'resampleAlg': 'bilinear',\n }\nprint(kwargs)\n\nnc_vars = ['sea_surface_temperature', 'quality_level']\n\n# Loop through subsetted files (use file_urls as the loop list) and warp into defined region from kwargs{}\n\nfor i in range(len(file_urls)):\n for j in range(len(nc_vars)): \n variable = nc_vars[j]\n \n # input filename\n src_filename = file_urls[i] \n print(\"source filename: \", src_filename)\n \n # load the netCDF 'layer' like sea_surface_temperature (variable)\n nc_file = 'NETCDF:' + src_filename + ':' + variable\n print(nc_file)\n \n # try/catch for GDAL steps. Dont work on empty netCDF file from subsetting operation \n try:\n src = gdal.Open(nc_file, gdalconst.GA_ReadOnly)\n\n # set output filename\n out_filename = download_dir + 'subset_reproject-' + variable + '-' + basename(file_urls[i]) \n ds = gdal.Warp(out_filename, src, **kwargs)\n print(\"\")\n\n del ds\n del src \n \n # add time dimenson to 'Band1' using NCO\n nco.ncecat(input=out_filename, output='tmp.nc', options=['-v Band1 -u time'])\n nco.ncks(input='tmp.nc', output=out_filename, options=['-v Band1'])\n\n # use NCO to copy (append with -A ) the time variable to the output and make it a record dimension\n nco.ncks(input=src_filename, output=out_filename, options=['-v time -A --mk_rec_dmn time'])\n\n except Exception as e:\n #Errors can happen when downloading subsetted files because some might \n #not actually fall in the bounding box, and are returned 'empty'\n print(f\" [{datetime.now()}] FAILURE: \\n\")\n \n else:\n print(f\" [{datetime.now()}] SUCCESS for: {out_filename}\")\n print()\n\nAdd variable level CF metadata information that GDAL stripped out using NCO commands. Use the ‘pynco’ module for NCO python bindings.\n\nnco = Nco()\n\nsstConfig = {\n 'scale_factor': 0.005, \n 'add_offset': 273.15,\n 'valid_max': 10000,\n 'valid_min': -1000,\n 'long_name': 'sea surface temperature',\n 'standard_name': 'sea_surface_skin_temperature',\n 'coverage_content_type': 'physicalMeasurement'\n }\n\nqualityConfig = {\n 'valid_max': 0,\n 'valid_min': 5, \n 'flag_values': '0b, 1b, 2b, 3b, 4b, 5b',\n 'flag_meanings': 'no_data bad_data worst_quality low_quality acceptable_quality best_quality',\n 'long_name': 'quality level of SST pixel',\n 'coverage_content_type': 'qualityInformation'\n }\n \nnc_vars = ['sea_surface_temperature', 'quality_level']\nos.chdir(download_dir)\n\nfor i in range(len(nc_vars)): \n variable = nc_vars[i]\n reg_ex = \"subset*\" + variable + \"*\"\n for file in glob.glob(reg_ex):\n if variable == 'sea_surface_temperature':\n print(\" -> Updating \"+ file)\n nco.ncrename(input=file, output=file, options=['-v .Band1,sea_surface_temperature'])\n \n\n # update the SST variable attributes\n nco.ncatted(input=file, output=file, options=['-a scale_factor,'+ variable + ',o,f,{scale_factor}'.format(**sstConfig)])\n nco.ncatted(input=file, output=file, options=['-a add_offset,'+ variable + ',o,f,{add_offset}'.format(**sstConfig)])\n nco.ncatted(input=file, output=file, options=['-a valid_min,'+ variable + ',o,s,{valid_min}'.format(**sstConfig)])\n nco.ncatted(input=file, output=file, options=['-a valid_max,'+ variable + ',o,s,{valid_max}'.format(**sstConfig)])\n nco.ncatted(input=file, output=file, options=['-a long_name,'+ variable + ',o,c,\"{long_name}\"'.format(**sstConfig)])\n nco.ncatted(input=file, output=file, options=['-a standard_name,'+ variable + ',o,c,{standard_name}'.format(**sstConfig)])\n nco.ncatted(input=file, output=file, options=['-a coverage_content_type,'+ variable + ',o,c,{coverage_content_type}'.format(**sstConfig)])\n \n elif variable == 'quality_level':\n print(\" -> Updating \"+ file)\n \n nco.ncrename(input=file, output=file, options=['-v .Band1,' + variable])\n\n # update the quality variable attributes\n nco.ncatted(input=file, output=file, options=['-a valid_min,'+ variable + ',o,s,{valid_min}'.format(**qualityConfig)])\n nco.ncatted(input=file, output=file, options=['-a valid_max,'+ variable + ',o,s,{valid_max}'.format(**qualityConfig)])\n nco.ncatted(input=file, output=file, options=['-a long_name,'+ variable + ',o,c,\"{long_name}\"'.format(**qualityConfig)])\n nco.ncatted(input=file, output=file, options=['-a flag_values,'+ variable + ',o,c,\"{flag_values}\"'.format(**qualityConfig)])\n nco.ncatted(input=file, output=file, options=['-a flag_meanings,'+ variable + ',o,c,\"{flag_meanings}\"'.format(**qualityConfig)])\n nco.ncatted(input=file, output=file, options=['-a coverage_content_type,'+ variable + ',o,c,{coverage_content_type}'.format(**qualityConfig)])\n \n \nprint( \"-Done-\\n\")\n\nCreate the MODIS SST Data Cube. Copy variable(s) to SST target files and catenate all of them into a final output netCDF file using NCO.\n\nnco = Nco()\nos.chdir(download_dir)\n\n\n# Loop through the SST files and add variable quality_level to SST file (target)\nreg_ex = \"subset_reproject-sea_surface_temperature*\"\n\nprint( \"Copying quality_level variables to target sst files . . .\")\nfor sst_file in glob.glob(reg_ex): \n quality_file = sst_file.replace(\"sea_surface_temperature\", \"quality_level\") \n nco.ncks(input=quality_file, output=sst_file, options=['-v quality_level -A'])\n \n \n# Create the data cube using NCO ncrcat command\nprint(\". . . -Done- \\n\\nCreating MODIS SST data cube . . . \")\nnco.ncrcat(input=glob.glob(reg_ex), output='MODIS_SST.data-cube.nc')\nprint(\". . . -Done- \\n\")\n\n\nRead the data with xarray and perform some plotting\n\ndataCube = download_dir + 'MODIS_SST.data-cube.nc'\nxds = xr.open_dataset(dataCube)\n\n# create objects for subplots\nfig, axes = plt.subplots(ncols=2)\n\n# plot a time series at a specific location\nxds.sea_surface_temperature.isel(lat = 200, lon = 300).plot(ax=axes[0], marker = '.', linestyle = 'None')\n\n# a histogram of all points in region of interest\nxds.sea_surface_temperature.plot(ax=axes[1])\n\nplt.tight_layout()\nplt.draw()\n\n# filter the dataset using quality information (quality_level value 4 and 5 are best data)\nqc_dataset = xds.where((xds['sea_surface_temperature'] < 310) & (xds['quality_level'] >= 4))\n\n# re-plot the time series at a specific location and the regional histogram\nfig, axes = plt.subplots(ncols=2)\nqc_dataset.sea_surface_temperature.isel(lat = 200, lon = 300).plot(ax=axes[0], marker = '.', linestyle = 'None')\nqc_dataset.sea_surface_temperature.plot(ax=axes[1])\n\nplt.tight_layout()\nplt.draw()"
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+ "text": "Plot results\nFinally, let’s plot the results! This will plot the lat/lon axes of the original ds DataArray.\n\nds_clim.air.sel(month=10).plot()\n\n<matplotlib.collections.QuadMesh at 0x7f22bb7acd90>"
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+ "title": "ECCO",
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- "text": "This notebook will demonstrate how to subset Level 2 data using a sea surface temperature product from the following collection: MODIS_A-JPL-L2P-v2019.0, GHRSST Level 2P Global Sea Surface Skin Temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua satellite (GDS2)."
+ "text": "The Estimating the Circulation and Climate of the Ocean (ECCO) project makes the best possible estimates of ocean circulation and its role in climate. ECCO combines state-of-the-art ocean circulation models with global ocean data sets. More information can be found on PO.DAAC’s ECCO webpage."
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- "section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata.\n\nfrom harmony import BBox, Client, Collection, Request, Environment, LinkType\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nimport tempfile\nimport getpass\nimport netrc\nimport json\nimport requests\nimport sys\nimport shutil\nimport xarray as xr\n\n\nFind a granule for subsetting\nBelow we call out a specific granule (G2524986524-POCLOUD) on which we will use the podaac L2 subsetter. Finding this information would complicate the tutorial- but po.daac has a tutorial available for using the CMR API to find collections and granules of interest. Please see this tutorial for that information.\n\ncollection = 'C1940473819-POCLOUD'\nvariable = 'sea_surface_temperature'\nvenue = 'prod'\n\n\n# Defaults\ncmr_root = 'cmr.earthdata.nasa.gov'\nharmony_root = 'https://harmony.earthdata.nasa.gov'\nedl_root = 'urs.earthdata.nasa.gov'"
+ "objectID": "quarto_text/ECCO.html#background",
+ "href": "quarto_text/ECCO.html#background",
+ "title": "ECCO",
+ "section": "",
+ "text": "The Estimating the Circulation and Climate of the Ocean (ECCO) project makes the best possible estimates of ocean circulation and its role in climate. ECCO combines state-of-the-art ocean circulation models with global ocean data sets. More information can be found on PO.DAAC’s ECCO webpage."
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- "section": "Subset of a PO.DAAC Granule",
- "text": "Subset of a PO.DAAC Granule\nWe can now build onto the root URL in order to actually perform a transformation. The first transformation is a subset of a selected granule. At this time, this requires discovering the granule id from CMR. That information can then be appended to the root URL and used to call Harmony with the help of the request library.\nAbove we show how to find a granule id for processing.\nNotes: The L2 subsetter current streams the data back to the user, and does not stage data in S3 for redirects. This is functionality we will be adding over time. It doesn’t work with URS backed files, which is coming in the next few weeks it only works on the show dataset, but\n\ncmr_url = \"https://\"+cmr_root+\"/search/granules.umm_json?collection_concept_id=\"+collection+\"&sort_key=-start_date&bounding_box=-90,-45.75,90,-45\"\n\nresponse = requests.get(cmr_url)\n\ngid=response.json()['items'][0]['meta']['concept-id']\nprint(response.json()['items'][0])\nprint(gid)\n\n{'meta': {'concept-type': 'granule', 'concept-id': 'G2525170373-POCLOUD', 'revision-id': 3, 'native-id': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0', 'provider-id': 'POCLOUD', 'format': 'application/vnd.nasa.cmr.umm+json', 'revision-date': '2022-10-25T21:12:59.142Z'}, 'umm': {'TemporalExtent': {'RangeDateTime': {'EndingDateTime': '2022-10-25T18:54:58.000Z', 'BeginningDateTime': '2022-10-25T18:50:01.000Z'}}, 'MetadataSpecification': {'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.4', 'Name': 'UMM-G', 'Version': '1.6.4'}, 'GranuleUR': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0', 'ProviderDates': [{'Type': 'Insert', 'Date': '2022-10-25T21:10:17.165Z'}, {'Type': 'Update', 'Date': '2022-10-25T21:10:17.165Z'}], 'SpatialExtent': {'HorizontalSpatialDomain': {'Geometry': {'BoundingRectangles': [{'WestBoundingCoordinate': -85.928, 'NorthBoundingCoordinate': -26.101, 'EastBoundingCoordinate': -54.117, 'SouthBoundingCoordinate': -47.37}], 'GPolygons': [{'Boundary': {'Points': [{'Longitude': -54.11689, 'Latitude': -43.06013}, {'Longitude': -58.42233, 'Latitude': -35.30318}, {'Longitude': -62.29779, 'Latitude': -26.1022}, {'Longitude': -73.93747, 'Latitude': -28.35286}, {'Longitude': -85.92834, 'Latitude': -29.50335}, {'Longitude': -84.04758, 'Latitude': -47.36956}, {'Longitude': -68.62455, 'Latitude': -46.17143}, {'Longitude': -54.11689, 'Latitude': -43.06013}]}}]}}}, 'DataGranule': {'ArchiveAndDistributionInformation': [{'SizeUnit': 'MB', 'Size': 20.64744758605957, 'Checksum': {'Value': 'e2d56b12c5fcbe7f10af1653834d76f6', 'Algorithm': 'MD5'}, 'SizeInBytes': 21650418, 'Name': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc'}, {'SizeUnit': 'MB', 'Size': 9.34600830078125e-05, 'Checksum': {'Value': 'a3c500f11b4d0af678f9e8de11397c97', 'Algorithm': 'MD5'}, 'SizeInBytes': 98, 'Name': '20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5'}], 'DayNightFlag': 'Unspecified', 'ProductionDateTime': '2022-10-25T21:06:25.000Z'}, 'CollectionReference': {'Version': '2019.0', 'ShortName': 'MODIS_A-JPL-L2P-v2019.0'}, 'RelatedUrls': [{'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc', 'Description': 'Download 20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc', 'Type': 'GET DATA'}, {'URL': 's3://podaac-ops-cumulus-protected/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc', 'Description': 'This link provides direct download access via S3 to the granule', 'Type': 'GET DATA VIA DIRECT ACCESS'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5', 'Description': 'Download 20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5', 'Type': 'EXTENDED METADATA'}, {'URL': 's3://podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5', 'Description': 'This link provides direct download access via S3 to the granule', 'Type': 'EXTENDED METADATA'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/s3credentials', 'Description': 'api endpoint to retrieve temporary credentials valid for same-region direct s3 access', 'Type': 'VIEW RELATED INFORMATION'}, {'URL': 'https://opendap.earthdata.nasa.gov/collections/C1940473819-POCLOUD/granules/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0', 'Type': 'USE SERVICE API', 'Subtype': 'OPENDAP DATA', 'Description': 'OPeNDAP request URL'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sea_surface_temperature.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.quality_level.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_bias.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}, {'URL': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-public/MODIS_A-JPL-L2P-v2019.0/20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.sses_standard_deviation.png', 'Type': 'GET RELATED VISUALIZATION', 'Subtype': 'DIRECT DOWNLOAD', 'MimeType': 'image/png'}]}}\nG2525170373-POCLOUD\n\n\n\nharmony_client = Client(env=Environment.PROD)\n\ncollection_id = Collection(collection) \n\nrequest = Request(\n collection=collection_id,\n spatial=BBox(-90,-45.75,90,45), # lat: (-45.75:45), lon: (-90:90)\n granule_id=gid \n)\n\nrequest.is_valid()\n\nTrue\n\n\n\n# sumbit request and monitor job\njob_id = harmony_client.submit(request)\nprint('\\n Waiting for the job to finish. . .\\n')\nresponse = harmony_client.result_json(job_id, show_progress=True)\nprint(\"\\n. . .DONE!\")\n\n\n Waiting for the job to finish. . .\n\n\n. . .DONE!\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\n\ntemp_dir = tempfile.mkdtemp()\nfutures = harmony_client.download_all(job_id, directory=temp_dir, overwrite=True)\nfile_names = [f.result() for f in futures]\nfile_names\n\n['C:\\\\Users\\\\nickles\\\\AppData\\\\Local\\\\Temp\\\\tmp2hna47qz\\\\20221025185001-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0_subsetted.nc4']\n\n\n\nds = xr.open_dataset(file_names[0])\nds\n\nlat_var = None\nlon_var = None\n\n# Determine the lat/lon coordinate names\nfor coord_name, coord in ds.coords.items():\n if 'units' not in coord.attrs:\n continue\n if coord.attrs['units'] == 'degrees_north':\n lat_var = coord_name\n if coord.attrs['units'] == 'degrees_east':\n lon_var = coord_name\n \n# If the lat/lon coordinates could not be determined, use l2ss-py get_coord_variable_names\nif not lat_var or not lon_var:\n from podaac.subsetter import subset\n lat_var_names, lon_var_names = subset.get_coord_variable_names(ds)\n lat_var = lat_var_names[0]\n lon_var = lon_var_names[0]\n\nprint(f'lat_var={lat_var}')\nprint(f'lon_var={lon_var}')\n\nlat_var=lat\nlon_var=lon\n\n\n\nif ds[variable].size == 0:\n print(\"No data in subsetted region. Exiting\")\n sys.exit(0)\n\n\nimport matplotlib.pyplot as plt\nimport math\n\nfig, axes = plt.subplots(ncols=3, nrows=math.ceil((len(ds.data_vars)/3)))\nfig.set_size_inches((15,15))\n\nfor count, xvar in enumerate(ds.data_vars):\n if ds[xvar].dtype == \"timedelta64[ns]\":\n continue\n #ds[xvar].astype('timedelta64[D]').plot(ax=axes[int(count/3)][count%3])\n ds[xvar].plot(ax=axes[int(count/3)][count%3])"
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+ "text": "Data Resources & Tutorials\n\nData Access\n\nIn-cloud - Direct Access to ECCO V4r4 Datasets in the Cloud\nIn this notebook, you will learn to 1) identify Amazon Web Services (AWS) S3 endpoints corresponding to two ECCO datasets of interest, 2) retrieve your AWS credentials which provide access to PO.DAAC data in AWS, 3) load the target netCDF files into two multi-file datasets with xarray, and 4) slice and plot the datasets as animated time series using matplotlib and cartopy. The notebook finishes by writing the animations to disk as MP4 files. The two variables analyzed in this example are global monthly sea surface height (SSH) data and monthly ocean temperature flux (TFLUX) data over the Gulf of Mexico.\n\n\nLocal Machine Download - Access to ECCO V4r4 Datasets on a Local Machine\nThis is a modified version of the In-cloud Access python notebook above to batch download ECCO data on a local machine.\n\n\n\nUse Case Demo\n\nECCO Science Use Case Jupyter Notebook Demonstration\nThis tutorial will use data from the ECCO model to derive spatial correlations between sea surface temperature anomaly and sea surface height anomaly through time for two regions of the Indian Ocean. The goal is to investigate the correlative characteristics of the Indian Ocean Dipole and how the east and west regions behave differently."
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- "title": "Harmony EOSS L2SS API Tutorial",
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- "text": "Verify the subsetting worked\nBounds used were:\n‘lat’: ‘(-45.75:45)’, ‘lon’: ‘(-90:90)’\n\nvar_ds = ds[variable]\nmsk = xr.ufuncs.logical_not(xr.ufuncs.isnan(var_ds.data.squeeze()))\n\nllat = ds[lat_var].where(msk)\nllon = ds[lon_var].where(msk)\n\nlat_max = llat.max()\nlat_min = llat.min()\n\nlon_min = llon.min()\nlon_max = llon.max()\n\nlon_min = (lon_min + 180) % 360 - 180\nlon_max = (lon_max + 180) % 360 - 180\n\nprint(lon_min)\nprint(lon_max)\nprint(lat_min)\nprint(lat_max)\n\nif lat_max <= 45 and lat_min >= -45.75:\n print(\"Successful Latitude subsetting\")\nelif xr.ufuncs.isnan(lat_max) and xr.ufuncs.isnan(lat_min):\n print(\"Partial Lat Success - no Data\")\nelse:\n assert False\n\n\nif lon_max <= 90 and lon_min >= -90:\n print(\"Successful Longitude subsetting\")\nelif xr.ufuncs.isnan(lon_max) and xr.ufuncs.isnan(lon_min):\n print(\"Partial Lon Success - no Data\")\nelse:\n assert False\n \n\n<xarray.DataArray 'lon' ()>\narray(-85.92834473)\n<xarray.DataArray 'lon' ()>\narray(-54.11688995)\n<xarray.DataArray 'lat' ()>\narray(-45.74999237)\n<xarray.DataArray 'lat' ()>\narray(-27.91536331)\nSuccessful Latitude subsetting\nSuccessful Longitude subsetting"
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- "text": "This notebook provides an overview of the capabilities offered through the Harmony API and SWOT L2 Reproject tool. While written for SWOT L2 data, it works with most any level 2 data for projecting to a normal grid. In this tutorial we will use MODIS L2 data to show the native file projected to equal-area-cylindracal projection using both Nearest Neighbor and Bi-linear interpolation.\nStanding on the shoulders of previous authors: Amy Steiker, Patrick Quinn"
+ "text": "The Surface Water and Ocean Topography (SWOT) mission aims to provide valuable data and information about the world’s oceans and its terrestrial surface water such as lakes, rivers, and wetlands. SWOT is being developed jointly by NASA and Centre National D’Etudes Spatiales (CNES), with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA). The satellite launched on December 16, 2022 and data is expected to be released to the public no earlier than Fall 2023. PO.DAAC is the NASA archive for the SWOT mission, and will be making data available via the NASA Earthdata Cloud (hosted in AWS) with direct download capabilities available. More information can be found on PO.DAAC’s SWOT webpage.\nPO.DAAC will host a variety of SWOT data products. Their product description documents can be found in the chart listing each dataset. Before these SWOT products are available, we have SWOT simulated datasets encompassing both oceanography and hydrology sample data. This data is not for analysis, but rather to become comfortable with future SWOT products data formats and access mechanisms."
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- "section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
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+ "text": "The Surface Water and Ocean Topography (SWOT) mission aims to provide valuable data and information about the world’s oceans and its terrestrial surface water such as lakes, rivers, and wetlands. SWOT is being developed jointly by NASA and Centre National D’Etudes Spatiales (CNES), with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA). The satellite launched on December 16, 2022 and data is expected to be released to the public no earlier than Fall 2023. PO.DAAC is the NASA archive for the SWOT mission, and will be making data available via the NASA Earthdata Cloud (hosted in AWS) with direct download capabilities available. More information can be found on PO.DAAC’s SWOT webpage.\nPO.DAAC will host a variety of SWOT data products. Their product description documents can be found in the chart listing each dataset. Before these SWOT products are available, we have SWOT simulated datasets encompassing both oceanography and hydrology sample data. This data is not for analysis, but rather to become comfortable with future SWOT products data formats and access mechanisms."
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- "section": "Import packages",
- "text": "Import packages\n\nfrom urllib import request, parse\nfrom http.cookiejar import CookieJar\nimport getpass\nimport netrc\nimport os\nimport requests\nimport json\nimport pprint\nfrom osgeo import gdal\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport rasterio\nfrom rasterio.plot import show\nimport numpy as np\nimport os\nimport time\nfrom netCDF4 import Dataset\n%matplotlib inline"
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+ "text": "SWOT Data Resources & Tutorials\n\nSearch & Download\nVia Graphical User Interface:\n\nFind/download simulated SWOT data on Earthdata Search\n\nProgrammatically: ie. within Python code workflows\n\nSearch and Download via earthaccess\nwith unique SWORD river reach ID\nwith unique Hydrologic Unit Code (HUC) basin ID\n\nVia Command Line - PO.DAAC subscriber/downloader examples:\nHydrology: These examples will download either the river vector files or the raster files for a location in Texas with multiple passes:\npodaac-data-downloader -c SWOT_SIMULATED_NA_CONTINENT_L2_HR_RiverSP_V1 -d ./SWOT_SIMULATED_NA_CONTINENT_L2_HR_RiverSP_V1 -start-date 2022-08-02T00:00:00Z --end-date 2022-08-22T00:00:00Z -b=\"-97,32.5,-96.5,33\"\npodaac-data-downloader -c SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 -d ./SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 --start-date 2022-08-02T00:00:00Z --end-date 2022-08-22T00:00:00Z -b=\"-97,32.5,-96.5,33\"\nOceanography: This example will download modeled sea surface heights:\npodaac-data-subscriber -c SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1 -d ./data/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1 --start-date 2015-12-30T00:00:00Z\npodaac-data-downloader -c SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1 -d ./data/SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_SCIENCE_V1 --start-date 2011-11-20T00:00:00Z --end-date 2011-11-20T12:00:00Z\n\nSee how to Download/Subscribe for more information on how to use the PO.DAAC subscriber/downloader.\n\n\n\nIn-cloud Access & Visualization\nAccess sample SWOT Hydrology data in the cloud\nAccess sample SWOT Oceanography data in the cloud\n\n\nGIS workflows\nSWOT: Through a GIS Lens StoryMap\nGIS shapefile exploration\nNetCDF to Geotiff Conversion\n\n\nTransform\nTransform SWOT Hydrology river reach shapefiles into time series"
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- "title": "Harmony API Introduction",
- "section": "Identify a data collection of interest",
- "text": "Identify a data collection of interest\nA CMR collection ID is needed to request services through Harmony. The collection ID can be determined using the CMR API. We will query the corresponding ID of a known collection short name, MODIS_A-JPL-L2P-v2019.0.\n\nparams = {\n 'short_name': 'MODIS_A-JPL-L2P-v2019.0',\n 'provider_id': 'POCLOUD'\n} # parameter dictionary with known CMR short_name\n\ncmr_collections_url = 'https://cmr.earthdata.nasa.gov/search/collections.json'\ncmr_response = requests.get(cmr_collections_url, params=params)\ncmr_results = json.loads(cmr_response.content) # Get json response from CMR collection metadata\n\ncollectionlist = [el['id'] for el in cmr_results['feed']['entry']]\nharmony_collection_id = collectionlist[0]\nprint(harmony_collection_id)\n\nC1940473819-POCLOUD\n\n\nWe can also view the MODIS_A-JPL-L2P-v2019.0 collection metadata to glean more information about the collection:\n\npprint.pprint(cmr_results)\n\n{'feed': {'entry': [{'archive_center': 'NASA/JPL/PODAAC',\n 'associations': {'services': ['S1962070864-POCLOUD',\n 'S2004184019-POCLOUD',\n 'S2153799015-POCLOUD',\n 'S2227193226-POCLOUD'],\n 'tools': ['TL2108419875-POCLOUD',\n 'TL2092786348-POCLOUD'],\n 'variables': ['V1997812737-POCLOUD',\n 'V1997812697-POCLOUD',\n 'V2112014688-POCLOUD',\n 'V1997812756-POCLOUD',\n 'V1997812688-POCLOUD',\n 'V1997812670-POCLOUD',\n 'V1997812724-POCLOUD',\n 'V2112014684-POCLOUD',\n 'V1997812701-POCLOUD',\n 'V1997812681-POCLOUD',\n 'V2112014686-POCLOUD',\n 'V1997812663-POCLOUD',\n 'V1997812676-POCLOUD',\n 'V1997812744-POCLOUD',\n 'V1997812714-POCLOUD']},\n 'boxes': ['-90 -180 90 180'],\n 'browse_flag': True,\n 'cloud_hosted': True,\n 'collection_data_type': 'SCIENCE_QUALITY',\n 'consortiums': ['GEOSS', 'EOSDIS'],\n 'coordinate_system': 'CARTESIAN',\n 'data_center': 'POCLOUD',\n 'dataset_id': 'GHRSST Level 2P Global Sea Surface Skin '\n 'Temperature from the Moderate Resolution '\n 'Imaging Spectroradiometer (MODIS) on the '\n 'NASA Aqua satellite (GDS2)',\n 'has_formats': True,\n 'has_spatial_subsetting': True,\n 'has_temporal_subsetting': True,\n 'has_transforms': False,\n 'has_variables': True,\n 'id': 'C1940473819-POCLOUD',\n 'links': [{'href': 'https://podaac.jpl.nasa.gov/Podaac/thumbnails/MODIS_A-JPL-L2P-v2019.0.jpg',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/browse#'},\n {'href': 'https://github.com/podaac/data-readers',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ghrsst/open/docs/GDS20r5.pdf',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://ghrsst.jpl.nasa.gov',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/flag/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/reprocessing/r2019/sst/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst4/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://oceancolor.gsfc.nasa.gov/atbd/sst/',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'http://www.ghrsst.org',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac.jpl.nasa.gov/forum/viewforum.php?f=18&sid=e2d67e5a01815fc6e39fcd2087ed8bc8',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://podaac.jpl.nasa.gov/CitingPODAAC',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'length': '75.0MB',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'},\n {'href': 'https://github.com/podaac/tutorials/blob/master/notebooks/MODIS_L2P_SST_DataCube.ipynb',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/documentation#'},\n {'href': 'https://search.earthdata.nasa.gov/search/granules?p=C1940473819-POCLOUD',\n 'hreflang': 'en-US',\n 'rel': 'http://esipfed.org/ns/fedsearch/1.1/data#'}],\n 'online_access_flag': True,\n 'orbit_parameters': {'inclination_angle': '98.1',\n 'number_of_orbits': '1.0',\n 'period': '98.4',\n 'swath_width': '2330.0'},\n 'organizations': ['NASA/JPL/PODAAC'],\n 'original_format': 'UMM_JSON',\n 'platforms': ['Aqua'],\n 'processing_level_id': '2',\n 'service_features': {'esi': {'has_formats': False,\n 'has_spatial_subsetting': False,\n 'has_temporal_subsetting': False,\n 'has_transforms': False,\n 'has_variables': False},\n 'harmony': {'has_formats': True,\n 'has_spatial_subsetting': True,\n 'has_temporal_subsetting': True,\n 'has_transforms': False,\n 'has_variables': True},\n 'opendap': {'has_formats': True,\n 'has_spatial_subsetting': True,\n 'has_temporal_subsetting': True,\n 'has_transforms': False,\n 'has_variables': True}},\n 'short_name': 'MODIS_A-JPL-L2P-v2019.0',\n 'summary': 'NASA produces skin sea surface temperature '\n '(SST) products from the Infrared (IR) '\n 'channels of the Moderate-resolution Imaging '\n 'Spectroradiometer (MODIS) onboard the Aqua '\n 'satellite. Aqua was launched by NASA on May '\n '4, 2002, into a sun synchronous, polar orbit '\n 'with a daylight ascending node at 1:30 pm, '\n 'formation flying in the A-train with other '\n 'Earth Observation Satellites (EOS), to study '\n 'the global dynamics of the Earth atmosphere, '\n 'land and oceans. MODIS captures data in 36 '\n 'spectral bands at a variety of spatial '\n 'resolutions. Two SST products can be present '\n 'in these files. The first is a skin SST '\n 'produced for both day and night (NSST) '\n 'observations, derived from the long wave IR '\n '11 and 12 micron wavelength channels, using a '\n 'modified nonlinear SST algorithm intended to '\n 'provide continuity of SST derived from '\n 'heritage and current NASA sensors. At night, '\n 'a second SST product is generated using the '\n 'mid-infrared 3.95 and 4.05 micron wavelength '\n 'channels which are unique to MODIS; the SST '\n 'derived from these measurements is identified '\n 'as SST4. The SST4 product has lower '\n 'uncertainty, but due to sun glint can only be '\n 'used at night. MODIS L2P SST data have a 1 km '\n 'spatial resolution at nadir and are stored in '\n '288 five minute granules per day. Full global '\n 'coverage is obtained every two days, with '\n 'coverage poleward of 32.3 degree being '\n 'complete each day. The production of MODIS '\n 'L2P SST files is part of the Group for High '\n 'Resolution Sea Surface Temperature (GHRSST) '\n 'project and is a joint collaboration between '\n 'the NASA Jet Propulsion Laboratory (JPL), the '\n 'NASA Ocean Biology Processing Group (OBPG), '\n 'and the Rosenstiel School of Marine and '\n 'Atmospheric Science (RSMAS). Researchers at '\n 'RSMAS are responsible for SST algorithm '\n 'development, error statistics and quality '\n 'flagging, while the OBPG, as the NASA ground '\n 'data system, is responsible for the '\n 'production of daily MODIS ocean products. JPL '\n 'acquires MODIS ocean granules from the OBPG '\n 'and reformats them to the GHRSST L2P netCDF '\n 'specification with complete metadata and '\n 'ancillary variables, and distributes the data '\n 'as the official Physical Oceanography Data '\n 'Archive (PO.DAAC) for SST. The R2019.0 '\n 'supersedes the previous R2014.0 datasets '\n 'which can be found at '\n 'https://doi.org/10.5067/GHMDA-2PJ02',\n 'time_start': '2002-07-04T00:00:00.000Z',\n 'title': 'GHRSST Level 2P Global Sea Surface Skin '\n 'Temperature from the Moderate Resolution '\n 'Imaging Spectroradiometer (MODIS) on the NASA '\n 'Aqua satellite (GDS2)',\n 'updated': '2019-12-02T22:59:24.849Z',\n 'version_id': '2019.0'}],\n 'id': 'https://cmr.earthdata.nasa.gov:443/search/collections.json?short_name=MODIS_A-JPL-L2P-v2019.0&provider_id=POCLOUD',\n 'title': 'ECHO dataset metadata',\n 'updated': '2022-10-25T21:32:46.472Z'}}\n\n\nNext we get a granule ID from this collection, G2525170359-POCLOUD.\n\ncmr_url = \"https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=\"+harmony_collection_id+\"&sort_key=-start_date\"\n\nresponse = requests.get(cmr_url)\n\ngid=response.json()['items'][0]['meta']['concept-id']\nprint(gid)\n\nG2525170359-POCLOUD"
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+ "href": "quarto_text/SWOT.html#additional-resources",
+ "title": "SWOT",
+ "section": "Additional Resources",
+ "text": "Additional Resources\n\n2022 SWOT Ocean Cloud Workshop\nhttps://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/\nThe goal of the workshop was to enable the (oceanography) science team to be ready for processing and handling the large volumes of SWOT SSH data in the cloud. Learning objectives focus on how to access the simulated SWOT L2 SSH data from Earthdata Cloud either by downloading or accessing the data on the cloud."
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- "title": "Harmony API Introduction",
- "section": "Access reprojected data",
- "text": "Access reprojected data\nThe Harmony API accepts reprojection requests with a given coordinate reference system using the outputCrs keyword. According to the Harmony API documentation, this keyword “recognizes CRS types that can be inferred by gdal, including EPSG codes, Proj4 strings, and OGC URLs (http://www.opengis.net/def/crs/…)”."
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+ "title": "Dataset Specific",
+ "section": "",
+ "text": "ECCO - Estimating the Circulation and Climate of the Ocean\nGHRSST - Group for High Resolution Sea Surface Temperature\nOPERA - Observational Products for End-Users from Remote Sensing Analysis\nSentinel-6A Michael Freilich Jason-CS\nSMAP - Soil Moisture Active Passive\nS-MODE - Submesoscale Ocean Dynamics and Vertical Transport Experiment\nSWOT - Surface Water and Ocean Topography"
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- "href": "notebooks/l2-regridding/reprojection notebook.html#the-practice-datasets-below-used-for-this-tutorial-are-no-longer-supported-for-details-about-the-harmony-api-see-this-tutorial-from-the-2021-cloud-hackathon-or-this-tutorial-introducing-the-harmony-py-library.",
- "title": "Harmony API Introduction",
- "section": "The practice datasets below used for this tutorial are no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.",
- "text": "The practice datasets below used for this tutorial are no longer supported, for details about the Harmony API see this tutorial from the 2021 Cloud Hackathon or this tutorial introducing the Harmony-py library.\nTwo examples below demonstrate inputting an EPSG code and Proj4 string using the global test granule from previous examples. First, let’s view the projection information of the granule in the native projection, using the variable subset example:"
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+ "text": "ECCO - Estimating the Circulation and Climate of the Ocean\nGHRSST - Group for High Resolution Sea Surface Temperature\nOPERA - Observational Products for End-Users from Remote Sensing Analysis\nSentinel-6A Michael Freilich Jason-CS\nSMAP - Soil Moisture Active Passive\nS-MODE - Submesoscale Ocean Dynamics and Vertical Transport Experiment\nSWOT - Surface Water and Ocean Topography"
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- "title": "Harmony API Introduction",
- "section": "Access Level 2 swath regridded data",
- "text": "Access Level 2 swath regridded data\nMoving outside of the harmony/gdal service, we will now request regridding from the sds/swot-reproject service using the C1940473819-POCLOUD.\nThe Harmony API accepts several query parameters related to regridding and interpolation in addition to the reprojection parameters above:\ninterpolation=<String> - Both near and bilinear are valid options\nscaleSize=x,y - 2 comma separated numbers as floats\nscaleExtent=xmin,ymin,xmax,ymax - 4 comma separated numbers as floats\nwidth=<Float>\nheight=<Float>\nAn error is returned if both scaleSize and width/height parameters are both provided (only one or the other can be used).\nRequest reprojection to Europe Lambert Conformal Conic with a new scale extent and nearest neighbor interpolation:\n\nharmony_root = 'https://harmony.earthdata.nasa.gov'\n\n# URL encode string using urllib parse package\nproj_string = '+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs' # proj4 of WGS 84 / NSIDC EASE-Grid 2.0 Global projection\n#l2proj_string = '+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'\nl2proj_encode = parse.quote(proj_string)\n\nregridConfig = {\n 'l2collection_id': 'C1940473819-POCLOUD',\n 'ogc-api-coverages_version': '1.0.0',\n 'variable': 'all',\n 'granuleid': 'G1234734747-POCLOUD',\n 'outputCrs': l2proj_encode,\n 'interpolation': 'near',\n 'width': 1000,\n 'height': 1000\n}\n\nregrid_url = harmony_root+'/{l2collection_id}/ogc-api-coverages/{ogc-api-coverages_version}/collections/{variable}/coverage/rangeset?&granuleid={granuleid}&outputCrs={outputCrs}&interpolation={interpolation}&height={height}&width={width}'.format(**regridConfig)\nprint('Request URL', regrid_url)\nregrid_response = request.urlopen(regrid_url)\nregrid_results = regrid_response.read()\n\nRequest URL https://harmony.earthdata.nasa.gov/C1940473819-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?&granuleid=G1234734747-POCLOUD&outputCrs=%2Bproj%3Dcea%20%2Blon_0%3D0%20%2Blat_ts%3D30%20%2Bx_0%3D0%20%2By_0%3D0%20%2Bellps%3DWGS84%20%2Btowgs84%3D0%2C0%2C0%2C0%2C0%2C0%2C0%20%2Bunits%3Dm%20%2Bno_defs&interpolation=near&height=1000&width=1000\n\n\nHTTPError: HTTP Error 401: Unauthorized\n\n\nThis reprojected and regridded output is downloaded to the Harmony outputs directory and we can inspect a variable to check for projection and grid dimension:\n\nregrid_file_name = 'regrid-near.nc'\nregrid_filepath = str(regrid_file_name)\nfile_ = open(regrid_filepath, 'wb')\nfile_.write(regrid_results)\nfile_.close()\n\n\nharmony_root = 'https://harmony.earthdata.nasa.gov'\n\n# URL encode string using urllib parse package\nproj_string = '+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs' # proj4 of WGS 84 / NSIDC EASE-Grid 2.0 Global projection\n#l2proj_string = '+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'\nl2proj_encode = parse.quote(proj_string)\n\nharmony_root = 'https://harmony.uat.earthdata.nasa.gov'\n\nregridConfig = {\n 'l2collection_id': 'C1234724470-POCLOUD',\n 'ogc-api-coverages_version': '1.0.0',\n 'variable': 'all',\n 'granuleid': 'G1234734747-POCLOUD',\n 'outputCrs': l2proj_encode,\n 'interpolation': 'bilinear',\n 'width': 1000,\n 'height': 1000\n}\n\nregrid_bi_url = harmony_root+'/{l2collection_id}/ogc-api-coverages/{ogc-api-coverages_version}/collections/{variable}/coverage/rangeset?&granuleid={granuleid}&outputCrs={outputCrs}&interpolation={interpolation}&height={height}&width={width}'.format(**regridConfig)\nprint('Request URL', regrid_bi_url)\nregrid_bi_response = request.urlopen(regrid_bi_url)\nregrid_bi_results = regrid_bi_response.read()\n\n\nregrid_bi_file_name = 'regrid-bi.nc'\nregrid_bi_filepath = str(regrid_bi_file_name)\nfile_ = open(regrid_bi_filepath, 'wb')\nfile_.write(regrid_bi_results)\nfile_.close()\n\nPrint the x and y dimensions to confirm that the output matches the requested scale extent in meters:\n\nimport xarray as xr\nreproject_ds = xr.open_dataset(regrid_filepath, drop_variables='time')\nprint(reproject_ds)\n\n\nimport xarray as xr\nreproject_bi_ds = xr.open_dataset(regrid_bi_filepath, drop_variables='time')\nprint(reproject_bi_ds)\n\n\noriginal_ds = xr.open_dataset('20200131234501-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc')\nprint(original_ds)\n\n\ng = reproject_ds.sea_surface_temperature.plot(robust=True)\ng.axes.set_title(\"Nearest Neighbor Interpolation\")\n\n\ng= reproject_bi_ds.sea_surface_temperature.plot(robust=True)\ng.axes.set_title(\"Bilinear Interpolation\")\n\n\ng = original_ds.sea_surface_temperature.plot(robust=True)\ng.axes.set_title(\"Native File\")\n\n\ng= original_ds.sea_surface_temperature.plot(x=\"lon\", y=\"lat\", robust=True)\ng.axes.set_title(\"Native, projected to Lat/Lon\")"
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+ "href": "quarto_text/DatasetSpecificExamples.html#gis-storymaps-of-select-datasets",
+ "title": "Dataset Specific",
+ "section": "GIS StoryMaps of Select Datasets:",
+ "text": "GIS StoryMaps of Select Datasets:\nPO.DAAC GIS StoryMap Collection Page"
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- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis notebook shows a simple way to maintain a local time series of Sentinel-6 NRT data using the CMR Search API. It downloads granules the ingested since the previous run to a designated data folder and overwrites a hidden file inside with the timestamp of the CMR Search request on success."
+ "text": "Tutorials showcasing the same workflows in in different locations: in the cloud hosted by Amazon Web Services (AWS) or on a local machine downloaded from the cloud.\nNote: NASA Earthdata is hosted in the us-west-2 region in the cloud.\n\nExample: Amazon Estuary Exploration\nThese tutorials explore the relationships between land water equivalent thickness, river height, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region from multiple datasets (GRACE-FO, Pre-SWOT MEaSUREs, OISSS, & MODIS).\nCloud Version | Local Version\n\n\nExample: Reservoir Surface Area\nThese tutorials estimate reservoir surface area from Harmonized Landsat-Sentinel (HLS) Imagery.\nCloud Version | Local Version - Undergoing updates shortly\n\n\nExample: Lake Extent\nThese tutorials explore OPERA Dynamic Surface Water Extent (DSWx) product: how to search for, download, visualize, and mosaic OPERA data over lake Powell.\nCloud Version | Local Version"
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- "section": "Before you start",
- "text": "Before you start\nBefore you beginning this tutorial, make sure you have an Earthdata account: https://urs.earthdata.nasa.gov for the operations envionrment (most common) or https://uat.urs.earthdata.nasa.gov for the UAT environment.\nAccounts are free to create and take just a moment to set up."
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+ "text": "Search for Data with Earthdata Search\nAuthenticate for NASA Earthdata Programmatically\nAccess Data Directly in Cloud (netCDF)\nDownload/Subscribe to Data"
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- "text": "Authentication setup\nWe need some boilerplate up front to log in to Earthdata Login. The function below will allow Python scripts to log into any Earthdata Login application programmatically. To avoid being prompted for credentials every time you run and also allow clients such as curl to log in, you can add the following to a .netrc (_netrc on Windows) file in your home directory:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nMake sure that this file is only readable by the current user or you will receive an error stating “netrc access too permissive.”\n$ chmod 0600 ~/.netrc\nYou’ll need to authenticate using the netrc method when running from command line with papermill. You can log in manually by executing the cell below when running in the notebook client in your browser.\n\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nimport getpass\nimport netrc\n\n\ndef setup_earthdata_login_auth(endpoint):\n \"\"\"\n Set up the request library so that it authenticates against the given Earthdata Login\n endpoint and is able to track cookies between requests. This looks in the .netrc file \n first and if no credentials are found, it prompts for them.\n\n Valid endpoints include:\n urs.earthdata.nasa.gov - Earthdata Login production\n \"\"\"\n try:\n username, _, password = netrc.netrc().authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n # FileNotFound = There's no .netrc file\n # TypeError = The endpoint isn't in the netrc file, causing the above to try unpacking None\n print('Please provide your Earthdata Login credentials to allow data access')\n print('Your credentials will only be passed to %s and will not be exposed in Jupyter' % (endpoint))\n username = input('Username:')\n password = getpass.getpass()\n\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n\n\nsetup_earthdata_login_auth('urs.earthdata.nasa.gov')\n\nPlease provide your Earthdata Login credentials to allow data access\nYour credentials will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter\n\n\nUsername: nickles\n ···········\n\n\n\nimport requests\nfrom os import makedirs\nfrom os.path import isdir, basename\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen, urlretrieve\nfrom datetime import datetime, timedelta\nfrom json import dumps, loads"
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+ "href": "quarto_text/HowTo.html#how-to",
+ "title": "How To’s",
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+ "text": "Search for Data with Earthdata Search\nAuthenticate for NASA Earthdata Programmatically\nAccess Data Directly in Cloud (netCDF)\nDownload/Subscribe to Data"
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- "title": "Access Sentinel-6 NRT Data",
- "section": "Hands-off workflow",
- "text": "Hands-off workflow\nThis workflow/notebook can be run routinely to maintain a time series of NRT data, downloading new granules as they become available in CMR.\nThe notebook writes/overwrites a file .update to the target data directory with each successful run. The file tracks to date and time of the most recent update to the time series of NRT granules using a timestamp in the format yyyy-mm-ddThh:mm:ssZ.\nThe timestamp matches the value used for the created_at parameter in the last successful run. This parameter finds the granules created within a range of datetimes. This workflow leverages the created_at parameter to search backwards in time for new granules ingested between the time of our timestamp and now.\nThe variables in the cell below determine the workflow behavior on its initial run:\n\nmins: Initialize a new local time series by starting with the granules ingested since ___ minutes ago.\ncmr: The domain of the target CMR instance, either cmr.earthdata.nasa.gov.\nccid: The unique CMR concept-id of the desired collection.\ndata: The path to a local directory in which to download/maintain a copy of the NRT granule time series.\n\n\ncmr = \"cmr.earthdata.nasa.gov\"\n\n# this function returns a concept id for a particular dataset\ndef get_collection(url: str=f\"https://{cmr}/search/collections.umm_json\", **params):\n return requests.get(url, params).json().get(\"items\")[0]\n\n#\n# This cell accepts parameters from command line with papermill: \n# https://papermill.readthedocs.io\n#\n# These variables should be set before the first run, then they \n# should be left alone. All subsequent runs expect the values \n# for cmr, ccid, data to be unchanged. The mins value has no \n# impact on subsequent runs.\n#\n\nmins = 20\n\nname = \"JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F\"\n\nccid = get_collection(ShortName=name).get(\"meta\").get(\"concept-id\")\n\ndata = \"resources/nrt\"\n\nThe variable data is pointed at a nearby folder resources/nrt by default. You should change data to a suitable download path on your file system. An unlucky sequence of git commands could disappear that folder and its downloads if your not careful. Just change it.\nThe Python imports relevant to the workflow\nThe search retrieves granules ingested during the last n minutes. A file in your local data dir file that tracks updates to your data directory, if one file exists. The CMR Search falls back on the ten minute window if not.\n\ntimestamp = (datetime.utcnow()-timedelta(minutes=mins)).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\ntimestamp\n\n'2022-11-08T00:15:46Z'\n\n\nThis cell will replace the timestamp above with the one read from the .update file in the data directory, if it exists.\n\nif not isdir(data):\n print(f\"NOTE: Making new data directory at '{data}'. (This is the first run.)\")\n makedirs(data)\nelse:\n try:\n with open(f\"{data}/.update\", \"r\") as f:\n timestamp = f.read()\n except FileNotFoundError:\n print(\"WARN: No .update in the data directory. (Is this the first run?)\")\n else:\n print(f\"NOTE: .update found in the data directory. (The last run was at {timestamp}.)\")\n\nNOTE: Making new data directory at 'resources/nrt'. (This is the first run.)\n\n\nThere are several ways to query for CMR updates that occured during a given timeframe. Read on in the CMR Search documentation:\n\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-new-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-revised-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-production-date (Granules)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-created-at (Granules)\n\nThe created_at parameter works for our purposes. It’s a granule search parameter that returns the records ingested since the input timestamp.\n\nparams = {\n 'scroll': \"true\",\n 'page_size': 2000,\n 'sort_key': \"-start_date\",\n 'collection_concept_id': ccid, \n 'created_at': timestamp,\n # Limit results to coverage for .5deg bbox in Gulf of Alaska:\n 'bounding_box': \"-146.5,57.5,-146,58\",\n}\n\nparams\n\n{'scroll': 'true',\n 'page_size': 2000,\n 'sort_key': '-start_date',\n 'collection_concept_id': 'C1968980576-POCLOUD',\n 'created_at': '2022-11-08T00:15:46Z',\n 'bounding_box': '-146.5,57.5,-146,58'}\n\n\nGet the query parameters as a string and then the complete search url:\n\nquery = urlencode(params)\nurl = f\"https://{cmr}/search/granules.umm_json?{query}\"\nprint(url)\n\nhttps://cmr.earthdata.nasa.gov/search/granules.umm_json?scroll=true&page_size=2000&sort_key=-start_date&collection_concept_id=C1968980576-POCLOUD&created_at=2022-11-08T00%3A15%3A46Z&bounding_box=-146.5%2C57.5%2C-146%2C58\n\n\nGet a new timestamp that represents the UTC time of the search. Then download the records in umm_json format for granules that match our search parameters:\n\nwith urlopen(url) as f:\n results = loads(f.read().decode())\n\nprint(f\"{results['hits']} new granules ingested for '{ccid}' since '{timestamp}'.\")\n\ntimestamp = datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n\n0 new granules ingested for 'C1968980576-POCLOUD' since '2022-11-08T00:15:46Z'.\n\n\nNeatly print the first granule record (if one was returned):\n\nif len(results['items'])>0:\n print(dumps(results['items'][0], indent=2))\n\nThe link for http access can be retrieved from each granule record’s RelatedUrls field. The download link is identified by \"Type\": \"GET DATA\" .\nSelect the download URL for each of the granule records:\n\ndownloads = [[u['URL'] for u in r['umm']['RelatedUrls'] if u['Type']==\"GET DATA\"][0] for r in results['items']]\ndownloads\n\n[]\n\n\nFinish by downloading the files to the data directory in a loop. Overwrite .update with a new timestamp on success.\n\nfor f in downloads:\n try:\n urlretrieve(f, f\"{data}/{basename(f)}\")\n except Exception as e:\n print(f\"[{datetime.now()}] FAILURE: {f}\\n\\n{e}\\n\")\n raise e\n else:\n print(f\"[{datetime.now()}] SUCCESS: {f}\")\n\nIf there were updates to the local time series during this run and no exceptions were raised during the download loop, then overwrite the timestamp file that tracks updates to the data folder (resources/nrt/.update):\n\nif len(results['items'])>0:\n with open(f\"{data}/.update\", \"w\") as f:\n f.write(timestamp)"
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+ "href": "quarto_text/HowTo.html#additional-resources",
+ "title": "How To’s",
+ "section": "Additional Resources",
+ "text": "Additional Resources\nFor more “how to” guides applicable to all NASA datasets, including PO.DAAC, visit the NASA Earthdata Cloud Cookbook."
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- "text": "This tutorial will show you how to authenticate with the PO.DAAC data archive, and gain access to the data in amazon S3 buckets. This allows you to natively list, copy, get data from the PO.DAAC archive using your preferred amazon methods (e.g. Python boto3, amazon SDK, aws cli).\nnote Direct S3 access is only available to users running in AWS, us-west-2 region. All other access must come from HTTP requests for PO.DAAC data\nimport boto3\nimport json\nimport requests\nimport xarray as xr\n%matplotlib inline"
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- "title": "Direct S3 Access tutorial",
- "section": "Get Temporary AWS Credentials for Access",
- "text": "Get Temporary AWS Credentials for Access\nS3 is an ‘object store’ hosted in AWS for cloud processing. Direct S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. Note, these temporary credentials are valid for only 1 hour. A netrc file is required to aquire these credentials. Use the NASA Earthdata Authentication to create a netrc file in your home directory. (Note: A NASA Earthdata Login is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.)\nThe following crediential is for PODAAC, but other credentials are needed to access data from other NASA DAACs.\n\ns3_cred_endpoint = 'https://archive.podaac.earthdata.nasa.gov/s3credentials'\n\nCreate a function to make a request to an endpoint for temporary credentials.\n\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\n\n\ntemp_creds_req = get_temp_creds()\n#temp_creds_req # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.\n\n\nList all datasets available using boto3\n\ns3_client = boto3.client(\n 's3',\n aws_access_key_id=temp_creds_req[\"accessKeyId\"],\n aws_secret_access_key=temp_creds_req[\"secretAccessKey\"],\n aws_session_token=temp_creds_req[\"sessionToken\"]\n)\n\n\ns3_client.list_objects(Bucket=\"podaac-ops-cumulus-protected\", Prefix=\"ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4/\")\n\n{'ResponseMetadata': {'RequestId': '1NM9KHE62SQMWDA3',\n 'HostId': '4Mq689UEnY9jo7SvuGDb/y3Go3XCNs1fjwW5TSDwWpGSqav8SGRkMetkYsIAxZLkn+tOsY4n5FU=',\n 'HTTPStatusCode': 200,\n 'HTTPHeaders': {'x-amz-id-2': '4Mq689UEnY9jo7SvuGDb/y3Go3XCNs1fjwW5TSDwWpGSqav8SGRkMetkYsIAxZLkn+tOsY4n5FU=',\n 'x-amz-request-id': '1NM9KHE62SQMWDA3',\n 'date': 'Thu, 03 Nov 2022 22:11:12 GMT',\n 'x-amz-bucket-region': 'us-west-2',\n 'content-type': 'application/xml',\n 'transfer-encoding': 'chunked',\n 'server': 'AmazonS3'},\n 'RetryAttempts': 0},\n 'IsTruncated': True,\n 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'ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4/ATM_SURFACE_TEMP_HUM_WIND_PRES_day_mean_1992-11-29_ECCO_V4r4_latlon_0p50deg.nc',\n 'LastModified': datetime.datetime(2021, 4, 9, 17, 41, 35, tzinfo=tzlocal()),\n 'ETag': '\"b3b57d9dd20873f90222c8bfef0d94aa-1\"',\n 'Size': 1885935,\n 'StorageClass': 'STANDARD'}],\n 'Name': 'podaac-ops-cumulus-protected',\n 'Prefix': 'ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4/',\n 'MaxKeys': 1000,\n 'EncodingType': 'url'}\n\n\n\n\nDownload a specific file within the cloud, open and plot a variable from it\n\ns3_client.download_file(\"podaac-ops-cumulus-protected\", \"ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4/ATM_SURFACE_TEMP_HUM_WIND_PRES_day_mean_1992-01-01_ECCO_V4r4_latlon_0p50deg.nc\",\"ATM_SURFACE_TEMP_HUM_WIND_PRES_day_mean_1992-01-01_ECCO_V4r4_latlon_0p50deg.nc\")\n\n\nds = xr.open_dataset(\"ATM_SURFACE_TEMP_HUM_WIND_PRES_day_mean_1992-01-01_ECCO_V4r4_latlon_0p50deg.nc\")\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 1, latitude: 360, longitude: 720, nv: 2)\nCoordinates:\n * time (time) datetime64[ns] 1992-01-01T18:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8\n time_bnds (time, nv) datetime64[ns] 1992-01-01T12:00:00 1992-01-02\n latitude_bnds (latitude, nv) float32 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0\n longitude_bnds (longitude, nv) float32 -180.0 -179.5 -179.5 ... 179.5 180.0\nDimensions without coordinates: nv\nData variables:\n EXFatemp (time, latitude, longitude) float32 ...\n EXFaqh (time, latitude, longitude) float32 ...\n EXFewind (time, latitude, longitude) float32 ...\n EXFnwind (time, latitude, longitude) float32 ...\n EXFwspee (time, latitude, longitude) float32 ...\n EXFpress (time, latitude, longitude) float32 ...\nAttributes: (12/57)\n acknowledgement: This research was carried out by the Jet Pr...\n author: Ian Fenty and Ou Wang\n cdm_data_type: Grid\n comment: Fields provided on a regular lat-lon grid. ...\n Conventions: CF-1.8, ACDD-1.3\n coordinates_comment: Note: the global 'coordinates' attribute de...\n ... ...\n time_coverage_duration: P1D\n time_coverage_end: 1992-01-02T00:00:00\n time_coverage_resolution: P1D\n time_coverage_start: 1992-01-01T12:00:00\n title: ECCO Atmosphere Surface Temperature, Humidi...\n uuid: 9142c796-4050-11eb-9101-0cc47a3f7ec5xarray.DatasetDimensions:time: 1latitude: 360longitude: 720nv: 2Coordinates: (6)time(time)datetime64[ns]1992-01-01T18:00:00axis :Tbounds :time_bndscoverage_content_type :coordinatelong_name :center time of averaging periodstandard_name :timearray(['1992-01-01T18:00:00.000000000'], dtype='datetime64[ns]')latitude(latitude)float32-89.75 -89.25 ... 89.25 89.75axis :Ybounds :latitude_bndscomment :uniform grid spacing from -89.75 to 89.75 by 0.5coverage_content_type :coordinatelong_name :latitude at grid cell centerstandard_name :latitudeunits :degrees_northarray([-89.75, -89.25, -88.75, ..., 88.75, 89.25, 89.75], dtype=float32)longitude(longitude)float32-179.8 -179.2 ... 179.2 179.8axis :Xbounds :longitude_bndscomment :uniform grid spacing from -179.75 to 179.75 by 0.5coverage_content_type :coordinatelong_name :longitude at grid cell centerstandard_name :longitudeunits :degrees_eastarray([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75],\n dtype=float32)time_bnds(time, nv)datetime64[ns]...comment :Start and end times of averaging period.coverage_content_type :coordinatelong_name :time bounds of averaging periodarray([['1992-01-01T12:00:00.000000000', '1992-01-02T00:00:00.000000000']],\n dtype='datetime64[ns]')latitude_bnds(latitude, nv)float32...coverage_content_type :coordinatelong_name :latitude bounds grid cellsarray([[-90. , -89.5],\n [-89.5, -89. ],\n [-89. , -88.5],\n ...,\n [ 88.5, 89. ],\n [ 89. , 89.5],\n [ 89.5, 90. ]], dtype=float32)longitude_bnds(longitude, nv)float32...coverage_content_type :coordinatelong_name :longitude bounds grid cellsarray([[-180. , -179.5],\n [-179.5, -179. ],\n [-179. , -178.5],\n ...,\n [ 178.5, 179. ],\n [ 179. , 179.5],\n [ 179.5, 180. ]], dtype=float32)Data variables: (6)EXFatemp(time, latitude, longitude)float32...coverage_content_type :modelResultlong_name :Atmosphere surface (2 m) air temperature standard_name :air_temperatureunits :degree_Kcomment :Surface (2 m) air temperature over open water. Note: sum of ERA-Interim surface air temperature and the control adjustment from ocean state estimation.valid_min :195.37054443359375valid_max :312.8451232910156[259200 values with dtype=float32]EXFaqh(time, latitude, longitude)float32...coverage_content_type :modelResultlong_name :Atmosphere surface (2 m) specific humidity standard_name :surface_specific_humidityunits :kg kg-1comment :Surface (2 m) specific humidity over open water. Note: sum of ERA-Interim surface specific humidity and the control adjustment from ocean state estimation.valid_min :-0.0014020215021446347valid_max :0.03014513850212097[259200 values with dtype=float32]EXFewind(time, latitude, longitude)float32...coverage_content_type :modelResultlong_name :Zonal (east-west) wind speedstandard_name :eastward_windunits :m s-1comment :Zonal (east-west) component of ocean surface wind. Note: EXFewind is calculated by interpolating the model's x and y components of wind velocity (EXFuwind and EXFvwind) to tracer cell centers and then finding the zonal component of the interpolated vectors. ECCO V4r4 is forced with wind stress (see EXFtaux, EXFtauy), not vector winds + bulk formulae. EXFewind is calculated by converting wind stress to vector wind using bulk formulae.valid_min :-33.524742126464844valid_max :39.48556900024414[259200 values with dtype=float32]EXFnwind(time, latitude, longitude)float32...coverage_content_type :modelResultlong_name :Meridional (north-south) wind speedstandard_name :northward_windunits :m s-1comment :Meridional (north-south) component of ocean surface wind. Note: EXFnwind is calculated by interpolating the model's x and y components of wind velocity (EXFuwind and EXFvwind) to tracer cell centers and then finding the meridional component of the interpolated vectors. ECCO V4r4 is forced with wind stress (see EXFtaux, EXFtauy), not vector winds + bulk formulae. EXFnwind is calculated by converting wind stress to vector wind using bulk formulae.valid_min :-30.042686462402344valid_max :33.95014190673828[259200 values with dtype=float32]EXFwspee(time, latitude, longitude)float32...coverage_content_type :modelResultlong_name :Wind speedstandard_name :wind_speedunits :m s-1comment :10-m wind speed magnitude (>= 0 ) over open water. Only used for the calculation of air-sea fluxes using bulk formulae. Note: not adjusted by the ocean state estimation and not necesarily consistent with EXFuwind and EXFvwind because EXFuwind and EXFvwind are calculated from EXFtaux and EXFtauy using bulk formulae. EXFwspee != sqrt(EXFuwind**2 + EXFvwind**2.valid_min :0.27271032333374023valid_max :45.87086486816406[259200 values with dtype=float32]EXFpress(time, latitude, longitude)float32...coverage_content_type :modelResultlong_name :Atmosphere surface pressurestandard_name :surface_air_pressureunits :N m-2comment :Atmospheric pressure field at sea level. Note: ERA-Interim atmospheric pressure, with air tides removed using a variety of methods. Not adjusted by the ocean state estimation.valid_min :92090.3125valid_max :106314.7734375[259200 values with dtype=float32]Attributes: (57)acknowledgement :This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.author :Ian Fenty and Ou Wangcdm_data_type :Gridcomment :Fields provided on a regular lat-lon grid. They have been mapped to the regular lat-lon grid from the original ECCO lat-lon-cap 90 (llc90) native model grid.Conventions :CF-1.8, ACDD-1.3coordinates_comment :Note: the global 'coordinates' attribute describes auxillary coordinates.creator_email :ecco-group@mit.educreator_institution :NASA Jet Propulsion Laboratory (JPL)creator_name :ECCO Consortiumcreator_type :groupcreator_url :https://ecco-group.orgdate_created :2020-12-17T02:13:54date_issued :2020-12-17T02:13:54date_metadata_modified :2021-03-15T22:03:08date_modified :2021-03-15T22:03:08geospatial_bounds_crs :EPSG:4326geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lat_resolution :0.5geospatial_lat_units :degrees_northgeospatial_lon_max :180.0geospatial_lon_min :-180.0geospatial_lon_resolution :0.5geospatial_lon_units :degrees_easthistory :Inaugural release of an ECCO Central Estimate solution to PO.DAACid :10.5067/ECG5D-ATM44institution :NASA Jet Propulsion Laboratory (JPL)instrument_vocabulary :GCMD instrument keywordskeywords :EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR INDICATORS > HUMIDITY > SPECIFIC HUMIDITY, EARTH SCIENCE > OCEANS > OCEAN PRESSURE > SEA LEVEL PRESSURE, EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE, EARTH SCIENCE > OCEANS > OCEAN WINDS > SURFACE WINDS, EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND SPEEDkeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordslicense :Public Domainmetadata_link :https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4naming_authority :gov.nasa.jplplatform :ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)platform_vocabulary :GCMD platform keywordsprocessing_level :L4product_name :ATM_SURFACE_TEMP_HUM_WIND_PRES_day_mean_1992-01-01_ECCO_V4r4_latlon_0p50deg.ncproduct_time_coverage_end :2018-01-01T00:00:00product_time_coverage_start :1992-01-01T12:00:00product_version :Version 4, Release 4program :NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)project :Estimating the Circulation and Climate of the Ocean (ECCO)publisher_email :podaac@podaac.jpl.nasa.govpublisher_institution :PO.DAACpublisher_name :Physical Oceanography Distributed Active Archive Center (PO.DAAC)publisher_type :institutionpublisher_url :https://podaac.jpl.nasa.govreferences :ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928source :The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint methodstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsummary :This dataset provides daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.time_coverage_duration :P1Dtime_coverage_end :1992-01-02T00:00:00time_coverage_resolution :P1Dtime_coverage_start :1992-01-01T12:00:00title :ECCO Atmosphere Surface Temperature, Humidity, Wind, and Pressure - Daily Mean 0.5 Degree (Version 4 Release 4)uuid :9142c796-4050-11eb-9101-0cc47a3f7ec5\n\n\n\nds.EXFwspee.plot()\n\n<matplotlib.collections.QuadMesh at 0x7f209099a340>\n\n\n\n\n\n\n\nSet up an s3fs session for Direct Access without downloading within the cloud\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint and then find the s3 paths to the data we want.\n\nimport s3fs\nimport os\n\nos.environ[\"AWS_ACCESS_KEY_ID\"] = temp_creds_req[\"accessKeyId\"]\nos.environ[\"AWS_SECRET_ACCESS_KEY\"] = temp_creds_req[\"secretAccessKey\"]\nos.environ[\"AWS_SESSION_TOKEN\"] = temp_creds_req[\"sessionToken\"]\n\ns3 = s3fs.S3FileSystem(anon=False)\n\ns3path = 's3://podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/202101*.nc'\n#s3path = 's3://podaac-ops-cumulus-protected/ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4/ATM_SURFACE_TEMP_HUM_WIND_PRES_day_mean_1992-01-*.nc'\nremote_files = s3.glob(s3path)\n\n\nremote_files\n\n['podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210102090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210103090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210104090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210105090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210106090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210107090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210108090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210109090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210110090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210111090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210112090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210113090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210114090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210115090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210116090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210117090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210118090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210119090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210120090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210121090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210122090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210123090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210124090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210125090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210126090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210127090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210128090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210129090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210130090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc',\n 'podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210131090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc']\n\n\n\nfileset = [s3.open(file) for file in remote_files]\n\nOpen all files and combine into one xarray dataset\n\ndata = xr.open_mfdataset(fileset, combine='by_coords', engine='h5netcdf' )\ndata\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 31, lat: 17999, lon: 36000)\nCoordinates:\n * time (time) datetime64[ns] 2021-01-01T09:00:00 ... 2021-01-3...\n * lat (lat) float32 -89.99 -89.98 -89.97 ... 89.97 89.98 89.99\n * lon (lon) float32 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\nData variables:\n analysed_sst (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n analysis_error (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n mask (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n sea_ice_fraction (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n dt_1km_data (time, lat, lon) timedelta64[ns] dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n sst_anomaly (time, lat, lon) float32 dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\nAttributes: (12/47)\n Conventions: CF-1.7\n title: Daily MUR SST, Final product\n summary: A merged, multi-sensor L4 Foundation SST anal...\n references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n institution: Jet Propulsion Laboratory\n history: created at nominal 4-day latency; replaced nr...\n ... ...\n project: NASA Making Earth Science Data Records for Us...\n publisher_name: GHRSST Project Office\n publisher_url: http://www.ghrsst.org\n publisher_email: ghrsst-po@nceo.ac.uk\n processing_level: L4\n cdm_data_type: gridxarray.DatasetDimensions:time: 31lat: 17999lon: 36000Coordinates: (3)time(time)datetime64[ns]2021-01-01T09:00:00 ... 2021-01-...long_name :reference time of sst fieldstandard_name :timeaxis :Tcomment :Nominal time of analyzed fieldsarray(['2021-01-01T09:00:00.000000000', '2021-01-02T09:00:00.000000000',\n '2021-01-03T09:00:00.000000000', '2021-01-04T09:00:00.000000000',\n '2021-01-05T09:00:00.000000000', '2021-01-06T09:00:00.000000000',\n '2021-01-07T09:00:00.000000000', '2021-01-08T09:00:00.000000000',\n '2021-01-09T09:00:00.000000000', '2021-01-10T09:00:00.000000000',\n '2021-01-11T09:00:00.000000000', '2021-01-12T09:00:00.000000000',\n '2021-01-13T09:00:00.000000000', '2021-01-14T09:00:00.000000000',\n '2021-01-15T09:00:00.000000000', '2021-01-16T09:00:00.000000000',\n '2021-01-17T09:00:00.000000000', '2021-01-18T09:00:00.000000000',\n '2021-01-19T09:00:00.000000000', '2021-01-20T09:00:00.000000000',\n '2021-01-21T09:00:00.000000000', '2021-01-22T09:00:00.000000000',\n '2021-01-23T09:00:00.000000000', '2021-01-24T09:00:00.000000000',\n '2021-01-25T09:00:00.000000000', '2021-01-26T09:00:00.000000000',\n '2021-01-27T09:00:00.000000000', '2021-01-28T09:00:00.000000000',\n '2021-01-29T09:00:00.000000000', '2021-01-30T09:00:00.000000000',\n '2021-01-31T09:00:00.000000000'], dtype='datetime64[ns]')lat(lat)float32-89.99 -89.98 ... 89.98 89.99long_name :latitudestandard_name :latitudeaxis :Yunits :degrees_northvalid_min :-90.0valid_max :90.0comment :geolocations inherited from the input data without correctionarray([-89.99, -89.98, -89.97, ..., 89.97, 89.98, 89.99], dtype=float32)lon(lon)float32-180.0 -180.0 ... 180.0 180.0long_name :longitudestandard_name :longitudeaxis :Xunits :degrees_eastvalid_min :-180.0valid_max :180.0comment :geolocations inherited from the input data without correctionarray([-179.99, -179.98, -179.97, ..., 179.98, 179.99, 180. ],\n dtype=float32)Data variables: (6)analysed_sst(time, lat, lon)float32dask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>long_name :analysed sea surface temperaturestandard_name :sea_surface_foundation_temperatureunits :kelvinvalid_min :-32767valid_max :32767comment :\"Final\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolationsource :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n74.83 GiB\n2.41 GiB\n\n\nShape\n(31, 17999, 36000)\n(1, 17999, 36000)\n\n\nCount\n93 Tasks\n31 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nanalysis_error\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated error standard deviation of analysed_sst\n\nunits :\n\nkelvin\n\nvalid_min :\n\n0\n\nvalid_max :\n\n32767\n\ncomment :\n\nuncertainty in \"analysed_sst\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n74.83 GiB\n2.41 GiB\n\n\nShape\n(31, 17999, 36000)\n(1, 17999, 36000)\n\n\nCount\n93 Tasks\n31 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nmask\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea/land field composite mask\n\nvalid_min :\n\n1\n\nvalid_max :\n\n31\n\nflag_masks :\n\n[ 1 2 4 8 16]\n\nflag_meanings :\n\nopen_sea land open_lake open_sea_with_ice_in_the_grid open_lake_with_ice_in_the_grid\n\ncomment :\n\nmask can be used to further filter the data.\n\nsource :\n\nGMT \"grdlandmask\", ice flag from sea_ice_fraction data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n74.83 GiB\n2.41 GiB\n\n\nShape\n(31, 17999, 36000)\n(1, 17999, 36000)\n\n\nCount\n93 Tasks\n31 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsea_ice_fraction\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea ice area fraction\n\nstandard_name :\n\nsea_ice_area_fraction\n\nvalid_min :\n\n0\n\nvalid_max :\n\n100\n\nsource :\n\nEUMETSAT OSI-SAF, copyright EUMETSAT\n\ncomment :\n\nice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n74.83 GiB\n2.41 GiB\n\n\nShape\n(31, 17999, 36000)\n(1, 17999, 36000)\n\n\nCount\n93 Tasks\n31 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\ndt_1km_data\n\n\n(time, lat, lon)\n\n\ntimedelta64[ns]\n\n\ndask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime to most recent 1km data\n\nvalid_min :\n\n-127\n\nvalid_max :\n\n127\n\nsource :\n\nMODIS and VIIRS pixels ingested by MUR\n\ncomment :\n\nThe grid value is hours between the analysis time and the most recent MODIS or VIIRS 1km L2P datum within 0.01 degrees from the grid point. \"Fill value\" indicates absence of such 1km data at the grid point.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n149.66 GiB\n4.83 GiB\n\n\nShape\n(31, 17999, 36000)\n(1, 17999, 36000)\n\n\nCount\n93 Tasks\n31 Chunks\n\n\nType\ntimedelta64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsst_anomaly\n\n\n(time, lat, lon)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 17999, 36000), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSST anomaly from a seasonal SST climatology based on the MUR data over 2003-2014 period\n\nunits :\n\nkelvin\n\nvalid_min :\n\n-32767\n\nvalid_max :\n\n32767\n\ncomment :\n\nanomaly reference to the day-of-year average between 2003 and 2014\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n74.83 GiB\n2.41 GiB\n\n\nShape\n(31, 17999, 36000)\n(1, 17999, 36000)\n\n\nCount\n93 Tasks\n31 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nAttributes: (47)Conventions :CF-1.7title :Daily MUR SST, Final productsummary :A merged, multi-sensor L4 Foundation SST analysis product from JPL.references :http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SSTinstitution :Jet Propulsion Laboratoryhistory :created at nominal 4-day latency; replaced nrt (1-day latency) version.comment :MUR = \"Multi-scale Ultra-high Resolution\"license :These data are available free of charge under data policy of JPL PO.DAAC.id :MUR-JPL-L4-GLOB-v04.1naming_authority :org.ghrsstproduct_version :04.1uuid :27665bc0-d5fc-11e1-9b23-0800200c9a66gds_version_id :2.0netcdf_version_id :4.1date_created :20210110T071843Zstart_time :20210101T090000Zstop_time :20210101T090000Ztime_coverage_start :20201231T210000Ztime_coverage_end :20210101T210000Zfile_quality_level :3source :MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAFplatform :Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Shipssensor :MODIS, AMSR2, AVHRR, in-situMetadata_Conventions :Unidata Observation Dataset v1.0metadata_link :http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1keywords :Oceans > Ocean Temperature > Sea Surface Temperaturekeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsouthernmost_latitude :-90.0northernmost_latitude :90.0westernmost_longitude :-180.0easternmost_longitude :180.0spatial_resolution :0.01 degreesgeospatial_lat_units :degrees northgeospatial_lat_resolution :0.01geospatial_lon_units :degrees eastgeospatial_lon_resolution :0.01acknowledgment :Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.creator_name :JPL MUR SST projectcreator_email :ghrsst@podaac.jpl.nasa.govcreator_url :http://mur.jpl.nasa.govproject :NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Programpublisher_name :GHRSST Project Officepublisher_url :http://www.ghrsst.orgpublisher_email :ghrsst-po@nceo.ac.ukprocessing_level :L4cdm_data_type :grid\n\n\n\ndata.analysed_sst.sel(lat=21.00, lon=-21.00).plot()"
+ "text": "This tutorial compares salinity from the SMAP satellite and Saildrone in-situ measurements. Both datasets are located within the cloud.\nMonitoring Changes in the Arctic Using Saildrone and SMAP Satellite\n\n\n\nThis tutorial explores the relationships between land water equivalent thickness, river height, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region from multiple datasets (GRACE-FO, Pre-SWOT MEaSUREs, OISSS, & MODIS).\nAmazon Estuary Exploration: In AWS Cloud Version"
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- "href": "notebooks/meetings_workshops/workshop_osm_2022/S6_OPeNDAP_Access_Gridding.html",
- "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
+ "objectID": "quarto_text/PairingCloudNoncloudData.html#multiple-cloud-datasets",
+ "href": "quarto_text/PairingCloudNoncloudData.html#multiple-cloud-datasets",
+ "title": "Workflows with Multiple Datasets",
"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\n#!rm *.nc*\nIn this tutorial you will learn how to access variable subsets from OPeNDAP in the Cloud."
+ "text": "This tutorial compares salinity from the SMAP satellite and Saildrone in-situ measurements. Both datasets are located within the cloud.\nMonitoring Changes in the Arctic Using Saildrone and SMAP Satellite\n\n\n\nThis tutorial explores the relationships between land water equivalent thickness, river height, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region from multiple datasets (GRACE-FO, Pre-SWOT MEaSUREs, OISSS, & MODIS).\nAmazon Estuary Exploration: In AWS Cloud Version"
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- "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
- "section": "Requirements",
- "text": "Requirements\nThis workflow was developed using Python 3.9 (and tested against versions 3.7, 3.8). The pyresample package is the only remaining dependency besides common packages like numpy and xarray. You may uncomment the first line of the following cell to install pyresample, if necessary. Then, import all the required Python packages.\n\n#!python -m pip install numpy pyresample xarray\nimport os\nimport tqdm\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport pyresample as pr\nimport matplotlib.pyplot as plt\nfrom concurrent.futures import ThreadPoolExecutor\nfrom pyresample.kd_tree import resample_gauss\nfrom io import StringIO\n\ncmr = \"cmr.earthdata.nasa.gov\"\n\ndef get_collection(url: str=f\"https://{cmr}/search/collections.umm_json\", **params):\n return requests.get(url, params).json().get(\"items\")[0]\n \ndef get_opendap(record: dict):\n for url in record.get(\"RelatedUrls\"):\n if 'opendap.earthdata.nasa.gov' in url.get(\"URL\"):\n return url.get(\"URL\")\n\ndef get_granules(ShortName: str, provider: str=\"POCLOUD\", page_size: int=200, **kwargs):\n url = f\"https://{cmr}/search/granules.umm_json\"\n params = dict(ShortName=ShortName, provider=\"POCLOUD\", page_size=page_size)\n granules = pd.DataFrame(requests.get(url, {**params,**kwargs}).json().get(\"items\"))\n granules['GranuleUR'] = granules.umm.apply(lambda x: x.get(\"GranuleUR\"))\n granules['OPeNDAP'] = granules.umm.apply(get_opendap)\n coverage = granules.umm.apply(lambda x: x.get(\"TemporalExtent\").get(\"RangeDateTime\").values()).apply(list)\n granules['Start'] = coverage.apply(sorted).apply(lambda x: x[0])\n granules['End'] = coverage.apply(sorted).apply(lambda x: x[1])\n return granules"
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+ "text": "Pairing Cloud and Non-cloud Data\nTutorials highlighting workflows that utilize both cloud and non-cloud data simultaneously.\n\nExample: River Heights Comparison\nThis tutorial explores the relationships between satellite and in situ river heights in the Mississippi River using Pre-SWOT MEaSUREs and USGS river height gauges.\nMississippi River Heights Exploration: Working with In Situ Measurements and Satellite Hydrology Data in the Cloud\n\n\nExample: Sea Surface Temperature Ocean Satellite & In-situ Comparison\nThis tutorial co-locates in-situ measurements and satellite data of sea surface temperature (SST) near the European coast for cross-validation of data or model validation. Datasets: Argo floats, MODIS-Aqua L2 SST, & MUR L4 SST.\nUse Case: Co-locate satellite and in-situ data for cross-validation - Undergoing updates shortly"
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- "text": "Dataset\n\nAbout the mission\nThis demo uses data acquired by the Sentinel-6A Michael Freilich (S6A) satellite altimetry mission, which provides precise measurements of ocean surface height. It is the latest iteration in a series of missions, which together provide an uninterupted sea surface height record going back more than 30 years.\nSatellite altimetry is a precise science carried out by Ocean Surface Topography researchers through the Jason-series radar altimetry missions. Instrument specifications, operational procedures, data calibration and analysis are sometimes referred to colloquially as “along-track altimetry” (a term that I find useful to understanding the data provided at level 2, like in the dataset we use here).\nLearn more through resources linked in the Appendix).\n\n\nAbout the data\nIn a nutshell:\n\nWhat? calibrated sea surface height measurements,\nWhere? from -66.0 to 66.0 degrees latitude,\nWhen? beginning in June 2021,\nHow? global coverage acquired every 10 days (1 cycle of 128 orbits)\n\n\nFigure: depicted data structure for level-2 along-track altimetry datasets from Sentinel-6A\nPO.DAAC typically refers to datasets by their ShortName: JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F. The ShortName contains the following info for all Sentinel-6 datasets at level 2:\n\nJASON_CS: referring to Jason Continuity of Service (Jason-CS), the mission series/instrument class;\nS6A: referring to Sentinel-6A (instead of Sentinel-6B, which is expected to launch in 2025);\nL2: Level 2, the data processing level;\nALT: Altimetry, the data product type and application;\nLR: Low Resolution, versus High Resolution (HR);\nRED: Reduced, the smaller of two datasets distributed at Level 2 (the other being Standard, which contains more variables)\nOST: Ocean Surface Topography, the science domain/team/community;\nNRT: Near Real Time, the data latency; i.e. accessible within 3 hours (vs. STC or NTC; lower latencies)"
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+ "text": "Here are some cheatsheets and guides helping visualize what working with NASA Earthdata Cloud data looks like, and how to get started!\n\nNASA Earthdata Cloud Overview\nCloud Access Pathways\nGetting Started Roadmaps: Cloud & Local Workflows\nTools & Services Roadmap\nCloud Terminology 101\nWorkflow Cheatsheet\nCheatsheet Terminology\n\n\n\nNASA Earthdata Cloud is the NASA archive of Earth observations and is hosted in Amazon Web Services (AWS) cloud with DAAC tools and services built for use “next to the data.” The NASA DAACs (data centers) are currently transitioning to this cloud-based environment. All PO.DAAC data will be housed in the cloud, and can be accessed through AWS. The cloud offers a scalable and effective way to address storage, network, and data movement concerns while offering a tremendous amount of flexibility to the user. Particularly if working with large data volumes, data access and processing would be more efficient if workflows are taking place in the cloud, avoiding having to download large data volumes. Data download will continue to be freely available to users, from the Earthdata Cloud archive.\n\n\nPublished Google Slide\n\n\n\nThree pathway examples, to interact and access data (and services) from and within the NASA Earthdata Cloud, are illustrated in the diagram. Green arrows and icons indicate working locally, after downloading data to your local machine, servers, or compute/storage space. Orange arrows and icons highlight a workflow within the cloud, setting up your own AWS EC2 cloud instance, or virtual machine, in the cloud next to the data. Blue arrows and icons also indicate a within the cloud workflow, through shareable cloud environments such as Binder or JupyterHub set up in an AWS cloud region. Note that each of these may have a range of cost models. EOSDIS data are being stored in the us-west-2 region of AWS cloud; we recommend setting up your cloud computing environment in the same region as the data for free and easy in-cloud access.\n\n\nPublished Google Slide\nA note on costing: What is free and what do I have to budget for, now that data is archived in the cloud?\n\nDownloading data from the Earthdata Cloud archive in AWS, to your local computer environment or local storage (e.g. servers) is and will continue to be free for the user.\nAccessing the data directly in the cloud (from us-west-2 S3 region) is free. Users will need a NASA Earthdata Login account and AWS credentials to access, but there is no cost associated with these authentication steps, which are in place for security reasons.\nAccessing data in the cloud via EOSDIS or DAAC cloud-based tools and services such as the CMR API, Harmony API, OPenDAP API (from us-west-2 S3 region) is free to the user. Having the tools and services “next to the data” in the cloud enables DAACs to support data reduction and transformation, more efficiently, on behalf of the user, so users only access the data they need.\nCloud computing environments (i.e. virtual machines in the cloud) for working with data in the cloud (beyond direct or via services provided access) such as data analysis or running models with the data, is user responsibility, and should be considered in budgeting. I.e. User would need to set up a cloud compute environment (such as an EC2 instance or JupyterLab) and are responsible for any storage and computing costs.\n\nThis means that even though direct data access in the cloud is free to the user, they would first need to have a cloud computing environment/machine to execute the data access step from, and then continue their analysis.\nDepending on whether that cloud environment is provided by the user themselves, user’s institution, community hubs like Pangeo or NASA Openscapes JupyterLab sandbox, this element of the workflow may require user accountability, budgeting and user financial maintenance.\n\n\n\n\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using an in-cloud workflow (i.e. bringing user code into the cloud, avoiding data download and performing data workflows “next to the data”).\n\n\nPublished Google Slide\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using a local machine (e.g. laptop) workflow, as data storage and computational work.\n\n\nPublished Google Slide\n\n\n\n\nBelow is a practical guide for learning about and selecting helpful tools or services for a given use case, focusing on how to find and access NASA Earthdata Cloud-archived data from local compute environment (e.g. laptop) or from a cloud computing workspace, with accompanying example tutorials. Once you follow your desired pathway, click on the respective blue notebook icon to get to the example tutorial. Note: these pathways are not exhaustive, there are many ways to accomplish these common steps, but these are some of our recommendations.\n\n\nPublished Google Slide\n\n\n\nCloud Terminology 101 for those new to commonly used cloud computing terms and phrases.\n\n\nPublished Google Slide\n\n\n\nThe following is a practical reference guide with links to tutorials and informational websites for users who are starting to take the conceptual pieces and explore and implement in their own workflows.\n\n\nPublished Google Slide\n\n\n\nTerminology cheatsheet to explain terms commonly used in cloud computing and those located on the NASA Earthdata Cloud Cheatsheet. See also NASA Earthdata Glossary.\n\n\nPublished Google Slide"
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- "text": "Discovery\nThe unique ‘concept-id’ assigned to each PO.DAAC dataset, or ‘collection’, within the Earthdata system is functionally the same as the ShortName in the context of PO.DAAC’s collections in the cloud (because we also assign unique ShortNames). This cell is downloading metadata to retrieve that identifier from an external source, then download metadata about the series of files that make up the time series for the cycle specified by the variable ‘cycle’, and merging to table.\n\nSearch for files/granules\nPick any cycle after cycle 25, which was around the time of the first release of data from S6A. (This cell calls three functions defined in the cell above.)\n\ncycle = 25\n\nname = \"JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F\"\n\nconcept_id = get_collection(ShortName=name).get(\"meta\").get(\"concept-id\")\n\ngranules = get_granules(name, cycle=cycle) \n\ngranules[['GranuleUR', 'Start', 'End']].set_index(\"GranuleUR\")\n\n\n\n\n\n\n\n\nStart\nEnd\n\n\nGranuleUR\n\n\n\n\n\n\nS6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02\n2021-07-13T16:26:44.514Z\n2021-07-13T18:22:34.471Z\n\n\nS6A_P4_2__LR_RED__NR_025_003_20210713T182234_20210713T201839_F02\n2021-07-13T18:22:34.522Z\n2021-07-13T20:18:39.482Z\n\n\nS6A_P4_2__LR_RED__NR_025_006_20210713T201839_20210713T215450_F02\n2021-07-13T20:18:39.532Z\n2021-07-13T21:54:50.473Z\n\n\nS6A_P4_2__LR_RED__NR_025_007_20210713T215450_20210713T234732_F02\n2021-07-13T21:54:50.523Z\n2021-07-13T23:47:32.482Z\n\n\nS6A_P4_2__LR_RED__NR_025_009_20210713T234732_20210714T014224_F02\n2021-07-13T23:47:32.533Z\n2021-07-14T01:42:24.454Z\n\n\n...\n...\n...\n\n\nS6A_P4_2__LR_RED__NR_025_245_20210723T050533_20210723T064603_F02\n2021-07-23T05:05:33.543Z\n2021-07-23T06:46:03.471Z\n\n\nS6A_P4_2__LR_RED__NR_025_247_20210723T064603_20210723T083817_F02\n2021-07-23T06:46:03.521Z\n2021-07-23T08:38:17.483Z\n\n\nS6A_P4_2__LR_RED__NR_025_249_20210723T083817_20210723T103256_F02\n2021-07-23T08:38:17.533Z\n2021-07-23T10:32:56.490Z\n\n\nS6A_P4_2__LR_RED__NR_025_251_20210723T103256_20210723T122904_F02\n2021-07-23T10:32:56.540Z\n2021-07-23T12:29:04.459Z\n\n\nS6A_P4_2__LR_RED__NR_025_253_20210723T122904_20210723T142514_F02\n2021-07-23T12:29:04.509Z\n2021-07-23T14:25:14.452Z\n\n\n\n\n125 rows × 2 columns"
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+ "text": "Here are some cheatsheets and guides helping visualize what working with NASA Earthdata Cloud data looks like, and how to get started!\n\nNASA Earthdata Cloud Overview\nCloud Access Pathways\nGetting Started Roadmaps: Cloud & Local Workflows\nTools & Services Roadmap\nCloud Terminology 101\nWorkflow Cheatsheet\nCheatsheet Terminology\n\n\n\nNASA Earthdata Cloud is the NASA archive of Earth observations and is hosted in Amazon Web Services (AWS) cloud with DAAC tools and services built for use “next to the data.” The NASA DAACs (data centers) are currently transitioning to this cloud-based environment. All PO.DAAC data will be housed in the cloud, and can be accessed through AWS. The cloud offers a scalable and effective way to address storage, network, and data movement concerns while offering a tremendous amount of flexibility to the user. Particularly if working with large data volumes, data access and processing would be more efficient if workflows are taking place in the cloud, avoiding having to download large data volumes. Data download will continue to be freely available to users, from the Earthdata Cloud archive.\n\n\nPublished Google Slide\n\n\n\nThree pathway examples, to interact and access data (and services) from and within the NASA Earthdata Cloud, are illustrated in the diagram. Green arrows and icons indicate working locally, after downloading data to your local machine, servers, or compute/storage space. Orange arrows and icons highlight a workflow within the cloud, setting up your own AWS EC2 cloud instance, or virtual machine, in the cloud next to the data. Blue arrows and icons also indicate a within the cloud workflow, through shareable cloud environments such as Binder or JupyterHub set up in an AWS cloud region. Note that each of these may have a range of cost models. EOSDIS data are being stored in the us-west-2 region of AWS cloud; we recommend setting up your cloud computing environment in the same region as the data for free and easy in-cloud access.\n\n\nPublished Google Slide\nA note on costing: What is free and what do I have to budget for, now that data is archived in the cloud?\n\nDownloading data from the Earthdata Cloud archive in AWS, to your local computer environment or local storage (e.g. servers) is and will continue to be free for the user.\nAccessing the data directly in the cloud (from us-west-2 S3 region) is free. Users will need a NASA Earthdata Login account and AWS credentials to access, but there is no cost associated with these authentication steps, which are in place for security reasons.\nAccessing data in the cloud via EOSDIS or DAAC cloud-based tools and services such as the CMR API, Harmony API, OPenDAP API (from us-west-2 S3 region) is free to the user. Having the tools and services “next to the data” in the cloud enables DAACs to support data reduction and transformation, more efficiently, on behalf of the user, so users only access the data they need.\nCloud computing environments (i.e. virtual machines in the cloud) for working with data in the cloud (beyond direct or via services provided access) such as data analysis or running models with the data, is user responsibility, and should be considered in budgeting. I.e. User would need to set up a cloud compute environment (such as an EC2 instance or JupyterLab) and are responsible for any storage and computing costs.\n\nThis means that even though direct data access in the cloud is free to the user, they would first need to have a cloud computing environment/machine to execute the data access step from, and then continue their analysis.\nDepending on whether that cloud environment is provided by the user themselves, user’s institution, community hubs like Pangeo or NASA Openscapes JupyterLab sandbox, this element of the workflow may require user accountability, budgeting and user financial maintenance.\n\n\n\n\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using an in-cloud workflow (i.e. bringing user code into the cloud, avoiding data download and performing data workflows “next to the data”).\n\n\nPublished Google Slide\n\n\n\nThe following is a conceptual roadmap for users getting started with NASA Earth Observations cloud-archived data using a local machine (e.g. laptop) workflow, as data storage and computational work.\n\n\nPublished Google Slide\n\n\n\n\nBelow is a practical guide for learning about and selecting helpful tools or services for a given use case, focusing on how to find and access NASA Earthdata Cloud-archived data from local compute environment (e.g. laptop) or from a cloud computing workspace, with accompanying example tutorials. Once you follow your desired pathway, click on the respective blue notebook icon to get to the example tutorial. Note: these pathways are not exhaustive, there are many ways to accomplish these common steps, but these are some of our recommendations.\n\n\nPublished Google Slide\n\n\n\nCloud Terminology 101 for those new to commonly used cloud computing terms and phrases.\n\n\nPublished Google Slide\n\n\n\nThe following is a practical reference guide with links to tutorials and informational websites for users who are starting to take the conceptual pieces and explore and implement in their own workflows.\n\n\nPublished Google Slide\n\n\n\nTerminology cheatsheet to explain terms commonly used in cloud computing and those located on the NASA Earthdata Cloud Cheatsheet. See also NASA Earthdata Glossary.\n\n\nPublished Google Slide"
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- "text": "Access\nThese functions have nothing to do with Earthdata or PO.DAAC services, all Python 3 standard library except for tqdm and wget:\n\ndef download(source: str):\n target = os.path.basename(source.split(\"?\")[0])\n if not os.path.isfile(target):\n !wget --quiet --continue --output-document $target $source\n return target\n\ndef download_all(urls: list, max_workers: int=12):\n with ThreadPoolExecutor(max_workers=max_workers) as pool:\n workers = pool.map(download, urls)\n return list(tqdm.tqdm(workers, total=len(urls)))\n\n\nExplore dataset variables\nThe S6A level 2 altimetry datasets include variables for sea surface height anomaly (SSHA), significant wave height (SWH), wind speed, others.\n\nopendap_url = f\"https://opendap.earthdata.nasa.gov/collections/{concept_id}\"\n\nurls = granules.GranuleUR.apply(lambda f: f\"{opendap_url}/granules/{f}.nc4\")\n \nurls.iloc[0].replace(\".nc4\", \".html\")\n\n'https://opendap.earthdata.nasa.gov/collections/C1968980576-POCLOUD/granules/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.html'\n\n\n\n\nGet subsets from OPeNDAP\nPrepare the full request urls by adding a comma-delimited list of variables, after the question mark ?.\n\nvariables = ['data_01_time',\n 'data_01_longitude',\n 'data_01_latitude',\n 'data_01_ku_ssha']\n\nquery = \",\".join(variables)\n\nreqs = urls.apply(lambda x: f\"{x}?{query}\")\n\nprint(reqs.iloc[0])\n\nhttps://opendap.earthdata.nasa.gov/collections/C1968980576-POCLOUD/granules/S6A_P4_2__LR_RED__NR_025_001_20210713T162644_20210713T182234_F02.nc4?data_01_time,data_01_longitude,data_01_latitude,data_01_ku_ssha\n\n\nThe function(s) below download granules from a remote source to a local target file, and reliably manage simultaneous streaming downloads divided between multiple threads.\n\nfiles = download_all(urls=reqs, max_workers=12)\n\n100%|██████████| 125/125 [00:53<00:00, 2.32it/s]\n\n\n\n!du -sh .\n\n16M .\n\n\nWhy this way?\nTo be explained…\nWill it scale?\nThe source netcdf files range from 2500KB to 3000KB per file. The OPeNDAP subsets that we just downloaded are around 100KB a pop. It took less than 10 minutes to download the same subsets for ~1700 files, that covers a period of roughly You can extrapolate to a reasonable estimate for time series of any length (even the whole mission).\nTotal size of the source data is ~4.25GB, based on:\n2500KB x 1700 = 4250000KB (4250 megabytes)\nversus, total size of the subset time series:\n100KB x 1700 = 17000KB (170 megabytes)\nPlot it to put this in context, because our goal is to produce one global grid for the entire cycle of data that we just downloaded.\n\n\nOpen, plot ssh time series\nSort the list of subset files to ensure they concatenate in proper order. Call open_mfdataset on the list to open all the subsets in memory as one dataset in xarray.\n\nds = xr.open_mfdataset(sorted(files), engine=\"netcdf4\")\n\nprint(ds)\n\n<xarray.Dataset>\nDimensions: (data_01_time: 827001)\nCoordinates:\n * data_01_time (data_01_time) datetime64[ns] 2021-07-13T16:26:45 ... ...\nData variables:\n data_01_longitude (data_01_time) float64 dask.array<chunksize=(6950,), meta=np.ndarray>\n data_01_latitude (data_01_time) float64 dask.array<chunksize=(6950,), meta=np.ndarray>\n data_01_ku_ssha (data_01_time) float64 dask.array<chunksize=(6950,), meta=np.ndarray>\nAttributes: (12/63)\n Convention: CF-1.7\n institution: EUMETSAT\n references: Sentinel-6_Jason-CS ALT Generic P...\n contact: ops@eumetsat.int\n radiometer_sensor_name: AMR-C\n doris_sensor_name: DORIS\n ... ...\n xref_solid_earth_tide: S6__P4_2__SETD_AX_20151008T000000...\n xref_surface_classification: S6__P4____SURF_AX_20151008T000000...\n xref_wind_speed_alt: S6A_P4_2__WNDL_AX_20151008T000000...\n product_name: S6A_P4_2__LR______20210713T162644...\n history: 2021-07-13 18:38:07 : Creation\\n2...\n history_json: [{\"$schema\":\"https:\\/\\/harmony.ea...\n\n\nTwo prerequisites to plot based on personal preference:\n\nrename all the variables to drop the group names (because I just think they’re too long as is)\nget a tuple with two timestamps for the start and end of the time series coverage for the cycle\n\nPlot the cycle as a series on a geographic plot, which should look just like the one at the top of this notebook:\n\nnew_variable_names = list(map(lambda x: x.split(\"_\")[-1], variables))\nmap_variable_names = dict(zip(variables, new_variable_names))\nds = ds.rename(map_variable_names).set_coords(['time','longitude','latitude']) # rename variables\n\ntimeframe = (str(ds.time.data[0]).split('T')[0],\n str(ds.time.data[-1]).split('T')[0]) # get timestamps tuple\n\nds.plot.scatter(y=\"latitude\", x=\"longitude\", hue=\"ssha\", \n vmin=-0.4, vmax=0.4, cmap=\"jet\", levels=9, \n aspect=2.5, size=6, s=1, )\nplt.ylim(-67., 67.)\nplt.xlim(0., 360.)\nplt.tight_layout()\nplt.title(f\"ssha for cycle {cycle} ({timeframe})\")\n\nText(0.5, 1.0, \"ssha for cycle 25 (('2021-07-13', '2021-07-23'))\")"
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+ "text": "Cloning the podaac/tutorials repository allows you to have a copy of all the PO.DAAC Cookbook Notebook tutorials on your own machine to use. Follow these instructions to clone our repository from GitHub documentation, selecting your machine type and what method you would like to use to clone at the top (GitHub Desktop is a useful interface if you would rather not use the command line).\nThe link for this particular repository is: https://github.com/podaac/tutorials.git In the command line or Git Bash change the working directory to the location where you would want the cloned directory and then type:\n$ git clone https://github.com/podaac/tutorials"
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- "text": "Process\n\n0.5-degree grid from ECCO V4r4 (int)\n\nAcknowledgement: pyresample approach shared by Ian Fenty, NASA JPL/ECCO.\n\nECCO V4r4 products are distributed in two spatial formats. One set of collections provides the ocean state estimates on the native model grid (LLC0090) and the other provides them after interpolating to a regular grid defined in geographic coordinates with horizontal cell size of 0.5-degrees. The latitude/longitude grid is distributed as its own collection in one netcdf file: https://search.earthdata.nasa.gov/search/granules?p=C2013583732-POCLOUD\nDownload the ECCO grid geometry netcdf from its https download endpoint in NASA Earthdata Cloud. Open the file and print the header content for the maskC variable, which contains a boolean mask representing the wet/dry state of the area contained in each cell of a 3d grid with dimensions mapped to Z, latitude, and longitude.\n\necco_file = download(\"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/\"\n \"ECCO_L4_GEOMETRY_05DEG_V4R4/GRID_GEOMETRY_ECCO_V4r4_latlon_0p50deg.nc\")\n\necco_grid = xr.open_dataset(ecco_file).isel(Z=0) # select 0 on z-axis\n\necco_mask = ecco_grid.maskC\n\nprint(ecco_grid)\n\n<xarray.Dataset>\nDimensions: (latitude: 360, longitude: 720, nv: 2)\nCoordinates:\n Z float32 -5.0\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 179.2 179.8\n latitude_bnds (latitude, nv) float32 ...\n longitude_bnds (longitude, nv) float32 ...\n Z_bnds (nv) float32 ...\nDimensions without coordinates: nv\nData variables:\n hFacC (latitude, longitude) float64 ...\n Depth (latitude, longitude) float64 ...\n area (latitude, longitude) float64 ...\n drF float32 ...\n maskC (latitude, longitude) bool ...\nAttributes: (12/57)\n acknowledgement: This research was carried out by the Jet...\n author: Ian Fenty and Ou Wang\n cdm_data_type: Grid\n comment: Fields provided on a regular lat-lon gri...\n Conventions: CF-1.8, ACDD-1.3\n coordinates_comment: Note: the global 'coordinates' attribute...\n ... ...\n references: ECCO Consortium, Fukumori, I., Wang, O.,...\n source: The ECCO V4r4 state estimate was produce...\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadat...\n summary: This dataset provides geometric paramete...\n title: ECCO Geometry Parameters for the 0.5 deg...\n uuid: b4795c62-86e5-11eb-9c5f-f8f21e2ee3e0\n\n\n\n\nGet ssha variable on 0.5-degree grid\nResample ssha data using kd-tree gaussian weighting neighbour approach. Define a function that implements the following steps:\n\nGenerate two 2d arrays of lats/lons using the permuted 1d coordinates from an input gridded dataset.\nDefine the target grid geometry using the 2d arrays of lats/lons.\nDefine the source grid geometry using the 1d arrays of lats/lons from an input dataset.\n\n\nimport warnings\n\ndef l2alt2grid(source: xr.DataArray, target: xr.DataArray, **options):\n nans = ~np.isnan(source.values)\n data = source.values[nans]\n\n lats = source.latitude.values[nans]\n lons = (source.longitude.values[nans] + 180) % 360 - 180\n src = pr.SwathDefinition(lons, lats)\n\n lons1d = target.longitude.values\n lats1d = target.latitude.values\n lons2d, lats2d = np.meshgrid(lons1d, lats1d)\n tgt = pr.SwathDefinition(lons2d, lats2d)\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=UserWarning)\n res, std, cnt = resample_gauss(src, data, tgt, **options)\n \n coords = {'latitude': lats1d, 'longitude': lons1d}\n return (xr.DataArray(res, coords=coords),\n xr.DataArray(std, coords=coords),\n xr.DataArray(cnt, coords=coords), )\n\nSet the gridding parameters in the python dictionary below; then pass it to the function as the last of three required positional arguments (the first two are the source dataset and the dataset that provides the target grid geometry).\n\ngridding_options = dict(\n radius_of_influence = 175000, \n sigmas = 25000,\n neighbours = 100,\n fill_value = np.nan,\n with_uncert = True\n)\n\nresult, stddev, counts = l2alt2grid(ds.ssha, ecco_mask, **gridding_options)\n\nresult.shape == (ecco_grid.latitude.size, \n ecco_grid.longitude.size)\n\nTrue\n\n\n\n\nPlot gridded ssha, gridding statistics\nPlot each array for the output ‘grid’ and the grid statistics ‘stddev’ and ‘counts’.\n\nresult.sel(latitude=slice(-66.,66.)).plot(cmap=\"jet\", vmin=-0.4, vmax=0.4, figsize=(15,6))\n\n<matplotlib.collections.QuadMesh at 0x7f47d0256520>\n\n\n\n\n\nLook at this plot and adjust gridding parameters as needed to refine ssha grid.\n\nstddev.sel(latitude=slice(-67.,67.)).plot(cmap=\"jet\", robust=True, figsize=(15,6))\n\n<matplotlib.collections.QuadMesh at 0x7f47e0302e50>\n\n\n\n\n\na guess: the yellow areas with lower counts correspond to pass positions at the start/end of the cycle.\n\ncounts.sel(latitude=slice(-67.,67.)).plot(cmap=\"jet\", robust=True, figsize=(15,6))\n\n<matplotlib.collections.QuadMesh at 0x7f47d0f560a0>\n\n\n\n\n\n\nresult.sel(latitude=slice(-66.,66.)).to_pandas().T.describe().T\n\n\n\n\n\n\n\n\ncount\nmean\nstd\nmin\n25%\n50%\n75%\nmax\n\n\nlatitude\n\n\n\n\n\n\n\n\n\n\n\n\n-65.75\n304.0\n0.209026\n0.830608\n-1.471174\n-0.069080\n0.039957\n0.259600\n6.104942\n\n\n-65.25\n316.0\n0.216497\n0.479879\n-0.793696\n0.002569\n0.080189\n0.301957\n3.173239\n\n\n-64.75\n326.0\n0.218253\n0.465126\n-0.593413\n0.008729\n0.085963\n0.296550\n2.885002\n\n\n-64.25\n332.0\n0.090540\n0.453919\n-0.909328\n-0.030323\n0.040090\n0.097437\n2.914544\n\n\n-63.75\n371.0\n0.029598\n0.379806\n-0.884937\n-0.064511\n0.001728\n0.062316\n2.879200\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n63.75\n310.0\n0.034539\n0.133022\n-0.580132\n-0.010876\n0.038027\n0.081060\n0.806144\n\n\n64.25\n314.0\n0.018076\n0.118825\n-0.690086\n-0.016610\n0.036694\n0.077421\n0.286705\n\n\n64.75\n311.0\n-0.085785\n1.264886\n-19.397700\n-0.022718\n0.041526\n0.080252\n1.397690\n\n\n65.25\n307.0\n4.177751\n42.431829\n-23.955439\n-0.043362\n0.043002\n0.090967\n505.812000\n\n\n65.75\n290.0\n3.392658\n34.873463\n-10.643720\n-0.075147\n0.058857\n0.105139\n430.164759\n\n\n\n\n264 rows × 8 columns"
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+ "text": "Cloning the podaac/tutorials repository allows you to have a copy of all the PO.DAAC Cookbook Notebook tutorials on your own machine to use. Follow these instructions to clone our repository from GitHub documentation, selecting your machine type and what method you would like to use to clone at the top (GitHub Desktop is a useful interface if you would rather not use the command line).\nThe link for this particular repository is: https://github.com/podaac/tutorials.git In the command line or Git Bash change the working directory to the location where you would want the cloned directory and then type:\n$ git clone https://github.com/podaac/tutorials"
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- "title": "Sentinel-6 MF L2 Altimetry: OPeNDAP Access and Gridding",
- "section": "Appendix",
- "text": "Appendix\n\nSentinel-6A MF\nThe Sentinel-6A Michael Freilich radar altimeter mission, or Sentinel-6, produces high-precision measurements of global sea-level. You can learn about the mission and ocean altimetry applications and datasets through the following links:\n\nhttps://www.nasa.gov/sentinel-6\nhttps://sealevel.jpl.nasa.gov/missions/jasoncs/\nhttps://sentinel-6.cnes.fr/en/JASON-CS/index.htm\nhttps://podaac.jpl.nasa.gov/Sentinel-6\n\n\n\nOcean Surface Topography\nThe primary contribution of satellite altimetry to satellite oceanography has been to:\n\nImprove the knowledge of ocean tides and develop global tide models.\nMonitor the variation of global mean sea level and its relationship to changes in ocean mass and heat content.\nMap the general circulation variability of the ocean, including the ocean mesoscale, over decades and in near real-time using multi-satellite altimetric sampling.\n\n\n\n\naltimetry\n\n\nThe Surface Water Ocean Topography (SWOT) mission represents the next-generation of sea surface height observation. It will bring together oceanography and hydrology to focus on gaining a better understanding of the world’s oceans and its terrestrial surface waters. U.S. and French oceanographers and hydrologists have joined forces to develop this new space mission to make the first global survey of Earth’s surface water, observe the fine details of the ocean’s surface topography and measure how water bodies change over time. The payload on SWOT will include a Jason-class radar altimeter that will serve to extend the time series of sea surface height data into the future, beyond the lifespan of Sentinel-6 MF, which is introduced immediately below. Read more about SWOT at: https://podaac.jpl.nasa.gov/SWOT/\n\n\nEarthdata Cloud Services Overview\nThis workflow example downloads subsets of the netcdf datasets via OPeNDAP for massive efficiency gains (network/compute).\nAccess for direct download:\n\nBrowse and download granules through Earthdata Search – https://search.earthdata.nasa.gov/search/granules?p=C1968980576-POCLOUD\nBrowse and download granules from HTTPS endpoints – https://cmr.earthdata.nasa.gov/virtual-directory/collections/C1968980576-POCLOUD\nBrowse and download granules from S3 endpoints (example forthcoming, assuming s3 direct access has been enabled for the collection)\n\nAccess through data services:\nData and metadata are also accessible in reduced forms through higher-level cloud data services, for example:\n\nhttps://harmony.earthdata.nasa.gov/ – Data reduction via on-demand subsetting and other high-level reformatting\n\nInterface to backend services such as data file format conversion, subsetting at L2+, regridding and reprojection at L3+, and more.\nCompatibility depends on the data processing level and data/file format, and so their expected behavior vary also.\nServices available through Harmony API reduce the technical burden on users by covering certain low-level data transformations that a user would normally have to apply themselves, even to simply subset a dataset from OPeNDAP.\n\nhttps://opendap.earthdata.nasa.gov/ – Data reduction via basic subsetting along coordinate dimensions and by variable\n\nRequires more familiarity with the contents of the target dataset, as well knowledge of how to select for data along the dimensions which correspond to space/time coordinates fitting the geographic and temporal coverage of interest.\nUser Guide: https://opendap.github.io/documentation/UserGuideComprehensive.html\n\n\n\n\nPython API References\n\nBash\n\nhttps://www.gnu.org/software/coreutils/manual/html_node/du-invocation.html\n\nPython\n\nhttps://docs.python.org/3/library/functions.html#map\n\nhttps://docs.python.org/3/library/functions.html#zip\nhttps://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor\n\nnumpy (https://numpy.org/doc/stable/reference)\n\nnumpy.ndarray.data\n\nnumpy.where\n\nnumpy.isnan\n\ndatetimes\n\nnumpy.sum\n\nnumpy.nansum\n\n\nxarray (https://xarray.pydata.org/en/stable)\n\nxarray.DataArray\n\nxarray.DataArray.values\n\nxarray.DataArray.mean\n\nxarray.DataArray.isel\nxarray.open_dataset\nxarray.DataArray.plot\nxarray.Dataset.rename\n\npyresample\n\npyresample.geometry.SwathDefinition\npyresample.kd_tree.resample_gauss"
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+ "title": "Tech Guides",
+ "section": "Tech How To’s & Useful Introductions",
+ "text": "Tech How To’s & Useful Introductions\n\nHow to Set Up an EC2 Instance - Earthdata Webinar outlining how to set up your own AWS cloud computer, an Elastic Computer Cloud (EC2)\nNASA earthaccess - a Python package to search, preview and access NASA datasets (on-prem or in the cloud) with a few lines of code\nGet started with GitHub - develop and share code in a collaborative environment\nIntroduction to Xarray - A useful Python Package while working with NASA Earthdata\nJupyter Lab, RStudio Desktop, or Visual Studio Code - useful coding workspaces"
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+ "section": "A Growing List of Resources",
+ "text": "A Growing List of Resources\n\nNASA Earthdata Cloud Cookbook - Tutorials & data recipes applicable to all NASA datasets\nNASA Earthdata: How to Cloud\nNASA Earthdata Cloud Primer - AWS cloud primer: helpful tutorials for how to set up your own EC2 cloud instance in AWS, attach storeage, move files back and forth, and more.\nSetting up Jupyter Notebooks in a user EC2 instance in AWS - helpful blog post for setting up jupyter notebooks in an EC2 instance in AWS. (Builds on the Cloud Primer tutorials, which are missing that next step)\nRunning the NASA Cloud Workshop notebooks with mybinder.org - by Eli Holmes, 2021 Cloud Hackathon Participant who then set up working in Binder"
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"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
+ "text": "The Downloader is useful if you need to download PO.DAAC data once in a while or prefer to do it “on-demand”. The Downloader makes no assumptions about the last time run or what is new in the archive, it simply uses the provided requests and downloads all matching data.\n\n\n\nThe subscriber is useful for users who need to continuously pull the latest data from the PO.DAAC archive. If you feed data into a model or real time process, the subscriber allows you to repeatedly run the script and only download the latest data.\n\n\n\nBoth subscriber and downloader require Python >= 3.7.\nThe subscriber and downloader scripts are available in the pypi python repository, it can be installed via pip:\npip install podaac-data-subscriber\nyou should now have access to the downloader and subscriber Command line interfaces.\n\nNote: If after installation, the podaac-data-subscriber or podaac-data-downloader commands are not available, you may need to add the script location to the PATH. This could be due to a User Install of the python package, which is common on shared systems where python packages are installed for the user (not the system). See Installing to the User Site and User Installs for more information on finding the location of installed scripts and adding them to the PATH.\n\nTo use the Subscriber or Downloader, you will need to have an Earthdata login account. You will also need a netrc file with your Earthdata Login credentials to access the data. Follow these authentication instructions to create your netrc if you do not have one already."
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- "section": "Set start and end dates",
- "text": "Set start and end dates\n\nstart_date = \"1992-01-01\"\nend_date = \"2002-12-31\"\n\n# break it down into Year, Month, Day (and minutes and seconds if desired) \n# as inputs to harmony.py call using datetime()\nstart_year = 2002\nstart_month = 1\nstart_day = 1\n\nend_year = 2017\nend_month = 12\nend_day = 31"
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+ "text": "The Downloader is useful if you need to download PO.DAAC data once in a while or prefer to do it “on-demand”. The Downloader makes no assumptions about the last time run or what is new in the archive, it simply uses the provided requests and downloads all matching data.\n\n\n\nThe subscriber is useful for users who need to continuously pull the latest data from the PO.DAAC archive. If you feed data into a model or real time process, the subscriber allows you to repeatedly run the script and only download the latest data.\n\n\n\nBoth subscriber and downloader require Python >= 3.7.\nThe subscriber and downloader scripts are available in the pypi python repository, it can be installed via pip:\npip install podaac-data-subscriber\nyou should now have access to the downloader and subscriber Command line interfaces.\n\nNote: If after installation, the podaac-data-subscriber or podaac-data-downloader commands are not available, you may need to add the script location to the PATH. This could be due to a User Install of the python package, which is common on shared systems where python packages are installed for the user (not the system). See Installing to the User Site and User Installs for more information on finding the location of installed scripts and adding them to the PATH.\n\nTo use the Subscriber or Downloader, you will need to have an Earthdata login account. You will also need a netrc file with your Earthdata Login credentials to access the data. Follow these authentication instructions to create your netrc if you do not have one already."
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- "section": "Spatial bounds (Region of Interest) – Not used",
- "text": "Spatial bounds (Region of Interest) – Not used\n\nwesternmost_longitude = 100.\neasternmost_longitude = 150.\nnorthermost_latitude = 30.\nsouthernmost_latitude = 0."
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+ "section": "Command (cmd) Line Examples",
+ "text": "Command (cmd) Line Examples\nThe dataset listing pages on the PO.DAAC Portal provide cmd line examples for each dataset respectively. For example, on a MUR SST dataset landing page, if you click the Download icon under Capabilities on the right side, the following script for the subscriber should be visible:\npodaac-data-subscriber -c MUR25-JPL-L4-GLOB-v04.2 -d ./data/MUR25-JPL-L4-GLOB-v04.2 --start-date 2002-08-31T21:00:00Z\nDownloading simulated SWOT Raster data over a specified region and time:\npodaac-data-downloader -c SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 -d ./SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 --start-date 2022-08-02T00:00:00Z --end-date 2022-08-22T00:00:00Z -b=\"-97,32.5,-96.5,33\"\nSubscribing to the [GRACE-FO Monthly Ocean Bottom Pressure Anomaly Dataset]:(https://podaac.jpl.nasa.gov/dataset/TELLUS_GRFO_L3_CSR_RL06_OCN_v04)\npodaac-data-subscriber -c TELLUS_GRFO_L3_CSR_RL06_OCN_v04 -d ./data/TELLUS_GRFO_L3_CSR_RL06_OCN_v04 --start-date 2018-05-22T00:00:00Z"
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- "section": "Setup the harmony-py service call and execute a request",
- "text": "Setup the harmony-py service call and execute a request\n\n# using the the harmony.py service, set up the request and exectue it\necco_collection = Collection(id=ccid)\ntime_range = {'start': datetime(start_year, start_month, start_day), 'stop': datetime(end_year, end_month, end_day)}\nprint(time_range)\n\nharmony_client = Client(env=Environment.PROD)\n\n# in this example set concatentae to 'False' because the monthly input time steps vary slightly\n# (not always centered in the middle of month)\necco_request = Request(collection=ecco_collection, temporal=time_range, format='application/x-zarr', concatenate='False')\n\n# sumbit request and monitor job\necco_job_id = harmony_client.submit(ecco_request)\nprint('\\n Waiting for the job to finish. . .\\n')\necco_response = harmony_client.result_json(ecco_job_id, show_progress=True)\nprint(\"\\n. . .DONE!\")\n\n{'start': datetime.datetime(2002, 1, 1, 0, 0), 'stop': datetime.datetime(2017, 12, 31, 0, 0)}\n\nWaiting for the job to finish. . .\n\n\n. . .DONE!\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\n\nYou can also wrap the creation of the Harmony request URL into one function. Shown here for legacy purposes (does not execute a Harmony request):\n\ndef get_harmony_url(ccid,start_date,end_date):\n \"\"\"\n Parameters:\n ===========\n ccid: string\n concept_id of the datset\n date_range: list\n [start_data, end_date] \n \n Return:\n =======\n url: the harmony URL used to perform the netcdf to zarr transformation\n \"\"\"\n \n base = f\"https://harmony.earthdata.nasa.gov/{ccid}\"\n hreq = f\"{base}/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset\"\n rurl = f\"{hreq}?format=application/x-zarr\"\n\n #print(rurl)\n\n subs = '&'.join([f'subset=time(\"{start_date}T00:00:00.000Z\":\"{end_date}T23:59:59.999Z\")']) \n #subs = subs + '&' + '&'.join([f'subset=lat({southernmost_latitude}:{northermost_latitude})'])\n #subs = subs + '&' + '&'.join([f'subset=lon({westernmost_longitude}:{easternmost_longitude})'])\n\n rurl = f\"{rurl}&{subs}\"\n return rurl\n\nccid='C2129189405-POCLOUD'\nprint(get_harmony_url(ccid,start_date,end_date))\n\n# this is the way you would execute it\n# response = requests.get(url=rurl).json()\n\nhttps://harmony.earthdata.nasa.gov/C2129189405-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?format=application/x-zarr&subset=time(\"1992-01-01T00:00:00.000Z\":\"2002-12-31T23:59:59.999Z\")"
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+ "section": "Tutorial Examples Utilizing the PO.DAAC Subscriber/Downloader:",
+ "text": "Tutorial Examples Utilizing the PO.DAAC Subscriber/Downloader:\n\nSWOT NetCDF to Geotiff Conversion"
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- "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
- "section": "Run 2nd Harmony netCDF-to-Zarr call",
- "text": "Run 2nd Harmony netCDF-to-Zarr call\n\nShortName = \"ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4\"\n\n# 1) Find new concept_id for this dataset\nresponse = requests.get(\n url='https://cmr.earthdata.nasa.gov/search/collections.umm_json', \n params={'provider': \"POCLOUD\",\n 'ShortName': ShortName,\n 'page_size': 1}\n)\n\nummc = response.json()['items'][0]\nccid = ummc['meta']['concept-id']\n\n# using the the harmony.py service, set up the request and exectue it\necco_collection = Collection(id=ccid)\ntime_range = {'start': datetime(start_year, start_month, start_day), 'stop': datetime(end_year, end_month, end_day)}\n\nharmony_client = Client(env=Environment.PROD)\n\n# in this example set concatentae to 'False' because the monthly input time steps vary slightly\n# (not always centered in the middle of month)\necco_request = Request(collection=ecco_collection, temporal=time_range, format='application/x-zarr', concatenate='False')\n\n# sumbit request and monitor job\necco_job_id = harmony_client.submit(ecco_request)\nprint('\\nWaiting for the job to finish. . .\\n')\necco_response = harmony_client.result_json(ecco_job_id, show_progress=True)\nprint(\"\\n. . .DONE!\")\n\n\nWaiting for the job to finish. . .\n\n\n. . .DONE!\n\n\n [ Processing: 100% ] |###################################################| [|]"
+ "objectID": "quarto_text/DataSubscriberDownloader.html#documentation",
+ "href": "quarto_text/DataSubscriberDownloader.html#documentation",
+ "title": "PO.DAAC Data Subscriber/Downloader",
+ "section": "Documentation",
+ "text": "Documentation"
},
{
- "objectID": "notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html#read-the-s3-zarr-endpoints-and-aggregate-to-single-zarr",
- "href": "notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html#read-the-s3-zarr-endpoints-and-aggregate-to-single-zarr",
- "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
- "section": "Read the S3 Zarr endpoints and aggregate to single Zarr",
- "text": "Read the S3 Zarr endpoints and aggregate to single Zarr\n\n# 1) read the AWS credentials\nprint(ecco_response['message'])\nwith requests.get(ecco_response['links'][2]['href']) as r:\n creds = r.json()\n\nprint( creds.keys() )\nprint(\"AWS credentials expire on: \", creds['Expiration'] )\n\n\n# 2) print root directory and read the s3 URLs into a list\ns3_dir2 = ecco_response['links'][3]['href']\nprint(\"root directory:\", s3_dir2)\ns3_urls2 = [u['href'] for u in ecco_response['links'][4:-1]]\n\n# sort the URLs in time order\ns3_urls2.sort()\n\n# 3) Autenticate AWS S3 credentials\ns3 = s3fs.S3FileSystem(\n key=creds['AccessKeyId'],\n secret=creds['SecretAccessKey'],\n token=creds['SessionToken'],\n client_kwargs={'region_name':'us-west-2'},\n)\n\n# 4) Read and concatenate into a single Zarr dataset\ntemp_ds = xr.concat([xr.open_zarr(s3.get_mapper(u)) for u in s3_urls2], dim=\"time\", coords='minimal')\n#temp_ds_group = xr.concat([xr.open_zarr(s3.get_mapper(u)) for u in s3_urls2], dim=\"time\", coords='minimal').groupby('time.month')\n\ntemp_ds\n\nThe job has completed successfully. Contains results in AWS S3. 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period\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.02 kiB\n16 B\n\n\nShape\n(193, 2)\n(1, 2)\n\n\nCount\n579 Tasks\n193 Chunks\n\n\nType\ndatetime64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\nData variables: (2)\n\n\n\n\n\nSALT\n\n\n(time, Z, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 50, 280, 280), meta=np.ndarray>\n\n\n\n\ncomment :\n\nDefined using CF convention 'Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term 'salinity' is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.' see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html\n\ncoverage_content_type :\n\nmodelResult\n\nlong_name :\n\nSalinity\n\nstandard_name :\n\nsea_water_salinity\n\nunits :\n\n1e-3\n\nvalid_max :\n\n41.26802444458008\n\nvalid_min :\n\n17.106637954711914\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.32 GiB\n14.95 MiB\n\n\nShape\n(193, 50, 360, 720)\n(1, 50, 280, 280)\n\n\nCount\n2509 Tasks\n1158 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nTHETA\n\n\n(time, Z, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 50, 280, 280), meta=np.ndarray>\n\n\n\n\ncomment :\n\nSea water potential temperature is the temperature a parcel of sea water would have if moved adiabatically to sea level pressure. Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g ho_{0} z$.\n\ncoverage_content_type :\n\nmodelResult\n\nlong_name :\n\nPotential temperature\n\nstandard_name :\n\nsea_water_potential_temperature\n\nunits :\n\ndegree_C\n\nvalid_max :\n\n36.032955169677734\n\nvalid_min :\n\n-2.2909388542175293\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.32 GiB\n14.95 MiB\n\n\nShape\n(193, 50, 360, 720)\n(1, 50, 280, 280)\n\n\nCount\n2509 Tasks\n1158 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nAttributes: (62)Conventions :CF-1.8, ACDD-1.3acknowledgement :This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.author :Ian Fenty and Ou Wangcdm_data_type :Gridcomment :Fields provided on a regular lat-lon grid. They have been mapped to the regular lat-lon grid from the original ECCO lat-lon-cap 90 (llc90) native model grid.coordinates_comment :Note: the global 'coordinates' attribute describes auxillary coordinates.creator_email :ecco-group@mit.educreator_institution :NASA Jet Propulsion Laboratory (JPL)creator_name :ECCO Consortiumcreator_type :groupcreator_url :https://ecco-group.orgdate_created :2020-12-18T09:50:20date_issued :2020-12-18T09:50:20date_metadata_modified :2021-03-15T22:06:59date_modified :2021-03-15T22:06:59geospatial_bounds_crs :EPSG:4326geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lat_resolution :0.5geospatial_lat_units :degrees_northgeospatial_lon_max :180.0geospatial_lon_min :-180.0geospatial_lon_resolution :0.5geospatial_lon_units :degrees_eastgeospatial_vertical_max :0.0geospatial_vertical_min :-6134.5geospatial_vertical_positive :upgeospatial_vertical_resolution :variablegeospatial_vertical_units :meterhistory :Inaugural release of an ECCO Central Estimate solution to PO.DAACid :10.5067/ECG5M-OTS44institution :NASA Jet Propulsion Laboratory (JPL)instrument_vocabulary :GCMD instrument keywordskeywords :EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY, EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > POTENTIAL TEMPERATUREkeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordslicense :Public Domainmetadata_link :https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4naming_authority :gov.nasa.jplplatform :ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)platform_vocabulary :GCMD platform keywordsprocessing_level :L4product_name :OCEAN_TEMPERATURE_SALINITY_mon_mean_2001-12_ECCO_V4r4_latlon_0p50deg.ncproduct_time_coverage_end :2018-01-01T00:00:00product_time_coverage_start :1992-01-01T12:00:00product_version :Version 4, Release 4program :NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)project :Estimating the Circulation and Climate of the Ocean (ECCO)publisher_email :podaac@podaac.jpl.nasa.govpublisher_institution :PO.DAACpublisher_name :Physical Oceanography Distributed Active Archive Center (PO.DAAC)publisher_type :institutionpublisher_url :https://podaac.jpl.nasa.govreferences :ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928source :The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint methodstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsummary :This dataset provides monthly-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.time_coverage_duration :P1Mtime_coverage_end :2002-01-01T00:00:00time_coverage_resolution :P1Mtime_coverage_start :2001-12-01T00:00:00title :ECCO Ocean Temperature and Salinity - Monthly Mean 0.5 Degree (Version 4 Release 4)uuid :7f718714-4159-11eb-8bbd-0cc47a3f819b"
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+ "section": "Step 2: Run the Script",
+ "text": "Step 2: Run the Script\nUsage:\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose]\n [-p PROVIDER] [--limit LIMIT] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n--cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --cycle must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the downloader.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nUnlike the ‘subscriber’, no ‘state’ is maintained for the downloader. if you re-run the downloader you’ll re-download all of the files again, unlike the subscriber which will download newly ingested data since the last run."
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- "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
- "section": "Do data masking, calculate a SST anomaly, and plot some figures",
- "text": "Do data masking, calculate a SST anomaly, and plot some figures\n\n# Mask for the good data. Everything else defaults to NaN\n# SST missing value 9.9692100e+36 \n# SSH missing value 9.9692100e+36\ncond = (ssh_ds < 1000)\nssh_ds_masked = ssh_ds['SSH'].where(cond)\n\ncond = (temp_ds < 1000)\ntemp_ds_masked = temp_ds['THETA'].where(cond)\n\n# Derive a SST climatology and subtract it from the SST to create SST anomaly and remove trends\nclimatology_mean = temp_ds_masked.groupby('time.month').mean('time',keep_attrs=True,skipna=False)\ntemp_ds_masked_anomaly = temp_ds_masked.groupby('time.month') - climatology_mean # subtract out longterm monthly mean\n\nfig,ax=plt.subplots(1,3,figsize=(25,5))\n\n# take a slice of the Indian Ocean and plot SSH, SST, SST anomaly\nssh_ds_masked['SSH'][6].sel(longitude=slice(40,120),latitude=slice(-30,20)).plot(ax=ax[0], vmin=-0.5,vmax=1.25)\ntemp_ds_masked['THETA'][6].sel(longitude=slice(40,120),latitude=slice(-30,20), Z=slice(0,-5)).plot(ax=ax[1], vmin=10,vmax=32)\ntemp_ds_masked_anomaly['THETA'][6].sel(longitude=slice(40,120),latitude=slice(-30,20), Z=slice(0,-5)).plot(ax=ax[2], vmin=-2,vmax=2)\n\n<matplotlib.collections.QuadMesh at 0x7fe46b00b460>"
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+ "section": "Note: netrc file",
+ "text": "Note: netrc file\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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- "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
- "section": "Perform the correlations in the east and west Indian Ocean",
- "text": "Perform the correlations in the east and west Indian Ocean\n\n# Western and Eastern Indian Ocean regions (WIO and EIO respectively)\n# EIO; 90 –110 E, 10 S–0N\n# WIO; 50 –70 E, 10 S–10 N\n\n# Group Eastern Indian Ocean data by month. This will make the correlation of all monthly values straightforwrd.\nssh_group = ssh_ds_masked['SSH'].sel(longitude=slice(90,110),latitude=slice(-10,0)).groupby('time.month')\n#temp_group = temp_ds_masked['THETA'].sel(longitude=slice(90,110),latitude=slice(-10,0), Z=-5.0).drop('Z').groupby('time.month')\ntemp_group = temp_ds_masked_anomaly['THETA'].sel(longitude=slice(90,110),latitude=slice(-10,0), Z=-5.0).drop('Z').groupby('time.month')\n\nprint(\" Running correlations in eastern Indian Ocean . . .\\n\") \ncorr = []\nfor month in range(1,13):\n corr.append(xr.corr(ssh_group[month], temp_group[month]))\n #print(\"\\nthe correlation in the east is: \" , xr.corr(ssh_group[month], temp_group[month]).values)\n \n# Do some plotting\nfig,ax=plt.subplots(1,2,figsize=(14,8))\n\nax[0].set_title(\"Spatial correlation in Eastern Indian Ocean\",fontsize=16)\nax[0].set_ylabel(\"Correlation\",fontsize=16)\nax[0].set_xlabel(\"Month\",fontsize=16)\nax[0].set_ylim([-1, 1])\nax[0].plot(corr)\n\n# Repeat for Western Indian Ocean\n# Group the data by month. This will make the correlation of all monthly values straightforwrd.\nssh_group = ssh_ds_masked['SSH'].sel(longitude=slice(50,70),latitude=slice(-10,10)).groupby('time.month')\n#temp_group = temp_ds_masked['THETA'].sel(longitude=slice(50,70),latitude=slice(-10,10), Z=-5.0).drop('Z').groupby('time.month')\ntemp_group = temp_ds_masked_anomaly['THETA'].sel(longitude=slice(50,70),latitude=slice(-10,10), Z=-5.0).drop('Z').groupby('time.month')\n\n\nprint(\" Running correlations in western Indian Ocean . . .\\n\") \ncorr2 =[]\nfor month in range(1,13):\n corr2.append(xr.corr(ssh_group[month], temp_group[month]))\n #print(\"\\nthe correlation in the west is: \" , xr.corr(ssh_group[month], temp_group[month]).values)\n \nax[1].set_title(\"Spatial correlation in Western Indian Ocean\",fontsize=16)\nax[1].set_ylabel(\"Correlation\",fontsize=16)\nax[1].set_xlabel(\"Month\",fontsize=16)\nax[1].set_ylim([-1, 1])\nax[1].plot(corr2)\n\n Running correlations in eastern Indian Ocean . . .\n\n Running correlations in western Indian Ocean . . ."
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+ "section": "Advanced Usage",
+ "text": "Advanced Usage\n\nDownload data by filename\nIf you’re aware of a file you want to download, you can use the -gr option to download by a filename. The -c (COLLECTION) and -d (directory) options are still required.\nThe -gr option works by taking the file name, removing the suffix and searching for a CMR entry called the granuleUR. Some examples of this include:\n\n\n\n\n\n\n\n\nCollection\nFilename\nCMR GranuleUR\n\n\n\n\nMUR25-JPL-L4-GLOB-v04.2\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2\n\n\nJASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07.nc\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07\n\n\n\nBecause of this behavior, granules without data suffixes and granules where the the UR does not directly follow this convention may not work as anticipated. We will be adding the ability to download by granuleUR in a future enhancement.\n\n\nDownload data by cycle\nSome PO.DAAC datasets are better suited for cycles based search instead of start and end times. To enabled this, we’ve added ‘cycle’ based downloading to the data-downloader. The following example will download data from cycle 42:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42\nThe cycle parameter can be repeated to specify multiple cycles:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42 --cycle 43 --cycle 44\n\n\nRequest data from another DAAC…\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z -ed 2014-07-01T00:46:02Z\n\n\nLogging\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\nControlling output directories\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\nDownloader behavior when a file already exists\nBy default, when the downloader is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the downloader will skip downloading that file.\nThis can drastically reduce the time for the downloader to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the downloader to always download matching files, by using –force/-f.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -f\n\n\nSetting a bounding rectangle for filtering results\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-downloader -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\" -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\nSetting extensions\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\nrun a post download process\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\nIn need of Help?\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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- "title": "Ocean Satellite and In-situ Comparison in the Cloud",
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- "text": "Summary\nHere, we compare salinity from the SMAP satellite and Saildrone in-situ measurements. Both datasets are located within the cloud.\n\nFollow along with the Data in Action story:\nBy the end of this notebook, you will have recreated a similar plot to the one featured in this Data-in-Action story:\nhttps://podaac.jpl.nasa.gov/DataAction-2021-10-05-Monitoring-Changes-in-the-Arctic-Using-Saildrone-SMAP-Satellite-and-Ocean-Models-Data\n\n\nShortnames of datasets used here:\nSMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5: https://podaac.jpl.nasa.gov/dataset/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5\nSAILDRONE_ARCTIC: https://podaac.jpl.nasa.gov/dataset/SAILDRONE_ARCTIC"
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+ "title": "PO.DAAC Data Subscriber/Downloader",
+ "section": "Run the Script",
+ "text": "Run the Script\nUsage:\nusage: PO.DAAC data subscriber [-h] -c COLLECTION -d OUTPUTDIRECTORY [-f] [-sd STARTDATE] [-ed ENDDATE] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-m MINUTES] [-e EXTENSIONS] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs. \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --minutes must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the subscriber.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nEvery time the script runs successfully (that is, no errors), a .update__<collectionname> file is created in your download directory with the last run timestamp. This timestamp will be used the next time the script is run. It will look for data between the timestamp in that file and the current time to determine new files to download."
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- "objectID": "notebooks/meetings_workshops/arctic_2019.html#requirements",
- "href": "notebooks/meetings_workshops/arctic_2019.html#requirements",
- "title": "Ocean Satellite and In-situ Comparison in the Cloud",
- "section": "Requirements",
- "text": "Requirements\n\n1. Compute environment\nThis tutorial can only be run in the following environments: - AWS instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n3. netrc File\nYou will need a .netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A .netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata tutorial.\n\n\nImport Libraries\n\n# To access dataset using Earthaccess\nimport earthaccess\n\n# To access dataset without Earthaccess\nimport os\nimport s3fs\nimport requests\nimport glob\n\n# To open dataset\nimport xarray as xr\n\n# For plotting\nimport matplotlib.pyplot as plt\nimport cartopy\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nfrom cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER"
+ "objectID": "quarto_text/DataSubscriberDownloader.html#note-cmr-times",
+ "href": "quarto_text/DataSubscriberDownloader.html#note-cmr-times",
+ "title": "PO.DAAC Data Subscriber/Downloader",
+ "section": "Note: CMR times",
+ "text": "Note: CMR times\nThere are numerous ‘times’ available to query on in CMR. For the default subscriber, we look at the ‘created at’ field, which will look for when a granule file was ingested into the archive. This means as PO.DAAC gets data, your subscriber will also get data, regardless of the time range within the granule itself."
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- "href": "notebooks/meetings_workshops/arctic_2019.html#smap-dataset",
- "title": "Ocean Satellite and In-situ Comparison in the Cloud",
- "section": "SMAP dataset",
- "text": "SMAP dataset\nSearch for and open this dataset as an example of using Earthaccess\n\nauth = earthaccess.login(strategy=\"netrc\")\n\nYou're now authenticated with NASA Earthdata Login\nUsing token with expiration date: 06/18/2023\nUsing .netrc file for EDL\n\n\n\nshort_name=\"SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5\"\n\nresults = earthaccess.search_data(\n short_name=short_name,\n cloud_hosted=True,\n temporal=(\"2019-05-01T00:00:00\", \"2019-10-01T00:00:00\"),\n bounding_box=(-170,65,-160,71) # (west, south, east, north)\n)\n\nGranules found: 122\n\n\n\nds_sss = xr.open_mfdataset(earthaccess.open(results))\n\n Opening 122 granules, approx size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\nplot_west = -170\nplot_east = -160\nplot_south = 60\nplot_north = 75\n\nlat_bnds, lon_bnds = [plot_south, plot_north], [plot_west+360, plot_east+360] # Turn the longitudes in (-180,0) to (0,360)\nds_sss_subset_0 = ds_sss.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_sss_subset_0['latitude'] = ds_sss_subset_0.lat\nds_sss_subset_0['longitude'] = ds_sss_subset_0.lon-360\nds_sss_subset = ds_sss_subset_0.swap_dims({'lat':'latitude', 'lon':'longitude'})\nds_sss_subset\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (longitude: 40, latitude: 60, time: 122,\n uncertainty_components: 9, iceflag_components: 3)\nCoordinates:\n lon (longitude) float32 190.1 190.4 ... 199.6 199.9\n lat (latitude) float32 60.12 60.38 60.62 ... 74.62 74.88\n * time (time) datetime64[ns] 2019-04-27T12:00:00 ... 201...\n * latitude (latitude) float32 60.12 60.38 60.62 ... 74.62 74.88\n * longitude (longitude) float32 -169.9 -169.6 ... -160.4 -160.1\nDimensions without coordinates: uncertainty_components, iceflag_components\nData variables: (12/19)\n nobs (time, latitude, longitude) float64 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n nobs_RF (time, latitude, longitude) float64 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n nobs_40km (time, latitude, longitude) float64 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sss_smap (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sss_smap_RF (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sss_smap_unc (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n ... ...\n fland (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n gice_est (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n surtep (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n winspd (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sea_ice_zones (time, latitude, longitude) int8 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n anc_sea_ice_flag (time, latitude, longitude, iceflag_components) int8 dask.array<chunksize=(1, 60, 40, 3), meta=np.ndarray>\nAttributes: (12/65)\n Conventions: CF-1.7, ACDD-1.3\n title: SMAP ocean surfac...\n version: V5.0 Validated Re...\n summary: The dataset conta...\n acknowledgement: Funded under Subc...\n processing_level: L3\n ... ...\n Source_of_SMAP_SSS_retrievals: T. Meissner, F. W...\n Source_of_ancillary_SST: Canada Meteorolog...\n Source_of_ancillary_CCMP_wind_speed: Mears, C. et al.,...\n Source_of_ancillary_AMSR2_sea_ice_flag_and_correction: Meissner, T. and ...\n Source_of_ancillary_land_mask: 1 km land/water m...\n Source_of_ancillary_reference_SSS_from_HYCOM: Hybrid Coordinate...xarray.DatasetDimensions:longitude: 40latitude: 60time: 122uncertainty_components: 9iceflag_components: 3Coordinates: (5)lon(longitude)float32190.1 190.4 190.6 ... 199.6 199.9standard_name :longitudeaxis :Xlong_name :center longitude of grid cellunits :degrees_eastvalid_min :0.0valid_max :360.0coverage_content_type :coordinatearray([190.125, 190.375, 190.625, 190.875, 191.125, 191.375, 191.625, 191.875,\n 192.125, 192.375, 192.625, 192.875, 193.125, 193.375, 193.625, 193.875,\n 194.125, 194.375, 194.625, 194.875, 195.125, 195.375, 195.625, 195.875,\n 196.125, 196.375, 196.625, 196.875, 197.125, 197.375, 197.625, 197.875,\n 198.125, 198.375, 198.625, 198.875, 199.125, 199.375, 199.625, 199.875],\n dtype=float32)lat(latitude)float3260.12 60.38 60.62 ... 74.62 74.88standard_name :latitudeaxis :Ylong_name :center latitude of grid cellunits :degrees_northvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875], dtype=float32)time(time)datetime64[ns]2019-04-27T12:00:00 ... 2019-10-...standard_name :timeaxis :Tlong_name :reference time of analyzed variable field corresponding to center of the product time intervalcoverage_content_type :coordinatearray(['2019-04-27T12:00:00.000000000', '2019-04-28T12:00:00.000000000',\n '2019-04-29T12:00:00.000000000', '2019-04-30T12:00:00.000000000',\n '2019-05-01T12:00:00.000000000', '2019-05-02T12:00:00.000000000',\n '2019-05-03T12:00:00.000000000', '2019-05-04T12:00:00.000000000',\n '2019-05-05T12:00:00.000000000', '2019-05-06T12:00:00.000000000',\n '2019-05-07T12:00:00.000000000', '2019-05-08T12:00:00.000000000',\n '2019-05-09T12:00:00.000000000', '2019-05-10T12:00:00.000000000',\n '2019-05-11T12:00:00.000000000', '2019-05-12T12:00:00.000000000',\n '2019-05-13T12:00:00.000000000', '2019-05-14T12:00:00.000000000',\n '2019-05-15T12:00:00.000000000', '2019-05-16T12:00:00.000000000',\n '2019-05-17T12:00:00.000000000', '2019-05-18T12:00:00.000000000',\n '2019-05-19T12:00:00.000000000', '2019-05-20T12:00:00.000000000',\n '2019-05-21T12:00:00.000000000', '2019-05-22T12:00:00.000000000',\n '2019-05-23T12:00:00.000000000', '2019-05-24T12:00:00.000000000',\n '2019-05-25T12:00:00.000000000', '2019-05-26T12:00:00.000000000',\n '2019-05-27T12:00:00.000000000', '2019-05-28T12:00:00.000000000',\n '2019-05-29T12:00:00.000000000', '2019-05-30T12:00:00.000000000',\n '2019-05-31T12:00:00.000000000', '2019-06-01T12:00:00.000000000',\n '2019-06-02T12:00:00.000000000', '2019-06-03T12:00:00.000000000',\n '2019-06-04T12:00:00.000000000', '2019-06-05T12:00:00.000000000',\n '2019-06-06T12:00:00.000000000', '2019-06-07T12:00:00.000000000',\n '2019-06-08T12:00:00.000000000', '2019-06-09T12:00:00.000000000',\n '2019-06-10T12:00:00.000000000', '2019-06-11T12:00:00.000000000',\n '2019-06-12T12:00:00.000000000', '2019-06-13T12:00:00.000000000',\n '2019-06-14T12:00:00.000000000', '2019-06-15T12:00:00.000000000',\n '2019-06-16T12:00:00.000000000', '2019-07-26T12:00:00.000000000',\n '2019-07-27T12:00:00.000000000', '2019-07-28T12:00:00.000000000',\n '2019-07-29T12:00:00.000000000', '2019-07-30T12:00:00.000000000',\n '2019-07-31T12:00:00.000000000', '2019-08-01T12:00:00.000000000',\n '2019-08-02T12:00:00.000000000', '2019-08-03T12:00:00.000000000',\n '2019-08-04T12:00:00.000000000', '2019-08-05T12:00:00.000000000',\n '2019-08-06T12:00:00.000000000', '2019-08-07T12:00:00.000000000',\n '2019-08-08T12:00:00.000000000', '2019-08-09T12:00:00.000000000',\n '2019-08-10T12:00:00.000000000', '2019-08-11T12:00:00.000000000',\n '2019-08-12T12:00:00.000000000', '2019-08-13T12:00:00.000000000',\n '2019-08-14T12:00:00.000000000', '2019-08-15T12:00:00.000000000',\n '2019-08-16T12:00:00.000000000', '2019-08-17T12:00:00.000000000',\n '2019-08-18T12:00:00.000000000', '2019-08-19T12:00:00.000000000',\n '2019-08-20T12:00:00.000000000', '2019-08-21T12:00:00.000000000',\n '2019-08-22T12:00:00.000000000', '2019-08-23T12:00:00.000000000',\n '2019-08-24T12:00:00.000000000', '2019-08-25T12:00:00.000000000',\n '2019-08-26T12:00:00.000000000', '2019-08-27T12:00:00.000000000',\n '2019-08-28T12:00:00.000000000', '2019-08-29T12:00:00.000000000',\n '2019-08-30T12:00:00.000000000', '2019-08-31T12:00:00.000000000',\n '2019-09-01T12:00:00.000000000', '2019-09-02T12:00:00.000000000',\n '2019-09-03T12:00:00.000000000', '2019-09-04T12:00:00.000000000',\n '2019-09-05T12:00:00.000000000', '2019-09-06T12:00:00.000000000',\n '2019-09-07T12:00:00.000000000', '2019-09-08T12:00:00.000000000',\n '2019-09-09T12:00:00.000000000', '2019-09-10T12:00:00.000000000',\n '2019-09-11T12:00:00.000000000', '2019-09-12T12:00:00.000000000',\n '2019-09-13T12:00:00.000000000', '2019-09-14T12:00:00.000000000',\n '2019-09-15T12:00:00.000000000', '2019-09-16T12:00:00.000000000',\n '2019-09-17T12:00:00.000000000', '2019-09-18T12:00:00.000000000',\n '2019-09-19T12:00:00.000000000', '2019-09-20T12:00:00.000000000',\n '2019-09-21T12:00:00.000000000', '2019-09-22T12:00:00.000000000',\n '2019-09-23T12:00:00.000000000', '2019-09-24T12:00:00.000000000',\n '2019-09-25T12:00:00.000000000', '2019-09-26T12:00:00.000000000',\n '2019-09-27T12:00:00.000000000', '2019-09-28T12:00:00.000000000',\n '2019-09-29T12:00:00.000000000', '2019-09-30T12:00:00.000000000',\n '2019-10-01T12:00:00.000000000', '2019-10-02T12:00:00.000000000',\n '2019-10-03T12:00:00.000000000', '2019-10-04T12:00:00.000000000'],\n dtype='datetime64[ns]')latitude(latitude)float3260.12 60.38 60.62 ... 74.62 74.88standard_name :latitudeaxis :Ylong_name :center latitude of grid cellunits :degrees_northvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875], dtype=float32)longitude(longitude)float32-169.9 -169.6 ... -160.4 -160.1array([-169.875, -169.625, -169.375, -169.125, -168.875, -168.625, -168.375,\n -168.125, -167.875, -167.625, -167.375, -167.125, -166.875, -166.625,\n -166.375, -166.125, -165.875, -165.625, -165.375, -165.125, -164.875,\n -164.625, -164.375, -164.125, -163.875, -163.625, -163.375, -163.125,\n -162.875, -162.625, -162.375, -162.125, -161.875, -161.625, -161.375,\n -161.125, -160.875, -160.625, -160.375, -160.125], dtype=float32)Data variables: (19)nobs(time, latitude, longitude)float64dask.array<chunksize=(1, 60, 40), meta=np.ndarray>long_name :Number of observations for L3 average of SSS smoothed to approx 70km resolutionstandard_name :number_of_observationsunits :1valid_min :1valid_max :480coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.23 MiB\n18.75 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\nnobs_RF\n\n\n(time, latitude, longitude)\n\n\nfloat64\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nNumber of observations for L3 average of rain filtered SSS smoothed to approx 70km resolution\n\nstandard_name :\n\nnumber_of_observations\n\nunits :\n\n1\n\nvalid_min :\n\n1\n\nvalid_max :\n\n480\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.23 MiB\n18.75 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nnobs_40km\n\n\n(time, latitude, longitude)\n\n\nfloat64\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nNumber of observations for L3 average of SSS at 40km resolution\n\nstandard_name :\n\nnumber_of_observations\n\nunits :\n\n1\n\nvalid_min :\n\n1\n\nvalid_max :\n\n480\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.23 MiB\n18.75 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_RF\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nRain filtered SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_unc\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntotal formal uncertainty estimate of SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_RF_unc\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntotal formal uncertainty estimate of rain filtered SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_unc_comp\n\n\n(time, uncertainty_components, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 9, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nformal uncertainty components of SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\ncomponents :\n\n1: ancillary wind speed random. 2: NEDT v-pol. 3: NEDT h-pol. 4: ancillary SST. 5: ancillary wind direction. 6: reflected galaxy. 7: land contamination. 8: sea ice contamination. 9: ancillary wind speed systematic.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n10.05 MiB\n84.38 kiB\n\n\nShape\n(122, 9, 60, 40)\n(1, 9, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_40km\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSMAP sea surface salinity at original 40km resolution\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_40km_unc\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntotal formal uncertainty estimate of SMAP sea surface salinity at original 40km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_40km_unc_comp\n\n\n(time, uncertainty_components, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 9, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nformal uncertainty components of SMAP sea surface salinity at original 40km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\ncomponents :\n\n1: ancillary wind speed random. 2: NEDT v-pol. 3: NEDT h-pol. 4: ancillary SST. 5: ancillary wind direction. 6: reflected galaxy. 7: land contamination. 8: sea ice contamination. 9: ancillary wind speed systematic.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n10.05 MiB\n84.38 kiB\n\n\nShape\n(122, 9, 60, 40)\n(1, 9, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_ref\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nReference sea surface salinity from HYCOM\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nreferenceInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ngland\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\naverage land fraction weighted by antenna gain\n\nstandard_name :\n\nland_area_fraction\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nfland\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\naverage land fraction within 3dB contour\n\nstandard_name :\n\nland_area_fraction\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ngice_est\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated sea ice fraction weighted by antenna gain\n\nstandard_name :\n\nsea_ice_area_fraction\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsurtep\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nstandard_name :\n\nsea_surface_temperature\n\nlong_name :\n\nAncillary sea surface temperature (from CMC)\n\nunits :\n\nKelvin\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n313.15\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwinspd\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nstandard_name :\n\nwind_speed\n\nlong_name :\n\nAncillary sea surface wind speed from CCMP NRT that is used in surface roughness correction\n\nunits :\n\nm s-1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n100.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsea_ice_zones\n\n\n(time, latitude, longitude)\n\n\nint8\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea-ice contamination zones at center day\n\nstandard_name :\n\nquality_flag\n\nunits :\n\n1\n\ncoverage_content_type :\n\nqualityInformation\n\nflag_meaning :\n\n0: open ocean scene. no sea-ice contamination. 1: likely sea-ice contamination in SMAP antenna sidelobes. SSS retrieved. 2: likely sea-ice contamination in SMAP antenna sidelobes. SSS retrieved. 3: likely sea-ice contamination in SMAP antenna mainlobe. SSS retrieved. 4: likely sea-ice contamination in SMAP antenna mainlobe. SSS retrieved. 5: likely sea-ice contamination in SMAP antenna mainlobe. no SSS retrieved. 6: AMSR2 50-km footprint contains land. sea-ice check not reliable. no SSS retrieved if AMSR-2 AS-ECV V8.2 sea-ice flag set. 7: no or invalid AMSR2 observation. sea-ice check not possible. no SSS retrieved if climatological sea-ice flag set.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n285.94 kiB\n2.34 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nint8 numpy.ndarray\n\n\n\n\n\n\n\n\n\nanc_sea_ice_flag\n\n\n(time, latitude, longitude, iceflag_components)\n\n\nint8\n\n\ndask.array<chunksize=(1, 60, 40, 3), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nancillary sea-ice detection indicator at center day\n\nstandard_name :\n\nquality_flag\n\nunits :\n\n1\n\ncoverage_content_type :\n\nqualityInformation\n\nflag_meaning :\n\ncomponent 1 of anc_sea_ice_flag: climatological sea-ice flag. component 2 of anc_sea_ice_flag: sea-ice flag from AMSR2 RSS AS-ECV V8.2 3-day map. component 3 of anc_sea_ice_flag: sea-ice flag from Meissner and Manaster.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n857.81 kiB\n7.03 kiB\n\n\nShape\n(122, 60, 40, 3)\n(1, 60, 40, 3)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nint8 numpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (3)timePandasIndexPandasIndex(DatetimeIndex(['2019-04-27 12:00:00', '2019-04-28 12:00:00',\n '2019-04-29 12:00:00', '2019-04-30 12:00:00',\n '2019-05-01 12:00:00', '2019-05-02 12:00:00',\n '2019-05-03 12:00:00', '2019-05-04 12:00:00',\n '2019-05-05 12:00:00', '2019-05-06 12:00:00',\n ...\n '2019-09-25 12:00:00', '2019-09-26 12:00:00',\n '2019-09-27 12:00:00', '2019-09-28 12:00:00',\n '2019-09-29 12:00:00', '2019-09-30 12:00:00',\n '2019-10-01 12:00:00', '2019-10-02 12:00:00',\n '2019-10-03 12:00:00', '2019-10-04 12:00:00'],\n dtype='datetime64[ns]', name='time', length=122, freq=None))latitudePandasIndexPandasIndex(Index([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875],\n dtype='float32', name='latitude'))longitudePandasIndexPandasIndex(Index([-169.875, -169.625, -169.375, -169.125, -168.875, -168.625, -168.375,\n -168.125, -167.875, -167.625, -167.375, -167.125, -166.875, -166.625,\n -166.375, -166.125, -165.875, -165.625, -165.375, -165.125, -164.875,\n -164.625, -164.375, -164.125, -163.875, -163.625, -163.375, -163.125,\n -162.875, -162.625, -162.375, -162.125, -161.875, -161.625, -161.375,\n -161.125, -160.875, -160.625, -160.375, -160.125],\n dtype='float32', name='longitude'))Attributes: (65)Conventions :CF-1.7, ACDD-1.3title :SMAP ocean surface salinityversion :V5.0 Validated Releasesummary :The dataset contains the Level 3 8-day running averages of the NASA/RSS Version 5.0 SMAP Salinity Retrieval Algorithm. It includes all necessary ancillary data and the results of all intermediate steps. The data are gridded on a regular 0.25 deg Earth grid. For details see the Release Notes at https://www.remss.com/missions/smap/salinity/.acknowledgement :Funded under Subcontract No.1664013 between JPL and RSS: Production System for NASA Ocean Salinity Science Team (OSST).processing_level :L3resolution :Spatial resolution: approx 70kmhistory :created by T. Meissnerdate_created :2022-03-29 T12:02:30-0700date_modified :2022-03-29 T12:02:30-0700date_issued :2022-03-29 T12:02:30-0700date_metadata_modified :2022-03-29 T12:02:30-0700institution :Remote Sensing Systems, Santa Rosa, CA, USAsource :RSS SMAP-SSS v5.0 algorithmplatform :SMAPinstrument :SMAP radiometerproject :Production System for NASA Ocean Salinity Science Team (OSST)keywords :SURFACE SALINITY, SALINITY, SMAP, NASA, RSSkeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :CF Standard Name Table v78license :Nonecreator_name :Thomas Meissner, Remote Sensing Systemscreator_email :meissner@remss.comcreator_url :http://www.remss.com/missions/smappublisher_name :Thomas Meissner, Frank Wentz, Andrew Manaster, Richard Lindsley, Marty Brewer, Michael Densberger, Remote Sensing Systemspublisher_email :meissner@remss.compublisher_url :http://www.remss.com/missions/smapid :10.5067/SMP50-3SPCSnaming_authority :gov.nasa.earthdatadataset_citation_authors :T. Meissner, F. Wentz, A. Manaster, R. Lindsley, M. Brewer, M. Densbergerdataset_citation_year :2022dataset_citation_product :Remote Sensing Systems SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8day runningdataset_citation_version :V5.0 Validated Releasedataset_citation_institution :Remote Sensing Systems, Santa Rosa, CA, USAdataset_citation_url :Available online at www.remss.com/missions/smapnetCDF_version_id :4comment :Major changes in V5.0: 1. sea-ice flag: based on AMSR-2 surface emissivties and discriminant analysis. 2. sea-ice correction included. 3. formal uncertainty estimates added.references :1. V5.0 Release Notes at https://www.remss.com/missions/smap/salinity/ 2. Meissner, T.; Wentz, F.J.; Le Vine, D.M. The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases. Remote Sens. 2018, 10, 1121. https://doi.org/10.3390/rs10071121 3. Meissner, T.; Manaster, A. SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures. Remote Sens. 2021, 13, 5120. https://doi.org/10.3390/rs13245120year_of_observation :2019center_day_of_observation :117first_orbit :22566last_orbit :22682time_coverage_start :2019-04-23T12:00:00Ztime_coverage_end :2019-05-01T12:00:00Ztime_coverage_duration :P8Dtime_coverage_resolution :P8Dcdm_data_type :gridgeospatial_bounds :2Dgeospatial_lat_min :-90.0geospatial_lat_max :90.0geospatial_lat_resolution :0.25geospatial_lat_units :degrees_northgeospatial_lon_min :0.0geospatial_lon_max :360.0geospatial_lon_resolution :0.25geospatial_lon_units :degrees_eastgeospatial_bounds_vertical_crs :EPSG:5831geospatial_vertical_min :0geospatial_vertical_max :0Source_of_SMAP_SSS_retrievals :T. Meissner, F. Wentz, A. Manaster, R. Lindsley, M. Brewer, M. Densberger, Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 5.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA doi: 10.5067/SMP50-2SOCS www.remss.com/missions/smap.Source_of_ancillary_SST :Canada Meteorological Center. 2016.GHRSST Level 4 CMC0.1deg Global Foundation Sea Surface Temperature Analysis (GDS version 2). Ver.3.3.doi: 10.5067/GHCMC-4FM03 http://dx.doi.org/10.5067/GHCMC-4FM03.Source_of_ancillary_CCMP_wind_speed :Mears, C. et al., 2018.Remote Sensing Systems CCMP NRT V2.0 wind speed and direction. Remote Sensing Systems, Santa Rosa, CA.Source_of_ancillary_AMSR2_sea_ice_flag_and_correction :Meissner, T. and A. Manaster, 2021. SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures. Remote Sens. 2021, 13, 5120. https://doi.org/10.3390/rs13245120.Source_of_ancillary_land_mask :1 km land/water mask from OCEAN DISCIPLINE PROCESSING SYSTEM (ODPS) based on World Vector Shoreline (WVS)database and World Data Bank. courtesy of Fred Patt, Goddard Space Flight Center, frederick.s.patt@nasa.gov.Source_of_ancillary_reference_SSS_from_HYCOM :Hybrid Coordinate Ocean Model, GLBa0.08/expt_90.9, Top layer salinity. Available at www.hycom.org.\n\n\n\nsubset_mean_values = ds_sss_subset.sss_smap.mean(dim = 'time', skipna = True)\nsubset_mean_values\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'sss_smap' (latitude: 60, longitude: 40)>\ndask.array<mean_agg-aggregate, shape=(60, 40), dtype=float32, chunksize=(60, 40), chunktype=numpy.ndarray>\nCoordinates:\n lon (longitude) float32 190.1 190.4 190.6 190.9 ... 199.4 199.6 199.9\n lat (latitude) float32 60.12 60.38 60.62 60.88 ... 74.38 74.62 74.88\n * latitude (latitude) float32 60.12 60.38 60.62 60.88 ... 74.38 74.62 74.88\n * longitude (longitude) float32 -169.9 -169.6 -169.4 ... -160.6 -160.4 -160.1xarray.DataArray'sss_smap'latitude: 60longitude: 40dask.array<chunksize=(60, 40), meta=np.ndarray>\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.38 kiB\n9.38 kiB\n\n\nShape\n(60, 40)\n(60, 40)\n\n\nDask graph\n1 chunks in 373 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\nCoordinates: (4)lon(longitude)float32190.1 190.4 190.6 ... 199.6 199.9standard_name :longitudeaxis :Xlong_name :center longitude of grid cellunits :degrees_eastvalid_min :0.0valid_max :360.0coverage_content_type :coordinatearray([190.125, 190.375, 190.625, 190.875, 191.125, 191.375, 191.625, 191.875,\n 192.125, 192.375, 192.625, 192.875, 193.125, 193.375, 193.625, 193.875,\n 194.125, 194.375, 194.625, 194.875, 195.125, 195.375, 195.625, 195.875,\n 196.125, 196.375, 196.625, 196.875, 197.125, 197.375, 197.625, 197.875,\n 198.125, 198.375, 198.625, 198.875, 199.125, 199.375, 199.625, 199.875],\n dtype=float32)lat(latitude)float3260.12 60.38 60.62 ... 74.62 74.88standard_name :latitudeaxis :Ylong_name :center latitude of grid cellunits :degrees_northvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875], dtype=float32)latitude(latitude)float3260.12 60.38 60.62 ... 74.62 74.88standard_name :latitudeaxis :Ylong_name :center latitude of grid cellunits :degrees_northvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875], dtype=float32)longitude(longitude)float32-169.9 -169.6 ... -160.4 -160.1array([-169.875, -169.625, -169.375, -169.125, -168.875, -168.625, -168.375,\n -168.125, -167.875, -167.625, -167.375, -167.125, -166.875, -166.625,\n -166.375, -166.125, -165.875, -165.625, -165.375, -165.125, -164.875,\n -164.625, -164.375, -164.125, -163.875, -163.625, -163.375, -163.125,\n -162.875, -162.625, -162.375, -162.125, -161.875, -161.625, -161.375,\n -161.125, -160.875, -160.625, -160.375, -160.125], dtype=float32)Indexes: (2)latitudePandasIndexPandasIndex(Index([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875],\n dtype='float32', name='latitude'))longitudePandasIndexPandasIndex(Index([-169.875, -169.625, -169.375, -169.125, -168.875, -168.625, -168.375,\n -168.125, -167.875, -167.625, -167.375, -167.125, -166.875, -166.625,\n -166.375, -166.125, -165.875, -165.625, -165.375, -165.125, -164.875,\n -164.625, -164.375, -164.125, -163.875, -163.625, -163.375, -163.125,\n -162.875, -162.625, -162.375, -162.125, -161.875, -161.625, -161.375,\n -161.125, -160.875, -160.625, -160.375, -160.125],\n dtype='float32', name='longitude'))Attributes: (0)"
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+ "title": "PO.DAAC Data Subscriber/Downloader",
+ "section": "Note: netrc file",
+ "text": "Note: netrc file\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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- "title": "Ocean Satellite and In-situ Comparison in the Cloud",
- "section": "Saildrone dataset",
- "text": "Saildrone dataset\nAccessing this dataset as an example of using s3fs\n\ns3_cred_endpoint = 'https://archive.podaac.earthdata.nasa.gov/s3credentials'\n\n\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\n\n\ntemp_creds_req = get_temp_creds()\n#temp_creds_req # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.\n\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})\n\n\nbucket = os.path.join('podaac-ops-cumulus-protected/','SAILDRONE_ARCTIC','saildrone-*-1_minutes-*.nc')\nsd_files = fs_s3.glob(bucket)\nsaildrone_files= [fs_s3.open(file) for file in sorted(sd_files)]\nlen(saildrone_files)\n\n2\n\n\n\nsd6 = xr.open_dataset(saildrone_files[0])\nsd7 = xr.open_dataset(saildrone_files[1])\nsd7\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (trajectory: 1, obs: 215731)\nCoordinates:\n latitude (trajectory, obs) float64 ...\n longitude (trajectory, obs) float64 ...\n time (trajectory, obs) datetime64[ns] ...\n * trajectory (trajectory) float32 1.037e+03\nDimensions without coordinates: obs\nData variables: (12/79)\n SOG (trajectory, obs) float64 ...\n SOG_FILTERED_MEAN (trajectory, obs) float64 ...\n SOG_FILTERED_STDDEV (trajectory, obs) float64 ...\n SOG_FILTERED_MAX (trajectory, obs) float64 ...\n SOG_FILTERED_MIN (trajectory, obs) float64 ...\n COG (trajectory, obs) float64 ...\n ... ...\n TEMP_O2_RBR_MEAN (trajectory, obs) float64 ...\n TEMP_O2_RBR_STDDEV (trajectory, obs) float64 ...\n CHLOR_WETLABS_MEAN (trajectory, obs) float64 ...\n CHLOR_WETLABS_STDDEV (trajectory, obs) float64 ...\n CHLOR_RBR_MEAN (trajectory, obs) float64 ...\n CHLOR_RBR_STDDEV (trajectory, obs) float64 ...\nAttributes: (12/45)\n title: Arctic NASA MISST 2019 Mission\n summary: Saildrone surface observational data for the N...\n ncei_template_version: NCEI_NetCDF_Trajectory_Template_v2.0\n Conventions: CF-1.6, ACDD-1.3\n netcdf_version: 4.6.3\n featureType: trajectory\n ... ...\n keywords_vocabulary: NASA/GCMD\n publisher_name: Saildrone\n publisher_url: www.saildrone.com\n publisher_email: support@saildrone.com\n acknowledgment: Saildrone. 2019. 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obs)datetime64[ns]...standard_name :timelong_name :time in secondsaxis :T[215731 values with dtype=datetime64[ns]]trajectory(trajectory)float321.037e+03long_name :Trajectory/Drone IDcf_role :trajectory_idcomment :A trajectory is a single deployment of a dronearray([1037.], dtype=float32)Data variables: (79)SOG(trajectory, obs)float64...standard_name :platform_speed_wrt_groundlong_name :Speed over groundunits :m s-1[215731 values with dtype=float64]SOG_FILTERED_MEAN(trajectory, obs)float64...standard_name :platform_speed_wrt_groundlong_name :Speed over ground one minute meanunits :m s-1[215731 values with dtype=float64]SOG_FILTERED_STDDEV(trajectory, obs)float64...standard_name :platform_speed_wrt_groundlong_name :Speed over ground one minute stddevunits :m s-1[215731 values with dtype=float64]SOG_FILTERED_MAX(trajectory, obs)float64...standard_name :platform_speed_wrt_groundlong_name :Speed over ground one minute maxunits :m s-1[215731 values with 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:Gill Anemometer (W182203)serial_number :W182203last_calibrated :2018-05-31installed_height :5.2vendor_name :Gillmodel_name :1590-PK-020model_product_page :http://gillinstruments.com/products/anemometer/windmaster.htmnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :50.0[215731 values with dtype=float64]WIND_HEIGHT_STDDEV(trajectory, obs)float64...standard_name :wind_measurement_height_rmslong_name :Wind measurement height SDunits :minstalled_date :2019-04-10T00:46:53.168598Zdevice_name :Gill Anemometer (W182203)serial_number :W182203last_calibrated :2018-05-31installed_height :5.2vendor_name :Gillmodel_name :1590-PK-020model_product_page :http://gillinstruments.com/products/anemometer/windmaster.htmnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :50.0[215731 values with dtype=float64]TEMP_AIR_MEAN(trajectory, obs)float64...standard_name :air_temperaturelong_name :Air temperatureunits :degrees_cinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_AIR_STDDEV(trajectory, obs)float64...standard_name :air_temperaturelong_name :Air temperature SDunits :degrees_cinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]RH_MEAN(trajectory, obs)float64...standard_name :relative_humiditylong_name :Relative humidityunits :percentinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]RH_STDDEV(trajectory, obs)float64...standard_name :relative_humiditylong_name :Relative humidity SDunits :percentinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]BARO_PRES_MEAN(trajectory, obs)float64...standard_name :air_pressurelong_name :Air pressureunits :hPainstalled_date :2019-04-09T22:03:51.977028Zdevice_name :Vaisala Barometer (5240536)serial_number :5240536last_calibrated :2018-01-03installed_height :0.2vendor_name :Vaisalamodel_name :PTB210model_product_page :http://www.vaisala.com/en/products/pressure/Pages/PTB210.aspxnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]BARO_PRES_STDDEV(trajectory, obs)float64...standard_name :air_pressurelong_name :Air pressure SDunits :hPainstalled_date :2019-04-09T22:03:51.977028Zdevice_name :Vaisala Barometer (5240536)serial_number :5240536last_calibrated :2018-01-03installed_height :0.2vendor_name :Vaisalamodel_name :PTB210model_product_page :http://www.vaisala.com/en/products/pressure/Pages/PTB210.aspxnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]PAR_AIR_MEAN(trajectory, obs)float64...standard_name :surface_downwelling_photosynthetic_photon_flux_in_airlong_name :Photosynthetically active radiation in airunits :micromol s-1 m-2installed_date :2019-04-09T18:07:44.339577Zdevice_name :LI-COR PAR (9658)serial_number :9658last_calibrated :2018-02-27installed_height :2.6vendor_name :LI-CORmodel_name :LI-192SAmodel_product_page :https://www.licor.com/env/products/light/quantum_underwater.htmlnominal_sampling_schedule :Always onupdate_period :1000.0[215731 values with dtype=float64]PAR_AIR_STDDEV(trajectory, obs)float64...standard_name :surface_downwelling_photosynthetic_photon_flux_in_airlong_name :Photosynthetically active radiation in air SDunits :micromol s-1 m-2installed_date :2019-04-09T18:07:44.339577Zdevice_name :LI-COR PAR (9658)serial_number :9658last_calibrated :2018-02-27installed_height :2.6vendor_name :LI-CORmodel_name :LI-192SAmodel_product_page :https://www.licor.com/env/products/light/quantum_underwater.htmlnominal_sampling_schedule :Always onupdate_period :1000.0[215731 values with dtype=float64]TEMP_IR_SKY_HULL_MEAN(trajectory, obs)float64...standard_name :sky_ir_thermo_temperature_filteredlong_name :Hull Sky IR Temperatureunits :degrees_cinstalled_date :2019-05-14T22:18:38.355856Zdevice_name :Heitronics Sky IR Pyrometer (02413)serial_number :02413installed_height :0.6vendor_name :Heitronicsmodel_name :CT09.10model_product_page :https://www.heitronics.com/en/infrarot-messtechnik/produkte/radiation-thermometers/compact-series/ct09-series/nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SKY_HULL_STDDEV(trajectory, obs)float64...standard_name :sky_ir_thermo_temperature_rmslong_name :Hull Sky IR Temperature SDunits :degrees_cinstalled_date :2019-05-14T22:18:38.355856Zdevice_name :Heitronics Sky IR Pyrometer (02413)serial_number :02413installed_height :0.6vendor_name :Heitronicsmodel_name :CT09.10model_product_page :https://www.heitronics.com/en/infrarot-messtechnik/produkte/radiation-thermometers/compact-series/ct09-series/nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_HULL_UNCOMP_MEAN(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Hull Sea IR Temperatureunits :degrees_cinstalled_date :2019-05-14T22:18:43.869843Zdevice_name :Heitronics Hull IR Pyrometer (12693)serial_number :12693last_calibrated :2018-05-16installed_height :0.6vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_HULL_UNCOMP_STDDEV(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Hull Sea IR Temperature SDunits :degrees_cinstalled_date :2019-05-14T22:18:43.869843Zdevice_name :Heitronics Hull IR Pyrometer (12693)serial_number :12693last_calibrated :2018-05-16installed_height :0.6vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_WING_UNCOMP_MEAN(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Wing Sea IR Temperatureunits :degrees_cinstalled_date :2019-04-09T18:19:51.135128Zdevice_name :Heitronics Wing IR Pyrometer (12605)serial_number :12605last_calibrated :2018-03-12installed_height :2.25vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_WING_UNCOMP_STDDEV(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Wing Sea IR Temperature SDunits :degrees_cinstalled_date :2019-04-09T18:19:51.135128Zdevice_name :Heitronics Wing IR Pyrometer (12605)serial_number :12605last_calibrated :2018-03-12installed_height :2.25vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]WAVE_DOMINANT_PERIOD(trajectory, obs)float64...standard_name :sea_surface_wave_period_at_variance_spectral_density_maximumlong_name :Dominant wave periodunits :sinstalled_date :2019-04-09T18:18:23.573574Zdevice_name :VectorNav Hull IMU (100035683)serial_number :100035683installed_height :0.34vendor_name :VectorNavmodel_name :VN-300model_product_page :https://www.vectornav.com/products/vn-300nominal_sampling_schedule :Always onupdate_period :50.0[215731 values with dtype=float64]WAVE_SIGNIFICANT_HEIGHT(trajectory, obs)float64...standard_name :sea_surface_wave_significant_heightlong_name :Significant wave heightunits :minstalled_date :2019-04-09T18:18:23.573574Zdevice_name :VectorNav Hull IMU (100035683)serial_number :100035683installed_height :0.34vendor_name :VectorNavmodel_name :VN-300model_product_page :https://www.vectornav.com/products/vn-300nominal_sampling_schedule :Always onupdate_period :50.0[215731 values with dtype=float64]TEMP_SBE37_MEAN(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperatureunits :degrees_cinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_SBE37_STDDEV(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperature SDunits :degrees_cinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]SAL_SBE37_MEAN(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinityunits :1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]SAL_SBE37_STDDEV(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinity SDunits :1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]COND_SBE37_MEAN(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivityunits :mS cm-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]COND_SBE37_STDDEV(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivity SDunits :mS cm-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_CTD_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperatureunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]TEMP_CTD_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperature SDunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]SAL_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinityunits :1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]SAL_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinity SDunits :1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]COND_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivityunits :mS cm-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]COND_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivity SDunits :mS cm-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_CONC_SBE37_MEAN(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentrationunits :micromol L-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_CONC_SBE37_STDDEV(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentration SDunits :micromol L-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_SAT_SBE37_MEAN(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturationunits :percentinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_SAT_SBE37_STDDEV(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturation SDunits :percentinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_CONC_RBR_MEAN(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentrationunits :micromol L-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_CONC_RBR_STDDEV(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentration SDunits :micromol L-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_SAT_RBR_MEAN(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturationunits :percentinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_SAT_RBR_STDDEV(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturation SDunits :percentinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]TEMP_O2_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperatureunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]TEMP_O2_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperature SDunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]CHLOR_WETLABS_MEAN(trajectory, obs)float64...standard_name :mass_concentration_of_chlorophyll_in_sea_waterlong_name :Chlorophyll concentrationunits :microgram L-1installed_date :2019-04-19T17:16:02.129225Zdevice_name :WET Labs Fluorometer (5599)serial_number :5599last_calibrated :2019-04-02installed_height :-0.5vendor_name :WET Labsmodel_name :FLSmodel_product_page :http://www.seabird.com/eco-flnominal_sampling_schedule :12s on, 48s off, centered at :00update_period :1000.0[215731 values with dtype=float64]CHLOR_WETLABS_STDDEV(trajectory, obs)float64...standard_name :mass_concentration_of_chlorophyll_in_sea_waterlong_name :Chlorophyll concentration SDunits :microgram L-1installed_date :2019-04-19T17:16:02.129225Zdevice_name :WET Labs Fluorometer (5599)serial_number :5599last_calibrated :2019-04-02installed_height :-0.5vendor_name :WET Labsmodel_name :FLSmodel_product_page :http://www.seabird.com/eco-flnominal_sampling_schedule :12s on, 48s off, centered at :00update_period :1000.0[215731 values with dtype=float64]CHLOR_RBR_MEAN(trajectory, obs)float64...standard_name :mass_concentration_of_chlorophyll_in_sea_waterlong_name :Chlorophyll concentrationunits :microgram L-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]CHLOR_RBR_STDDEV(trajectory, obs)float64...standard_name :mass_concentration_of_chlorophyll_in_sea_waterlong_name :Chlorophyll concentration SDunits :microgram L-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]Indexes: (1)trajectoryPandasIndexPandasIndex(Index([1037.0], dtype='float32', name='trajectory'))Attributes: (45)title :Arctic NASA MISST 2019 Missionsummary :Saildrone surface observational data for the NOPP NASA-funded Arctic MSST campaign, 5/14/2019-10/11/2019ncei_template_version :NCEI_NetCDF_Trajectory_Template_v2.0Conventions :CF-1.6, ACDD-1.3netcdf_version :4.6.3featureType :trajectorycdm_data_type :Trajectorystandard_name_vocabulary :CF Standard Name Table v58description :Saildrone NetCDF Formatcreator_name :Saildroneplatform :Saildroneinstitution :Saildronecreator_email :support@saildrone.comcreator_url :https://saildrone.com/date_created :2019-12-04T19:13:20.502874Ztime_coverage_start :2019-05-14T23:00:00.000Ztime_coverage_end :2019-10-11T18:30:00.000Ztime_coverage_resolution :PT1Mtime_coverage_duration :P149DT19H30M0Sarea :Bering and Chukchi Seasdata_mode :delayed-modedrone_id :1037id :75156naming_authority :saildrone.comuuid :9f27a3ef-b53a-4dac-af8c-f87677a2c28fproject :NASA Multi-Sensor Improved Sea Surface Temperature Projectsource :Saildronelicense :2019 CC-BY SAILDRONE Inc. All Rights Reserved. These Data and any resultant Product are the property of SAILDRONE. At SAILDRONE’s sole discretion, these Data may be used for research or educational activities only. You may not use, share or sell the Data for any other purpose including for commercial purposes, or alternatively, have any unauthorized third party use or sell the Data, either for any research, educational and/or commercial purpose(s), without the express prior consent of SAILDRONE.nodc_template_version :NODC_NetCDF_Trajectory_Template_v2.0wmo_id :4803915geospatial_lat_min :53.8444032geospatial_lat_max :75.4970304geospatial_lat_units :degrees_northgeospatial_lon_min :-168.7037952geospatial_lon_max :-146.129856geospatial_lon_units :degrees_easthistory :created post-cruise 1/2020product_version :v01.0keywords :Temperature, Salinity, Wind Vectors, Air Temperature, Humidity, Current Velocity, Saildrone, Arctic, Berring Sea, Chukchi Sea, NASA, NOAAkeywords_vocabulary :NASA/GCMDpublisher_name :Saildronepublisher_url :www.saildrone.compublisher_email :support@saildrone.comacknowledgment :Saildrone. 2019. Saildrone Arctic field campaign surface and ADCP measurements. Ver. 01.0. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/SDRON-NOPP0processing_level :Level 2"
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+ "text": "Advanced Usage\n\nRequest data from another DAAC…\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z\n\n\nLogging\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\nControlling output directories\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. 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Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the subscriber to always download matching files, by using –force/-f.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -f\n\n\nRunning as a Cron job\nTo automatically run and update a local file system with data files from a collection, one can use a syntax like the following:\n10 * * * * podaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d /path/to/data/VIIRS_N20-OSPO-L2P-v2.61 -e .nc -e .h5 -m 60 -b=\"-180,-90,180,90\" --verbose >> ~/.subscriber.log\n\nThis will run every hour at ten minutes passed, and output will be appended to a local file called ~/.subscriber.log\n\n\nSetting a bounding rectangle for filtering results\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\"\n\n\nSetting extensions\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\nrun a post download process\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\nIn need of Help?\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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- "text": "Plot salinity side-by-side from SMAP and from Saildrone vehicle 1036\n\nfig = plt.figure(figsize= (16,10))\n\nrows = 1\ncolumns = 2\nax = fig.add_subplot(rows, columns, 1, projection=ccrs.PlateCarree())\nax.add_feature(cartopy.feature.RIVERS)\nax.coastlines()\nax.set_extent([plot_west, plot_east, plot_south, plot_north])\ns = subset_mean_values.plot.pcolormesh(add_colorbar = False)\ngl = ax.gridlines(color='gray',alpha=0.6,draw_labels=True) \nplt.title('SMAP, May-Oct 2019')\ngl.top_labels = False\ngl.left_labels = True\ngl.right_labels = False\ngl.xformatter = LONGITUDE_FORMATTER\ngl.yformatter = LATITUDE_FORMATTER\ncb = plt.colorbar(s, ax = ax)\ncb.set_label('Salinity (PSU)')\n\nax = fig.add_subplot(rows, columns, 2, projection=ccrs.PlateCarree())\nax.add_feature(cartopy.feature.RIVERS)\nax.coastlines()\nax.set_extent([plot_west, plot_east, plot_south, plot_north])\ns6 = plt.scatter(sd6.longitude, sd6.latitude, s = 0.5, c = sd6.SAL_SBE37_MEAN, vmin = 25, vmax = 35, cmap = 'rainbow', transform = ccrs.PlateCarree())\ngl = ax.gridlines(color='gray',alpha=0.6,draw_labels=True)\nplt.title('Saildrone vehicle 1036, May-Oct 2019')\ngl.top_labels = False\ngl.left_labels = True\ngl.right_labels = False\ngl.xformatter = LONGITUDE_FORMATTER\ngl.yformatter = LATITUDE_FORMATTER\ncb = plt.colorbar(s, ax = ax)\ncb.set_label('Salinity (PSU)')\nplt.savefig('salinity_comparison_sd1036.png')"
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+ "text": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) product suite is designed to collect data from satellite radar and optical instruments to generate three products:\n\na near-global Dynamic Surface Water Extent (DSWx) product suite\na near-global land-surface Disturbance (DIST) product suite\na North America land-surface Displacement (DISP) product suite\n\nOnly DSWx products will be distributed through PO.DAAC. More information can be found on PO.DAAC’s OPERA webpage."
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- "text": "Plot salinity side-by-side from SMAP and from Saildrone vehicle 1037\n\nfig = plt.figure(figsize= (16,10))\n\nrows = 1\ncolumns = 2\nax = fig.add_subplot(rows, columns, 1, projection=ccrs.PlateCarree())\nax.add_feature(cartopy.feature.RIVERS)\nax.coastlines()\nax.set_extent([plot_west, plot_east, plot_south, plot_north])\ns = subset_mean_values.plot.pcolormesh(add_colorbar=False)\ngl = ax.gridlines(color='gray',alpha=0.6,draw_labels=True) \nplt.title('SMAP, May-Oct 2019')\ngl.top_labels = False\ngl.left_labels = True\ngl.right_labels = False\ngl.xformatter = LONGITUDE_FORMATTER\ngl.yformatter = LATITUDE_FORMATTER\ncb = plt.colorbar(s, ax = ax)\ncb.set_label('Salinity (PSU)')\n\nax = fig.add_subplot(rows, columns, 2, projection=ccrs.PlateCarree())\nax.add_feature(cartopy.feature.RIVERS)\nax.coastlines()\nax.set_extent([plot_west, plot_east, plot_south, plot_north])\ns6 = plt.scatter(sd7.longitude, sd7.latitude, s = 0.5, c = sd7.SAL_SBE37_MEAN, vmin = 25, vmax = 35, cmap = 'rainbow', transform = ccrs.PlateCarree())\ngl = ax.gridlines(color='gray',alpha=0.6,draw_labels=True)\nplt.title('Saildrone vehicle 1037, May-Oct 2019')\ngl.top_labels = False\ngl.left_labels = True\ngl.right_labels = False\ngl.xformatter = LONGITUDE_FORMATTER\ngl.yformatter = LATITUDE_FORMATTER\ncb = plt.colorbar(s, ax = ax)\ncb.set_label('Salinity (PSU)')\nplt.savefig('salinity_comparison_sd1037.png')"
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+ "title": "OPERA",
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+ "text": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) product suite is designed to collect data from satellite radar and optical instruments to generate three products:\n\na near-global Dynamic Surface Water Extent (DSWx) product suite\na near-global land-surface Disturbance (DIST) product suite\na North America land-surface Displacement (DISP) product suite\n\nOnly DSWx products will be distributed through PO.DAAC. More information can be found on PO.DAAC’s OPERA webpage."
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+ "text": "Data Resources & Tutorials\n\nImagery Exploration\nSOTO by Worldview - explore OPERA imagery in a GUI\nVideo Tutorial: Exploring OPERA Surface Water Extent Data in NASA Worldview\n\n\nSearch & Download\nVia Graphical User Interface:\nFind/download OPERA data on Earthdata Search\nVia Command Line - PO.DAAC subscriber/downloader example:\npodaac-data-subscriber -c OPERA_L3_DSWX-HLS_PROVISIONAL_V1 -d ./data/OPERA_L3_DSWX-HLS_PROVISIONAL_V1 --start-date 2023-04-04T00:00:00Z -e .tif\n\nSee how to Download/Subscribe for more information on how to use the PO.DAAC subscriber/downloader.\n\n\n\nWorkflow tutorials: Access & Visualization\nAWS Cloud: OPERA Dynamic Surface Water Extent (DSWx) Data - How to search for, download, visualize, and mosaic OPERA data over lake Powell while working in the cloud.\nLocal Machine: OPERA Dynamic Surface Water Extent (DSWx) Data - How to search for, download, visualize, and mosaic OPERA data over lake Powell while working on a local machine.\n\n\nGIS workflows\nStoryMap: Exploring Water Surface Extent with Satellite Data"
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"section": "",
- "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nfrom pystac_client import Client \nfrom collections import defaultdict \nimport json\nimport geopandas\nimport geoviews as gv\nfrom cartopy import crs\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport os\nimport requests\nimport boto3\nimport numpy as np\nimport xarray as xr\nimport rasterio as rio\nfrom rasterio.session import AWSSession\nfrom rasterio.plot import show\nimport rioxarray\nimport geoviews as gv\nimport hvplot.xarray\nimport holoviews as hv\nfrom tqdm import tqdm\nfrom pprint import pprint\nimport time\ngv.extension('bokeh', 'matplotlib')"
+ "text": "Earthdata Forum - For general questions about PO.DAAC data or data access, see the Earthdata Forum and specify PO.DAAC.\n\n\nTutorials GitHub Issues Page - For technical questions about contributing to the Cookbook or reporting issues about any tutorials in the Cookbook, create a new GitHub Issue in our podaac/tutorials repository."
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+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
+ "section": "",
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nfrom pystac_client import Client \nfrom collections import defaultdict \nimport json\nimport geopandas\nimport geoviews as gv\nfrom cartopy import crs\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport os\nimport requests\nimport boto3\nimport numpy as np\nimport xarray as xr\nimport rasterio as rio\nfrom rasterio.session import AWSSession\nfrom rasterio.plot import show\nimport rioxarray\nimport geoviews as gv\nimport hvplot.xarray\nimport holoviews as hv\nfrom tqdm import tqdm\nfrom pprint import pprint\ngv.extension('bokeh', 'matplotlib')"
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+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
"section": "Initiate Data Search",
"text": "Initiate Data Search\n\nSTAC_URL = 'https://cmr.earthdata.nasa.gov/stac'\nprovider_cat = Client.open(STAC_URL)\ncatalog = Client.open(f'{STAC_URL}/LPCLOUD/')\n#collections = ['HLSL30.v2.0', 'HLSS30.v2.0']\ncollections = ['HLSL30.v2.0']"
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"section": "Define Date Range and Region of Interest",
"text": "Define Date Range and Region of Interest\n\ndate_range = \"2021-01/2022-01\"\nroi = {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.60835266113281,\n 39.49874248613119\n ],\n [\n -121.26983642578124,\n 39.49874248613119\n ],\n [\n -121.26983642578124,\n 39.756824261131406\n ],\n [\n -121.60835266113281,\n 39.756824261131406\n ],\n [\n -121.60835266113281,\n 39.49874248613119\n ]\n ]\n ]\n }\n }['geometry']\nbase = gv.tile_sources.EsriImagery.opts(width=650, height=500)\nReservoir = gv.Polygons(roi['coordinates']).opts(line_color='yellow', line_width=10, color=None)\nReservoir * base"
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"section": "Search for HLS imagery matching search criteria",
"text": "Search for HLS imagery matching search criteria\n\nsearch = catalog.search(\n collections=collections,\n intersects=roi,\n datetime=date_range,\n limit=100\n)\n\nitem_collection = search.get_all_items()\nsearch.matched()\n\n50"
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+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
"section": "Filter imagery for low cloud images and identify image bands needed for water classification",
"text": "Filter imagery for low cloud images and identify image bands needed for water classification\n\ns30_bands = ['B8A', 'B03'] # S30 bands for NDWI calculation and quality filtering -> NIR, GREEN, Quality \nl30_bands = ['B05', 'B03'] # L30 bands for NDWI calculation and quality filtering -> NIR, GREEN, Quality \ncloudcover = 10\n\n\nndwi_band_links = []\n\nfor i in item_collection:\n if i.properties['eo:cloud_cover'] <= cloudcover:\n if i.collection_id == 'HLSS30.v2.0':\n #print(i.properties['eo:cloud_cover'])\n ndwi_bands = s30_bands\n elif i.collection_id == 'HLSL30.v2.0':\n #print(i.properties['eo:cloud_cover'])\n ndwi_bands = l30_bands\n\n for a in i.assets:\n if any(b==a for b in ndwi_bands):\n ndwi_band_links.append(i.assets[a].href)\n\n\nndwi_band_links[:10]\n\n['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021016T184526.v2.0/HLS.L30.T10TFK.2021016T184526.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021016T184526.v2.0/HLS.L30.T10TFK.2021016T184526.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021048T184520.v2.0/HLS.L30.T10TFK.2021048T184520.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021048T184520.v2.0/HLS.L30.T10TFK.2021048T184520.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021048T184520.v2.0/HLS.L30.T10SFJ.2021048T184520.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021048T184520.v2.0/HLS.L30.T10SFJ.2021048T184520.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021064T184513.v2.0/HLS.L30.T10SFJ.2021064T184513.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10SFJ.2021064T184513.v2.0/HLS.L30.T10SFJ.2021064T184513.v2.0.B05.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021064T184513.v2.0/HLS.L30.T10TFK.2021064T184513.v2.0.B03.tif',\n 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TFK.2021064T184513.v2.0/HLS.L30.T10TFK.2021064T184513.v2.0.B05.tif']\n\n\n\ntile_dicts = defaultdict(list) \n\n\nfor l in ndwi_band_links:\n tile = l.split('.')[-6]\n tile_dicts[tile].append(l)\n\n\ntile_dicts.keys()\n\ndict_keys(['T10TFK', 'T10SFJ'])\n\n\n\ntile_links = tile_dicts['T10SFJ']\n\n\nbands_dicts = defaultdict(list)\nfor b in tile_links:\n band = b.split('.')[-2]\n bands_dicts[band].append(b)\nfor i in bands_dicts:\n print(i)\n\nB03\nB05"
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
- "section": "Download identified images to local computer",
- "text": "Download identified images to local computer\n\nos.makedirs(\"downloads\", exist_ok=True)\n\n\ndef download(url: str, fname: str):\n resp = requests.get(url, stream=True)\n total = int(resp.headers.get('content-length', 0))\n with open(fname, 'wb') as file, tqdm(\n desc=fname,\n ncols=110,\n total=total,\n unit='iB',\n unit_scale=True,\n unit_divisor=1024,\n ) as bar:\n for data in resp.iter_content(chunk_size=1024):\n size = file.write(data)\n bar.update(size)\n\n\npath_dicts = defaultdict(list)\nprint('Begin Downloading Imagery')\nstart_time = time.time()\nfor key in bands_dicts:\n url = bands_dicts[key]\n for u in url:\n filename = u.split('/')[-1]\n path = './downloads/' + filename\n download(u,path)\n path_dicts[key].append(path)\nprint('Download Complete')\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n\nBegin Downloading Imagery\nDownload Complete\n--- 195.1027250289917 seconds ---\n\n\n./downloads/HLS.L30.T10SFJ.2021048T184520.v2.0.B03.tif: 100%|███████████| 24.2M/24.2M [00:01<00:00, 13.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021064T184513.v2.0.B03.tif: 100%|███████████| 23.9M/23.9M [00:01<00:00, 13.7MiB/s]\n./downloads/HLS.L30.T10SFJ.2021080T184505.v2.0.B03.tif: 100%|███████████| 23.8M/23.8M [00:02<00:00, 10.7MiB/s]\n./downloads/HLS.L30.T10SFJ.2021096T184501.v2.0.B03.tif: 100%|███████████| 23.7M/23.7M [00:01<00:00, 13.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184454.v2.0.B03.tif: 100%|███████████| 23.4M/23.4M [00:01<00:00, 13.2MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184518.v2.0.B03.tif: 100%|███████████| 22.3M/22.3M [00:01<00:00, 12.5MiB/s]\n./downloads/HLS.L30.T10SFJ.2021128T184447.v2.0.B03.tif: 100%|███████████| 23.6M/23.6M [00:01<00:00, 14.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021176T184509.v2.0.B03.tif: 100%|███████████| 23.9M/23.9M [00:01<00:00, 13.7MiB/s]\n./downloads/HLS.L30.T10SFJ.2021192T184511.v2.0.B03.tif: 100%|███████████| 24.0M/24.0M [00:01<00:00, 14.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021224T184524.v2.0.B03.tif: 100%|███████████| 22.2M/22.2M [00:01<00:00, 13.2MiB/s]\n./downloads/HLS.L30.T10SFJ.2021240T184529.v2.0.B03.tif: 100%|███████████| 22.5M/22.5M [00:01<00:00, 13.6MiB/s]\n./downloads/HLS.L30.T10SFJ.2021256T184533.v2.0.B03.tif: 100%|███████████| 23.3M/23.3M [00:02<00:00, 12.2MiB/s]\n./downloads/HLS.L30.T10SFJ.2021272T184537.v2.0.B03.tif: 100%|███████████| 23.9M/23.9M [00:01<00:00, 13.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2021288T184542.v2.0.B03.tif: 100%|███████████| 24.0M/24.0M [00:01<00:00, 14.1MiB/s]\n./downloads/HLS.L30.T10SFJ.2021336T184538.v2.0.B03.tif: 100%|███████████| 23.5M/23.5M [00:01<00:00, 14.4MiB/s]\n./downloads/HLS.L30.T10SFJ.2022019T184528.v2.0.B03.tif: 100%|███████████| 24.4M/24.4M [00:01<00:00, 15.6MiB/s]\n./downloads/HLS.L30.T10SFJ.2021048T184520.v2.0.B05.tif: 100%|███████████| 26.9M/26.9M [00:02<00:00, 11.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2021064T184513.v2.0.B05.tif: 100%|███████████| 26.6M/26.6M [00:02<00:00, 13.8MiB/s]\n./downloads/HLS.L30.T10SFJ.2021080T184505.v2.0.B05.tif: 100%|███████████| 26.6M/26.6M [00:03<00:00, 7.98MiB/s]\n./downloads/HLS.L30.T10SFJ.2021096T184501.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:02<00:00, 9.77MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184454.v2.0.B05.tif: 100%|███████████| 26.0M/26.0M [00:01<00:00, 14.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021112T184518.v2.0.B05.tif: 100%|███████████| 24.9M/24.9M [00:02<00:00, 9.89MiB/s]\n./downloads/HLS.L30.T10SFJ.2021128T184447.v2.0.B05.tif: 100%|███████████| 26.1M/26.1M [00:02<00:00, 9.87MiB/s]\n./downloads/HLS.L30.T10SFJ.2021176T184509.v2.0.B05.tif: 100%|███████████| 26.3M/26.3M [00:02<00:00, 13.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021192T184511.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:02<00:00, 10.1MiB/s]\n./downloads/HLS.L30.T10SFJ.2021224T184524.v2.0.B05.tif: 100%|███████████| 25.3M/25.3M [00:02<00:00, 12.5MiB/s]\n./downloads/HLS.L30.T10SFJ.2021240T184529.v2.0.B05.tif: 100%|███████████| 25.1M/25.1M [00:02<00:00, 12.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2021256T184533.v2.0.B05.tif: 100%|███████████| 26.2M/26.2M [00:01<00:00, 14.0MiB/s]\n./downloads/HLS.L30.T10SFJ.2021272T184537.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:02<00:00, 12.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021288T184542.v2.0.B05.tif: 100%|███████████| 26.4M/26.4M [00:01<00:00, 14.3MiB/s]\n./downloads/HLS.L30.T10SFJ.2021336T184538.v2.0.B05.tif: 100%|███████████| 26.8M/26.8M [00:02<00:00, 10.9MiB/s]\n./downloads/HLS.L30.T10SFJ.2022019T184528.v2.0.B05.tif: 100%|███████████| 27.3M/27.3M [00:01<00:00, 14.9MiB/s]"
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- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#locate-images-in-amazon-s3-storage",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#locate-images-in-amazon-s3-storage",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
+ "section": "Locate Images in Amazon S3 Storage",
+ "text": "Locate Images in Amazon S3 Storage\n\npath_dicts = defaultdict(list)\nfor l in bands_dicts['B05']:\n s3l = l.replace('https://data.lpdaac.earthdatacloud.nasa.gov/', 's3://')\n path_dicts['B05'].append(s3l)\n \ns3paths_LB3 = []\nfor l in bands_dicts['B03']:\n s3l = l.replace('https://data.lpdaac.earthdatacloud.nasa.gov/', 's3://')\n if s3l[38:39] == 'L':\n path_dicts['B03'].append(s3l)\n\n\ns3_cred_endpoint = 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\ntemp_creds_req = get_temp_creds()\nsession = boto3.Session(aws_access_key_id=temp_creds_req['accessKeyId'], \n aws_secret_access_key=temp_creds_req['secretAccessKey'],\n aws_session_token=temp_creds_req['sessionToken'],\n region_name='us-west-2')\n\n\nrio_env = rio.Env(AWSSession(session),\n GDAL_DISABLE_READDIR_ON_OPEN='EMPTY_DIR',\n GDAL_HTTP_COOKIEFILE=os.path.expanduser('~/cookies.txt'),\n GDAL_HTTP_COOKIEJAR=os.path.expanduser('~/cookies.txt'))\nrio_env.__enter__()\n\n<rasterio.env.Env at 0x7fd7e12fc580>"
+ },
+ {
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#load-images-and-visualize",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#load-images-and-visualize",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
"section": "Load images and visualize",
- "text": "Load images and visualize\n\ndef time_index_from_filenames(file_links):\n return [datetime.strptime(f.split('.')[-5], '%Y%jT%H%M%S') for f in file_links]\n\n\ntime = xr.Variable('time', time_index_from_filenames(path_dicts['B03']))\nchunks=dict(band=1, x=512, y=512)\nhls_ts_da_LB3 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B03']], dim=time)\nhls_ts_da_LB5 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B05']], dim=time)\nhls_ts_da_LB3 = hls_ts_da_LB3.rio.reproject(\"epsg:4326\")\nhls_ts_da_LB5 = hls_ts_da_LB5.rio.reproject(\"epsg:4326\")\n\n\nhls_ts_da_data_LB3 = hls_ts_da_LB3.load()\nhls_ts_da_data_LB5 = hls_ts_da_LB5.load()\nhls_ts_da_data_LB3 = hls_ts_da_data_LB3.rio.clip([roi])\nhls_ts_da_data_LB5 = hls_ts_da_data_LB5.rio.clip([roi])\n\n\nhls_ts_da_data_LB5.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='gray').opts(clim=(0,2000))"
+ "text": "Load images and visualize\n\ndef time_index_from_filenames(file_links):\n return [datetime.strptime(f.split('.')[-5], '%Y%jT%H%M%S') for f in file_links]\n\n\ntime = xr.Variable('time', time_index_from_filenames(path_dicts['B03']))\nchunks=dict(band=1, x=512, y=512)\nhls_ts_da_LB3 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B03']], dim=time)\nhls_ts_da_LB5 = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in path_dicts['B05']], dim=time)\nhls_ts_da_LB3 = hls_ts_da_LB3.rio.reproject(\"epsg:4326\")\nhls_ts_da_LB5 = hls_ts_da_LB5.rio.reproject(\"epsg:4326\")\n\n\nhls_ts_da_data_LB3 = hls_ts_da_LB3.load()\nhls_ts_da_data_LB5 = hls_ts_da_LB5.load()\nhls_ts_da_data_LB3 = hls_ts_da_data_LB3.rio.clip([roi])\nhls_ts_da_data_LB5 = hls_ts_da_data_LB5.rio.clip([roi])\n\n\nhls_ts_da_data_LB5.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='gray').opts(clim=(0,2000))\n\nNameError: name 'hls_ts_da_data_LB5' is not defined"
},
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- "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#caclulate-normalized-difference-water-index-ndwi-and-classify-innundated-areas",
- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#caclulate-normalized-difference-water-index-ndwi-and-classify-innundated-areas",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#caclulate-normalized-difference-water-index-ndwi-and-classify-innundated-areas",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
"section": "Caclulate Normalized Difference Water Index (NDWI) and Classify Innundated Areas",
- "text": "Caclulate Normalized Difference Water Index (NDWI) and Classify Innundated Areas\n\nLB3 = hls_ts_da_data_LB3 \nLB5 = hls_ts_da_data_LB5\nNDWI = (LB3-LB5)/(LB3+LB5)\nNDWI.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='coolwarm').opts(clim=(-0.5,0.5))\n\n\nwater = NDWI>0\nwater.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='PuOr').opts(clim=(0,1))"
+ "text": "Caclulate Normalized Difference Water Index (NDWI) and Classify Innundated Areas\n\nLB3 = hls_ts_da_data_LB3 \nLB5 = hls_ts_da_data_LB5\nNDWI = (LB3-LB5)/(LB3+LB5)\nNDWI.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='coolwarm').opts(clim=(-0.5,0.5))\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\nwater = NDWI>0\nwater.hvplot.image(x='x', y='y', rasterize=True, width=600, height=400, colorbar=True, cmap='PuOr').opts(clim=(0,1))"
},
{
- "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#caclulate-surface-area-of-reservoir-and-plot-time-series",
- "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Local.html#caclulate-surface-area-of-reservoir-and-plot-time-series",
- "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Local Machine Version",
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#caclulate-surface-area-of-reservoir-and-plot-time-series",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html#caclulate-surface-area-of-reservoir-and-plot-time-series",
+ "title": "Estimating Reservoir Surface Area From Harmonized Landsat-Sentinel (HLS) Imagery – Cloud Version",
"section": "Caclulate surface area of reservoir and plot time series",
- "text": "Caclulate surface area of reservoir and plot time series\n\nif water.variable.max() == True:\n water_real = water*30*30\nwater_area = water_real.sum(axis=(1,2))\n\n%matplotlib inline\n\nfig, ax = plt.subplots()\n(water_area[:]/1000000).plot(ax=ax, linewidth=2, linestyle = '-', marker='o')\nax.set_title(\"Surface area of waterbody in km2\")\nax.set_ylabel('Area [km^2]')"
- },
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- "title": "A newer version of this Notebook exists here.",
- "section": "",
- "text": "This Jupyter Notebook contains examples related to querying river reaches (segments) using the SWOT River Database (SWORD) Feature Translation Service (FTS), previewing (visualizing) the queried features, and using FTS results to query NASA’s Common Metadata Repository (CMR) data.\nExample Use Case: In this example, we are using FTS to geospatially search a single river reach, multiple reaches, and river nodes within the database. We then use geospatial coordinates of the features (here river reaches/node along the Kasai River, a tributary of the Congo River in Africa) to query against a dataset in CMR, namely Pre SWOT Hydrology.\nResources - SWOT River Database (SWORD) data can be found here: https://zenodo.org/record/4917236#.YTKLPd9lCST - Other SWOT SWORD documentation can be found here: https://swot.jpl.nasa.gov/documents/4031/ - MEaSUREs - Pre-Surface Water and Ocean Topography (Pre-SWOT) Hydrology data can be found here: https://podaac.jpl.nasa.gov/MEaSUREs-Pre-SWOT?sections=data\n\n\nThere are three python dependencies that must be available to the python kernel running this notebook.\n\nplotly (https://pypi.org/project/plotly/)\nkaleido (https://pypi.org/project/kaleido/)\ngeojson (https://pypi.org/project/geojson/)\n\nThe next cell installs them when the cell is run.\n\n!pip install plotly geojson kaleido\n\nRequirement already satisfied: plotly in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (5.1.0)\nRequirement already satisfied: geojson in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (2.5.0)\nRequirement already satisfied: kaleido in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (0.2.1)\nRequirement already satisfied: tenacity>=6.2.0 in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (from plotly) (8.0.1)\nRequirement already satisfied: six in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (from plotly) (1.15.0)\n\n\nFirst, we define a function to query the FTS service for a single reach, multiple reaches, or river nodes. This function returns the properties of river reaches including name, length, coordinates as well as individual node properties.\n\nimport requests\nimport json\nimport geojson\nimport pprint\nimport plotly.graph_objects as go\n\nfrom IPython.display import JSON, Image\n\ndef response_to_FeatureCollection(response):\n \"\"\"\n This function will return a geojson.FeatureCollection representation of the features found\n in the provided response.\n Parameters\n ----------\n response : requests.Response\n Response object returned from a GET request on the FTS rivers endpoint.\n Returns\n -------\n geojson.FeatureCollection\n FeatureCollection containing all features extracted from the response.\n \"\"\"\n featureList = []\n for reach_id, reach_json in response.json()['results'].items():\n reach_feature = geojson.loads(json.dumps(reach_json['geojson']))\n reach_feature['properties']={k:v for k,v in reach_json.items() if k not in ['geojson', 'geometry']}\n featureList.append(reach_feature)\n featureCollection = geojson.FeatureCollection(featureList)\n return featureCollection\n\n\nWe define another function to calculate the center coordinates of features. This function returns the center of a single reach or multiple reaches.\n\ndef estimate_center_of_FeatureCollection(featureCollection):\n \"\"\"\n This function does a very simplistic estimation of the center of all features in the given FeatureCollection.\n Parameters\n ----------\n featureCollection : geojson.FeatureCollection\n Estimate the center lon, lat of this FeatureCollection.\n Returns\n -------\n tuple(float, float)\n Estimated center longitude, center latitude\n \"\"\"\n lats = [xy[1] for feature in featureCollection['features'] for xy in feature['coordinates']]\n lons = [xy[0] for feature in featureCollection['features'] for xy in feature['coordinates']]\n\n center_lat = (min(lats) + max(lats)) / 2\n center_lon = (min(lons) + max(lons)) / 2\n \n return center_lon, center_lat"
+ "text": "Caclulate surface area of reservoir and plot time series\n\nif water.variable.max() == True:\n water_real = water*30*30\nwater_area = water_real.sum(axis=(1,2))\n\n%matplotlib inline\n\nfig, ax = plt.subplots()\n(water_area[:]/1000000).plot(ax=ax, linewidth=2, linestyle = '-', marker='o')\nax.set_title(\"Surface area of waterbody in km2\")\nax.set_ylabel('Area [km^2]')\n\nText(0, 0.5, 'Area [km^2]')"
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- "title": "A newer version of this Notebook exists here.",
+ "objectID": "notebooks/meetings_workshops/swot_ea_hackweek_2022/River_Heights_in_the_Cloud.html",
+ "href": "notebooks/meetings_workshops/swot_ea_hackweek_2022/River_Heights_in_the_Cloud.html",
+ "title": "Mississippi River Heights Exploration:",
"section": "",
- "text": "This Jupyter Notebook contains examples related to querying river reaches (segments) using the SWOT River Database (SWORD) Feature Translation Service (FTS), previewing (visualizing) the queried features, and using FTS results to query NASA’s Common Metadata Repository (CMR) data.\nExample Use Case: In this example, we are using FTS to geospatially search a single river reach, multiple reaches, and river nodes within the database. We then use geospatial coordinates of the features (here river reaches/node along the Kasai River, a tributary of the Congo River in Africa) to query against a dataset in CMR, namely Pre SWOT Hydrology.\nResources - SWOT River Database (SWORD) data can be found here: https://zenodo.org/record/4917236#.YTKLPd9lCST - Other SWOT SWORD documentation can be found here: https://swot.jpl.nasa.gov/documents/4031/ - MEaSUREs - Pre-Surface Water and Ocean Topography (Pre-SWOT) Hydrology data can be found here: https://podaac.jpl.nasa.gov/MEaSUREs-Pre-SWOT?sections=data\n\n\nThere are three python dependencies that must be available to the python kernel running this notebook.\n\nplotly (https://pypi.org/project/plotly/)\nkaleido (https://pypi.org/project/kaleido/)\ngeojson (https://pypi.org/project/geojson/)\n\nThe next cell installs them when the cell is run.\n\n!pip install plotly geojson kaleido\n\nRequirement already satisfied: plotly in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (5.1.0)\nRequirement already satisfied: geojson in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (2.5.0)\nRequirement already satisfied: kaleido in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (0.2.1)\nRequirement already satisfied: tenacity>=6.2.0 in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (from plotly) (8.0.1)\nRequirement already satisfied: six in /Users/catoaida/opt/anaconda3/envs/mygdal2/lib/python3.7/site-packages (from plotly) (1.15.0)\n\n\nFirst, we define a function to query the FTS service for a single reach, multiple reaches, or river nodes. This function returns the properties of river reaches including name, length, coordinates as well as individual node properties.\n\nimport requests\nimport json\nimport geojson\nimport pprint\nimport plotly.graph_objects as go\n\nfrom IPython.display import JSON, Image\n\ndef response_to_FeatureCollection(response):\n \"\"\"\n This function will return a geojson.FeatureCollection representation of the features found\n in the provided response.\n Parameters\n ----------\n response : requests.Response\n Response object returned from a GET request on the FTS rivers endpoint.\n Returns\n -------\n geojson.FeatureCollection\n FeatureCollection containing all features extracted from the response.\n \"\"\"\n featureList = []\n for reach_id, reach_json in response.json()['results'].items():\n reach_feature = geojson.loads(json.dumps(reach_json['geojson']))\n reach_feature['properties']={k:v for k,v in reach_json.items() if k not in ['geojson', 'geometry']}\n featureList.append(reach_feature)\n featureCollection = geojson.FeatureCollection(featureList)\n return featureCollection\n\n\nWe define another function to calculate the center coordinates of features. This function returns the center of a single reach or multiple reaches.\n\ndef estimate_center_of_FeatureCollection(featureCollection):\n \"\"\"\n This function does a very simplistic estimation of the center of all features in the given FeatureCollection.\n Parameters\n ----------\n featureCollection : geojson.FeatureCollection\n Estimate the center lon, lat of this FeatureCollection.\n Returns\n -------\n tuple(float, float)\n Estimated center longitude, center latitude\n \"\"\"\n lats = [xy[1] for feature in featureCollection['features'] for xy in feature['coordinates']]\n lons = [xy[0] for feature in featureCollection['features'] for xy in feature['coordinates']]\n\n center_lat = (min(lats) + max(lats)) / 2\n center_lon = (min(lons) + max(lons)) / 2\n \n return center_lon, center_lat"
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.*"
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- "title": "A newer version of this Notebook exists here.",
- "section": "Query CMR by Coordinates",
- "text": "Query CMR by Coordinates\nHere is a another useful example of the Feature Translation Service. We can use results obtained from the FTS to then directly and automatically query data using CMR. We will use the coordinate information of a single reach to search for granules available through the Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2 data.\nWe query CMR using the previously used reach ID of 13227000061 over Kasai River, a tributary of the Congo River in Africa.\n\nresponse = requests.get(\"https://fts.podaac.earthdata.nasa.gov/rivers/reach/13227000061\")\nfeatureCollection = response_to_FeatureCollection(response)\n\npprint.pprint(response.json(), compact=True, width=60, depth=2)\n\n{'hits': 1,\n 'results': {'13227000061': {...}},\n 'search on': {'exact': False,\n 'page_number': 1,\n 'page_size': 100,\n 'parameter': 'reach'},\n 'status': '200 OK',\n 'time': '4.289 ms.'}\n\n\nThe next cell queries CMR using the coordinates of the reach. Note that coordinates should be listed in the format lon1, lat1, lon2, lat2, lon3, lat3, and so on. The CMR json response proivides a link to the data file (granule) from the Pre SWOT Hydroology GRRATS Daily River Heights data collection that overlaps the geospatial search from FTS-SWORD for the river reaches of interest, e.g. \"href\": \"https://podaac-tools.jpl.nasa.gov/drive/files/allData/preswot_hydrology/L2/rivers/daily//Africa_Congo1kmdaily.nc\"\n\nCOLLECTION_ID = \"C1674168562-PODAAC\" # Pre SWOT Hydrology GRRATS Daily River Heights and Storage Version 2\n\n# this function returns the unique set of lon,lat coordinates. When quering CMR, we need to delete repeated points with the same lon,lat\ndef get_unique_numbers(numbers):\n unique = []\n\n for number in numbers:\n if number in unique:\n continue\n else:\n unique.append(number)\n return unique\n\n# derive lon,lat of nodes along the reach\nlatlon = [xy[:] for feature in featureCollection['features'] for xy in feature['coordinates']]\n\n# delete the repeated nodes if available\nunique_lonlat=get_unique_numbers(latlon)\n\n# create one single list of lon,lat coordinates \nflat_list = []\nfor sublist in unique_lonlat:\n for item in sublist:\n flat_list.append(item)\n\n#extra steps to create a string of the list\nflat_list_string = str(flat_list)[1:-1]\nlonlat_nodes = flat_list_string.replace(\" \", \"\")\n\n\n#query CMR\ncmr_response = requests.get(\"https://cmr.earthdata.nasa.gov/search/granules.json?line={}&echo_collection_id={}&pretty=True\".format(lonlat_nodes, COLLECTION_ID))\n\n\n# Make it look nice\nprint(json.dumps(cmr_response.json(), indent = 4))\n\n{\n \"feed\": {\n \"updated\": \"2021-09-09T22:02:30.060Z\",\n \"id\": 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+ "title": "Mississippi River Heights Exploration:",
+ "section": "Working with In Situ Measurements and Satellite Hydrology Data in the Cloud",
+ "text": "Working with In Situ Measurements and Satellite Hydrology Data in the Cloud\n\nLearning Objectives\n\nAccess data from the cloud (Pre-SWOT MEaSUREs river heights) and utilize in tandem with locally hosted dataset (USGS gauges)\nSearch for products using Earthdata Search GUI\nAccess datasets using xarray and visualize\n\nThis tutorial explores the relationships between satellite and in situ river heights in the Mississippi River using the data sets listed below. The notebook is designed to be executed in Amazon Web Services (AWS) (in us-west-2 region where the cloud data is located)."
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- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Summary & Learning Objectives",
- "text": "Summary & Learning Objectives\n\nNotebook showcasing how to work with OPERA DSWx data in the cloud\n\nUtilizing the earthaccess Python package. For more information visit: https://nsidc.github.io/earthaccess/\nOption to query the new dataset based on users choice; either by classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’), etc.\nVisualizing the dataset based on its classified layer values.\nMosaicking multiple layers into a single layer."
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+ "title": "Mississippi River Heights Exploration:",
+ "section": "Datasets",
+ "text": "Datasets\nThe tutorial itself will use two different datasets:\n1. PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\n\nDOI: https://doi.org/10.5067/PSGRA-DA2V2\n\nThe NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual river height gauges from various altimeter satellites. \n2. USGS Water Data for the Nations River Gauges\n\nURL: https://dashboard.waterdata.usgs.gov/app/nwd/?region=lower48&aoi=default\n\nRiver heights are obtained from the United States Geologic Survey (USGS) National Water Dashboard."
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- "text": "Requirements\n\n1. Compute environment\nThis tutorial can only be run in the following environment: - AWS instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via and s3fs session; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nImport Packages\n\nimport os\nimport requests \nimport json\nimport boto3\nimport s3fs\nfrom osgeo import gdal\nimport rasterio as rio\nfrom rasterio.plot import show\nfrom rasterio.merge import merge\nfrom rasterio.io import MemoryFile\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes \nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\nimport numpy as np\nfrom pathlib import Path\nfrom urllib.request import urlretrieve\nfrom json import dumps\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store"
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+ "title": "Mississippi River Heights Exploration:",
+ "section": "Needed Packages",
+ "text": "Needed Packages\n\nimport os\nimport glob\nimport s3fs\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport hvplot.xarray\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy\nimport csv\nfrom datetime import datetime\nfrom os.path import isfile, basename, abspath"
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- "text": "Authentication with earthaccess\nIn this notebook, we will be calling the authentication in the below cell.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)"
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+ "title": "Mississippi River Heights Exploration:",
+ "section": "Get Temporary AWS Credentials for Access",
+ "text": "Get Temporary AWS Credentials for Access\nS3 is an ‘object store’ hosted in AWS for cloud processing. Direct S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. Note, these temporary credentials are valid for only 1 hour. A netrc file is required to aquire these credentials. Use the NASA Earthdata Authentication to create a netrc file in your home directory. (Note: A NASA Earthdata Login is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.)\nThe following crediential is for PODAAC, but other credentials are needed to access data from other NASA DAACs.\n\ns3_cred_endpoint = 'https://archive.podaac.earthdata.nasa.gov/s3credentials'\n\nCreate a function to make a request to an endpoint for temporary credentials.\n\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\n\n\ntemp_creds_req = get_temp_creds()\n#temp_creds_req # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.\n\n\nSet up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint.\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})"
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- "section": "Set up an s3fs session for Direct Access",
- "text": "Set up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data provider.\n\nfs_s3 = Store(auth).get_s3fs_session(daac=\"PODAAC\", provider=\"POCLOUD\")"
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+ "title": "Mississippi River Heights Exploration:",
+ "section": "Pre-SWOT MEaSUREs River Heights",
+ "text": "Pre-SWOT MEaSUREs River Heights\nThe shortname for MEaSUREs is ‘PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2’ with the concept ID: C2036882359-POCLOUD. The guidebook explains the details of the Pre-SWOT MEaSUREs data.\nOur desired MEaSUREs variable is river height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Mississippi River. Time is between April 8, 1993 and April 20, 2019.\nFor this dataset, we found the cloud-enabled data directly using the Earthdata Search (see the Earthdata Search for downloading data tutorial) by searching directly for the concept ID, and locating the granule needed, G2105959746-POCLOUD, that will show us the Mississippi river.\n\n\n\nimage.png\n\n\nThe s3 link for this granule can be found in it’s meta data by viewing the details of the granule from the button with three vertical dots in the corner. The s3 link is under ‘relatedURLs’, or it can be found by going through the download process and instead of downloading, clicking the tab entitled “AWS S3 Access.”\n\n\n\nimage.png\n\n\nLet’s access the netCDF file from an s3 bucket and look at the data structure.\n\ns3_MEaSUREs_url = 's3://podaac-ops-cumulus-protected/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2/North_America_Mississippi1kmdaily.nc'\n\n\ns3_file_obj = fs_s3.open(s3_MEaSUREs_url, mode='rb')\n\n\nds_MEaSUREs = xr.open_dataset(s3_file_obj, engine='h5netcdf')\nds_MEaSUREs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (X: 2766, Y: 2766, distance: 2766, time: 9440,\n charlength: 26)\nCoordinates:\n * time (time) datetime64[ns] 1993-04-14T17:00:38.973026816 ...\nDimensions without coordinates: X, Y, distance, charlength\nData variables:\n lon (X) float64 -89.35 -89.35 -89.36 ... -92.42 -92.43\n lat (Y) float64 29.27 29.28 29.29 ... 44.56 44.56 44.57\n FD (distance) float64 10.01 1.01e+03 ... 2.765e+06\n height (distance, time) float64 ...\n sat (charlength, time) |S1 ...\n storage (distance, time) float64 ...\n IceFlag (time) float64 nan nan nan nan nan ... nan nan nan nan\n LakeFlag (distance) float64 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0\n Storage_uncertainty (distance, time) float64 ...\nAttributes: (12/40)\n title: GRRATS (Global River Radar Altimetry Time ...\n Conventions: CF-1.6, ACDD-1.3\n institution: Ohio State University, School of Earth Sci...\n source: MEaSUREs OSU Storage toolbox 2018\n keywords: EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n keywords_vocabulary: Global Change Master Directory (GCMD)\n ... ...\n geospatial_lat_max: 44.56663081735837\n geospatial_lat_units: degree_north\n geospatial_vertical_max: 201.5947042200621\n geospatial_vertical_min: -2.2912740783007286\n geospatial_vertical_units: m\n geospatial_vertical_positive: upxarray.DatasetDimensions:X: 2766Y: 2766distance: 2766time: 9440charlength: 26Coordinates: (1)time(time)datetime64[ns]1993-04-14T17:00:38.973026816 .....long_name :timestandard_name :timeaxis :Tarray(['1993-04-14T17:00:38.973026816', '1993-04-15T17:00:38.973026816',\n '1993-04-16T17:00:38.973026816', ..., '2019-04-16T15:38:57.639261696',\n '2019-04-17T15:38:57.639261696', '2019-04-18T15:38:57.639261696'],\n dtype='datetime64[ns]')Data variables: (9)lon(X)float64...units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xarray([-89.345393, -89.351435, -89.356849, ..., -92.404921, -92.416217,\n -92.42713 ])lat(Y)float64...units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yarray([29.273779, 29.281522, 29.288734, ..., 44.558685, 44.562254, 44.566631])FD(distance)float64...long_name :distance_from_river_mouthunits :kmcomment :This is the distance along the river centerline from the river mouth to this VS.array([1.000915e+01, 1.010009e+03, 2.010009e+03, ..., 2.763010e+06,\n 2.764010e+06, 2.765010e+06])height(distance, time)float64...units :mpositive :uplong_name :interpolated_heightsstandard_name :heightvalid_min :-2.2912740783007286valid_max :201.5947042200621comment :A time flow distance grid of river water heights with respect to the EGM08 Geoid).-9999 fill values are for missing data, while -9995 fill values are for lakes and reservoirs.[26111040 values with dtype=float64]sat(charlength, time)|S1...long_name :satellitecomment :The satellite the measurement is derived from.[245440 values with dtype=|S1]storage(distance, time)float64...units :km3positive :uplong_name :river_channel_storagecomment :A time flow distance grid of river channel storage values.[26111040 values with dtype=float64]IceFlag(time)float64...long_name :Ice_Flagvalid_range :0, 1flag_masks :1flag_meaning :Time of ice covercomment : This is a flag for masking out times of Ice Cover.array([nan, nan, nan, ..., nan, nan, nan])LakeFlag(distance)float64...long_name :Lake_Flagvalid_range :0, 1flag_masks :1flag_meaning :River sections that are lakes or reservoirscomment : This is a flag for masking out setions that are lakes or reservoirs.array([0., 0., 0., ..., 1., 1., 1.])Storage_uncertainty(distance, time)float64...units :km3positive :uplong_name :river_channel_storage_uncertaintycomment :A time flow distance grid of river channel storage uncertainty values.[26111040 values with dtype=float64]Attributes: (40)title :GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Mississippi RiverConventions :CF-1.6, ACDD-1.3institution :Ohio State University, School of Earth Sciencessource :MEaSUREs OSU Storage toolbox 2018keywords :EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHTkeywords_vocabulary :Global Change Master Directory (GCMD)cdm_data_type :stationcreator_name :Coss,Stevecreator_email :Coss.31@osu.eduproject :MEaSUREs OSUprogram :NASA Earth Science Data Systems (ESDS)publisher_name :PO.DAAC (Physical Oceanography Distributed Active Archive Center)publisher_email :podaac@podaac.jpl.nasa.govpublisher_url :podaac.jpl.nasa.govpublisher_type :Institutionpublisher_institution :PO.DAACprocessing_level :L2doi :10.5067/PSGRA-DA2V2history :This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements. platform :ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)platform_vocabulary :NASA/GCMD Platform Keywords. Version 8.6instrument :RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)instrument_vocabulary :NASA/GCMD Platform Keywords. Version 8.6references :in review :doi.org/10.5194/essd-2019-84id :GRRATS(Global River Radar Altimeter Time Series) 1km/dailysummary :The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.time_coverage_resolution :1 daydate_created :2021-06-30T10:30:44time_coverage_start :1992-04-14T17:00:38time_coverage_end :2018-04-18T15:38:57geospatial_lon_min :-92.42712987654255geospatial_lon_max :-89.09954782976848geospatial_lon_units :degree_eastgeospatial_lat_min :29.27377910202398geospatial_lat_max :44.56663081735837geospatial_lat_units :degree_northgeospatial_vertical_max :201.5947042200621geospatial_vertical_min :-2.2912740783007286geospatial_vertical_units :mgeospatial_vertical_positive :up\n\n\n\nPlot a subset of the data\nPlotting the river distances and associated heights on the map at time t=9069 (March 16, 2018) using plt.\n\nfig = plt.figure(figsize=[11,7]) \nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent([-100, -70, 25, 45])\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()"
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- "section": "Search Using earthaccess for OPERA DSWx",
- "text": "Search Using earthaccess for OPERA DSWx\nEach dataset has it’s own unique collection ID. For the OPERA_L3_DSWX-HLS_PROVISIONAL_V1 dataset, we can find the collection ID here.\nFor this tutorial, we are looking at the Lake Powell Reservoir.\nWe used bbox finder to get the exact coordinates for our area of interest.\nWe want to look at two different times for comparison: 04/11/2023 and 05/02/2023. To find these dates, let’s search for all the data granules between the two.\n\nQuery = DataGranules().concept_id(\"C2617126679-POCLOUD\").bounding_box(-111.144811,36.980121,-110.250799,37.915625).temporal(\"2023-04-11T00:00:00\",\"2023-05-02T23:59:59\")\nprint(f\"Granule hits: {Query.hits()}\")\ncloud_granules = Query.get()\n# is this a cloud hosted data granule?\ncloud_granules[0].cloud_hosted\n\nGranule hits: 50\n\n\nTrue\n\n\n\n# Let's pretty print this\ncloud_granules[0]\n\n\n\n \n \n \n \n \n \n \n Data: OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B01_WTR.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B02_BWTR.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B03_CONF.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B04_DIAG.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B05_WTR-1.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B06_WTR-2.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B07_LAND.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B08_SHAD.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B09_CLOUD.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B10_DEM.tif\n Size: 0 MB\n Spatial: {'HorizontalSpatialDomain': {'Geometry': {'BoundingRectangles': [{'WestBoundingCoordinate': -111, 'SouthBoundingCoordinate': 37.04, 'EastBoundingCoordinate': -109.749, 'NorthBoundingCoordinate': 38.036}]}}}\n \n \n \n \n \n \n \n \n\n\n\nGet S3 Bucket links from search results\nBecause we are working within the AWS cloud, let’s get the S3 bucket links for the 8 desired files. We want to query the dataset based on a specific classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’).\nOPERA has 10 different available layers. We will look at ‘B01_WTR’ which is the Water Classification (WTR) layer of the OPERA DSWx dataset. Details on each available layer and the data product can be found here.\n\n#extract S3 bucket links\ndata_links = [g.data_links(access=\"direct\") for g in cloud_granules]\ndata_links[0]\n\n['s3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B01_WTR.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B02_BWTR.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B03_CONF.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B04_DIAG.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B05_WTR-1.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B06_WTR-2.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B07_LAND.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B08_SHAD.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B09_CLOUD.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B10_DEM.tif']\n\n\n\n#add the S3 bucket links to a list, here we are looking for B01_WTR layer and two dates specified earlier\ns3 = []\nfor r in data_links:\n for l in r:\n if 'B01_WTR' in l: \n if '20230411' in l:\n s3.append(l)\n if '20230502' in l:\n s3.append(l)\n\nprint(len(s3))\n\n8\n\n\n\napril = []\nmay = []\nfor s in s3:\n if '20230411' in s:\n april.append(s)\n if '20230502' in s:\n may.append(s)\n\nSince we are looking at two seperate times, we can create two lists, one for each date, which will be used to mosaic based on its respective time range later."
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+ "title": "Mississippi River Heights Exploration:",
+ "section": "USGS Gauge River Heights",
+ "text": "USGS Gauge River Heights\nIn situ measurements on the Mississippi River can be obtained from the United States Geologic Survey (USGS) National Water Dashboard.\nHere, we zoom into one of the streamgauges toward the outlet of the Mississippi River, Monitoring location 07374525: Mississippi River at Belle Chasse, LA highlighted in green. \nIf the point is selected, data can be obtained for the particular location. This gauge is located at lon, lat: (-89.977847, 29.85715084) and we can obtain gauge height for the time period October 2008 - 2021.\nOnce the text file is downloaded for the gauge heights, and the headers removed, it can be uploaded to the cloud as a dataframe to work alongside the MEaSUREs data. Click the upload files button in the top left corner to do so.\n\ndf_gauge_data = pd.read_csv('Mississippi_outlet_gauge.txt', delimiter = \"\\t\")\n#clean data and convert units to meters\ndf_gauge_data.columns = [\"agency\", \"site_no\", \"datetime\", \"river_height\", \"qual_code\"]\ndf_gauge_data['datetime'] = pd.to_datetime(df_gauge_data['datetime']) \ndf_gauge_data['river_height'] = df_gauge_data['river_height']*0.3048\ndf_gauge_data\n\n\n\n\n\n\n\n\nagency\nsite_no\ndatetime\nriver_height\nqual_code\n\n\n\n\n0\nUSGS\n7374525\n2008-10-29\n2.322576\nA\n\n\n1\nUSGS\n7374525\n2008-10-30\n2.337816\nA\n\n\n2\nUSGS\n7374525\n2008-10-31\n2.368296\nA\n\n\n3\nUSGS\n7374525\n2008-11-01\n2.356104\nA\n\n\n4\nUSGS\n7374525\n2008-11-02\n2.459736\nA\n\n\n...\n...\n...\n...\n...\n...\n\n\n4807\nUSGS\n7374525\n2021-12-27\n2.819400\nA\n\n\n4808\nUSGS\n7374525\n2021-12-28\n2.901696\nA\n\n\n4809\nUSGS\n7374525\n2021-12-29\n2.919984\nA\n\n\n4810\nUSGS\n7374525\n2021-12-30\n2.874264\nA\n\n\n4811\nUSGS\n7374525\n2021-12-31\n2.819400\nA\n\n\n\n\n4812 rows × 5 columns\n\n\n\n\nPlot the data\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(df_gauge_data.datetime, df_gauge_data.river_height, color = 'darkorange')\n\nplt.xlabel('Date')\nplt.ylabel('USGS River Height (m)')\n\nplt.title('Mississippi River Gauge 07374525, 2008-2021')\nplt.grid()\nplt.show()\n\n\n\n\n\n\nFind the same location in the MEaSUREs Dataset using lat/lon\nThe closest location in the MEaSUREs dataset to the gauge (-89.977847, 29.85715084) is at index 106 where lon, lat is (-89.976628, 29.855369). We’ll use this for comparison.\n\nds_MEaSUREs.lat[106]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'lat' ()>\narray(29.855369)\nAttributes:\n units: degrees_north\n long_name: latitude\n standard_name: latitude\n axis: Yxarray.DataArray'lat'29.86array(29.855369)Coordinates: (0)Attributes: (4)units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Y\n\n\n\nds_MEaSUREs.lon[106]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'lon' ()>\narray(-89.976628)\nAttributes:\n units: degrees_east\n long_name: longitude\n standard_name: longitude\n axis: Xxarray.DataArray'lon'-89.98array(-89.976628)Coordinates: (0)Attributes: (4)units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :X\n\n\n\nfig = plt.figure(figsize=[14,7]) \nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent([-90.5, -89.5, 29.3, 30])\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon[106], ds_MEaSUREs.lat[106], lw=1)\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()\n\n\n\n\n\n\nCombined timeseries plot of river heights from each source\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(df_gauge_data.datetime[0:3823], df_gauge_data.river_height[0:3823], color = 'darkorange')\nds_MEaSUREs.height[106,5657:9439].plot(color='darkblue')\n\nplt.xlabel('Date')\nplt.ylabel('River Height (m)')\nplt.legend(['USGS', 'MEaSUREs'], loc='lower right')\n\nplt.grid()\nplt.show()\n\n\n\n\n\n\nLooks like the datums need fixing!\nThe USGS gauge datum is 6.58 feet below NAVD88 GEOID12B EPOCH 2010, while the MEaSUREs datum is height above the WGS84 Earth Gravitational Model (EGM 08) geoid, causing this discrepancy."
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- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Visualizing the Dataset",
- "text": "Visualizing the Dataset\nLet’s now visualize an individual layer for a single file that was downloaded using Rasterio to read the GeoTIFF image.\n\ns3_url = s3[2]\n\n\ns3_file_obj1 = fs_s3.open(s3_url, mode='rb')\n\n\ndsw = rio.open(s3_file_obj1)\ndsw\n\n<open DatasetReader name='/vsipythonfilelike/068314bf-c361-41a7-85ac-9bb1356e63b2/068314bf-c361-41a7-85ac-9bb1356e63b2' mode='r'>\n\n\nOPERA is a single band image with specific classified rgb values.\nThis requires to read the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\nimage = dsw.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw2 = color_array[image]\n\n\nfig, ax = plt.subplots(figsize=(15,10))\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nax.legend(handles=patches,\n bbox_to_anchor=(1.28, 1),\n facecolor=\"gainsboro\")\n\nplt.imshow(dsw2)\nplt.show()"
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+ "title": "Mississippi River Heights Exploration:",
+ "section": "Use Case: Validation",
+ "text": "Use Case: Validation\nTo validate the MEaSUREs dataset, the authors of the dataset actually compare relative heights between gauges, as opposed to absolute heights, in order to avoid the influence of datum errors and the lack of correspondence between satellite ground tracks and gauge locations. They calculate relative heights by removing the long-term mean of difference between the sample pairs of virtual station heights and the stage measured by the stream gauges. We’ll repeat this method below for completeness and calculate the Nash-Sutcliffe Efficiency (NSE) value.\n\n#create dataframes of the two dataset river heights so values can be subtracted easier (the datasets have different numbers of observations)\ng_height_df = pd.DataFrame()\nm_height_df = pd.DataFrame()\ng_height_df['time'] = df_gauge_data.datetime[0:3823].dt.date\ng_height_df['gauge_height'] = df_gauge_data.river_height[0:3823]\nm_height_df['time'] = ds_MEaSUREs.time[5657:9439].dt.date\nm_height_df['MEaSUREs_height'] = ds_MEaSUREs.height[106,5657:9439]\n#merge into one by time\nheight_df = pd.merge(g_height_df, m_height_df, on='time', how='left')\nheight_df\n\n\n\n\n\n\n\n\ntime\ngauge_height\nMEaSUREs_height\n\n\n\n\n0\n2008-10-29\n2.322576\n-0.238960\n\n\n1\n2008-10-30\n2.337816\n-0.209417\n\n\n2\n2008-10-31\n2.368296\n-0.180987\n\n\n3\n2008-11-01\n2.356104\n-0.178015\n\n\n4\n2008-11-02\n2.459736\n-0.177945\n\n\n...\n...\n...\n...\n\n\n3819\n2019-04-13\n5.312664\n1.132784\n\n\n3820\n2019-04-14\n5.236464\n1.152391\n\n\n3821\n2019-04-15\n5.230368\n1.172064\n\n\n3822\n2019-04-16\n5.221224\n1.192314\n\n\n3823\n2019-04-17\n5.202936\n1.208963\n\n\n\n\n3824 rows × 3 columns\n\n\n\n\ndiff = height_df.gauge_height - height_df.MEaSUREs_height\nmean_diff = diff.mean()\nmean_diff\n\n3.451153237576882\n\n\n\nheight_df['relative_gauge_height'] = height_df.gauge_height - mean_diff\n\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(height_df.time, height_df.relative_gauge_height, color = 'darkorange')\nplt.plot(height_df.time, height_df.MEaSUREs_height, color = 'darkblue')\n\nplt.xlabel('Date')\nplt.ylabel('River Height (m)')\nplt.legend(['USGS', 'MEaSUREs'], loc='lower right')\n\nplt.grid()\nplt.show()\n\n\n\n\n\nNash Sutcliffe Efficiency\n\nNSE = 1-(np.sum((height_df.MEaSUREs_height-height_df.relative_gauge_height)**2)/np.sum((height_df.relative_gauge_height-np.mean(height_df.relative_gauge_height))**2))\nNSE\n\n-0.2062487355865772\n\n\nNSE for Oct 2013 - Sept 2014 water year:\n\nfig = plt.figure(figsize=[14,7]) \nplt.plot(height_df.time[1799:2163], height_df.relative_gauge_height[1799:2163], color = 'darkorange')\nplt.plot(height_df.time[1799:2163], height_df.MEaSUREs_height[1799:2163], color = 'darkblue')\n\nplt.xlabel('Date')\nplt.ylabel('River Height (m)')\nplt.legend(['USGS', 'MEaSUREs'], loc='lower right')\n\nplt.grid()\nplt.show()\n\n\n\n\n\nNSE_2014 = 1-(np.sum((height_df.MEaSUREs_height[1799:2163]-height_df.relative_gauge_height[1799:2163])**2)/np.sum((height_df.relative_gauge_height[1799:2163]-np.mean(height_df.relative_gauge_height[1799:2163]))**2))\nNSE_2014\n\n0.18294061649986848\n\n\n\n\nPossible Explanations for discrepancies\n\nMultiple satellites, different return periods\nData interpolation\nSatellite tracks instead of swaths like SWOT will have, spatial interpolation\nRadar altimeter performance varies, was not designed to measure rivers\n\nMEaSUREs is comprised of the Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolation river heights from ERS1, ERS2, TOPEX/Poseidon, OSTM/Jason-2, Envisat, and Jason-3 that are interpolated and processed to create continuous heights for the study over the temporal range of the altimeters used. Each satellite has differing return periods (ie. Jason has a 10-day revist, Envisat 35 days) so to fill the data gaps, perhaps much needed to be interpolated and caused misalignment. In addition, the satellite tracks of these altimeter satellites do not capture entire river reaches with wide swath tracks like the Surface Water and Ocean Topography (SWOT) mission will do in the future. Thus locations observed among satellites may be different and data interpolated spatially, increasing errors. Also, radar altimeter performance varies dramatically across rivers and across Virtual Stations, as the creators of the dataset mention in the guidebook.\nIn addition, the authors note that the Mississippi NSE values ranged from -0.22 to 0.96 with an average of 0.43 when evaluating the dataset, so it looks like we unintentionally honed into one of the stations with the worst statistics on the Mississippi River."
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- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Mosaic Multiple OPERA Layers",
- "text": "Mosaic Multiple OPERA Layers\nWhen creating a mosaic, make sure the temporal range is correct/matching.\nThe mosaic is being created because we have 4 results from the bounding box area provided. If you receive more than 1 result and would like to see a single raster image of all the results, mosaicking is the solution. We define the function below to merge the tiff files for each date and return the composite raster into memory.\n\ndef raster2mosaic(date):\n datasets = []\n for file in date:\n file_path = f\"{file}\"\n file_obj = fs_s3.open(file_path)\n dataset = rio.open(file_obj)\n datasets.append(dataset)\n mosaic, output = merge(datasets) #the merge function will mosaic the raster images\n \n #Saving the output of the mosaicked raster image to memory\n memfile = MemoryFile()\n with memfile.open(driver='GTiff', count = 1, width= mosaic.shape[1], height=mosaic.shape[2] , dtype=np.uint8, transform=output) as dst:\n dst.write(mosaic)\n mosaic_bytes = memfile.read()\n with MemoryFile(mosaic_bytes) as memfile:\n dataset1 = memfile.open()\n raster = dataset1.read(1)\n return raster\n\n\naprilmos = raster2mosaic(april)\nmaymos = raster2mosaic(may)"
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+ "title": "Mississippi River Heights Exploration:",
+ "section": "Conclusions",
+ "text": "Conclusions\n\nRegardless, the workflow works!\nData from the cloud (Pre-SWOT MEaSUREs river heights) is used in tandem with in situ measurements (USGS gauges)\nTime and download space saved"
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- "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
- "section": "Visualizing the Mosaic",
- "text": "Visualizing the Mosaic\nTo visualize the mosaic, you must utilize the single layer colormap.\nThis will be the ‘dsw’ variable used earlier to visualize a single layer. Similarly reading the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw3 = color_array[aprilmos]\n\n\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw4 = color_array[maymos]\n\n\nfig = plt.figure(figsize=(20, 15))\n\nrows = 1\ncolumns = 2\n\n# Lake Powell 04/11/2023\nfig.add_subplot(rows, columns, 1)\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\nplt.imshow(dsw3)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\n# Lake Powell 05/02/2023\nfig.add_subplot(rows, columns, 2)\nplt.title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\nplt.imshow(dsw4)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nplt.show()\n\n\n\n\n\nTo take a closer look at a specific area of the image, we can create an inset map of a specified area.\n\nfig, ax = plt.subplots(1, 2, figsize=(20, 15))\n\nax[0].imshow(dsw3)\nax[0].set_title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\nax_ins1 = ax[0].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins1.imshow(dsw3)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins1.set_xlim(x1, x2)\nax_ins1.set_ylim(y1, y2)\nax_ins1.set_xticklabels('')\nax_ins1.set_yticklabels('')\n\nax[0].indicate_inset_zoom(ax_ins1, edgecolor='black')\n\nax[1].imshow(dsw4)\nax[1].set_title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nax_ins2 = ax[1].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins2.imshow(dsw4)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins2.set_xlim(x1, x2)\nax_ins2.set_ylim(y1, y2)\nax_ins2.set_xticklabels('')\nax_ins2.set_yticklabels('')\n\nax[1].indicate_inset_zoom(ax_ins2, edgecolor='black')\n\nplt.show()"
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+ "title": "Ocean Satellite and In-situ Comparison in the Cloud",
+ "section": "Summary",
+ "text": "Summary\nHere, we compare salinity from the SMAP satellite and Saildrone in-situ measurements. Both datasets are located within the cloud.\n\nFollow along with the Data in Action story:\nBy the end of this notebook, you will have recreated a similar plot to the one featured in this Data-in-Action story:\nhttps://podaac.jpl.nasa.gov/DataAction-2021-10-05-Monitoring-Changes-in-the-Arctic-Using-Saildrone-SMAP-Satellite-and-Ocean-Models-Data\n\n\nShortnames of datasets used here:\nSMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5: https://podaac.jpl.nasa.gov/dataset/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5\nSAILDRONE_ARCTIC: https://podaac.jpl.nasa.gov/dataset/SAILDRONE_ARCTIC"
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- "href": "notebooks/datasets/smap_imerg_tutorial.html#summary",
- "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
- "section": "Summary:",
- "text": "Summary:\nThis tutorial uses the Earthdata Search (https://search.earthdata.nasa.gov/) to download the data on your local machine. You will need to create an account in order to download the data."
+ "objectID": "notebooks/meetings_workshops/arctic_2019.html#requirements",
+ "href": "notebooks/meetings_workshops/arctic_2019.html#requirements",
+ "title": "Ocean Satellite and In-situ Comparison in the Cloud",
+ "section": "Requirements",
+ "text": "Requirements\n\n1. Compute environment\nThis tutorial can only be run in the following environments: - AWS instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n3. netrc File\nYou will need a .netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A .netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata tutorial.\n\n\nImport Libraries\n\n# To access dataset using Earthaccess\nimport earthaccess\n\n# To access dataset without Earthaccess\nimport os\nimport s3fs\nimport requests\nimport glob\n\n# To open dataset\nimport xarray as xr\n\n# For plotting\nimport matplotlib.pyplot as plt\nimport cartopy\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nfrom cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER"
},
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- "objectID": "notebooks/datasets/smap_imerg_tutorial.html#datasets",
- "href": "notebooks/datasets/smap_imerg_tutorial.html#datasets",
- "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
- "section": "Datasets:",
- "text": "Datasets:\n\nJPL SMAP L3 Dataset: https://podaac.jpl.nasa.gov/dataset/SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5?ids=&values=&search=Smap Level 3&provider=POCLOUD\nGPM IMERG Late Precipitation: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDL_06/summary?keywords=gpm imerg"
+ "objectID": "notebooks/meetings_workshops/arctic_2019.html#smap-dataset",
+ "href": "notebooks/meetings_workshops/arctic_2019.html#smap-dataset",
+ "title": "Ocean Satellite and In-situ Comparison in the Cloud",
+ "section": "SMAP dataset",
+ "text": "SMAP dataset\nSearch for and open this dataset as an example of using Earthaccess\n\nauth = earthaccess.login(strategy=\"netrc\")\n\nYou're now authenticated with NASA Earthdata Login\nUsing token with expiration date: 06/18/2023\nUsing .netrc file for EDL\n\n\n\nshort_name=\"SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5\"\n\nresults = earthaccess.search_data(\n short_name=short_name,\n cloud_hosted=True,\n temporal=(\"2019-05-01T00:00:00\", \"2019-10-01T00:00:00\"),\n bounding_box=(-170,65,-160,71) # (west, south, east, north)\n)\n\nGranules found: 122\n\n\n\nds_sss = xr.open_mfdataset(earthaccess.open(results))\n\n Opening 122 granules, approx size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\nplot_west = -170\nplot_east = -160\nplot_south = 60\nplot_north = 75\n\nlat_bnds, lon_bnds = [plot_south, plot_north], [plot_west+360, plot_east+360] # Turn the longitudes in (-180,0) to (0,360)\nds_sss_subset_0 = ds_sss.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_sss_subset_0['latitude'] = ds_sss_subset_0.lat\nds_sss_subset_0['longitude'] = ds_sss_subset_0.lon-360\nds_sss_subset = ds_sss_subset_0.swap_dims({'lat':'latitude', 'lon':'longitude'})\nds_sss_subset\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (longitude: 40, latitude: 60, time: 122,\n uncertainty_components: 9, iceflag_components: 3)\nCoordinates:\n lon (longitude) float32 190.1 190.4 ... 199.6 199.9\n lat (latitude) float32 60.12 60.38 60.62 ... 74.62 74.88\n * time (time) datetime64[ns] 2019-04-27T12:00:00 ... 201...\n * latitude (latitude) float32 60.12 60.38 60.62 ... 74.62 74.88\n * longitude (longitude) float32 -169.9 -169.6 ... -160.4 -160.1\nDimensions without coordinates: uncertainty_components, iceflag_components\nData variables: (12/19)\n nobs (time, latitude, longitude) float64 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n nobs_RF (time, latitude, longitude) float64 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n nobs_40km (time, latitude, longitude) float64 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sss_smap (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sss_smap_RF (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sss_smap_unc (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n ... ...\n fland (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n gice_est (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n surtep (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n winspd (time, latitude, longitude) float32 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n sea_ice_zones (time, latitude, longitude) int8 dask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n anc_sea_ice_flag (time, latitude, longitude, iceflag_components) int8 dask.array<chunksize=(1, 60, 40, 3), meta=np.ndarray>\nAttributes: (12/65)\n Conventions: CF-1.7, ACDD-1.3\n title: SMAP ocean surfac...\n version: V5.0 Validated Re...\n summary: The dataset conta...\n acknowledgement: Funded under Subc...\n processing_level: L3\n ... ...\n Source_of_SMAP_SSS_retrievals: T. Meissner, F. W...\n Source_of_ancillary_SST: Canada Meteorolog...\n Source_of_ancillary_CCMP_wind_speed: Mears, C. et al.,...\n Source_of_ancillary_AMSR2_sea_ice_flag_and_correction: Meissner, T. and ...\n Source_of_ancillary_land_mask: 1 km land/water m...\n Source_of_ancillary_reference_SSS_from_HYCOM: Hybrid Coordinate...xarray.DatasetDimensions:longitude: 40latitude: 60time: 122uncertainty_components: 9iceflag_components: 3Coordinates: (5)lon(longitude)float32190.1 190.4 190.6 ... 199.6 199.9standard_name :longitudeaxis :Xlong_name :center longitude of grid cellunits :degrees_eastvalid_min :0.0valid_max :360.0coverage_content_type :coordinatearray([190.125, 190.375, 190.625, 190.875, 191.125, 191.375, 191.625, 191.875,\n 192.125, 192.375, 192.625, 192.875, 193.125, 193.375, 193.625, 193.875,\n 194.125, 194.375, 194.625, 194.875, 195.125, 195.375, 195.625, 195.875,\n 196.125, 196.375, 196.625, 196.875, 197.125, 197.375, 197.625, 197.875,\n 198.125, 198.375, 198.625, 198.875, 199.125, 199.375, 199.625, 199.875],\n dtype=float32)lat(latitude)float3260.12 60.38 60.62 ... 74.62 74.88standard_name :latitudeaxis :Ylong_name :center latitude of grid cellunits :degrees_northvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875], dtype=float32)time(time)datetime64[ns]2019-04-27T12:00:00 ... 2019-10-...standard_name :timeaxis :Tlong_name :reference time of analyzed variable field corresponding to center of the product time intervalcoverage_content_type :coordinatearray(['2019-04-27T12:00:00.000000000', '2019-04-28T12:00:00.000000000',\n '2019-04-29T12:00:00.000000000', '2019-04-30T12:00:00.000000000',\n '2019-05-01T12:00:00.000000000', '2019-05-02T12:00:00.000000000',\n '2019-05-03T12:00:00.000000000', '2019-05-04T12:00:00.000000000',\n '2019-05-05T12:00:00.000000000', '2019-05-06T12:00:00.000000000',\n '2019-05-07T12:00:00.000000000', '2019-05-08T12:00:00.000000000',\n '2019-05-09T12:00:00.000000000', '2019-05-10T12:00:00.000000000',\n '2019-05-11T12:00:00.000000000', '2019-05-12T12:00:00.000000000',\n '2019-05-13T12:00:00.000000000', '2019-05-14T12:00:00.000000000',\n '2019-05-15T12:00:00.000000000', '2019-05-16T12:00:00.000000000',\n '2019-05-17T12:00:00.000000000', '2019-05-18T12:00:00.000000000',\n '2019-05-19T12:00:00.000000000', '2019-05-20T12:00:00.000000000',\n '2019-05-21T12:00:00.000000000', '2019-05-22T12:00:00.000000000',\n '2019-05-23T12:00:00.000000000', '2019-05-24T12:00:00.000000000',\n '2019-05-25T12:00:00.000000000', '2019-05-26T12:00:00.000000000',\n '2019-05-27T12:00:00.000000000', '2019-05-28T12:00:00.000000000',\n '2019-05-29T12:00:00.000000000', '2019-05-30T12:00:00.000000000',\n '2019-05-31T12:00:00.000000000', '2019-06-01T12:00:00.000000000',\n '2019-06-02T12:00:00.000000000', '2019-06-03T12:00:00.000000000',\n '2019-06-04T12:00:00.000000000', '2019-06-05T12:00:00.000000000',\n '2019-06-06T12:00:00.000000000', '2019-06-07T12:00:00.000000000',\n '2019-06-08T12:00:00.000000000', '2019-06-09T12:00:00.000000000',\n '2019-06-10T12:00:00.000000000', '2019-06-11T12:00:00.000000000',\n '2019-06-12T12:00:00.000000000', '2019-06-13T12:00:00.000000000',\n '2019-06-14T12:00:00.000000000', '2019-06-15T12:00:00.000000000',\n '2019-06-16T12:00:00.000000000', '2019-07-26T12:00:00.000000000',\n '2019-07-27T12:00:00.000000000', '2019-07-28T12:00:00.000000000',\n '2019-07-29T12:00:00.000000000', '2019-07-30T12:00:00.000000000',\n '2019-07-31T12:00:00.000000000', '2019-08-01T12:00:00.000000000',\n '2019-08-02T12:00:00.000000000', '2019-08-03T12:00:00.000000000',\n '2019-08-04T12:00:00.000000000', '2019-08-05T12:00:00.000000000',\n '2019-08-06T12:00:00.000000000', '2019-08-07T12:00:00.000000000',\n '2019-08-08T12:00:00.000000000', '2019-08-09T12:00:00.000000000',\n '2019-08-10T12:00:00.000000000', '2019-08-11T12:00:00.000000000',\n '2019-08-12T12:00:00.000000000', '2019-08-13T12:00:00.000000000',\n '2019-08-14T12:00:00.000000000', '2019-08-15T12:00:00.000000000',\n '2019-08-16T12:00:00.000000000', '2019-08-17T12:00:00.000000000',\n '2019-08-18T12:00:00.000000000', '2019-08-19T12:00:00.000000000',\n '2019-08-20T12:00:00.000000000', '2019-08-21T12:00:00.000000000',\n '2019-08-22T12:00:00.000000000', '2019-08-23T12:00:00.000000000',\n '2019-08-24T12:00:00.000000000', '2019-08-25T12:00:00.000000000',\n '2019-08-26T12:00:00.000000000', '2019-08-27T12:00:00.000000000',\n '2019-08-28T12:00:00.000000000', '2019-08-29T12:00:00.000000000',\n '2019-08-30T12:00:00.000000000', '2019-08-31T12:00:00.000000000',\n '2019-09-01T12:00:00.000000000', '2019-09-02T12:00:00.000000000',\n '2019-09-03T12:00:00.000000000', '2019-09-04T12:00:00.000000000',\n '2019-09-05T12:00:00.000000000', '2019-09-06T12:00:00.000000000',\n '2019-09-07T12:00:00.000000000', '2019-09-08T12:00:00.000000000',\n '2019-09-09T12:00:00.000000000', '2019-09-10T12:00:00.000000000',\n '2019-09-11T12:00:00.000000000', '2019-09-12T12:00:00.000000000',\n '2019-09-13T12:00:00.000000000', '2019-09-14T12:00:00.000000000',\n '2019-09-15T12:00:00.000000000', '2019-09-16T12:00:00.000000000',\n '2019-09-17T12:00:00.000000000', '2019-09-18T12:00:00.000000000',\n '2019-09-19T12:00:00.000000000', '2019-09-20T12:00:00.000000000',\n '2019-09-21T12:00:00.000000000', '2019-09-22T12:00:00.000000000',\n '2019-09-23T12:00:00.000000000', '2019-09-24T12:00:00.000000000',\n '2019-09-25T12:00:00.000000000', '2019-09-26T12:00:00.000000000',\n '2019-09-27T12:00:00.000000000', '2019-09-28T12:00:00.000000000',\n '2019-09-29T12:00:00.000000000', '2019-09-30T12:00:00.000000000',\n '2019-10-01T12:00:00.000000000', '2019-10-02T12:00:00.000000000',\n '2019-10-03T12:00:00.000000000', '2019-10-04T12:00:00.000000000'],\n dtype='datetime64[ns]')latitude(latitude)float3260.12 60.38 60.62 ... 74.62 74.88standard_name :latitudeaxis :Ylong_name :center latitude of grid cellunits :degrees_northvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875], dtype=float32)longitude(longitude)float32-169.9 -169.6 ... -160.4 -160.1array([-169.875, -169.625, -169.375, -169.125, -168.875, -168.625, -168.375,\n -168.125, -167.875, -167.625, -167.375, -167.125, -166.875, -166.625,\n -166.375, -166.125, -165.875, -165.625, -165.375, -165.125, -164.875,\n -164.625, -164.375, -164.125, -163.875, -163.625, -163.375, -163.125,\n -162.875, -162.625, -162.375, -162.125, -161.875, -161.625, -161.375,\n -161.125, -160.875, -160.625, -160.375, -160.125], dtype=float32)Data variables: (19)nobs(time, latitude, longitude)float64dask.array<chunksize=(1, 60, 40), meta=np.ndarray>long_name :Number of observations for L3 average of SSS smoothed to approx 70km resolutionstandard_name :number_of_observationsunits :1valid_min :1valid_max :480coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.23 MiB\n18.75 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\nnobs_RF\n\n\n(time, latitude, longitude)\n\n\nfloat64\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nNumber of observations for L3 average of rain filtered SSS smoothed to approx 70km resolution\n\nstandard_name :\n\nnumber_of_observations\n\nunits :\n\n1\n\nvalid_min :\n\n1\n\nvalid_max :\n\n480\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.23 MiB\n18.75 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nnobs_40km\n\n\n(time, latitude, longitude)\n\n\nfloat64\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nNumber of observations for L3 average of SSS at 40km resolution\n\nstandard_name :\n\nnumber_of_observations\n\nunits :\n\n1\n\nvalid_min :\n\n1\n\nvalid_max :\n\n480\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n2.23 MiB\n18.75 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat64 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_RF\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nRain filtered SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_unc\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntotal formal uncertainty estimate of SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_RF_unc\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntotal formal uncertainty estimate of rain filtered SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_unc_comp\n\n\n(time, uncertainty_components, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 9, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nformal uncertainty components of SMAP sea surface salinity smoothed to approx 70km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\ncomponents :\n\n1: ancillary wind speed random. 2: NEDT v-pol. 3: NEDT h-pol. 4: ancillary SST. 5: ancillary wind direction. 6: reflected galaxy. 7: land contamination. 8: sea ice contamination. 9: ancillary wind speed systematic.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n10.05 MiB\n84.38 kiB\n\n\nShape\n(122, 9, 60, 40)\n(1, 9, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_40km\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nSMAP sea surface salinity at original 40km resolution\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nphysicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_40km_unc\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntotal formal uncertainty estimate of SMAP sea surface salinity at original 40km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_smap_40km_unc_comp\n\n\n(time, uncertainty_components, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 9, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nformal uncertainty components of SMAP sea surface salinity at original 40km resolution\n\nstandard_name :\n\nsea_surface_salinity standard_error\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nqualityInformation\n\ncomponents :\n\n1: ancillary wind speed random. 2: NEDT v-pol. 3: NEDT h-pol. 4: ancillary SST. 5: ancillary wind direction. 6: reflected galaxy. 7: land contamination. 8: sea ice contamination. 9: ancillary wind speed systematic.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n10.05 MiB\n84.38 kiB\n\n\nShape\n(122, 9, 60, 40)\n(1, 9, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_ref\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nReference sea surface salinity from HYCOM\n\nstandard_name :\n\nsea_surface_salinity\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\ncoverage_content_type :\n\nreferenceInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ngland\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\naverage land fraction weighted by antenna gain\n\nstandard_name :\n\nland_area_fraction\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nfland\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\naverage land fraction within 3dB contour\n\nstandard_name :\n\nland_area_fraction\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\ngice_est\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated sea ice fraction weighted by antenna gain\n\nstandard_name :\n\nsea_ice_area_fraction\n\nunits :\n\n1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n1.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsurtep\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nstandard_name :\n\nsea_surface_temperature\n\nlong_name :\n\nAncillary sea surface temperature (from CMC)\n\nunits :\n\nKelvin\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n313.15\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nwinspd\n\n\n(time, latitude, longitude)\n\n\nfloat32\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nstandard_name :\n\nwind_speed\n\nlong_name :\n\nAncillary sea surface wind speed from CCMP NRT that is used in surface roughness correction\n\nunits :\n\nm s-1\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n100.0\n\ncoverage_content_type :\n\nauxiliaryInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.12 MiB\n9.38 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nfloat32 numpy.ndarray\n\n\n\n\n\n\n\n\n\nsea_ice_zones\n\n\n(time, latitude, longitude)\n\n\nint8\n\n\ndask.array<chunksize=(1, 60, 40), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nsea-ice contamination zones at center day\n\nstandard_name :\n\nquality_flag\n\nunits :\n\n1\n\ncoverage_content_type :\n\nqualityInformation\n\nflag_meaning :\n\n0: open ocean scene. no sea-ice contamination. 1: likely sea-ice contamination in SMAP antenna sidelobes. SSS retrieved. 2: likely sea-ice contamination in SMAP antenna sidelobes. SSS retrieved. 3: likely sea-ice contamination in SMAP antenna mainlobe. SSS retrieved. 4: likely sea-ice contamination in SMAP antenna mainlobe. SSS retrieved. 5: likely sea-ice contamination in SMAP antenna mainlobe. no SSS retrieved. 6: AMSR2 50-km footprint contains land. sea-ice check not reliable. no SSS retrieved if AMSR-2 AS-ECV V8.2 sea-ice flag set. 7: no or invalid AMSR2 observation. sea-ice check not possible. no SSS retrieved if climatological sea-ice flag set.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n285.94 kiB\n2.34 kiB\n\n\nShape\n(122, 60, 40)\n(1, 60, 40)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nint8 numpy.ndarray\n\n\n\n\n\n\n\n\n\nanc_sea_ice_flag\n\n\n(time, latitude, longitude, iceflag_components)\n\n\nint8\n\n\ndask.array<chunksize=(1, 60, 40, 3), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nancillary sea-ice detection indicator at center day\n\nstandard_name :\n\nquality_flag\n\nunits :\n\n1\n\ncoverage_content_type :\n\nqualityInformation\n\nflag_meaning :\n\ncomponent 1 of anc_sea_ice_flag: climatological sea-ice flag. component 2 of anc_sea_ice_flag: sea-ice flag from AMSR2 RSS AS-ECV V8.2 3-day map. component 3 of anc_sea_ice_flag: sea-ice flag from Meissner and Manaster.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n857.81 kiB\n7.03 kiB\n\n\nShape\n(122, 60, 40, 3)\n(1, 60, 40, 3)\n\n\nDask graph\n122 chunks in 368 graph layers\n\n\nData type\nint8 numpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (3)timePandasIndexPandasIndex(DatetimeIndex(['2019-04-27 12:00:00', '2019-04-28 12:00:00',\n '2019-04-29 12:00:00', '2019-04-30 12:00:00',\n '2019-05-01 12:00:00', '2019-05-02 12:00:00',\n '2019-05-03 12:00:00', '2019-05-04 12:00:00',\n '2019-05-05 12:00:00', '2019-05-06 12:00:00',\n ...\n '2019-09-25 12:00:00', '2019-09-26 12:00:00',\n '2019-09-27 12:00:00', '2019-09-28 12:00:00',\n '2019-09-29 12:00:00', '2019-09-30 12:00:00',\n '2019-10-01 12:00:00', '2019-10-02 12:00:00',\n '2019-10-03 12:00:00', '2019-10-04 12:00:00'],\n dtype='datetime64[ns]', name='time', length=122, freq=None))latitudePandasIndexPandasIndex(Index([60.125, 60.375, 60.625, 60.875, 61.125, 61.375, 61.625, 61.875, 62.125,\n 62.375, 62.625, 62.875, 63.125, 63.375, 63.625, 63.875, 64.125, 64.375,\n 64.625, 64.875, 65.125, 65.375, 65.625, 65.875, 66.125, 66.375, 66.625,\n 66.875, 67.125, 67.375, 67.625, 67.875, 68.125, 68.375, 68.625, 68.875,\n 69.125, 69.375, 69.625, 69.875, 70.125, 70.375, 70.625, 70.875, 71.125,\n 71.375, 71.625, 71.875, 72.125, 72.375, 72.625, 72.875, 73.125, 73.375,\n 73.625, 73.875, 74.125, 74.375, 74.625, 74.875],\n dtype='float32', name='latitude'))longitudePandasIndexPandasIndex(Index([-169.875, -169.625, -169.375, -169.125, -168.875, -168.625, -168.375,\n -168.125, -167.875, -167.625, -167.375, -167.125, -166.875, -166.625,\n -166.375, -166.125, -165.875, -165.625, -165.375, -165.125, -164.875,\n -164.625, -164.375, -164.125, -163.875, -163.625, -163.375, -163.125,\n -162.875, -162.625, -162.375, -162.125, -161.875, -161.625, -161.375,\n -161.125, -160.875, -160.625, -160.375, -160.125],\n dtype='float32', name='longitude'))Attributes: (65)Conventions :CF-1.7, ACDD-1.3title :SMAP ocean surface salinityversion :V5.0 Validated Releasesummary :The dataset contains the Level 3 8-day running averages of the NASA/RSS Version 5.0 SMAP Salinity Retrieval Algorithm. It includes all necessary ancillary data and the results of all intermediate steps. The data are gridded on a regular 0.25 deg Earth grid. For details see the Release Notes at https://www.remss.com/missions/smap/salinity/.acknowledgement :Funded under Subcontract No.1664013 between JPL and RSS: Production System for NASA Ocean Salinity Science Team (OSST).processing_level :L3resolution :Spatial resolution: approx 70kmhistory :created by T. Meissnerdate_created :2022-03-29 T12:02:30-0700date_modified :2022-03-29 T12:02:30-0700date_issued :2022-03-29 T12:02:30-0700date_metadata_modified :2022-03-29 T12:02:30-0700institution :Remote Sensing Systems, Santa Rosa, CA, USAsource :RSS SMAP-SSS v5.0 algorithmplatform :SMAPinstrument :SMAP radiometerproject :Production System for NASA Ocean Salinity Science Team (OSST)keywords :SURFACE SALINITY, SALINITY, SMAP, NASA, RSSkeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsstandard_name_vocabulary :CF Standard Name Table v78license :Nonecreator_name :Thomas Meissner, Remote Sensing Systemscreator_email :meissner@remss.comcreator_url :http://www.remss.com/missions/smappublisher_name :Thomas Meissner, Frank Wentz, Andrew Manaster, Richard Lindsley, Marty Brewer, Michael Densberger, Remote Sensing Systemspublisher_email :meissner@remss.compublisher_url :http://www.remss.com/missions/smapid :10.5067/SMP50-3SPCSnaming_authority :gov.nasa.earthdatadataset_citation_authors :T. Meissner, F. Wentz, A. Manaster, R. Lindsley, M. Brewer, M. Densbergerdataset_citation_year :2022dataset_citation_product :Remote Sensing Systems SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8day runningdataset_citation_version :V5.0 Validated Releasedataset_citation_institution :Remote Sensing Systems, Santa Rosa, CA, USAdataset_citation_url :Available online at www.remss.com/missions/smapnetCDF_version_id :4comment :Major changes in V5.0: 1. sea-ice flag: based on AMSR-2 surface emissivties and discriminant analysis. 2. sea-ice correction included. 3. formal uncertainty estimates added.references :1. V5.0 Release Notes at https://www.remss.com/missions/smap/salinity/ 2. Meissner, T.; Wentz, F.J.; Le Vine, D.M. The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases. Remote Sens. 2018, 10, 1121. https://doi.org/10.3390/rs10071121 3. Meissner, T.; Manaster, A. SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures. Remote Sens. 2021, 13, 5120. https://doi.org/10.3390/rs13245120year_of_observation :2019center_day_of_observation :117first_orbit :22566last_orbit :22682time_coverage_start :2019-04-23T12:00:00Ztime_coverage_end :2019-05-01T12:00:00Ztime_coverage_duration :P8Dtime_coverage_resolution :P8Dcdm_data_type :gridgeospatial_bounds :2Dgeospatial_lat_min :-90.0geospatial_lat_max :90.0geospatial_lat_resolution :0.25geospatial_lat_units :degrees_northgeospatial_lon_min :0.0geospatial_lon_max :360.0geospatial_lon_resolution :0.25geospatial_lon_units :degrees_eastgeospatial_bounds_vertical_crs :EPSG:5831geospatial_vertical_min :0geospatial_vertical_max :0Source_of_SMAP_SSS_retrievals :T. Meissner, F. Wentz, A. Manaster, R. Lindsley, M. Brewer, M. Densberger, Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 5.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA doi: 10.5067/SMP50-2SOCS www.remss.com/missions/smap.Source_of_ancillary_SST :Canada Meteorological Center. 2016.GHRSST Level 4 CMC0.1deg Global Foundation Sea Surface Temperature Analysis (GDS version 2). Ver.3.3.doi: 10.5067/GHCMC-4FM03 http://dx.doi.org/10.5067/GHCMC-4FM03.Source_of_ancillary_CCMP_wind_speed :Mears, C. et al., 2018.Remote Sensing Systems CCMP NRT V2.0 wind speed and direction. Remote Sensing Systems, Santa Rosa, CA.Source_of_ancillary_AMSR2_sea_ice_flag_and_correction :Meissner, T. and A. Manaster, 2021. SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures. Remote Sens. 2021, 13, 5120. https://doi.org/10.3390/rs13245120.Source_of_ancillary_land_mask :1 km land/water mask from OCEAN DISCIPLINE PROCESSING SYSTEM (ODPS) based on World Vector Shoreline (WVS)database and World Data Bank. courtesy of Fred Patt, Goddard Space Flight Center, frederick.s.patt@nasa.gov.Source_of_ancillary_reference_SSS_from_HYCOM :Hybrid Coordinate Ocean Model, GLBa0.08/expt_90.9, Top layer salinity. 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- "text": "Learning Objectives:\nUses python to plot the SMAP sea surface salinity anomalies over the ocean and the IMERG precipitation over the land."
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+ "text": "Saildrone dataset\nAccessing this dataset as an example of using s3fs\n\ns3_cred_endpoint = 'https://archive.podaac.earthdata.nasa.gov/s3credentials'\n\n\ndef get_temp_creds():\n temp_creds_url = s3_cred_endpoint\n return requests.get(temp_creds_url).json()\n\n\ntemp_creds_req = get_temp_creds()\n#temp_creds_req # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.\n\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})\n\n\nbucket = os.path.join('podaac-ops-cumulus-protected/','SAILDRONE_ARCTIC','saildrone-*-1_minutes-*.nc')\nsd_files = fs_s3.glob(bucket)\nsaildrone_files= [fs_s3.open(file) for file in sorted(sd_files)]\nlen(saildrone_files)\n\n2\n\n\n\nsd6 = xr.open_dataset(saildrone_files[0])\nsd7 = xr.open_dataset(saildrone_files[1])\nsd7\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (trajectory: 1, obs: 215731)\nCoordinates:\n latitude (trajectory, obs) float64 ...\n longitude (trajectory, obs) float64 ...\n time (trajectory, obs) datetime64[ns] ...\n * trajectory (trajectory) float32 1.037e+03\nDimensions without coordinates: obs\nData variables: (12/79)\n SOG (trajectory, obs) float64 ...\n SOG_FILTERED_MEAN (trajectory, obs) float64 ...\n SOG_FILTERED_STDDEV (trajectory, obs) float64 ...\n SOG_FILTERED_MAX (trajectory, obs) float64 ...\n SOG_FILTERED_MIN (trajectory, obs) float64 ...\n COG (trajectory, obs) float64 ...\n ... ...\n TEMP_O2_RBR_MEAN (trajectory, obs) float64 ...\n TEMP_O2_RBR_STDDEV (trajectory, obs) float64 ...\n CHLOR_WETLABS_MEAN (trajectory, obs) float64 ...\n CHLOR_WETLABS_STDDEV (trajectory, obs) float64 ...\n CHLOR_RBR_MEAN (trajectory, obs) float64 ...\n CHLOR_RBR_STDDEV (trajectory, obs) float64 ...\nAttributes: (12/45)\n title: Arctic NASA MISST 2019 Mission\n summary: Saildrone surface observational data for the N...\n ncei_template_version: NCEI_NetCDF_Trajectory_Template_v2.0\n Conventions: CF-1.6, ACDD-1.3\n netcdf_version: 4.6.3\n featureType: trajectory\n ... ...\n keywords_vocabulary: NASA/GCMD\n publisher_name: Saildrone\n publisher_url: www.saildrone.com\n publisher_email: support@saildrone.com\n acknowledgment: Saildrone. 2019. 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(W182203)serial_number :W182203last_calibrated :2018-05-31installed_height :5.2vendor_name :Gillmodel_name :1590-PK-020model_product_page :http://gillinstruments.com/products/anemometer/windmaster.htmnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :50.0[215731 values with dtype=float64]GUST_WND_STDDEV(trajectory, obs)float64...standard_name :wind_speed_of_gustlong_name :Wind gust speed SDunits :m s-1installed_date :2019-04-10T00:46:53.168598Zdevice_name :Gill Anemometer (W182203)serial_number :W182203last_calibrated :2018-05-31installed_height :5.2vendor_name :Gillmodel_name :1590-PK-020model_product_page :http://gillinstruments.com/products/anemometer/windmaster.htmnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :50.0[215731 values with dtype=float64]WIND_HEIGHT_MEAN(trajectory, obs)float64...standard_name :wind_measurement_height_filteredlong_name :Wind measurement heightunits :minstalled_date :2019-04-10T00:46:53.168598Zdevice_name :Gill Anemometer (W182203)serial_number :W182203last_calibrated :2018-05-31installed_height :5.2vendor_name :Gillmodel_name :1590-PK-020model_product_page :http://gillinstruments.com/products/anemometer/windmaster.htmnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :50.0[215731 values with dtype=float64]WIND_HEIGHT_STDDEV(trajectory, obs)float64...standard_name :wind_measurement_height_rmslong_name :Wind measurement height SDunits :minstalled_date :2019-04-10T00:46:53.168598Zdevice_name :Gill Anemometer (W182203)serial_number :W182203last_calibrated :2018-05-31installed_height :5.2vendor_name :Gillmodel_name :1590-PK-020model_product_page :http://gillinstruments.com/products/anemometer/windmaster.htmnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :50.0[215731 values with dtype=float64]TEMP_AIR_MEAN(trajectory, obs)float64...standard_name :air_temperaturelong_name :Air temperatureunits :degrees_cinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_AIR_STDDEV(trajectory, obs)float64...standard_name :air_temperaturelong_name :Air temperature SDunits :degrees_cinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]RH_MEAN(trajectory, obs)float64...standard_name :relative_humiditylong_name :Relative humidityunits :percentinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]RH_STDDEV(trajectory, obs)float64...standard_name :relative_humiditylong_name :Relative humidity SDunits :percentinstalled_date :2019-04-09T18:21:02.735703Zdevice_name :Rotronic AT/RH (0020208767)serial_number :0020208767last_calibrated :2017-05-10installed_height :2.3vendor_name :Rotronicmodel_name :HC2-S3nominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]BARO_PRES_MEAN(trajectory, obs)float64...standard_name :air_pressurelong_name :Air pressureunits :hPainstalled_date :2019-04-09T22:03:51.977028Zdevice_name :Vaisala Barometer (5240536)serial_number :5240536last_calibrated :2018-01-03installed_height :0.2vendor_name :Vaisalamodel_name :PTB210model_product_page :http://www.vaisala.com/en/products/pressure/Pages/PTB210.aspxnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]BARO_PRES_STDDEV(trajectory, obs)float64...standard_name :air_pressurelong_name :Air pressure SDunits :hPainstalled_date :2019-04-09T22:03:51.977028Zdevice_name :Vaisala Barometer (5240536)serial_number :5240536last_calibrated :2018-01-03installed_height :0.2vendor_name :Vaisalamodel_name :PTB210model_product_page :http://www.vaisala.com/en/products/pressure/Pages/PTB210.aspxnominal_sampling_schedule :60s on, 240s off, centered at :00update_period :1000.0[215731 values with dtype=float64]PAR_AIR_MEAN(trajectory, obs)float64...standard_name :surface_downwelling_photosynthetic_photon_flux_in_airlong_name :Photosynthetically active radiation in airunits :micromol s-1 m-2installed_date :2019-04-09T18:07:44.339577Zdevice_name :LI-COR PAR (9658)serial_number :9658last_calibrated :2018-02-27installed_height :2.6vendor_name :LI-CORmodel_name :LI-192SAmodel_product_page :https://www.licor.com/env/products/light/quantum_underwater.htmlnominal_sampling_schedule :Always onupdate_period :1000.0[215731 values with dtype=float64]PAR_AIR_STDDEV(trajectory, obs)float64...standard_name :surface_downwelling_photosynthetic_photon_flux_in_airlong_name :Photosynthetically active radiation in air SDunits :micromol s-1 m-2installed_date :2019-04-09T18:07:44.339577Zdevice_name :LI-COR PAR (9658)serial_number :9658last_calibrated :2018-02-27installed_height :2.6vendor_name :LI-CORmodel_name :LI-192SAmodel_product_page :https://www.licor.com/env/products/light/quantum_underwater.htmlnominal_sampling_schedule :Always onupdate_period :1000.0[215731 values with dtype=float64]TEMP_IR_SKY_HULL_MEAN(trajectory, obs)float64...standard_name :sky_ir_thermo_temperature_filteredlong_name :Hull Sky IR Temperatureunits :degrees_cinstalled_date :2019-05-14T22:18:38.355856Zdevice_name :Heitronics Sky IR Pyrometer (02413)serial_number :02413installed_height :0.6vendor_name :Heitronicsmodel_name :CT09.10model_product_page :https://www.heitronics.com/en/infrarot-messtechnik/produkte/radiation-thermometers/compact-series/ct09-series/nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SKY_HULL_STDDEV(trajectory, obs)float64...standard_name :sky_ir_thermo_temperature_rmslong_name :Hull Sky IR Temperature SDunits :degrees_cinstalled_date :2019-05-14T22:18:38.355856Zdevice_name :Heitronics Sky IR Pyrometer (02413)serial_number :02413installed_height :0.6vendor_name :Heitronicsmodel_name :CT09.10model_product_page :https://www.heitronics.com/en/infrarot-messtechnik/produkte/radiation-thermometers/compact-series/ct09-series/nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_HULL_UNCOMP_MEAN(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Hull Sea IR Temperatureunits :degrees_cinstalled_date :2019-05-14T22:18:43.869843Zdevice_name :Heitronics Hull IR Pyrometer (12693)serial_number :12693last_calibrated :2018-05-16installed_height :0.6vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_HULL_UNCOMP_STDDEV(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Hull Sea IR Temperature SDunits :degrees_cinstalled_date :2019-05-14T22:18:43.869843Zdevice_name :Heitronics Hull IR Pyrometer (12693)serial_number :12693last_calibrated :2018-05-16installed_height :0.6vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_WING_UNCOMP_MEAN(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Wing Sea IR Temperatureunits :degrees_cinstalled_date :2019-04-09T18:19:51.135128Zdevice_name :Heitronics Wing IR Pyrometer (12605)serial_number :12605last_calibrated :2018-03-12installed_height :2.25vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_IR_SEA_WING_UNCOMP_STDDEV(trajectory, obs)float64...standard_name :sea_surface_skin_temperaturelong_name :Wing Sea IR Temperature SDunits :degrees_cinstalled_date :2019-04-09T18:19:51.135128Zdevice_name :Heitronics Wing IR Pyrometer (12605)serial_number :12605last_calibrated :2018-03-12installed_height :2.25vendor_name :Heitronicsmodel_name :CT15.10nominal_sampling_schedule :30s on, 270s off, centered at :00update_period :1000.0[215731 values with dtype=float64]WAVE_DOMINANT_PERIOD(trajectory, obs)float64...standard_name :sea_surface_wave_period_at_variance_spectral_density_maximumlong_name :Dominant wave periodunits :sinstalled_date :2019-04-09T18:18:23.573574Zdevice_name :VectorNav Hull IMU (100035683)serial_number :100035683installed_height :0.34vendor_name :VectorNavmodel_name :VN-300model_product_page :https://www.vectornav.com/products/vn-300nominal_sampling_schedule :Always onupdate_period :50.0[215731 values with dtype=float64]WAVE_SIGNIFICANT_HEIGHT(trajectory, obs)float64...standard_name :sea_surface_wave_significant_heightlong_name :Significant wave heightunits :minstalled_date :2019-04-09T18:18:23.573574Zdevice_name :VectorNav Hull IMU (100035683)serial_number :100035683installed_height :0.34vendor_name :VectorNavmodel_name :VN-300model_product_page :https://www.vectornav.com/products/vn-300nominal_sampling_schedule :Always onupdate_period :50.0[215731 values with dtype=float64]TEMP_SBE37_MEAN(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperatureunits :degrees_cinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_SBE37_STDDEV(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperature SDunits :degrees_cinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]SAL_SBE37_MEAN(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinityunits :1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]SAL_SBE37_STDDEV(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinity SDunits :1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]COND_SBE37_MEAN(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivityunits :mS cm-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]COND_SBE37_STDDEV(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivity SDunits :mS cm-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]TEMP_CTD_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperatureunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]TEMP_CTD_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperature SDunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]SAL_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinityunits :1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]SAL_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_practical_salinitylong_name :Seawater salinity SDunits :1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]COND_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivityunits :mS cm-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]COND_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_electrical_conductivitylong_name :Seawater conductivity SDunits :mS cm-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_CONC_SBE37_MEAN(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentrationunits :micromol L-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_CONC_SBE37_STDDEV(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentration SDunits :micromol L-1installed_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_SAT_SBE37_MEAN(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturationunits :percentinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_SAT_SBE37_STDDEV(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturation SDunits :percentinstalled_date :2019-04-19T17:14:55.229566Zdevice_name :Sea-Bird Conductivity/Temp/ODO (20729)serial_number :20729last_calibrated :2019-04-13installed_height :-0.5vendor_name :Sea-Birdmodel_name :SBE37-SMP-ODO Microcatnominal_sampling_schedule :12s on, 588s off, centered at :00update_period :1000.0[215731 values with dtype=float64]O2_CONC_RBR_MEAN(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentrationunits :micromol L-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_CONC_RBR_STDDEV(trajectory, obs)float64...standard_name :mole_concentration_of_dissolved_molecular_oxygen_in_sea_waterlong_name :Oxygen concentration SDunits :micromol L-1installed_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_SAT_RBR_MEAN(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturationunits :percentinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]O2_SAT_RBR_STDDEV(trajectory, obs)float64...standard_name :fractional_saturation_of_oxygen_in_sea_waterlong_name :Oxygen saturation SDunits :percentinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]TEMP_O2_RBR_MEAN(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperatureunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]TEMP_O2_RBR_STDDEV(trajectory, obs)float64...standard_name :sea_water_temperaturelong_name :Seawater temperature SDunits :degrees_cinstalled_date :2019-04-09T18:00:36.867996Zdevice_name :RBR CTD/ODO/Chl-A (040821)serial_number :040821last_calibrated :2018-05-16installed_height :-0.53vendor_name :RBRmodel_name :Saildrone^3nominal_sampling_schedule :12s on, 48s off, centered at :00update_period :500.0[215731 values with dtype=float64]CHLOR_WETLABS_MEAN(trajectory, obs)float64...standard_name :mass_concentration_of_chlorophyll_in_sea_waterlong_name :Chlorophyll concentrationunits :microgram L-1installed_date :2019-04-19T17:16:02.129225Zdevice_name :WET Labs Fluorometer (5599)serial_number :5599last_calibrated :2019-04-02installed_height :-0.5vendor_name :WET Labsmodel_name :FLSmodel_product_page :http://www.seabird.com/eco-flnominal_sampling_schedule :12s on, 48s off, centered at :00update_period :1000.0[215731 values with dtype=float64]CHLOR_WETLABS_STDDEV(trajectory, obs)float64...standard_name :mass_concentration_of_chlorophyll_in_sea_waterlong_name :Chlorophyll concentration SDunits :microgram L-1installed_date :2019-04-19T17:16:02.129225Zdevice_name :WET Labs Fluorometer (5599)serial_number :5599last_calibrated :2019-04-02installed_height :-0.5vendor_name :WET Labsmodel_name :FLSmodel_product_page :http://www.seabird.com/eco-flnominal_sampling_schedule :12s on, 48s off, centered at :00update_period :1000.0[215731 values with dtype=float64]CHLOR_RBR_MEAN(trajectory, obs)float64...standard_name 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All Rights Reserved. These Data and any resultant Product are the property of SAILDRONE. At SAILDRONE’s sole discretion, these Data may be used for research or educational activities only. You may not use, share or sell the Data for any other purpose including for commercial purposes, or alternatively, have any unauthorized third party use or sell the Data, either for any research, educational and/or commercial purpose(s), without the express prior consent of SAILDRONE.nodc_template_version :NODC_NetCDF_Trajectory_Template_v2.0wmo_id :4803915geospatial_lat_min :53.8444032geospatial_lat_max :75.4970304geospatial_lat_units :degrees_northgeospatial_lon_min :-168.7037952geospatial_lon_max :-146.129856geospatial_lon_units :degrees_easthistory :created post-cruise 1/2020product_version :v01.0keywords :Temperature, Salinity, Wind Vectors, Air Temperature, Humidity, Current Velocity, Saildrone, Arctic, Berring Sea, Chukchi Sea, NASA, NOAAkeywords_vocabulary :NASA/GCMDpublisher_name :Saildronepublisher_url :www.saildrone.compublisher_email :support@saildrone.comacknowledgment :Saildrone. 2019. Saildrone Arctic field campaign surface and ADCP measurements. Ver. 01.0. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at https://doi.org/10.5067/SDRON-NOPP0processing_level :Level 2"
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+ "section": "",
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
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- "text": "Calculates the mean\n\n#subsets to the location of the pacific coast and California \nsubset_location = allsubset_files.where((allsubset_files.latitude>20)&(allsubset_files.latitude<50)&(allsubset_files.longitude>-140)&(allsubset_files.longitude<-100), drop=True)\n\n#calculates the mean for the 'smap_sss' variable\nsubset_mean_values = np.nanmean(subset_location['smap_sss'], axis=0)\n\n#gets rid of past mean value if you were to run this code again with different dates\nif 'backup_subset_mean_values' in globals():\n del backup_subset_mean_values\n \n#plots the figure and saves it to your output path\nfig = plt.figure(figsize= (16,10))\nax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\ns = plt.pcolormesh(subset_location.longitude, subset_location.latitude, subset_mean_values, vmin = 33, vmax= 35, cmap = 'rainbow', transform = ccrs.PlateCarree())\ncb = plt.colorbar(s)\ncb.set_label('psu')\nax.set_title(f'SSS mean over 12/26 to 1/16 (2015-2023)', size = 24)\nax.grid()\nax.add_feature(cfeature.OCEAN)\nax.add_feature(cfeature.LAND)\nax.add_feature(cfeature.LAKES)\nax.add_feature(cfeature.RIVERS)\nax.add_feature(cfeature.STATES)\nax.coastlines()\nax.set_xlim(-130, -115)\nax.set_ylim(32, 42.5)\n\n#saves figure to output path \nplt.savefig(outputpath+datetime.now().strftime(\"%Y%m%d-%H%M%S\")+'.png',dpi=400, facecolor='w', transparent=False, bbox_inches='tight')\n\n/var/folders/f0/dgnqgvtx46513by9cdh6fnjw0000gq/T/ipykernel_66947/1610522150.py:5: RuntimeWarning: Mean of empty slice\n subset_mean_values = np.nanmean(subset_location['smap_sss'], axis=0)"
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+ "title": "Amazon Estuary Exploration:",
+ "section": "Cloud Direct Download Version",
+ "text": "Cloud Direct Download Version\nThis tutorial is one of two jupyter notebook versions of the same use case exploring multiple satellite data products over the Amazon Estuary. In this version, we use data that has been downloaded onto our local machine from the cloud.\n\nLearning Objectives\n\nCompare cloud access methods (in tandem with notebook “Amazon Estuary Exploration: In Cloud AWS Version”)\nSearch for data products using earthaccess Python library\nAccess datasets using xarray and visualize using hvplot or plot tools\n\nThis tutorial explores the relationships between river height, land water equivalent thickness, sea surface salinity, and sea surface temperature in the Amazon River estuary and coastal region from multiple datasets listed below. The contents are useful for the ocean, coastal, and terrestrial hydrosphere communities, showcasing how to use cloud datasets and services. This notebook is meant to be executed locally."
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- "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
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- "text": "plot the anomalies for subset bounds\n\n#Plots precip data only over land, using a colorscale\nmode_choice = 2 \n\n#currently, the line below is commented but you can uncomment this line and it will display raw SSS values (doesnt calculate anomalies) \n#calc_anomaly = 0 \n\n#calculate anomalies relative to the mean\ncalc_anomaly = 1 \n\n#Replace with your dictory for IMERG precip data\ndirectory = os.path.join(base_directory, 'Desktop/imerg_precip')\nif 'backup_subset_mean_values' not in globals():\n backup_subset_mean_values = subset_mean_values\n\n#raw sss values\nif calc_anomaly == 0:\n subset_mean_values = 0\n smin = 31\n smax = 34.5 \n smap = 'rainbow'\n\n#anomaly values\nif calc_anomaly != 0:\n subset_mean_values = backup_subset_mean_values \n smin = -1\n smax = +1 \n smap = 'seismic'\n\n#Replace with your directory \nsss_subset = sorted(glob.glob(os.path.join(base_directory, 'Desktop/jpl_smap_l3/SMAP*.nc*')))\n\n#grabs the sss file date for the subset location and plots anomalies\nfor filename in sss_subset:\n file_date = filename.split('_')[-3].replace('.nc', '')\n updated_file_date = file_date[0:4]+'-'+file_date[4:6]+'-'+file_date[6:8]\n if int(file_date) < int(subset_bounds[0]) or int(file_date) > int(subset_bounds[1]):\n continue\n print(file_date)\n sss_ds = xr.open_dataset(filename)\n try:\n sss_ds = sss_ds.where((sss_ds.latitude>20)&(sss_ds.latitude<50)&(sss_ds.longitude>-140)&(sss_ds.longitude<-100), drop=True)- subset_mean_values\n if sss_ds.smap_sss.size == 0:\n continue\n except Exception as e:\n print(e)\n continue\n plt.rcParams.update({\"font.size\": 24})\n fig = plt.figure(figsize= (16,10))\n ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n\n #grabs the precip file date for the subset location and plots \n substring = file_date\n precip_counter = 0\n for file_path in glob.glob(os.path.join(directory, f'*{substring}*')):\n precip_counter += 1\n if precip_counter >1:\n crashnow\n print(file_path)\n precip = xr.open_dataset(file_path)\n precip = precip.where((precip.lat>20)&(precip.lat<50)&(precip.lon>-140)&(precip.lon<-100),drop=True)\n val = 24*precip.HQprecipitation[0,:,:]/(0.5*precip.HQprecipitation_cnt[0,:,:])\n if mode_choice == 2: \n p = plt.pcolormesh(precip.lon,precip.lat,val.transpose(), cmap = 'viridis', vmax = 20, vmin = 0, transform = ccrs.PlateCarree())\n cb = plt.colorbar(p, fraction=0.046, pad=0.04)\n cb.set_label('mm/day')\n\n #the line below plots the raw sss values if its uncommented\n s = plt.pcolormesh(sss_ds.longitude, sss_ds.latitude, sss_ds.smap_sss, vmin = smin, vmax = smax, cmap = smap, transform = ccrs.PlateCarree())\n \n #adds the colorbar and features to the plot\n cb = plt.colorbar(s, location = 'left', fraction=0.046, pad=0.04)\n if calc_anomaly == 1:\n cb.set_label(f'psu anomalies wrt mean of 12/26 to 1/16 (2015-23)')\n ax.set_title(f'SSS Anomaly over ocean, Precipitation on land\\n{updated_file_date}', size = 24)\n if calc_anomaly != 1:\n cb.set_label('psu')\n ax.set_title(f'SSS over ocean, Precipitation on land\\n{updated_file_date}', size = 24)\n ax.grid()\n ax.add_feature(cfeature.OCEAN)\n ax.add_feature(cfeature.LAND)\n ax.add_feature(cfeature.LAKES)\n ax.add_feature(cfeature.RIVERS)\n ax.add_feature(cfeature.STATES)\n ax.coastlines()\n ax.set_xlim(-130, -115)\n ax.set_ylim(32, 42.5)\n\n #saves figure to output path \n plt.savefig(outputpath+datetime.now().strftime(\"%Y%m%d-%H%M%S\")+'.png',dpi=400, facecolor='w', transparent=False, bbox_inches='tight')\n\n/var/folders/f0/dgnqgvtx46513by9cdh6fnjw0000gq/T/ipykernel_66947/576375500.py:48: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n fig = plt.figure(figsize= (16,10))"
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+ "href": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#cloud-datasets",
+ "title": "Amazon Estuary Exploration:",
+ "section": "Cloud Datasets",
+ "text": "Cloud Datasets\nThe tutorial itself will use four different datasets:\n1. TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3\n\nDOI: https://doi.org/10.5067/TEMSC-3JC63\n\nThe Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) satellite land water equivalent (LWE) thicknesses will be used to observe seasonal changes in water storage around the river. When discharge is high, the change in water storage will increase, thus highlighting a wet season. \n2. PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\n\nDOI: https://doi.org/10.5067/PSGRA-DA2V2\n\nThe NASA Pre-SWOT Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program virtual gauges will be used as a proxy for Surface Water and Ocean Topography (SWOT) discharge until SWOT products are available. MEaSUREs contains river height products, not discharge, but river height is directly related to discharge and thus will act as a good substitute.\n3. OISSS_L4_multimission_7day_v1\n\nDOI: https://doi.org/10.5067/SMP10-4U7CS\n\nOptimally Interpolated Sea surface salinity (OISSS) is a level 4 product that combines the records from Aquarius (Sept 2011-June 2015), the Soil Moisture Active Passive (SMAP) satellite (April 2015-present), and ESAs Soil Moisture Ocean Salinity (SMOS) data to fill in data gaps.\n4. MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0\n\nDOI: https://doi.org/10.5067/MODAM-MO9N9\n\nSea surface temperature is obtained from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the Aqua satellite. \nMore details on available collections are on the PO.DAAC Cloud Earthdata Search Portal. For more information on the PO.DAAC transition to the cloud, please visit: https://podaac.jpl.nasa.gov/cloud-datasets/about\n\nNote: NASA Earthdata Login Required\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below."
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- "objectID": "notebooks/datasets/enso_MUR_tutorial_final.html",
- "href": "notebooks/datasets/enso_MUR_tutorial_final.html",
- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "",
- "text": "Author: Julie Sanchez, NASA JPL PO.DAAC"
+ "objectID": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#needed-packages",
+ "href": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#needed-packages",
+ "title": "Amazon Estuary Exploration:",
+ "section": "Needed Packages",
+ "text": "Needed Packages\n\nimport glob\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport hvplot.xarray\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy\nfrom datetime import datetime\nimport os\nfrom os.path import isfile, basename, abspath\nimport dask\ndask.config.set({\"array.slicing.split_large_chunks\": False})\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\n\n\n\n\n\n\n\n\n\n\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)"
},
{
- "objectID": "notebooks/datasets/enso_MUR_tutorial_final.html#summary",
- "href": "notebooks/datasets/enso_MUR_tutorial_final.html#summary",
- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "Summary",
- "text": "Summary\n\nEl Niño-Southern Oscillation (ENSO) is a climate pattern in the Pacific Ocean that has two phases: El Niño (warm/wet phase) and La Niña (cold/dry phase). ENSO has global impacts on wildfires, weather, and ecosystems. We have been experiencing La Niña conditions for the last 2 and a half years. The last El Niño event occurred in 2015/2016 and a weak El Niño event was also experienced during the winter of 2018/2019.\nThis tutorial uses the SST anomaly variable derived from a MUR climatology dataset - MUR25-JPL-L4-GLOB-v04.2 (average between 2003 and 2014). This tutorial uses the PO.DAAC Downloader which downloads data to your local computer and uses the data to run a notebook using python. The following code produces the sea surface temperature anomalies (SSTA) over the Pacific Ocean."
+ "objectID": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#liquid-water-equivalent-lwe-thickness-grace-grace-fo",
+ "href": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#liquid-water-equivalent-lwe-thickness-grace-grace-fo",
+ "title": "Amazon Estuary Exploration:",
+ "section": "Liquid Water Equivalent (LWE) Thickness (GRACE & GRACE-FO)",
+ "text": "Liquid Water Equivalent (LWE) Thickness (GRACE & GRACE-FO)\n\nSearch for GRACE LWE Thickness data\nSuppose we are interested in LWE data from the dataset (DOI:10.5067/TEMSC-3JC62) described on this PO.DAAC dataset landing page: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3\nFrom the landing page, we see the dataset Short Name under the Information tab. (For this dataset it is “TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3”) We will be using this to search for the necessary granules.\n\n#earthaccess search\ngrace_results = earthaccess.search_data(short_name=\"TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3\")\n\nGranules found: 1\n\n\n\n#download data into folder on local machine\nearthaccess.download(grace_results, \"./grace_data\")\n\n Getting 1 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n['GRCTellus.JPL.200204_202303.GLO.RL06.1M.MSCNv03CRI.nc']\n\n\n\n\nOpen file using xarray.\n\n#open dataset for visualization\nds_GRACE = xr.open_mfdataset(\"./grace_data/GRCTellus.JPL.200204_202303.GLO.RL06.1M.MSCNv03CRI.nc\", engine=\"h5netcdf\")\nds_GRACE\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lon: 720, lat: 360, time: 219, bounds: 2)\nCoordinates:\n * lon (lon) float64 0.25 0.75 1.25 1.75 ... 358.2 358.8 359.2 359.8\n * lat (lat) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * time (time) datetime64[ns] 2002-04-17T12:00:00 ... 2023-03-16T1...\nDimensions without coordinates: bounds\nData variables:\n lwe_thickness (time, lat, lon) float64 dask.array<chunksize=(219, 360, 720), meta=np.ndarray>\n uncertainty (time, lat, lon) float64 dask.array<chunksize=(219, 360, 720), meta=np.ndarray>\n lat_bounds (lat, bounds) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>\n lon_bounds (lon, bounds) float64 dask.array<chunksize=(720, 2), meta=np.ndarray>\n time_bounds (time, bounds) datetime64[ns] dask.array<chunksize=(219, 2), meta=np.ndarray>\n land_mask (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>\n scale_factor (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>\n mascon_ID (lat, lon) float64 dask.array<chunksize=(360, 720), meta=np.ndarray>\nAttributes: (12/53)\n Conventions: CF-1.6, ACDD-1.3, ISO 8601\n Metadata_Conventions: Unidata Dataset Discovery v1.0\n standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata ...\n title: JPL GRACE and GRACE-FO MASCON RL06.1Mv03 CRI\n summary: Monthly gravity solutions from GRACE and G...\n keywords: Solid Earth, Geodetics/Gravity, Gravity, l...\n ... ...\n C_30_substitution: TN-14; Loomis et al., 2019, Geophys. Res. ...\n user_note_1: The accelerometer on the GRACE-B spacecraf...\n user_note_2: The accelerometer on the GRACE-D spacecraf...\n journal_reference: Watkins, M. M., D. N. Wiese, D.-N. Yuan, C...\n CRI_filter_journal_reference: Wiese, D. N., F. W. Landerer, and M. M. Wa...\n date_created: 2023-05-22T06:05:03Zxarray.DatasetDimensions:lon: 720lat: 360time: 219bounds: 2Coordinates: (3)lon(lon)float640.25 0.75 1.25 ... 359.2 359.8units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :Xvalid_min :0.25valid_max :359.75bounds :lon_boundsarray([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,\n 3.5975e+02])lat(lat)float64-89.75 -89.25 ... 89.25 89.75units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Yvalid_min :-89.75valid_max :89.75bounds :lat_boundsarray([-89.75, -89.25, -88.75, ..., 88.75, 89.25, 89.75])time(time)datetime64[ns]2002-04-17T12:00:00 ... 2023-03-...long_name :timestandard_name :timeaxis :Tbounds :time_boundsarray(['2002-04-17T12:00:00.000000000', '2002-05-10T12:00:00.000000000',\n '2002-08-16T12:00:00.000000000', ..., '2023-01-16T12:00:00.000000000',\n '2023-02-15T00:00:00.000000000', '2023-03-16T12:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (8)lwe_thickness(time, lat, lon)float64dask.array<chunksize=(219, 360, 720), meta=np.ndarray>units :cmlong_name :Liquid_Water_Equivalent_Thicknessstandard_name :Liquid_Water_Equivalent_Thicknessgrid_mapping :WGS84valid_min :-1986.9763606523888valid_max :965.4782725418918comment :Coastline Resolution Improvement (CRI) filter is applied\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n433.08 MiB\n433.08 MiB\n\n\nShape\n(219, 360, 720)\n(219, 360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\nuncertainty\n\n\n(time, lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(219, 360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\ncm\n\nlong_name :\n\nuncertainty\n\nstandard_name :\n\nuncertainty\n\ngrid_mapping :\n\nWGS84\n\nvalid_min :\n\n0.15854006805783352\n\nvalid_max :\n\n53.34469598560085\n\ncomment :\n\n1-sigma uncertainty: not for each 0.5 degree grid cell, but for each 3-degree mascon estimate\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n433.08 MiB\n433.08 MiB\n\n\nShape\n(219, 360, 720)\n(219, 360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nlat_bounds\n\n\n(lat, bounds)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 2), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlatitude boundaries\n\nunits :\n\ndegrees_north\n\ncomment :\n\nlatitude values at the north and south bounds of each pixel\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n5.62 kiB\n5.62 kiB\n\n\nShape\n(360, 2)\n(360, 2)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nlon_bounds\n\n\n(lon, bounds)\n\n\nfloat64\n\n\ndask.array<chunksize=(720, 2), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nlongitude boundaries\n\nunits :\n\ndegrees_east\n\ncomment :\n\nlongitude values at the west and east bounds of each pixel\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n11.25 kiB\n11.25 kiB\n\n\nShape\n(720, 2)\n(720, 2)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\ntime_bounds\n\n\n(time, bounds)\n\n\ndatetime64[ns]\n\n\ndask.array<chunksize=(219, 2), meta=np.ndarray>\n\n\n\n\nlong_name :\n\ntime boundaries\n\ncomment :\n\ntime bounds for each time value, i.e. the first day and last day included in the monthly solution\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.42 kiB\n3.42 kiB\n\n\nShape\n(219, 2)\n(219, 2)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\ndatetime64[ns]\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nland_mask\n\n\n(lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\nbinary\n\nlong_name :\n\nLand_Mask\n\nstandard_name :\n\nLand_Mask\n\ndescription :\n\nLand Mask that was used with the CRI filter\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.98 MiB\n1.98 MiB\n\n\nShape\n(360, 720)\n(360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nscale_factor\n\n\n(lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\ndimensionless\n\nlong_name :\n\nScale_Factor_CRI\n\nstandard_name :\n\nScale_Factor_CRI\n\nvalid_min :\n\n-99999.0\n\nvalid_max :\n\n24.133988467789724\n\ndescription :\n\nGridded scale factors to be used with mascon solution that has the CRI filter applied; based on CLM data from 2002-2009\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.98 MiB\n1.98 MiB\n\n\nShape\n(360, 720)\n(360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nmascon_ID\n\n\n(lat, lon)\n\n\nfloat64\n\n\ndask.array<chunksize=(360, 720), meta=np.ndarray>\n\n\n\n\nunits :\n\ndimensionless\n\nlong_name :\n\nMascon_Identifier\n\nstandard_name :\n\nMascon_ID\n\nvalid_min :\n\n1\n\nvalid_max :\n\n4551\n\ndescription :\n\nMascon identifier mapped to the grid\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n1.98 MiB\n1.98 MiB\n\n\nShape\n(360, 720)\n(360, 720)\n\n\nCount\n2 Tasks\n1 Chunks\n\n\nType\nfloat64\nnumpy.ndarray\n\n\n\n\n\n\n\n\nIndexes: (3)lonPandasIndexPandasIndex(Float64Index([ 0.25, 0.75, 1.25, 1.75, 2.25, 2.75, 3.25, 3.75,\n 4.25, 4.75,\n ...\n 355.25, 355.75, 356.25, 356.75, 357.25, 357.75, 358.25, 358.75,\n 359.25, 359.75],\n dtype='float64', name='lon', length=720))latPandasIndexPandasIndex(Float64Index([-89.75, -89.25, -88.75, -88.25, -87.75, -87.25, -86.75, -86.25,\n -85.75, -85.25,\n ...\n 85.25, 85.75, 86.25, 86.75, 87.25, 87.75, 88.25, 88.75,\n 89.25, 89.75],\n dtype='float64', name='lat', length=360))timePandasIndexPandasIndex(DatetimeIndex(['2002-04-17 12:00:00', '2002-05-10 12:00:00',\n '2002-08-16 12:00:00', '2002-09-16 00:00:00',\n '2002-10-16 12:00:00', '2002-11-16 00:00:00',\n '2002-12-16 12:00:00', '2003-01-16 12:00:00',\n '2003-02-15 00:00:00', '2003-03-16 12:00:00',\n ...\n '2022-06-16 00:00:00', '2022-07-16 12:00:00',\n '2022-08-16 12:00:00', '2022-09-16 00:00:00',\n '2022-10-16 12:00:00', '2022-11-16 00:00:00',\n '2022-12-16 12:00:00', '2023-01-16 12:00:00',\n '2023-02-15 00:00:00', '2023-03-16 12:00:00'],\n dtype='datetime64[ns]', name='time', length=219, freq=None))Attributes: (53)Conventions :CF-1.6, ACDD-1.3, ISO 8601Metadata_Conventions :Unidata Dataset Discovery v1.0standard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Convention-1.6title :JPL GRACE and GRACE-FO MASCON RL06.1Mv03 CRIsummary :Monthly gravity solutions from GRACE and GRACE-FO as determined from the JPL RL06.1Mv03 mascon solution - with CRI filter appliedkeywords :Solid Earth, Geodetics/Gravity, Gravity, liquid_water_equivalent_thicknesskeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordsplatform :GRACE and GRACE-FOinstitution :NASA/JPLcreator_name :David Wiesecreator_email :grace@podaac.jpl.nasa.govcreator_url :https://grace.jpl.nasa.govcreator_type :groupcreator_institution :NASA/JPLpublisher_name :Physical Oceanography Distributed Active Archive Centerpublisher_email :podaac@jpl.nasa.govpublisher_url :https://podaac.jpl.nasa.govpublisher_type :grouppublisher_institution :NASA/JPLproject :NASA Gravity Recovery and Climate Experiment (GRACE) and NASA Gravity Recovery and Climate Experiment Follow-On (GRACE-FO)program :NASA Earth Science System Pathfinder and NASA Earth Systematic Missions Programid :10.5067/TEMSC-3JC62naming_authority :org.doi.dxsource :GRACE and GRACE-FO JPL RL06.1Mv03-CRIprocessing_level :2 and 3acknowledgement :GRACE is a joint mission of NASA (USA) and DLR (Germany). GRACE-FO is a joint mission of NASA (USA) and the German Research Center for Geosciences (GFZ). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAClicense :https://science.nasa.gov/earth-science/earth-science-data/data-information-policyproduct_version :v3.0time_epoch :2002-01-01T00:00:00Ztime_coverage_start :2002-04-16T00:00:00Ztime_coverage_end :2023-03-16T23:59:59Zgeospatial_lat_min :-89.75geospatial_lat_max :89.75geospatial_lat_units :degrees_northgeospatial_lat_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconsgeospatial_lon_min :0.25geospatial_lon_max :359.75geospatial_lon_units :degrees_eastgeospatial_lon_resolution :0.5 degree grid; however the native resolution of the data is 3-degree equal-area masconstime_mean_removed :2004.000 to 2009.999months_missing :2002-06;2002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05;2018-08-2018-09postprocess_1 : OCEAN_ATMOSPHERE_DEALIAS_MODEL (GAD), MONTHLY_AVE, ADDED BACK TO OCEAN PIXELS ONLYpostprocess_2 :Water density used to convert to equivalent water height: 1000 kg/m^3postprocess_3 :Coastline Resolution Improvement (CRI) filter has been applied to separate land/ocean mass within mascons that span coastlinesGIA_removed :ICE6G-D; Peltier, W. R., D. F. Argus, and R. Drummond (2018) Comment on the paper by Purcell et al. 2016 entitled An assessment of ICE-6G_C (VM5a) glacial isostatic adjustment model, J. Geophys. Res. Solid Earth, 122.geocenter_correction :We use a version of TN-13 based on the JPL masconsC_20_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929C_30_substitution :TN-14; Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929. This substitution is made for all months after August 2016.user_note_1 :The accelerometer on the GRACE-B spacecraft was turned off after August 2016. After this date, the accelerometer on GRACE-A was used to derive the non-gravitational accelerations acting on GRACE-B using a transplant procedure. This has led to a subsequent degradation in the quality of the gravity fields derived. The uncertainties in this file have been scaled to accomodate this degradation.user_note_2 :The accelerometer on the GRACE-D spacecraft began performing sub-optimally after June 21, 2018. After this date, the accelerometer on GRACE-C is used to derive the non-gravitational accelerations acting on GRACE-D using a transplant procedure. The uncertainties in the file have been scaled to accomodate this degradation using the current best state of knowledge.journal_reference :Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015) Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res., 120, doi:10.1002/2014JB011547. CRI_filter_journal_reference :Wiese, D. N., F. W. Landerer, and M. M. Watkins (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res., 52, doi:10.1002/2016WR019344. date_created :2023-05-22T06:05:03Z\n\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data to plot with hvplot.\n\nlat_bnds, lon_bnds = [-18, 10], [275, 330] #degrees east for longitude\nds_GRACE_subset = ds_GRACE.sel(lat=slice(*lat_bnds), lon=slice(*lon_bnds))\nds_GRACE_subset\n\nds_GRACE_subset.lwe_thickness.hvplot.image(y='lat', x='lon', cmap='bwr_r',).opts(clim=(-80,80))"
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- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "Requirements",
- "text": "Requirements\n\n1. Earthdata Login\n\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n\n2. netrc File\n\nYou will need a .netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A .netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata tutorial.\n\n\n\n3. PO.DAAC Data Downloader\n\nTo download the data via command line, this tutorial uses PO.DAAC’s Data Downloader. The downloader can be installed using these instructions The Downloader is useful if you need to download PO.DAAC data once in a while or prefer to do it “on-demand”. The Downloader makes no assumptions about the last time run or what is new in the archive, it simply uses the provided requests and downloads all matching data."
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+ "title": "Amazon Estuary Exploration:",
+ "section": "River heights (Pre-SWOT MEaSUREs)",
+ "text": "River heights (Pre-SWOT MEaSUREs)\nThe shortname for MEaSUREs is ‘PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2’.\nOur desired variable is height (meters above EGM2008 geoid) for this exercise, which can be subset by distance and time. Distance represents the distance from the river mouth, in this example, the Amazon estuary. Time is between April 8, 1993 and April 20, 2019.\nTo get the data for the exact area we need, we have set the boundaries of (-74.67188,-4.51279,-51.04688,0.19622) as reflected in our earthaccess data search.\n\nMEaSUREs_results = earthaccess.search_data(short_name=\"PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2\", temporal = (\"1993-04-08\", \"2019-04-20\"), bounding_box=(-74.67188,-4.51279,-51.04688,0.19622))\n\nGranules found: 1\n\n\n\nearthaccess.download(MEaSUREs_results, \"./MEaSUREs_data\")\n\n Getting 1 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n['South_America_Amazon1kmdaily.nc']\n\n\n\nds_MEaSUREs = xr.open_dataset(\"./MEaSUREs_data/South_America_Amazon1kmdaily.nc\", engine=\"h5netcdf\")\nds_MEaSUREs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (X: 3311, Y: 3311, distance: 3311, time: 9469,\n charlength: 26)\nCoordinates:\n * time (time) datetime64[ns] 1993-04-08T15:20:40.665117184 ...\nDimensions without coordinates: X, Y, distance, charlength\nData variables:\n lon (X) float64 ...\n lat (Y) float64 ...\n FD (distance) float64 ...\n height (distance, time) float64 ...\n sat (charlength, time) |S1 ...\n storage (distance, time) float64 ...\n IceFlag (time) float64 ...\n LakeFlag (distance) float64 ...\n Storage_uncertainty (distance, time) float64 ...\nAttributes: (12/40)\n title: GRRATS (Global River Radar Altimetry Time ...\n Conventions: CF-1.6, ACDD-1.3\n institution: Ohio State University, School of Earth Sci...\n source: MEaSUREs OSU Storage toolbox 2018\n keywords: EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURF...\n keywords_vocabulary: Global Change Master Directory (GCMD)\n ... ...\n geospatial_lat_max: -0.6550700975069503\n geospatial_lat_units: degree_north\n geospatial_vertical_max: 92.7681246287056\n geospatial_vertical_min: -3.5634095181633763\n geospatial_vertical_units: m\n geospatial_vertical_positive: upxarray.DatasetDimensions:X: 3311Y: 3311distance: 3311time: 9469charlength: 26Coordinates: (1)time(time)datetime64[ns]1993-04-08T15:20:40.665117184 .....long_name :timestandard_name :timeaxis :Tarray(['1993-04-08T15:20:40.665117184', '1993-04-09T15:20:40.665117184',\n '1993-04-10T15:20:40.665117184', ..., '2019-04-18T03:39:13.243964928',\n '2019-04-19T03:39:13.243964928', '2019-04-20T03:39:13.243964928'],\n dtype='datetime64[ns]')Data variables: (9)lon(X)float64...units :degrees_eastlong_name :longitudestandard_name :longitudeaxis :X[3311 values with dtype=float64]lat(Y)float64...units :degrees_northlong_name :latitudestandard_name :latitudeaxis :Y[3311 values with dtype=float64]FD(distance)float64...long_name :distance_from_river_mouthunits :kmcomment :This is the distance along the river centerline from the river mouth to this VS.[3311 values with dtype=float64]height(distance, time)float64...units :mpositive :uplong_name :interpolated_heightsstandard_name :heightvalid_min :-3.5634095181633763valid_max :92.7681246287056comment :A time flow distance grid of river water heights with respect to the EGM08 Geoid).-9999 fill values are for missing data, while -9995 fill values are for lakes and reservoirs.[31351859 values with dtype=float64]sat(charlength, time)|S1...long_name :satellitecomment :The satellite the measurement is derived from.[246194 values with dtype=|S1]storage(distance, time)float64...units :km3positive :uplong_name :river_channel_storagecomment :A time flow distance grid of river channel storage values.[31351859 values with dtype=float64]IceFlag(time)float64...long_name :Ice_Flagvalid_range :0, 1flag_masks :1flag_meaning :Time of ice covercomment : This is a flag for masking out times of Ice Cover.[9469 values with dtype=float64]LakeFlag(distance)float64...long_name :Lake_Flagvalid_range :0, 1flag_masks :1flag_meaning :River sections that are lakes or reservoirscomment : This is a flag for masking out setions that are lakes or reservoirs.[3311 values with dtype=float64]Storage_uncertainty(distance, time)float64...units :km3positive :uplong_name :river_channel_storage_uncertaintycomment :A time flow distance grid of river channel storage uncertainty values.[31351859 values with dtype=float64]Indexes: (1)timePandasIndexPandasIndex(DatetimeIndex(['1993-04-08 15:20:40.665117184',\n '1993-04-09 15:20:40.665117184',\n '1993-04-10 15:20:40.665117184',\n '1993-04-11 15:20:40.665117184',\n '1993-04-12 15:20:40.665117184',\n '1993-04-13 15:20:40.665117184',\n '1993-04-14 15:20:40.665117184',\n '1993-04-15 15:20:40.665117184',\n '1993-04-16 15:20:40.665117184',\n '1993-04-17 15:20:40.665117184',\n ...\n '2019-04-11 03:39:13.243964928',\n '2019-04-12 03:39:13.243964928',\n '2019-04-13 03:39:13.243964928',\n '2019-04-14 03:39:13.243964928',\n '2019-04-15 03:39:13.243964928',\n '2019-04-16 03:39:13.243964928',\n '2019-04-17 03:39:13.243964928',\n '2019-04-18 03:39:13.243964928',\n '2019-04-19 03:39:13.243964928',\n '2019-04-20 03:39:13.243964928'],\n dtype='datetime64[ns]', name='time', length=9469, freq=None))Attributes: (40)title :GRRATS (Global River Radar Altimetry Time Series)1km daily interpolation for the Amazon RiverConventions :CF-1.6, ACDD-1.3institution :Ohio State University, School of Earth Sciencessource :MEaSUREs OSU Storage toolbox 2018keywords :EARTH SCIENCE,TERRESTRIAL HYDROSPHERE,SURFACE WATER,SURFACE WATER PROCESSES/MEASUREMENTS,STAGE HEIGHTkeywords_vocabulary :Global Change Master Directory (GCMD)cdm_data_type :stationcreator_name :Coss,Stevecreator_email :Coss.31@osu.eduproject :MEaSUREs OSUprogram :NASA Earth Science Data Systems (ESDS)publisher_name :PO.DAAC (Physical Oceanography Distributed Active Archive Center)publisher_email :podaac@podaac.jpl.nasa.govpublisher_url :podaac.jpl.nasa.govpublisher_type :Institutionpublisher_institution :PO.DAACprocessing_level :L2doi :10.5067/PSGRA-DA2V2history :This GRRATS product adds data river surface height data from ERS1, ERS2, TOPEX/Poseidon and Jason-3 to expand the temporal coverage of the product. GRRATS1kd includes interpolated daily 1km resolution height measurements as well as river channel storage measurements. platform :ERS-1(L2),ERS-2(L2),TOPEX/POSEIDON(L2), Jason-1(L2),OSTM/Jason-2(L2),Jason-3(L2),Envisat(L2)platform_vocabulary :NASA/GCMD Platform Keywords. Version 8.6instrument :RA(L2),RA-2(L2),ALT(TOPEX)(L2),POSEIDON-2(L2),POSEIDON-3(L2),POSEIDON-3b(L2)instrument_vocabulary :NASA/GCMD Platform Keywords. Version 8.6references :in review :doi.org/10.5194/essd-2019-84id :GRRATS(Global River Radar Altimeter Time Series) 1km/dailysummary :The Global River Radar Altimeter Time Series (GRRATS) 1km/daily interpolations are river heights from ERS1, ERS2, TOPEX/Poseidon OSTM/Jason-2 Envisat and Jason-3 that are interpolated and processed to create a continuous heights for the study over the temporal range of the altimeters used. The purpose of these heights are to provide satellite altimetric river height data in a form that is more recognizable to the observational community and as a way to get users use to using satellite data for river hydrology.time_coverage_resolution :1 daydate_created :2021-06-30T08:03:41time_coverage_start :1992-04-08T15:20:40time_coverage_end :2018-04-20T03:39:13geospatial_lon_min :-73.35433106652545geospatial_lon_max :-51.0426448887506geospatial_lon_units :degree_eastgeospatial_lat_min :-4.3804275867636875geospatial_lat_max :-0.6550700975069503geospatial_lat_units :degree_northgeospatial_vertical_max :92.7681246287056geospatial_vertical_min :-3.5634095181633763geospatial_vertical_units :mgeospatial_vertical_positive :up\n\n\n\nPlot a subset of the data\nPlotting the river distances and associated heights on the map at time t=9069 (March 16, 2018) using plt.\n\nfig = plt.figure(figsize=[11,7]) \nax = plt.axes(projection=ccrs.PlateCarree())\nax.coastlines()\nax.set_extent([-85, -30, -20, 20])\nax.add_feature(cartopy.feature.RIVERS)\n\nplt.scatter(ds_MEaSUREs.lon, ds_MEaSUREs.lat, lw=1, c=ds_MEaSUREs.height[:,9069])\nplt.colorbar(label='Interpolated River Heights (m)')\nplt.clim(-10,100)\n\nplt.show()"
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- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "Learning Objectives",
- "text": "Learning Objectives\n\nIntroduction to the PO.DAAC Data Downloader\nLearn how to plot the SSTA for the ENSO 3.4 Region and a timeseries"
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+ "title": "Amazon Estuary Exploration:",
+ "section": "Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)",
+ "text": "Sea Surface Salinity (Multi-mission: SMAP, Aquarius, SMOS)\nThe shortname for this dataset is ‘OISSS_L4_multimission_7day_v1’. This dataset contains hundreds of granules, by using earthaccess search, we access 998 granules.\nSince this dataset has more than 1 granule that we want to open for visualization, we have to establish the full file path in a different way. For the previous datasets, we could list the exact file, but that would be difficult to do with hundreds of granules. Therefore, the extra step to recurse through the directory to access all files.\n\n#earthaccess search\nsss_results = earthaccess.search_data(short_name=\"OISSS_L4_multimission_7day_v1\")\n\nGranules found: 998\n\n\n\n#earthaccess download\nsss_files = earthaccess.download(sss_results, \"./sss_data\")\n\n Getting 998 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\n\n#ensures that all files are included in the path\nsss_path = [os.path.join(\"./sss_data\", f) \n for pth, dirs, files in os.walk(\"./sss_data\") for f in files]\n\n\nds_sss = xr.open_mfdataset(sss_path,\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks='auto',\n engine='h5netcdf')\nds_sss\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (longitude: 1440, latitude: 720, time: 998)\nCoordinates:\n * longitude (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n * latitude (latitude) float32 -89.88 -89.62 ... 89.62 89.88\n * time (time) datetime64[ns] 2011-08-28 ... 2022-08-02\nData variables:\n sss (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\n sss_empirical_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 962), meta=np.ndarray>\n sss_uncertainty (latitude, longitude, time) float32 dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\nAttributes: (12/42)\n Conventions: CF-1.8, ACDD-1.3\n standard_name_vocabulary: CF Standard Name Table v27\n Title: Multi-Mission Optimally Interpolated Sea S...\n Short_Name: OISSS_L4_multimission_7d_v1\n Version: V1.0\n Processing_Level: Level 4\n ... ...\n geospatial_lat_resolution: 0.25\n geospatial_lat_units: degrees_north\n geospatial_lon_min: -180.0\n geospatial_lon_max: 180.0\n geospatial_lon_resolution: 0.25\n geospatial_lon_units: degrees_eastxarray.DatasetDimensions:longitude: 1440latitude: 720time: 998Coordinates: (3)longitude(longitude)float32-179.9 -179.6 ... 179.6 179.9long_name :longitudestandard_name :longitudeunits :degrees_eastaxis :Xvalid_min :-180.0valid_max :180.0coverage_content_type :coordinatearray([-179.875, -179.625, -179.375, ..., 179.375, 179.625, 179.875],\n dtype=float32)latitude(latitude)float32-89.88 -89.62 ... 89.62 89.88long_name :latitudestandard_name :latitudeunits :degrees_northaxis :Yvalid_min :-90.0valid_max :90.0coverage_content_type :coordinatearray([-89.875, -89.625, -89.375, ..., 89.375, 89.625, 89.875],\n dtype=float32)time(time)datetime64[ns]2011-08-28 ... 2022-08-02long_name :center day of a time period over which satellite Level 2 SSS data have been collected for OISSS analysisstandard_name :timeaxis :Tcoverage_content_type :coordinatearray(['2011-08-28T00:00:00.000000000', '2011-09-01T00:00:00.000000000',\n '2011-09-05T00:00:00.000000000', ..., '2022-07-25T00:00:00.000000000',\n '2022-07-29T00:00:00.000000000', '2022-08-02T00:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (3)sss(latitude, longitude, time)float32dask.array<chunksize=(720, 1440, 1), meta=np.ndarray>long_name :sea surface salinitystandard_name :sea_surface_salinityunits :1e-3valid_min :0.0valid_max :45.0add_factor :0.0coverage_content_type :physicalMeasurement\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n13461 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\nsss_empirical_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array<chunksize=(720, 1440, 962), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n3.72 GiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 962)\n\n\nCount\n338 Tasks\n37 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nsss_uncertainty\n\n\n(latitude, longitude, time)\n\n\nfloat32\n\n\ndask.array<chunksize=(720, 1440, 1), meta=np.ndarray>\n\n\n\n\nlong_name :\n\nestimated empirical uncertainty of multi-mission OISSS\n\nunits :\n\n1e-3\n\nvalid_min :\n\n0.0\n\nvalid_max :\n\n45.0\n\nadd_factor :\n\n0.0\n\ncoordinates :\n\ntime longitude latudude\n\ncoverage_content_type :\n\nqualityInformation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n3.85 GiB\n150.29 MiB\n\n\nShape\n(720, 1440, 998)\n(720, 1440, 38)\n\n\nCount\n8654 Tasks\n961 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nIndexes: (3)longitudePandasIndexPandasIndex(Float64Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625,\n -178.375, -178.125, -177.875, -177.625,\n ...\n 177.625, 177.875, 178.125, 178.375, 178.625, 178.875,\n 179.125, 179.375, 179.625, 179.875],\n dtype='float64', name='longitude', length=1440))latitudePandasIndexPandasIndex(Float64Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375,\n -88.125, -87.875, -87.625,\n ...\n 87.625, 87.875, 88.125, 88.375, 88.625, 88.875, 89.125,\n 89.375, 89.625, 89.875],\n dtype='float64', name='latitude', length=720))timePandasIndexPandasIndex(DatetimeIndex(['2011-08-28', '2011-09-01', '2011-09-05', '2011-09-09',\n '2011-09-13', '2011-09-17', '2011-09-21', '2011-09-25',\n '2011-09-29', '2011-10-03',\n ...\n '2022-06-27', '2022-07-01', '2022-07-05', '2022-07-09',\n '2022-07-13', '2022-07-17', '2022-07-21', '2022-07-25',\n '2022-07-29', '2022-08-02'],\n dtype='datetime64[ns]', name='time', length=998, freq=None))Attributes: (42)Conventions :CF-1.8, ACDD-1.3standard_name_vocabulary :CF Standard Name Table v27Title :Multi-Mission Optimally Interpolated Sea Surface Salinity 7-Day Global Dataset V1.0Short_Name :OISSS_L4_multimission_7d_v1Version :V1.0Processing_Level :Level 4source :Aquarius V5.0 Level 2 SSS; SMAP RSS V4.0 Level 2 SSS_40km; SMOS Level 2 SSS L2OS version 662sourse_of_input_Aquarius_SSS :Aquarius Official Release Level 2 Sea Surface Salinity & Wind Speed Cal Data V5.0. Distributed by PO.DAAC at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L2_SSS_CAL_V5sourse_of_input_SMAP_SSS :Meissner, T., F. Wentz, A. Manaster, R. Lindsley, 2019. Remote Sensing Systems SMAP L2C Sea Surface Salinity, Version 4.0 Validated Release, Remote Sensing Systems, Santa Rosa, CA, USA, Available online at www.remss.com/missions/smap.sourse_of_input_SMOS_SSS :ESA SMOS online dissemination service at https://smos-diss.eo.esa.int/oads/accessplatform :Aquarius/SAC-D, SMAP, SMOSinstrument :Aquarius radiometer, SMAP radiometer, SMOS MIRASCreation_Date :2023-01-16T04:04:41ZCreator_Name :Oleg MelnichenkoCreator_Email :oleg@hawaii.eduCreator_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBProject :NASA Ocean SalinityKeywords :Sea Surface Salinity, SSS, Aquarius, SMAP, Optimum Interpolation, OISSSKeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science KeywordsInstitution :IPRC/SOEST, University of Hawaii, Honolulu, HI; Remote Sensing Systems (RSS), Santa Rosa, CAPublisher_Name :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzPublisher_Email :oleg@hawaii.edu.orgPublisher_URL :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLBDataset_Citation_Authors :Oleg Melnichenko, Peter Hacker, James Potemra, Thomas Meissner, Frank WentzDataset_Citation_Year :2021Dataset_Citation_Product :Aquarius/SMAP Sea Surface Salinity Optimum Interpolation AnalysisTechnical_Notes :http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdfyear_of_observation :2022month_of_observation :3day_of_observation :11time_coverage_start :2022-03-07T12:00:00Ztime_coverage_end :2022-03-15T12:00:00Ztime_coverage_resolution :P7Dcdm_data_type :gridgeospatial_lat_min :-90.0geospatial_lat_max :90.0geospatial_lat_resolution :0.25geospatial_lat_units :degrees_northgeospatial_lon_min :-180.0geospatial_lon_max :180.0geospatial_lon_resolution :0.25geospatial_lon_units :degrees_east\n\n\n\nPlot a subset of the data\nUse the function xarray.DataSet.sel to select a subset of the data at the outlet of the Amazon to plot at time t=0 (August 28, 2011) with hvplot.\n\nlat_bnds, lon_bnds = [-2, 6], [-52, -44] \nds_sss_subset = ds_sss.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))\nds_sss_subset\n\nds_sss_subset.sss[:,:,0].hvplot()"
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- "href": "notebooks/datasets/enso_MUR_tutorial_final.html#download-data-in-the-command-line-using-the-po.daac-data-downloader",
- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "Download Data in the Command Line using the PO.DAAC Data Downloader",
- "text": "Download Data in the Command Line using the PO.DAAC Data Downloader\nIn your terminal, go to the folder you want to download the files to – this will be important to remember. You will need to put your path name in the code below. Copy and paste each line (below) into your terminal. If you have all the prerequisites, the files will download to your folder:\npodaac-data-downloader -h\n\npodaac-data-subscriber -c MUR25-JPL-L4-GLOB-v04.2 -d ./data/MUR25-JPL-L4-GLOB-v04.2 --start-date 2022-12-1T00:00:00Z -ed 2023-04-24T23:59:00Z -d ."
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- "href": "notebooks/datasets/enso_MUR_tutorial_final.html#import-packages",
- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "Import Packages",
- "text": "Import Packages\n\n# Import packages \nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport xarray as xr\nimport matplotlib.ticker as mticker\nimport netCDF4 as nc\nimport numpy as np\nimport datetime as dt\nimport glob\nimport hvplot.xarray\nimport pandas as pd\n\n\n\n\n\n\n\n\n\n\n\nInput your folder directory where you used the Downloader to store the data\n\ndir = '/Users/your_user_name/folder_name/'"
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- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "Open and Plot Sea Surface Temperature Anomalies",
- "text": "Open and Plot Sea Surface Temperature Anomalies\n\n# Read the April 24 2023 NetCDF file\nds = xr.open_dataset(dir +'20230424090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc')\n\n# Extract the required variables\nlon = ds['lon']\nlat = ds['lat']\nsst_anomaly = ds['sst_anomaly']\n\n\n# Create the figure\nfig = plt.figure(figsize=(10, 10))\nax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(-150, 10))\n\n\n# Plot the sst_anomaly data with vmin and vmax\npcm = ax.pcolormesh(ds.lon, ds.lat, ds.sst_anomaly[0], transform=ccrs.PlateCarree(), cmap='rainbow', vmin=-2, vmax=2)\n\n\n# Plot the equator line\nax.plot(np.arange(360), np.zeros((360)), transform=ccrs.PlateCarree(), color='black')\n\n\n# Define the El Niño 1 + 2 region\nenso_bounds_lon = [-90, -80, -80, -90, -90]\nenso_bounds_lat = [-10, -10, 0, 0, -10]\n# Plot the Enso region box\nax.plot(enso_bounds_lon, enso_bounds_lat, transform=ccrs.PlateCarree(), color='black', linewidth=2)\n\n\n# Define the El Niño 3 region\nenso_bounds_lon2 = [-150, -90, -90, -150, -150]\nenso_bounds_lat2 = [-5, -5, 5, 5, -5]\n# Plot great circle equations for Enso region 3 (accounts for the curve)\nfor i in range(4):\n circle_lon = np.linspace(enso_bounds_lon2[i], enso_bounds_lon2[i+1], 100)\n circle_lat = np.linspace(enso_bounds_lat2[i], enso_bounds_lat2[i+1], 100)\n ax.plot(circle_lon, circle_lat, transform=ccrs.PlateCarree(), color='brown', linestyle='--', linewidth=2)\n\n\n# Add coastlines and gridlines\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.LAND, facecolor='gray')\nax.add_feature(cfeature.LAKES)\nax.add_feature(cfeature.RIVERS)\n\n\n#Set tick locations and labels for the colorbar\ncbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, fraction=0.04)\ncbar.set_label('SST Anomaly', color = 'white')\ncbar.set_ticks([-2, -1, 0, 1, 2])\ncbar.set_ticklabels([-2, -1, 0, 1, 2]) \ncbar.ax.tick_params(color='white')\ncbar.ax.xaxis.set_ticklabels(cbar.ax.get_xticks(), color='white')\ncbar.ax.yaxis.set_ticklabels(cbar.ax.get_yticks(), color='white')\n\n\n# Add white text on top left and right\nfig.text(0.02, 0.95, 'APR 24 2023', color='white', fontsize=20, ha='left', va='top')\nfig.text(0.98, 0.95, 'MUR SSTA', color='white', fontsize=20, ha='right', va='top')\n\n\n# #Background set to black\nfig.set_facecolor('black')\n\n\nplt.show()"
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- "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
- "section": "Plot a Time Series of MUR SSTA",
- "text": "Plot a Time Series of MUR SSTA\n\n# Open data from December to April\nds2 = xr.open_mfdataset(dir + '20*.nc*', combine='by_coords')\n\n# Grab the time values\ntimes = ds2.time.values\n\n# Select the El Niño 1+2 region\nsubset_ds = ds2.sel(lat=slice(-10, 0)).sel(lon=slice(-90, -80))\n\n# Select ssta for El Niño 1+2 region\ndata = subset_ds.sst_anomaly.values\ndata_means = [np.nanmean(step) for step in data]\n\n# Select the El Niño 3 region\nsubset_ds2 = ds2.sel(lat=slice(-5, 5)).sel(lon=slice(-150, -90)) \ndata2 = subset_ds2.sst_anomaly.values\ndata_means2 = [np.nanmean(step) for step in data2]\n\n# Plot the figure with labels\nfig = plt.figure(figsize=(20,6))\nplt.title('MUR SST Anomaly in El Niño 1+2 and El Niño 3 Regions', fontsize=20)\nplt.ylabel('Anomaly in Degrees C', fontsize=16)\nplt.tick_params(labelsize=12) \nplt.grid(True)\n\nplt.plot(times, data_means, color='black', linewidth=4, label='Niño 1+2')\nplt.plot(times, data_means2[:len(times)], color='brown', linewidth=4, linestyle='--', label='Niño 3')\n\nplt.ylim(-4, 4)\n\n# Add legend with labels\nplt.legend(fontsize=16) \n\n# Increase label size\nplt.xticks(fontsize=16)\nplt.yticks(fontsize=16)\n\nplt.show()"
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+ "href": "notebooks/meetings_workshops/workshop_osm_2022/Cloud_DirectDownload_AmazonRiver_Estuary_Exploration.html#sea-surface-temperature-modis",
+ "title": "Amazon Estuary Exploration:",
+ "section": "Sea Surface Temperature (MODIS)",
+ "text": "Sea Surface Temperature (MODIS)\nMODIS has SST data with separate files on a monthly basis. By using earthaccess search with the shortname: “MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0”, and filtering by time to coincide when SSS started (2011), we access 143 granules.\nGet a list of files so we can open them all at once, creating an xarray dataset using the open_mfdataset() function to “read in” all of the netCDF4 files in one call. MODIS does not have a built-in time variable like SSS, but it is subset by latitude and longitude coordinates. We need to combine the files using the nested format with a created ‘time’ dimension.\n\nsss_results = earthaccess.search_data(short_name=\"MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0\", temporal = (\"2011-01-01\", \"2023-01-01\"))\n\nGranules found: 143\n\n\n\nsss_files = earthaccess.download(sss_results, \"./modis_data\")\n\n Getting 143 granules, approx download size: 0.0 GB\n\n\n\n\n\n\n\n\n\n\n\nMODIS did not come with a time variable, so it needs to be extracted from the file names and added in the file preprocessing so files can be successfully concatenated.\n\n#repeat this step since we are accessing multiple granules\nmodis_path = [os.path.join(\"./modis_data\", f) \n for pth, dirs, files in os.walk(\"./modis_data\") for f in files]\n\n\n#function for time dimension added to each netCDF file\ndef preprocessing(ds): \n file_name = ds.product_name \n file_date = basename(file_name).split(\"_\")[2][:6]\n file_date_c = datetime.strptime(file_date, \"%Y%m\")\n time_point = [file_date_c]\n ds.coords['time'] = ('time', time_point) #expand the dimensions to include time\n return ds\n\nds_MODIS = xr.open_mfdataset(modis_path, combine='by_coords', join='override', preprocess = preprocessing)\nds_MODIS\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 143, lat: 2160, lon: 4320, rgb: 3, eightbitcolor: 256)\nCoordinates:\n * lat (lat) float32 89.96 89.88 89.79 89.71 ... -89.79 -89.88 -89.96\n * lon (lon) float32 -180.0 -179.9 -179.8 -179.7 ... 179.8 179.9 180.0\n * time (time) datetime64[ns] 2011-01-01 2011-02-01 ... 2023-01-01\nDimensions without coordinates: rgb, eightbitcolor\nData variables:\n sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n qual_sst4 (time, lat, lon) float32 dask.array<chunksize=(1, 2160, 4320), meta=np.ndarray>\n palette (time, lon, lat, rgb, eightbitcolor) uint8 dask.array<chunksize=(1, 4320, 2160, 3, 256), meta=np.ndarray>\nAttributes: (12/59)\n product_name: AQUA_MODIS.20110101_20110131.L3m.MO.SST...\n instrument: MODIS\n title: MODISA Level-3 Standard Mapped Image\n project: Ocean Biology Processing Group (NASA/GS...\n platform: Aqua\n temporal_range: month\n ... ...\n publisher_url: https://oceandata.sci.gsfc.nasa.gov\n processing_level: L3 Mapped\n cdm_data_type: grid\n data_bins: 4834400\n data_minimum: -1.635\n data_maximum: 32.06999xarray.DatasetDimensions:time: 143lat: 2160lon: 4320rgb: 3eightbitcolor: 256Coordinates: (3)lat(lat)float3289.96 89.88 89.79 ... -89.88 -89.96long_name :Latitudeunits :degrees_northstandard_name :latitudevalid_min :-90.0valid_max :90.0array([ 89.958336, 89.875 , 89.79167 , ..., -89.791664, -89.87501 ,\n -89.958336], dtype=float32)lon(lon)float32-180.0 -179.9 ... 179.9 180.0long_name :Longitudeunits :degrees_eaststandard_name :longitudevalid_min :-180.0valid_max :180.0array([-179.95833, -179.875 , -179.79166, ..., 179.79167, 179.87502,\n 179.95836], dtype=float32)time(time)datetime64[ns]2011-01-01 ... 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- "text": "The original source for this document is https://nasa-openscapes.github.io/2021-Cloud-Workshop-AGU/tutorials/01_Earthdata_Search.html\nThis tutorial guides you through how to use Earthdata Search for NASA Earth observations search and discovery, and how to connect the search output (e.g. download or access links) to a programmatic workflow (locally or from within the cloud).\n\nStep 1. Go to Earthdata Search and Login\nGo to Earthdata Search https://search.earthdata.nasa.gov and use your Earthdata login credentials to log in. If you do not have an Earthdata account, please see the Workshop Prerequisites for guidance.\n\n\nStep 2. Search for dataset of interest\nUse the search box in the upper left to type key words. In this example we are interested in the ECCO dataset, hosted by the PO.DAAC. This dataset is available from the NASA Earthdata Cloud archive hosted in AWS cloud.\nClick on the “Available from AWS Cloud” filter option on the left. Here, 104 matching collections were found with the basic ECCO search.\n\n\n\nFigure caption: Search for ECCO data available in AWS cloud in Earthdata Search portal\n\n\nLet’s refine our search further. Let’s search for ECCO monthly SSH in the search box (which will produce 39 matching collections), and for the time period for year 2015. The latter can be done using the calendar icon on the left under the search box.\nScroll down the list of returned matches until we see the dataset of interest, in this case ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4).\nWe can click on the (i) icon for the dataset to read more details, including the dataset shortname (helpful for programmatic workflows) just below the dataset name; here ECCO_L4_SSH_05DEG_MONTHLY_V4R4.\n\n\n\nFigure caption: Refine search, set temporal bounds, get more information\n\n\n\n\nStep 3. Explore the dataset details, including Cloud Access information\nOnce we clicked the (i), scrolling down the info page for the dataset we will see Cloud Access information, such as:\n\nwhether the dataset is available in the cloud\nthe cloud Region (all NASA Earthdata Cloud data is/will be in us-west-2 region)\nthe S3 storage bucket and object prefix where this data is located\nlink that generates AWS S3 Credentials for in-cloud data access (we will cover this in the Direct Data Access Tutorials)\nlink to documentation describing the In-region Direct S3 Access to Buckets. Note: these will be unique depending on the DAAC where the data is archived. (We will show examples of direct in-region access in Tutorial 3.)\n\n\n\n\nFigure caption: Cloud access info in EDS\n\n\n\n\n\nFigure caption: Documentation describing the In-region Direct S3 Access to Buckets\n\n\nPro Tip: Clicking on “For Developers” to exapnd will provide programmatic endpoints such as those for the CMR API, and more. CMR API and CMR STAC API tutorials can be found on the 2021 Cloud Hackathon website.\nFor now, let’s say we are intersted in getting download link(s) or access link(s) for specific data files (granules) within this collection.\nAt the top of the dataset info section, click on Search Results, which will take us back to the list of datasets matching our search parameters. Clicking on the dataset (here again it’s the same ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4)) we now see a list of files (granules) that are part of the dataset (collection).\n\n\nStep 4. Customize the download or data access\nClick on the green + symbol to add a few files to our project. Here we added the first 3 listed for 2015. Then click on the green button towards the bottom that says “Download”. This will take us to another page with options to customize our download or access link(s).\n\n\n\nFigure caption: Select granules and click download\n\n\n\n4.a. Entire file content\nLet’s stay we are interested in the entire file content, so we select the “Direct Download” option (as opposed to other options to subset or transform the data):\n\n\n\nFigure caption: Customize your download or access\n\n\nClicking the green Download Data button again, will take us to the final page for instructions to download and links for data access in the cloud. You should see three tabs: Download Files, AWS S3 Access, Download Script:\n \nThe Download Files tab provides the https:// links for downloading the files locally. E.g.: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc\nThe AWS S3 Access tab provides the S3:// links, which is what we would use to access the data directly in-region (us-west-2) within the AWS cloud (an example will be shown in Tutorial 3). E.g.: s3://podaac-ops-cumulus-protected/ECCO_L4_SSH_05DEG_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_latlon_0p50deg.nc where s3 indicates data is stored in AWS S3 storage, podaac-ops-cumulus-protected is the bucket, and ECCO_L4_SSH_05DEG_MONTHLY_V4R4 is the object prefix (the latter two are also listed in the dataset collection information under Cloud Access (step 3 above)).\nTip: Another quicker way to find the bucket and object prefix is from the list of data files the search returns. Next to the + green button is a grey donwload symbol. Click on that to see the Download Files https:// links or on the AWS S3 Access to get the direct S3:// access links, which contain the bucket and object prefix where data is stored.\n\n\n4.b. Subset or transform before download or access\nDAAC tools and services are also being migrated or developed in the cloud, next to that data. These include the Harmony API and OPeNDAP in the cloud, as a few examples.\nWe can leverage these cloud-based services on cloud-archived data to reduce or transform the data (depending on need) before getting the access links regardless of whether we prefer to download the data and work on a local machine or whether we want to access the data in the cloud (from a cloud workspace). These can be useful data reduction services that support a faster time to science.\nHarmony\nHarmony allows you to seamlessly analyze Earth observation data from different NASA data centers. These services (API endpoints) provide data reduction (e.g. subsetting) and transfromation services (e.g. convert netCDF data to Zarr cloud optimized format).\n\n\n\nFigure caption: Leverage Harmony cloud-based data transformation services\n\n\nWhen you click the final green Download button, the links provided are to data that had been transformed based on our selections on the previous screen (here chosing to use the Harmony service to reformat the data to Zarr). These data are staged for us in an S3 bucket in AWS, and we can use the s3:// links to access those specific data. This service also provides STAC access links. This particular example is applicable if your workflow is in the AWS us-west-2 region.\n\n\n\nFigure caption: Harmony-staged data in S3\n\n\n\n\n\nStep 5. Integrate file links into programmatic workflow, locally or in the AWS cloud.\nIn tutorial 3 Direct Data Access, we will work programmatically in the cloud to access datasets of interest, to get us set up for further scientific analysis of choice. There are several ways to do this. One way to connect the search part of the workflow we just did in Earthdata Search to our next steps working in the cloud is to simply copy/paste the s3:// links provides in Step 4 above into a JupyterHub notebook or script in our cloud workspace, and continue the data analysis from there.\nOne could also copy/paste the s3:// links and save them in a text file, then open and read the text file in the notebook or script in the JupyterHub in the cloud.\nTutorial 3 will pick up from here and cover these next steps in more detail."
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+ "section": "Time Series Comparison",
+ "text": "Time Series Comparison\nPlot each dataset for the time period 2011-2019.\nFirst, we need to average all pixels in the subset lat/lon per time for sea surface salinity and sea surface temperature to set up for the graphs.\n\nsss_mean = []\nfor t in np.arange(len(ds_sss_subset.time)):\n sss_mean.append(np.nanmean(ds_sss_subset.sss[:,:,t].values))\n\n#sss_mean\n\n\n#MODIS\nsst_MODIS_mean = []\nfor t in np.arange(len(ds_MODIS_subset.time)):\n sst_MODIS_mean.append(np.nanmean(ds_MODIS_subset.sst4[t,:,:].values))\n \n#sst_MODIS_mean\n\n\nCombined timeseries plot of river height and LWE thickness\nBoth datasets are mapped for the outlet of the Amazon River into the estuary.\n\n#plot river height and land water equivalent thickness\nfig, ax1 = plt.subplots(figsize=[12,7])\n\n#plot river height\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\n\n#plot LWE thickness on secondary axis\nax2 = ax1.twinx()\nax2.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\n\nax1.set_xlabel('Date')\nax2.set_ylabel('Land Water Equivalent Thickness (cm)', color='darkorange')\nax1.set_ylabel('River Height (m)', color='darkblue')\nax2.legend(['GRACE-FO'], loc='upper right')\nax1.legend(['Pre-SWOT MEaSUREs'], loc='lower right')\n\nplt.title('Amazon Estuary, 2011-2019 Lat, Lon = (-0.7, -50)')\nax1.grid()\nplt.show()\n\n\n\n\nLWE thickness captures the seasonality of Pre-SWOT MEaSUREs river heights well, and so LWE thickness can be compared to all other variables as a representative of the seasonality of both measurements for the purpose of this notebook.\n\n\nCombined timeseries plots of salinity and LWE thickness, followed by temperature\n\n#Combined Subplots\nfig = plt.figure(figsize=(10,10))\n\nax1 = fig.add_subplot(211)\nplt.title('Amazon Estuary, 2011-2019')\nax2 = ax1.twinx()\nax3 = plt.subplot(212)\nax4 = ax3.twinx()\n\n#lwe thickness\nax1.plot(ds_GRACE_subset.time[107:179], ds_GRACE_subset.lwe_thickness[107:179,34,69], color = 'darkorange')\nax1.set_ylabel('LWE Thickness (cm)', color='darkorange')\nax1.grid()\n\n#sea surface salinity\nax2.plot(ds_sss_subset.time[0:750], sss_mean[0:750], 'g')\nax2.set_ylabel('SSS (psu)', color='g')\n\n#sea surface temperature\nax3.plot(ds_MODIS_subset.time[7:108], sst_MODIS_mean[7:108], 'darkred')\nax3.set_ylabel('SST (deg C)', color='darkred')\nax3.grid()\n\n#river height at outlet\nds_MEaSUREs.height[16,6689:9469].plot(color='darkblue')\nax4.set_ylabel('River Height (m)', color='darkblue')\n\nText(0, 0.5, 'River Height (m)')\n\n\n\n\n\nMeasurements of LWE thickness and SSS follow expected patterns. When lwe thickness is at its lowest, indicating less water is flowing through during the drought, salinity is at its highest. Without high volume of water pouring into the estuary, salinity increases. We can see that temperature is shifted a bit in time from river height as well at the outlet, a relationship that could be further explored."
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- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
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- "text": "Getting Started\nIn this notebook, we will access monthly sea surface height from ECCO V4r4 (10.5067/ECG5D-SSH44). The data are provided as a time series of monthly netCDFs on a 0.5-degree latitude/longitude grid.\nWe will access the data from inside the AWS cloud (us-west-2 region, specifically) and load a time series made of multiple netCDF datasets into a single xarray dataset. This approach leverages S3 native protocols for efficient access to the data.\n\n\nRequirements\n\nAWS\nThis notebook should be running in an EC2 instance in AWS region us-west-2, as previously mentioned. We recommend using an EC2 with at least 8GB of memory available.\nThe notebook was developed and tested using a t2.large instance (2 cpus; 8GB memory).\n\n\nPython 3\nMost of these imports are from the Python standard library. However, you will need to install these packages into your Python 3 environment if you have not already done so:\n\ns3fs\nrequests\npandas\nxarray\nmatplotlib\ncartopy\n\n\nimport s3fs\nimport requests\nimport numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport cartopy\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeat\nfrom json import dumps\nfrom io import StringIO\nfrom os.path import dirname, join\nfrom IPython.display import HTML\n\nplt.rcParams.update({'font.size': 14})\n\nMake a folder to write some outputs, if needed:\n\n!mkdir -p outputs/\n\n\n\n\nInputs\nConfigure one input: the ShortName of the desired dataset from ECCO V4r4. In this case it’s the following string that unique identifies the collection of monthly, 0.5-degree sea surface height data.\n\nShortName = \"ECCO_L4_SSH_05DEG_MONTHLY_V4R4\"\n\n\n\nEarthdata Login\nYou should have a .netrc file set up like:\nmachine urs.earthdata.nasa.gov login <username> password <password>\n\n\nDirect access from S3\nSet up an s3fs session for authneticated access to ECCO netCDF files in s3:\n\ndef begin_s3_direct_access(url: str=\"https://archive.podaac.earthdata.nasa.gov/s3credentials\"):\n response = requests.get(url).json()\n return s3fs.S3FileSystem(key=response['accessKeyId'],\n secret=response['secretAccessKey'],\n token=response['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})\n\nfs = begin_s3_direct_access()\n\ntype(fs)\n\ns3fs.core.S3FileSystem"
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- "text": "Datasets\n\nsea surface height (0.5-degree gridded, monthly)\nECCO_L4_SSH_05DEG_MONTHLY_V4R4\nGet a list of netCDF files located at the S3 path corresponding to the ECCO V4r4 monthly sea surface height dataset on the 0.5-degree latitude/longitude grid.\n\nssh_Files = fs.glob(join(\"podaac-ops-cumulus-protected/\", ShortName, \"*2015*.nc\"))\n\nlen(ssh_Files)\n\n12\n\n\nOpen with the netCDF files using the s3fs package, then load them all at once into a concatenated xarray dataset.\n\nssh_Dataset = xr.open_mfdataset(\n paths=[fs.open(f) for f in ssh_Files],\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks={'latitude': 60, # These were chosen arbitrarily. You must specify \n 'longitude': 120, # chunking that is suitable to the data and target\n 'time': 100} # analysis.\n)\n\nssh = ssh_Dataset.SSH\n\nprint(ssh)\n\n<xarray.DataArray 'SSH' (time: 12, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(12, 360, 720), dtype=float32, chunksize=(1, 60, 120), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-16T12:00:00 ... 2015-12-16T12:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: [-1.88057721]\n valid_max: [1.42077196]\n\n\n\n\nPlot the gridded sea surface height time series\nBut only the timesteps beginning in 2015:\n\nssh_after_201x = ssh[ssh['time.year']>=2015,:,:]\n\nprint(ssh_after_201x)\n\n<xarray.DataArray 'SSH' (time: 12, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(12, 360, 720), dtype=float32, chunksize=(1, 60, 120), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-16T12:00:00 ... 2015-12-16T12:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n long_name: Dynamic sea surface height anomaly\n standard_name: sea_surface_height_above_geoid\n units: m\n comment: Dynamic sea surface height anomaly above the geoi...\n valid_min: [-1.88057721]\n valid_max: [1.42077196]\n\n\nPlot the grid for the first time step using a Robinson projection. Define a helper function for consistency throughout the notebook:\n\ndef make_figure(proj):\n fig = plt.figure(figsize=(16,6))\n ax = fig.add_subplot(1, 1, 1, projection=proj)\n ax.add_feature(cfeat.LAND)\n ax.add_feature(cfeat.OCEAN)\n ax.add_feature(cfeat.COASTLINE)\n ax.add_feature(cfeat.BORDERS, linestyle='dotted')\n return fig, ax\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\nssh_after_201x.isel(time=0).plot(ax=ax, transform=ccrs.PlateCarree(), cmap='Spectral_r')\n\n<matplotlib.collections.QuadMesh at 0x7fae2533d730>\n\n\n\n\n\nNow plot the whole time series (post-2010) in an animation and write it to an mp4 file called ecco_monthly_ssh_grid_2015_to_x.mp4:\n\ndef get_animation(var, cmap: str=\"Spectral_r\"):\n \"\"\"Get time series animation for input xarray dataset\"\"\"\n\n def draw_map(i: int, add_colorbar: bool):\n data = var[i]\n m = data.plot(ax=ax, \n transform=ccrs.PlateCarree(),\n add_colorbar=add_colorbar,\n vmin=var.valid_min, \n vmax=var.valid_max,\n cmap=cmap)\n plt.title(str(data.time.values)[:7])\n return m\n\n def init():\n return draw_map(0, add_colorbar=True)\n \n def animate(i):\n return draw_map(i, add_colorbar=False)\n\n return init, animate\n\nNow make the animation using the function:\n\nfig, ax = make_figure(proj=ccrs.Robinson())\n\ninit, animate = get_animation(ssh_after_201x)\n\nani = animation.FuncAnimation(fig=fig, \n func=animate, \n frames=ssh_after_201x.time.size, \n init_func=init, \n interval=0.2, \n blit=False, \n repeat=False)\n\n# Now save the animation to an MP4 file:\nani.save('outputs/ecco_monthly_ssh_grid_2015_to_x.mp4', writer=animation.FFMpegWriter(fps=8))\n\nplt.close(fig)\n\nRender the animation in the ipynb:\n\n#HTML(ani.to_html5_video())\n\n\n\ntflux (0.5-degree gridded, daily)\nNow we will do something similar to access daily, gridded (0.5-degree) ocean and sea-ice surface heat fluxes (10.5067/ECG5D-HEA44). Read more about the dataset and the rest of the ECCO V4r4 product suite on the PO.DAAC Web Portal.\nUse a “glob” pattern when listing the S3 bucket contents such that only netCDFs from January 2015 are represented in the resulting list of paths.\n\ntflux_Files = fs.glob(join(\"podaac-ops-cumulus-protected/\", \"ECCO_L4_HEAT_FLUX_05DEG_DAILY_V4R4\", \"*2015-01*.nc\"))\n\nlen(tflux_Files)\n\n31\n\n\nNow open them all as one xarray dataset just like before. Open and pass the 365 netCDF files to the xarray.open_mfdataset constructor so that we can operate on them as a single aggregated dataset.\n\ntflux_Dataset = xr.open_mfdataset(\n paths=[fs.open(f) for f in tflux_Files],\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks={'latitude': 60, # These were chosen arbitrarily. You must specify \n 'longitude': 120, # chunking that is suitable to the data and target\n 'time': 100} # analysis.\n)\n\ntflux = tflux_Dataset.TFLUX\n\nprint(tflux)\n\n<xarray.DataArray 'TFLUX' (time: 31, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(31, 360, 720), dtype=float32, chunksize=(1, 60, 120), chunktype=numpy.ndarray>\nCoordinates:\n * time (time) datetime64[ns] 2015-01-01T12:00:00 ... 2015-01-31T12:00:00\n * latitude (latitude) float32 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float32 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\nAttributes:\n coverage_content_type: modelResult\n direction: >0 increases potential temperature (THETA)\n long_name: Rate of change of ocean heat content per m2 accou...\n units: W m-2\n comment: The rate of change of ocean heat content due to h...\n valid_min: [-1713.51220703]\n valid_max: [870.31304932]\n\n\nSelect a region over the Gulf of Mexico and spatially subset it from the larger dataset by slicing on the latitude and longitude axes.\n\ntflux_gom = tflux.sel(latitude=slice(15, 40), \n longitude=slice(-105, -70))\n\nprint(tflux_gom.shape)\n\n(31, 50, 70)\n\n\n\ntflux_gom.isel(time=0).plot()\n\n<matplotlib.collections.QuadMesh at 0x7fae1689a550>\n\n\n\n\n\nPlot the Jan 2015 surface heat flux as a gridded time series animation over the GOM study region.\n\nfig, ax = make_figure(proj=ccrs.Mercator())\n\nax.coastlines()\nax.set_extent([tflux_gom.longitude.min(), \n tflux_gom.longitude.max(), \n tflux_gom.latitude.min(), \n tflux_gom.latitude.max()])\n\ninit, animate = get_animation(tflux_gom, cmap=\"RdBu\")\n\n# Plot a time series animation write it to an mp4 file:\nani = animation.FuncAnimation(fig=fig, \n func=animate, \n frames=tflux_gom.time.size, \n init_func=init, \n interval=0.2, \n blit=False, \n repeat=False)\n\nani.save('outputs/ecco_daily_tflux_gom_2015.mp4', writer=animation.FFMpegWriter(fps=8))\n\nplt.close(fig)"
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
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- "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
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+ "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
+ "section": "Set start and end dates",
+ "text": "Set start and end dates\n\nstart_date = \"1992-01-01\"\nend_date = \"2002-12-31\"\n\n# break it down into Year, Month, Day (and minutes and seconds if desired) \n# as inputs to harmony.py call using datetime()\nstart_year = 2002\nstart_month = 1\nstart_day = 1\n\nend_year = 2017\nend_month = 12\nend_day = 31"
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- "section": "Why do we need xarray?",
- "text": "Why do we need xarray?\nAs Geoscientists, we often work with time series of data with two or more dimensions: a time series of calibrated, orthorectified satellite images; two-dimensional grids of surface air temperature from an atmospheric reanalysis; or three-dimensional (level, x, y) cubes of ocean salinity from an ocean model. These data are often provided in GeoTIFF, NetCDF or HDF format with rich and useful metadata that we want to retain, or even use in our analysis. Common analyses include calculating means, standard deviations and anomalies over time or one or more spatial dimensions (e.g. zonal means). Model output often includes multiple variables that you want to apply similar analyses to.\n\n\n\nA schematic of multi-dimensional data\n\n\nThe schematic above shows a typical data structure for multi-dimensional data. There are two data cubes, one for temperature and one for precipitation. Common coordinate variables, in this case latitude, longitude and time are associated with each variable. Each variable, including coordinate variables, will have a set of attributes: name, units, missing value, etc. The file containing the data may also have attributes: source of the data, model name coordinate reference system if the data are projected. Writing code using low-level packages such as netcdf4 and numpy to read the data, then perform analysis, and write the results to file is time consuming and prone to errors."
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+ "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
+ "section": "Spatial bounds (Region of Interest) – Not used",
+ "text": "Spatial bounds (Region of Interest) – Not used\n\nwesternmost_longitude = 100.\neasternmost_longitude = 150.\nnorthermost_latitude = 30.\nsouthernmost_latitude = 0."
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- "text": "What is xarray\nxarray is an open-source project and python package to work with labelled multi-dimensional arrays. It is leverages numpy, pandas, matplotlib and dask to build Dataset and DataArray objects with built-in methods to subset, analyze, interpolate, and plot multi-dimensional data. It makes working with multi-dimensional data cubes efficient and fun. It will change your life for the better. You’ll be more attractive, more interesting, and better equiped to take on lifes challenges."
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+ "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
+ "section": "Setup the harmony-py service call and execute a request",
+ "text": "Setup the harmony-py service call and execute a request\n\n# using the the harmony.py service, set up the request and exectue it\necco_collection = Collection(id=ccid)\ntime_range = {'start': datetime(start_year, start_month, start_day), 'stop': datetime(end_year, end_month, end_day)}\nprint(time_range)\n\nharmony_client = Client(env=Environment.PROD)\n\n# in this example set concatentae to 'False' because the monthly input time steps vary slightly\n# (not always centered in the middle of month)\necco_request = Request(collection=ecco_collection, temporal=time_range, format='application/x-zarr', concatenate='False')\n\n# sumbit request and monitor job\necco_job_id = harmony_client.submit(ecco_request)\nprint('\\n Waiting for the job to finish. . .\\n')\necco_response = harmony_client.result_json(ecco_job_id, show_progress=True)\nprint(\"\\n. . .DONE!\")\n\n{'start': datetime.datetime(2002, 1, 1, 0, 0), 'stop': datetime.datetime(2017, 12, 31, 0, 0)}\n\nWaiting for the job to finish. . .\n\n\n. . .DONE!\n\n\n [ Processing: 100% ] |###################################################| [|]\n\n\n\nYou can also wrap the creation of the Harmony request URL into one function. Shown here for legacy purposes (does not execute a Harmony request):\n\ndef get_harmony_url(ccid,start_date,end_date):\n \"\"\"\n Parameters:\n ===========\n ccid: string\n concept_id of the datset\n date_range: list\n [start_data, end_date] \n \n Return:\n =======\n url: the harmony URL used to perform the netcdf to zarr transformation\n \"\"\"\n \n base = f\"https://harmony.earthdata.nasa.gov/{ccid}\"\n hreq = f\"{base}/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset\"\n rurl = f\"{hreq}?format=application/x-zarr\"\n\n #print(rurl)\n\n subs = '&'.join([f'subset=time(\"{start_date}T00:00:00.000Z\":\"{end_date}T23:59:59.999Z\")']) \n #subs = subs + '&' + '&'.join([f'subset=lat({southernmost_latitude}:{northermost_latitude})'])\n #subs = subs + '&' + '&'.join([f'subset=lon({westernmost_longitude}:{easternmost_longitude})'])\n\n rurl = f\"{rurl}&{subs}\"\n return rurl\n\nccid='C2129189405-POCLOUD'\nprint(get_harmony_url(ccid,start_date,end_date))\n\n# this is the way you would execute it\n# response = requests.get(url=rurl).json()\n\nhttps://harmony.earthdata.nasa.gov/C2129189405-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?format=application/x-zarr&subset=time(\"1992-01-01T00:00:00.000Z\":\"2002-12-31T23:59:59.999Z\")"
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- "text": "What you will learn from this tutorial\nIn this tutorial you will learn how to:\n\nload a netcdf file into xarray\ninterrogate the Dataset and understand the difference between DataArray and Dataset\nsubset a Dataset\ncalculate annual and monthly mean fields\ncalculate a time series of zonal means\nplot these results\n\nAs always, we’ll start by importing xarray. We’ll follow convention by giving the module the shortname xr\n\nimport xarray as xr\nxr.set_options(keep_attrs=True)\nimport hvplot.xarray\n\n\n\n\n\n\n\n\n\n\n\nI’m going to use one of xarray’s tutorial datasets. In this case, air temperature from the NCEP reanalysis. I’ll assign the result of the open_dataset to ds. I may change this to access a dataset directly\n\nds = xr.tutorial.open_dataset(\"air_temperature\")\n\nAs we are in an interactive environment, we can just type ds to see what we have.\n\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lat: 25, time: 2920, lon: 53)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nData variables:\n air (time, lat, lon) float32 ...\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:lat: 25time: 2920lon: 53Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (1)air(time, lat, lon)float32...long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ][3869000 values with dtype=float32]Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html\n\n\nFirst thing to notice is that ds is an xarray.Dataset object. It has dimensions, lat, lon, and time. It also has coordinate variables with the same names as these dimensions. These coordinate variables are 1-dimensional. This is a NetCDF convention. The Dataset contains one data variable, air. This has dimensions (time, lat, lon).\nClicking on the document icon reveals attributes for each variable. Clicking on the disk icon reveals a representation of the data.\nEach of the data and coordinate variables can be accessed and examined using the variable name as a key.\n\nds.air\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n[3869000 values with dtype=float32]\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degK\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920lat: 25lon: 53...[3869000 values with dtype=float32]Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\n\nds['air']\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n[3869000 values with dtype=float32]\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degK\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920lat: 25lon: 53...[3869000 values with dtype=float32]Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nThese are xarray.DataArray objects. This is the basic building block for xarray.\nVariables can also be accessed as attributes of ds.\n\nds.time\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'time' (time: 2920)>\narray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n standard_name: time\n long_name: Timexarray.DataArray'time'time: 29202013-01-01 2013-01-01T06:00:00 ... 2014-12-31T18:00:00array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Coordinates: (1)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (2)standard_name :timelong_name :Time\n\n\nA major difference between accessing a variable as an attribute versus using a key is that the attribute is read-only but the key method can be used to update the variable. For example, if I want to convert the units of air from Kelvin to degrees Celsius.\n\nds['air'] = ds.air - 273.15\n\nThis approach can also be used to add new variables\n\nds['air_kelvin'] = ds.air + 273.15\n\nIt is helpful to update attributes such as units, this saves time, confusion and mistakes, especially when you save the dataset.\n\nds['air'].attrs['units'] = 'degC'\n\n\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lat: 25, time: 2920, lon: 53)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nData variables:\n air (time, lat, lon) float32 -31.95 -30.65 -29.65 ... 23.04 22.54\n air_kelvin (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:lat: 25time: 2920lon: 53Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (2)air(time, lat, lon)float32-31.95 -30.65 ... 23.04 22.54long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n -37.649994, -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n -37.850006, -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n -36.759995, -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 ,\n 21.950012, 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 ,\n 22.75 , 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 ,\n 23.640015, 23.450012]],\n\n [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n -39.550003, -37.350006],\n [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n -40.649994, -37.449997],\n [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n -39.759995, -34.649994],\n...\n [ 20.540009, 20.73999 , 22.23999 , ..., 21.940002,\n 21.540009, 21.140015],\n [ 23.140015, 24.040009, 24.440002, ..., 22.140015,\n 21.940002, 21.23999 ],\n [ 24.640015, 25.23999 , 25.339996, ..., 22.540009,\n 22.339996, 22.040009]],\n\n [[-28.059998, -28.86 , -29.86 , ..., -31.460007,\n -31.660004, -31.36 ],\n [-23.259995, -23.86 , -24.759995, ..., -33.559998,\n -32.86 , -31.460007],\n [-10.160004, -10.959991, -11.76001 , ..., -33.259995,\n -30.559998, -26.86 ],\n ...,\n [ 20.640015, 20.540009, 21.940002, ..., 22.140015,\n 21.940002, 21.540009],\n [ 22.940002, 23.73999 , 24.040009, ..., 22.540009,\n 22.540009, 22.040009],\n [ 24.540009, 24.940002, 24.940002, ..., 23.339996,\n 23.040009, 22.540009]]], dtype=float32)air_kelvin(time, lat, lon)float32241.2 242.5 243.5 ... 296.2 295.7long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 ,\n 238.59999],\n [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999,\n 239.29999],\n [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 ,\n 241.7 ],\n ...,\n [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 ,\n 294.69998],\n [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 ,\n 295.19998],\n [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 ,\n 296.6 ]],\n\n [[242.09999, 242.7 , 243.09999, ..., 232. , 233.59999,\n 235.79999],\n [243.59999, 244.09999, 244.2 , ..., 231. , 232.5 ,\n 235.7 ],\n [253.2 , 252.89 , 252.09999, ..., 230.79999, 233.39 ,\n 238.5 ],\n...\n [293.69 , 293.88998, 295.38998, ..., 295.09 , 294.69 ,\n 294.29 ],\n [296.29 , 297.19 , 297.59 , ..., 295.29 , 295.09 ,\n 294.38998],\n [297.79 , 298.38998, 298.49 , ..., 295.69 , 295.49 ,\n 295.19 ]],\n\n [[245.09 , 244.29 , 243.29 , ..., 241.68999, 241.48999,\n 241.79 ],\n [249.89 , 249.29 , 248.39 , ..., 239.59 , 240.29 ,\n 241.68999],\n [262.99 , 262.19 , 261.38998, ..., 239.89 , 242.59 ,\n 246.29 ],\n ...,\n [293.79 , 293.69 , 295.09 , ..., 295.29 , 295.09 ,\n 294.69 ],\n [296.09 , 296.88998, 297.19 , ..., 295.69 , 295.69 ,\n 295.19 ],\n [297.69 , 298.09 , 298.09 , ..., 296.49 , 296.19 ,\n 295.69 ]]], dtype=float32)Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"
+ "objectID": "notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html#run-2nd-harmony-netcdf-to-zarr-call",
+ "href": "notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html#run-2nd-harmony-netcdf-to-zarr-call",
+ "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
+ "section": "Run 2nd Harmony netCDF-to-Zarr call",
+ "text": "Run 2nd Harmony netCDF-to-Zarr call\n\nShortName = \"ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4\"\n\n# 1) Find new concept_id for this dataset\nresponse = requests.get(\n url='https://cmr.earthdata.nasa.gov/search/collections.umm_json', \n params={'provider': \"POCLOUD\",\n 'ShortName': ShortName,\n 'page_size': 1}\n)\n\nummc = response.json()['items'][0]\nccid = ummc['meta']['concept-id']\n\n# using the the harmony.py service, set up the request and exectue it\necco_collection = Collection(id=ccid)\ntime_range = {'start': datetime(start_year, start_month, start_day), 'stop': datetime(end_year, end_month, end_day)}\n\nharmony_client = Client(env=Environment.PROD)\n\n# in this example set concatentae to 'False' because the monthly input time steps vary slightly\n# (not always centered in the middle of month)\necco_request = Request(collection=ecco_collection, temporal=time_range, format='application/x-zarr', concatenate='False')\n\n# sumbit request and monitor job\necco_job_id = harmony_client.submit(ecco_request)\nprint('\\nWaiting for the job to finish. . .\\n')\necco_response = harmony_client.result_json(ecco_job_id, show_progress=True)\nprint(\"\\n. . .DONE!\")\n\n\nWaiting for the job to finish. . .\n\n\n. . .DONE!\n\n\n [ Processing: 100% ] |###################################################| [|]"
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- "title": "Xarray",
- "section": "Subsetting and Indexing",
- "text": "Subsetting and Indexing\nSubsetting and indexing methods depend on whether you are working with a Dataset or DataArray. A DataArray can be accessed using positional indexing just like a numpy array. To access the temperature field for the first time step, you do the following.\n\nds['air'][0,:,:]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (lat: 25, lon: 53)>\narray([[-31.949997, -30.649994, -29.649994, ..., -40.350006, -37.649994,\n -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006, -37.850006,\n -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997, -36.759995,\n -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 , 21.950012,\n 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 , 22.75 ,\n 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 , 23.640015,\n 23.450012]], dtype=float32)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n time datetime64[ns] 2013-01-01\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'lat: 25lon: 53-31.95 -30.65 -29.65 -29.15 -29.05 ... 24.64 24.45 23.75 23.64 23.45array([[-31.949997, -30.649994, -29.649994, ..., -40.350006, -37.649994,\n -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006, -37.850006,\n -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997, -36.759995,\n -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 , 21.950012,\n 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 , 22.75 ,\n 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 , 23.640015,\n 23.450012]], dtype=float32)Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time()datetime64[ns]2013-01-01standard_name :timelong_name :Timearray('2013-01-01T00:00:00.000000000', dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nNote this returns a DataArray with coordinates but not attributes.\nHowever, the real power is being able to access variables using coordinate variables. I can get the same subset using the following. (It’s also more explicit about what is being selected and robust in case I modify the DataArray and expect the same output.)\n\nds['air'].sel(time='2013-01-01').time\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'time' (time: 4)>\narray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\nAttributes:\n standard_name: time\n long_name: Timexarray.DataArray'time'time: 42013-01-01 2013-01-01T06:00:00 2013-01-01T12:00:00 2013-01-01T18:00:00array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')Coordinates: (1)time(time)datetime64[ns]2013-01-01 ... 2013-01-01T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (2)standard_name :timelong_name :Time\n\n\n\nds.air.sel(time='2013-01-01')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 4, lat: 25, lon: 53)>\narray([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n -37.649994, -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n -37.850006, -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n -36.759995, -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 ,\n 21.950012, 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 ,\n 22.75 , 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 ,\n 23.640015, 23.450012]],\n\n [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n -39.550003, -37.350006],\n [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n -40.649994, -37.449997],\n [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n -39.759995, -34.649994],\n...\n [ 22.450012, 22.25 , 22.25 , ..., 23.140015,\n 22.140015, 21.850006],\n [ 23.049988, 23.350006, 23.140015, ..., 23.25 ,\n 22.850006, 22.450012],\n [ 23.25 , 23.140015, 23.25 , ..., 23.850006,\n 23.850006, 23.640015]],\n\n [[-31.259995, -31.350006, -31.350006, ..., -38.759995,\n -37.649994, -35.550003],\n [-26.850006, -27.850006, -28.949997, ..., -42.259995,\n -41.649994, -38.649994],\n [-16.549988, -18.449997, -21.050003, ..., -42.449997,\n -41.350006, -37.050003],\n ...,\n [ 23.450012, 23.25 , 22.850006, ..., 23.350006,\n 22.640015, 22.140015],\n [ 23.850006, 24.350006, 23.950012, ..., 23.640015,\n 23.450012, 23.140015],\n [ 24.350006, 24.549988, 24.350006, ..., 24.640015,\n 24.850006, 24.75 ]]], dtype=float32)\nCoordinates:\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n * time (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 4lat: 25lon: 53-31.95 -30.65 -29.65 -29.15 -29.05 ... 25.45 25.05 24.64 24.85 24.75array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\n -37.649994, -34.550003],\n [-29.350006, -28.649994, -28.449997, ..., -40.350006,\n -37.850006, -33.850006],\n [-23.149994, -23.350006, -24.259995, ..., -39.949997,\n -36.759995, -31.449997],\n ...,\n [ 23.450012, 23.049988, 23.25 , ..., 22.25 ,\n 21.950012, 21.549988],\n [ 22.75 , 23.049988, 23.640015, ..., 22.75 ,\n 22.75 , 22.049988],\n [ 23.140015, 23.640015, 23.950012, ..., 23.75 ,\n 23.640015, 23.450012]],\n\n [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\n -39.550003, -37.350006],\n [-29.550003, -29.050003, -28.949997, ..., -42.149994,\n -40.649994, -37.449997],\n [-19.949997, -20.259995, -21.050003, ..., -42.350006,\n -39.759995, -34.649994],\n...\n [ 22.450012, 22.25 , 22.25 , ..., 23.140015,\n 22.140015, 21.850006],\n [ 23.049988, 23.350006, 23.140015, ..., 23.25 ,\n 22.850006, 22.450012],\n [ 23.25 , 23.140015, 23.25 , ..., 23.850006,\n 23.850006, 23.640015]],\n\n [[-31.259995, -31.350006, -31.350006, ..., -38.759995,\n -37.649994, -35.550003],\n [-26.850006, -27.850006, -28.949997, ..., -42.259995,\n -41.649994, -38.649994],\n [-16.549988, -18.449997, -21.050003, ..., -42.449997,\n -41.350006, -37.050003],\n ...,\n [ 23.450012, 23.25 , 22.850006, ..., 23.350006,\n 22.640015, 22.140015],\n [ 23.850006, 24.350006, 23.950012, ..., 23.640015,\n 23.450012, 23.140015],\n [ 24.350006, 24.549988, 24.350006, ..., 24.640015,\n 24.850006, 24.75 ]]], dtype=float32)Coordinates: (3)lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2013-01-01T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', '2013-01-01T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nI can also do slices. I’ll extract temperatures for the state of Colorado. The bounding box for the state is [-109 E, -102 E, 37 N, 41 N].\nIn the code below, pay attention to both the order of the coordinates and the range of values. The first value of the lat coordinate variable is 41 N, the second value is 37 N. Unfortunately, xarray expects slices of coordinates to be in the same order as the coordinates. Note lon is 0 to 360 not -180 to 180, and I let python calculate it for me within the slice.\n\nds.air.sel(lat=slice(41.,37.), lon=slice(360-109,360-102))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920, lat: 2, lon: 3)>\narray([[[-10.049988 , -9.25 , -8.75 ],\n [ -6.25 , -6.549988 , -6.3599854]],\n\n [[-18.149994 , -14.950012 , -9.950012 ],\n [-13.649994 , -11.049988 , -7.25 ]],\n\n [[-20.449997 , -18.649994 , -13.359985 ],\n [-19.350006 , -16.950012 , -11.25 ]],\n\n ...,\n\n [[-24.460007 , -28.259995 , -25.759995 ],\n [-16.959991 , -24.059998 , -24.059998 ]],\n\n [[-24.36 , -26.160004 , -23.460007 ],\n [-15.959991 , -22.86 , -22.960007 ]],\n\n [[-17.559998 , -15.359985 , -13.660004 ],\n [-13.76001 , -15.959991 , -14.459991 ]]], dtype=float32)\nCoordinates:\n * lat (lat) float32 40.0 37.5\n * lon (lon) float32 252.5 255.0 257.5\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920lat: 2lon: 3-10.05 -9.25 -8.75 -6.25 -6.55 ... -15.36 -13.66 -13.76 -15.96 -14.46array([[[-10.049988 , -9.25 , -8.75 ],\n [ -6.25 , -6.549988 , -6.3599854]],\n\n [[-18.149994 , -14.950012 , -9.950012 ],\n [-13.649994 , -11.049988 , -7.25 ]],\n\n [[-20.449997 , -18.649994 , -13.359985 ],\n [-19.350006 , -16.950012 , -11.25 ]],\n\n ...,\n\n [[-24.460007 , -28.259995 , -25.759995 ],\n [-16.959991 , -24.059998 , -24.059998 ]],\n\n [[-24.36 , -26.160004 , -23.460007 ],\n [-15.959991 , -22.86 , -22.960007 ]],\n\n [[-17.559998 , -15.359985 , -13.660004 ],\n [-13.76001 , -15.959991 , -14.459991 ]]], dtype=float32)Coordinates: (3)lat(lat)float3240.0 37.5standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([40. , 37.5], dtype=float32)lon(lon)float32252.5 255.0 257.5standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([252.5, 255. , 257.5], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nWhat if we want temperature for a point, for example Denver, CO (39.72510678889283 N, -104.98785545855408 E). xarray can handle this! If we just want data from the nearest grid point, we can use sel and specify the method as “nearest”.\n\ndenver_lat, denver_lon = 39.72510678889283, -104.98785545855408\n\n\nds.air.sel(lat=denver_lat, lon=360+denver_lon, method='nearest').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nIf we want to interpolate, we can use interp(). In this case I use linear or bilinear interpolation.\ninterp() can also be used to resample data to a new grid and even reproject data\n\nds.air.interp(lat=denver_lat, lon=360+denver_lon, method='linear')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.DataArray 'air' (time: 2920)>\narray([ -8.95085077, -14.49752791, -18.43715163, ..., -27.78736503,\n -25.78552388, -15.41780902])\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n lat float64 39.73\n lon float64 255.0\nAttributes:\n long_name: 4xDaily Air temperature at sigma level 995\n units: degC\n precision: 2\n GRIB_id: 11\n GRIB_name: TMP\n var_desc: Air temperature\n dataset: NMC Reanalysis\n level_desc: Surface\n statistic: Individual Obs\n parent_stat: Other\n actual_range: [185.16 322.1 ]xarray.DataArray'air'time: 2920-8.951 -14.5 -18.44 -11.33 -8.942 ... -22.4 -27.79 -25.79 -15.42array([ -8.95085077, -14.49752791, -18.43715163, ..., -27.78736503,\n -25.78552388, -15.41780902])Coordinates: (3)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')lat()float6439.73standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray(39.72510679)lon()float64255.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray(255.01214454)Attributes: (11)long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]\n\n\nsel() and interp() can also be used on Dataset objects.\n\nds.sel(lat=slice(41,37), lon=slice(360-109,360-102))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (lat: 2, time: 2920, lon: 3)\nCoordinates:\n * lat (lat) float32 40.0 37.5\n * lon (lon) float32 252.5 255.0 257.5\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\nData variables:\n air (time, lat, lon) float32 -10.05 -9.25 -8.75 ... -15.96 -14.46\n air_kelvin (time, lat, lon) float32 263.1 263.9 264.4 ... 259.4 257.2 258.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:lat: 2time: 2920lon: 3Coordinates: (3)lat(lat)float3240.0 37.5standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([40. , 37.5], dtype=float32)lon(lon)float32252.5 255.0 257.5standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([252.5, 255. , 257.5], dtype=float32)time(time)datetime64[ns]2013-01-01 ... 2014-12-31T18:00:00standard_name :timelong_name :Timearray(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n dtype='datetime64[ns]')Data variables: (2)air(time, lat, lon)float32-10.05 -9.25 ... -15.96 -14.46long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[-10.049988 , -9.25 , -8.75 ],\n [ -6.25 , -6.549988 , -6.3599854]],\n\n [[-18.149994 , -14.950012 , -9.950012 ],\n [-13.649994 , -11.049988 , -7.25 ]],\n\n [[-20.449997 , -18.649994 , -13.359985 ],\n [-19.350006 , -16.950012 , -11.25 ]],\n\n ...,\n\n [[-24.460007 , -28.259995 , -25.759995 ],\n [-16.959991 , -24.059998 , -24.059998 ]],\n\n [[-24.36 , -26.160004 , -23.460007 ],\n [-15.959991 , -22.86 , -22.960007 ]],\n\n [[-17.559998 , -15.359985 , -13.660004 ],\n [-13.76001 , -15.959991 , -14.459991 ]]], dtype=float32)air_kelvin(time, lat, lon)float32263.1 263.9 264.4 ... 257.2 258.7long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[263.1 , 263.9 , 264.4 ],\n [266.9 , 266.6 , 266.79 ]],\n\n [[255. , 258.19998, 263.19998],\n [259.5 , 262.1 , 265.9 ]],\n\n [[252.7 , 254.5 , 259.79 ],\n [253.79999, 256.19998, 261.9 ]],\n\n ...,\n\n [[248.68999, 244.89 , 247.39 ],\n [256.19 , 249.09 , 249.09 ]],\n\n [[248.79 , 246.98999, 249.68999],\n [257.19 , 250.29 , 250.18999]],\n\n [[255.59 , 257.79 , 259.49 ],\n [259.38998, 257.19 , 258.69 ]]], dtype=float32)Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html\n\n\n\nds.interp(lat=denver_lat, lon=360+denver_lon, method='linear')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 2920)\nCoordinates:\n * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n lat float64 39.73\n lon float64 255.0\nData variables:\n air (time) float64 -8.951 -14.5 -18.44 ... -27.79 -25.79 -15.42\n air_kelvin (time) float64 264.2 258.7 254.7 261.8 ... 245.4 247.4 257.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). 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+ "objectID": "notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html#read-the-s3-zarr-endpoints-and-aggregate-to-single-zarr",
+ "href": "notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html#read-the-s3-zarr-endpoints-and-aggregate-to-single-zarr",
+ "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
+ "section": "Read the S3 Zarr endpoints and aggregate to single Zarr",
+ "text": "Read the S3 Zarr endpoints and aggregate to single Zarr\n\n# 1) read the AWS credentials\nprint(ecco_response['message'])\nwith requests.get(ecco_response['links'][2]['href']) as r:\n creds = r.json()\n\nprint( creds.keys() )\nprint(\"AWS credentials expire on: \", creds['Expiration'] )\n\n\n# 2) print root directory and read the s3 URLs into a list\ns3_dir2 = ecco_response['links'][3]['href']\nprint(\"root directory:\", s3_dir2)\ns3_urls2 = [u['href'] for u in ecco_response['links'][4:-1]]\n\n# sort the URLs in time order\ns3_urls2.sort()\n\n# 3) Autenticate AWS S3 credentials\ns3 = s3fs.S3FileSystem(\n key=creds['AccessKeyId'],\n secret=creds['SecretAccessKey'],\n token=creds['SessionToken'],\n client_kwargs={'region_name':'us-west-2'},\n)\n\n# 4) Read and concatenate into a single Zarr dataset\ntemp_ds = xr.concat([xr.open_zarr(s3.get_mapper(u)) for u in s3_urls2], dim=\"time\", coords='minimal')\n#temp_ds_group = xr.concat([xr.open_zarr(s3.get_mapper(u)) for u in s3_urls2], dim=\"time\", coords='minimal').groupby('time.month')\n\ntemp_ds\n\nThe job has completed successfully. 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Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g ho_{0} z$.\n\ncoverage_content_type :\n\nmodelResult\n\nlong_name :\n\nPotential temperature\n\nstandard_name :\n\nsea_water_potential_temperature\n\nunits :\n\ndegree_C\n\nvalid_max :\n\n36.032955169677734\n\nvalid_min :\n\n-2.2909388542175293\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArray\nChunk\n\n\n\n\nBytes\n9.32 GiB\n14.95 MiB\n\n\nShape\n(193, 50, 360, 720)\n(1, 50, 280, 280)\n\n\nCount\n2509 Tasks\n1158 Chunks\n\n\nType\nfloat32\nnumpy.ndarray\n\n\n\n\n\n\n\n\n\nAttributes: (62)Conventions :CF-1.8, ACDD-1.3acknowledgement :This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.author :Ian Fenty and Ou Wangcdm_data_type :Gridcomment :Fields provided on a regular lat-lon grid. They have been mapped to the regular lat-lon grid from the original ECCO lat-lon-cap 90 (llc90) native model grid.coordinates_comment :Note: the global 'coordinates' attribute describes auxillary coordinates.creator_email :ecco-group@mit.educreator_institution :NASA Jet Propulsion Laboratory (JPL)creator_name :ECCO Consortiumcreator_type :groupcreator_url :https://ecco-group.orgdate_created :2020-12-18T09:50:20date_issued :2020-12-18T09:50:20date_metadata_modified :2021-03-15T22:06:59date_modified :2021-03-15T22:06:59geospatial_bounds_crs :EPSG:4326geospatial_lat_max :90.0geospatial_lat_min :-90.0geospatial_lat_resolution :0.5geospatial_lat_units :degrees_northgeospatial_lon_max :180.0geospatial_lon_min :-180.0geospatial_lon_resolution :0.5geospatial_lon_units :degrees_eastgeospatial_vertical_max :0.0geospatial_vertical_min :-6134.5geospatial_vertical_positive :upgeospatial_vertical_resolution :variablegeospatial_vertical_units :meterhistory :Inaugural release of an ECCO Central Estimate solution to PO.DAACid :10.5067/ECG5M-OTS44institution :NASA Jet Propulsion Laboratory (JPL)instrument_vocabulary :GCMD instrument keywordskeywords :EARTH SCIENCE SERVICES > MODELS > EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY, EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > POTENTIAL TEMPERATUREkeywords_vocabulary :NASA Global Change Master Directory (GCMD) Science Keywordslicense :Public Domainmetadata_link :https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4naming_authority :gov.nasa.jplplatform :ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)platform_vocabulary :GCMD platform keywordsprocessing_level :L4product_name :OCEAN_TEMPERATURE_SALINITY_mon_mean_2001-12_ECCO_V4r4_latlon_0p50deg.ncproduct_time_coverage_end :2018-01-01T00:00:00product_time_coverage_start :1992-01-01T12:00:00product_version :Version 4, Release 4program :NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)project :Estimating the Circulation and Climate of the Ocean (ECCO)publisher_email :podaac@podaac.jpl.nasa.govpublisher_institution :PO.DAACpublisher_name :Physical Oceanography Distributed Active Archive Center (PO.DAAC)publisher_type :institutionpublisher_url :https://podaac.jpl.nasa.govreferences :ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., & Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928source :The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint methodstandard_name_vocabulary :NetCDF Climate and Forecast (CF) Metadata Conventionsummary :This dataset provides monthly-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.time_coverage_duration :P1Mtime_coverage_end :2002-01-01T00:00:00time_coverage_resolution :P1Mtime_coverage_start :2001-12-01T00:00:00title :ECCO Ocean Temperature and Salinity - Monthly Mean 0.5 Degree (Version 4 Release 4)uuid :7f718714-4159-11eb-8bbd-0cc47a3f819b"
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- "text": "Analysis\nAs a simple example, let’s try to calculate a mean field for the whole time range.\n\nds.mean(dim='time').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nWe can also calculate a zonal mean (averaging over longitude)\n\nds.mean(dim='lon').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nOther aggregation methods include min(), max(), std(), along with others.\n\nds.std(dim='time').hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\nThe data we have are in 6h timesteps. This can be resampled to daily or monthly. If you are familiar with pandas, xarray uses the same methods.\n\nds.resample(time='M').mean().hvplot()\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\nds_mon = ds.resample(time='M').mean()\nds_mon\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (time: 24, lat: 25, lon: 53)\nCoordinates:\n * time (time) datetime64[ns] 2013-01-31 2013-02-28 ... 2014-12-31\n * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0\n * lon (lon) float32 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0\nData variables:\n air (time, lat, lon) float32 -28.68 -28.49 -28.48 ... 24.57 24.56\n air_kelvin (time, lat, lon) float32 244.5 244.7 244.7 ... 297.7 297.7 297.7\nAttributes:\n Conventions: COARDS\n title: 4x daily NMC reanalysis (1948)\n description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n platform: Model\n references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...xarray.DatasetDimensions:time: 24lat: 25lon: 53Coordinates: (3)time(time)datetime64[ns]2013-01-31 ... 2014-12-31array(['2013-01-31T00:00:00.000000000', '2013-02-28T00:00:00.000000000',\n '2013-03-31T00:00:00.000000000', '2013-04-30T00:00:00.000000000',\n '2013-05-31T00:00:00.000000000', '2013-06-30T00:00:00.000000000',\n '2013-07-31T00:00:00.000000000', '2013-08-31T00:00:00.000000000',\n '2013-09-30T00:00:00.000000000', '2013-10-31T00:00:00.000000000',\n '2013-11-30T00:00:00.000000000', '2013-12-31T00:00:00.000000000',\n '2014-01-31T00:00:00.000000000', '2014-02-28T00:00:00.000000000',\n '2014-03-31T00:00:00.000000000', '2014-04-30T00:00:00.000000000',\n '2014-05-31T00:00:00.000000000', '2014-06-30T00:00:00.000000000',\n '2014-07-31T00:00:00.000000000', '2014-08-31T00:00:00.000000000',\n '2014-09-30T00:00:00.000000000', '2014-10-31T00:00:00.000000000',\n '2014-11-30T00:00:00.000000000', '2014-12-31T00:00:00.000000000'],\n dtype='datetime64[ns]')lat(lat)float3275.0 72.5 70.0 ... 20.0 17.5 15.0standard_name :latitudelong_name :Latitudeunits :degrees_northaxis :Yarray([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n 15. ], dtype=float32)lon(lon)float32200.0 202.5 205.0 ... 327.5 330.0standard_name :longitudelong_name :Longitudeunits :degrees_eastaxis :Xarray([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n 325. , 327.5, 330. ], dtype=float32)Data variables: (2)air(time, lat, lon)float32-28.68 -28.49 ... 24.57 24.56long_name :4xDaily Air temperature at sigma level 995units :degCprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[-28.68323 , -28.486452 , -28.479755 , ..., -30.658554 ,\n -29.743628 , -28.474194 ],\n [-26.076784 , -26.127504 , -26.4225 , ..., -32.5679 ,\n -31.105167 , -28.442825 ],\n [-22.770565 , -23.31516 , -24.042498 , ..., -31.165657 ,\n -28.38291 , -24.144924 ],\n ...,\n [ 22.688152 , 22.00097 , 21.773153 , ..., 22.218397 ,\n 21.734531 , 21.118395 ],\n [ 23.31952 , 23.16702 , 22.698233 , ..., 22.43775 ,\n 22.190727 , 21.715578 ],\n [ 23.903486 , 23.89203 , 23.585333 , ..., 23.154608 ,\n 22.947426 , 22.889124 ]],\n\n [[-32.41607 , -32.44866 , -32.738483 , ..., -31.54482 ,\n -30.430185 , -29.205448 ],\n [-31.216885 , -31.08063 , -31.236965 , ..., -32.135708 ,\n -30.825186 , -28.42241 ],\n [-27.826433 , -28.123934 , -28.78045 , ..., -29.734114 ,\n -27.383936 , -23.491434 ],\n...\n [ 24.899088 , 24.200085 , 24.072004 , ..., 24.861843 ,\n 24.510258 , 23.995668 ],\n [ 25.815008 , 25.661922 , 25.121607 , ..., 24.954088 ,\n 25.071083 , 24.735588 ],\n [ 26.023424 , 26.06767 , 25.74576 , ..., 25.566338 ,\n 25.591848 , 25.630259 ]],\n\n [[-26.348473 , -26.260897 , -26.380894 , ..., -33.07903 ,\n -32.067986 , -30.868315 ],\n [-25.419994 , -24.849277 , -24.405483 , ..., -34.531376 ,\n -32.82783 , -30.179682 ],\n [-23.181051 , -23.56476 , -23.574757 , ..., -35.446938 ,\n -31.91259 , -26.923311 ],\n ...,\n [ 23.299198 , 22.541454 , 22.60839 , ..., 23.378307 ,\n 23.067505 , 22.662996 ],\n [ 24.295895 , 24.286139 , 24.031782 , ..., 23.80259 ,\n 23.908312 , 23.579037 ],\n [ 24.897346 , 25.076134 , 24.909689 , ..., 24.547583 ,\n 24.573233 , 24.560413 ]]], dtype=float32)air_kelvin(time, lat, lon)float32244.5 244.7 244.7 ... 297.7 297.7long_name :4xDaily Air temperature at sigma level 995units :degKprecision :2GRIB_id :11GRIB_name :TMPvar_desc :Air temperaturedataset :NMC Reanalysislevel_desc :Surfacestatistic :Individual Obsparent_stat :Otheractual_range :[185.16 322.1 ]array([[[244.4667 , 244.66354, 244.67027, ..., 242.49142, 243.40633,\n 244.67577],\n [247.07323, 247.02248, 246.7275 , ..., 240.58205, 242.04489,\n 244.70726],\n [250.37941, 249.83484, 249.10748, ..., 241.98434, 244.76712,\n 249.00505],\n ...,\n [295.83795, 295.15085, 294.9229 , ..., 295.36826, 294.88437,\n 294.26828],\n [296.46942, 296.31686, 295.84802, ..., 295.5876 , 295.34058,\n 294.86536],\n [297.05316, 297.0418 , 296.73517, ..., 296.30438, 296.09732,\n 296.0389 ]],\n\n [[240.73384, 240.7013 , 240.4115 , ..., 241.60518, 242.71988,\n 243.94455],\n [241.93309, 242.06935, 241.913 , ..., 241.01428, 242.32481,\n 244.72758],\n [245.32361, 245.0261 , 244.36955, ..., 243.41588, 245.7661 ,\n 249.65858],\n...\n [298.04895, 297.35007, 297.22195, ..., 298.01172, 297.66013,\n 297.14554],\n [298.96484, 298.81186, 298.27136, ..., 298.10403, 298.22104,\n 297.88547],\n [299.17334, 299.2175 , 298.89566, ..., 298.71625, 298.74167,\n 298.7802 ]],\n\n [[246.80156, 246.88907, 246.76907, ..., 240.07089, 241.08206,\n 242.2817 ],\n [247.72998, 248.30064, 248.74443, ..., 238.61859, 240.3222 ,\n 242.97026],\n [249.96893, 249.58516, 249.57521, ..., 237.70308, 241.23743,\n 246.22667],\n ...,\n [296.4491 , 295.6914 , 295.75824, ..., 296.52817, 296.21747,\n 295.8128 ],\n [297.44586, 297.43613, 297.1817 , ..., 296.95242, 297.05823,\n 296.72897],\n [298.0472 , 298.22598, 298.0595 , ..., 297.6975 , 297.72318,\n 297.71024]]], dtype=float32)Attributes: (5)Conventions :COARDStitle :4x daily NMC reanalysis (1948)description :Data is from NMC initialized reanalysis\n(4x/day). These are the 0.9950 sigma level values.platform :Modelreferences :http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html\n\n\nThis is a really short time series but as an example, let’s calculate a monthly climatology (at least for 2 months). For this we can use groupby()\n\nds_clim = ds_mon.groupby(ds_mon.time.dt.month).mean()"
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+ "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
+ "section": "Do data masking, calculate a SST anomaly, and plot some figures",
+ "text": "Do data masking, calculate a SST anomaly, and plot some figures\n\n# Mask for the good data. Everything else defaults to NaN\n# SST missing value 9.9692100e+36 \n# SSH missing value 9.9692100e+36\ncond = (ssh_ds < 1000)\nssh_ds_masked = ssh_ds['SSH'].where(cond)\n\ncond = (temp_ds < 1000)\ntemp_ds_masked = temp_ds['THETA'].where(cond)\n\n# Derive a SST climatology and subtract it from the SST to create SST anomaly and remove trends\nclimatology_mean = temp_ds_masked.groupby('time.month').mean('time',keep_attrs=True,skipna=False)\ntemp_ds_masked_anomaly = temp_ds_masked.groupby('time.month') - climatology_mean # subtract out longterm monthly mean\n\nfig,ax=plt.subplots(1,3,figsize=(25,5))\n\n# take a slice of the Indian Ocean and plot SSH, SST, SST anomaly\nssh_ds_masked['SSH'][6].sel(longitude=slice(40,120),latitude=slice(-30,20)).plot(ax=ax[0], vmin=-0.5,vmax=1.25)\ntemp_ds_masked['THETA'][6].sel(longitude=slice(40,120),latitude=slice(-30,20), Z=slice(0,-5)).plot(ax=ax[1], vmin=10,vmax=32)\ntemp_ds_masked_anomaly['THETA'][6].sel(longitude=slice(40,120),latitude=slice(-30,20), Z=slice(0,-5)).plot(ax=ax[2], vmin=-2,vmax=2)\n\n<matplotlib.collections.QuadMesh at 0x7fe46b00b460>"
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- "title": "Xarray",
- "section": "Plot results",
- "text": "Plot results\nFinally, let’s plot the results! This will plot the lat/lon axes of the original ds DataArray.\n\nds_clim.air.sel(month=10).plot()\n\n<matplotlib.collections.QuadMesh at 0x7f22bb7acd90>"
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+ "href": "notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html#perform-the-correlations-in-the-east-and-west-indian-ocean",
+ "title": "The spatial Correlation between sea surface temperature anomaly and sea surface height anomaly in the Indian Ocean – A demo using ECCO",
+ "section": "Perform the correlations in the east and west Indian Ocean",
+ "text": "Perform the correlations in the east and west Indian Ocean\n\n# Western and Eastern Indian Ocean regions (WIO and EIO respectively)\n# EIO; 90 –110 E, 10 S–0N\n# WIO; 50 –70 E, 10 S–10 N\n\n# Group Eastern Indian Ocean data by month. This will make the correlation of all monthly values straightforwrd.\nssh_group = ssh_ds_masked['SSH'].sel(longitude=slice(90,110),latitude=slice(-10,0)).groupby('time.month')\n#temp_group = temp_ds_masked['THETA'].sel(longitude=slice(90,110),latitude=slice(-10,0), Z=-5.0).drop('Z').groupby('time.month')\ntemp_group = temp_ds_masked_anomaly['THETA'].sel(longitude=slice(90,110),latitude=slice(-10,0), Z=-5.0).drop('Z').groupby('time.month')\n\nprint(\" Running correlations in eastern Indian Ocean . . .\\n\") \ncorr = []\nfor month in range(1,13):\n corr.append(xr.corr(ssh_group[month], temp_group[month]))\n #print(\"\\nthe correlation in the east is: \" , xr.corr(ssh_group[month], temp_group[month]).values)\n \n# Do some plotting\nfig,ax=plt.subplots(1,2,figsize=(14,8))\n\nax[0].set_title(\"Spatial correlation in Eastern Indian Ocean\",fontsize=16)\nax[0].set_ylabel(\"Correlation\",fontsize=16)\nax[0].set_xlabel(\"Month\",fontsize=16)\nax[0].set_ylim([-1, 1])\nax[0].plot(corr)\n\n# Repeat for Western Indian Ocean\n# Group the data by month. This will make the correlation of all monthly values straightforwrd.\nssh_group = ssh_ds_masked['SSH'].sel(longitude=slice(50,70),latitude=slice(-10,10)).groupby('time.month')\n#temp_group = temp_ds_masked['THETA'].sel(longitude=slice(50,70),latitude=slice(-10,10), Z=-5.0).drop('Z').groupby('time.month')\ntemp_group = temp_ds_masked_anomaly['THETA'].sel(longitude=slice(50,70),latitude=slice(-10,10), Z=-5.0).drop('Z').groupby('time.month')\n\n\nprint(\" Running correlations in western Indian Ocean . . .\\n\") \ncorr2 =[]\nfor month in range(1,13):\n corr2.append(xr.corr(ssh_group[month], temp_group[month]))\n #print(\"\\nthe correlation in the west is: \" , xr.corr(ssh_group[month], temp_group[month]).values)\n \nax[1].set_title(\"Spatial correlation in Western Indian Ocean\",fontsize=16)\nax[1].set_ylabel(\"Correlation\",fontsize=16)\nax[1].set_xlabel(\"Month\",fontsize=16)\nax[1].set_ylim([-1, 1])\nax[1].plot(corr2)\n\n Running correlations in eastern Indian Ocean . . .\n\n Running correlations in western Indian Ocean . . ."
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- "title": "How to Access Data Directly in Cloud (netCDF)",
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+ "href": "notebooks/Tutorials_TEMPLATE.html",
+ "title": "Tutorial Title",
"section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository."
+ "text": "Internal Note:"
},
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- "objectID": "external/Direct_S3_Access_NetCDF.html#summary",
- "href": "external/Direct_S3_Access_NetCDF.html#summary",
- "title": "How to Access Data Directly in Cloud (netCDF)",
+ "objectID": "notebooks/Tutorials_TEMPLATE.html#summary",
+ "href": "notebooks/Tutorials_TEMPLATE.html#summary",
+ "title": "Tutorial Title",
"section": "Summary",
- "text": "Summary\nIn this notebook, we will access monthly sea surface height from ECCO V4r4 (10.5067/ECG5D-SSH44). The data are provided as a time series of monthly netCDFs on a 0.5-degree latitude/longitude grid.\nWe will access the data from inside the AWS cloud (us-west-2 region, specifically) and load a time series made of multiple netCDF datasets into an xarray dataset. This approach leverages S3 native protocols for efficient access to the data."
+ "text": "Summary\n[Add summary/tutorial description here]"
},
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- "objectID": "external/Direct_S3_Access_NetCDF.html#requirements",
- "href": "external/Direct_S3_Access_NetCDF.html#requirements",
- "title": "How to Access Data Directly in Cloud (netCDF)",
+ "objectID": "notebooks/Tutorials_TEMPLATE.html#requirements",
+ "href": "notebooks/Tutorials_TEMPLATE.html#requirements",
+ "title": "Tutorial Title",
"section": "Requirements",
- "text": "Requirements\n\n1. AWS instance running in us-west-2\nNASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n3. netrc File\nYou will need a netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
+ "text": "Requirements\n\n1. Compute environment\ninternal note (remove this note in final tutorial): keep one or both of these Req 1 depending on environment required to run the noteook\nThis tutorial can be run in the following environments: - AWS instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. - Local compute environment e.g. laptop, server: this tutorial can be run on your local machine\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n3. netrc File\nYou will need a .netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A .netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata tutorial.\ninternal note (delete in final tutorial) You can use the netrc approach in the notebook or leverage the earthaccess package.\n\n\n4. Additional Requirements\nAny other requirements needed for reproducing this tutorial."
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- "objectID": "external/Direct_S3_Access_NetCDF.html#learning-objectives",
- "href": "external/Direct_S3_Access_NetCDF.html#learning-objectives",
- "title": "How to Access Data Directly in Cloud (netCDF)",
+ "objectID": "notebooks/Tutorials_TEMPLATE.html#learning-objectives",
+ "href": "notebooks/Tutorials_TEMPLATE.html#learning-objectives",
+ "title": "Tutorial Title",
"section": "Learning Objectives",
- "text": "Learning Objectives\n\nhow to retrieve temporary S3 credentials for in-region direct S3 bucket access\nhow to define a dataset of interest and find netCDF files in S3 bucket\nhow to perform in-region direct access of ECCO_L4_SSH_05DEG_MONTHLY_V4R4 data in S3\nhow to plot the data"
+ "text": "Learning Objectives\n\nenter objective\nenter objective\n…"
},
{
- "objectID": "external/Direct_S3_Access_NetCDF.html#import-packages",
- "href": "external/Direct_S3_Access_NetCDF.html#import-packages",
- "title": "How to Access Data Directly in Cloud (netCDF)",
+ "objectID": "notebooks/Tutorials_TEMPLATE.html#import-packages",
+ "href": "notebooks/Tutorials_TEMPLATE.html#import-packages",
+ "title": "Tutorial Title",
"section": "Import Packages",
- "text": "Import Packages\n\nimport os\nimport requests\nimport s3fs\nimport xarray as xr\nimport hvplot.xarray"
+ "text": "Import Packages\ninternal note (delete in final tutorial): update the cell below for specific tutorial\n\n# e.g. \nimport os\nimport requests\nimport s3fs\nimport xarray as xr\nimport hvplot.xarray\n\nInternal Note (delete in final tutorial): The following section is optional. Keep if working in the cloud, remove if tutorial is for local workflow."
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- "objectID": "external/Direct_S3_Access_NetCDF.html#get-temporary-aws-credentials",
- "href": "external/Direct_S3_Access_NetCDF.html#get-temporary-aws-credentials",
- "title": "How to Access Data Directly in Cloud (netCDF)",
- "section": "Get Temporary AWS Credentials",
- "text": "Get Temporary AWS Credentials\nDirect S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. For now, each NASA DAAC has different AWS credentials endpoints. Below are some of the credential endpoints to various DAACs:\n\ns3_cred_endpoint = {\n 'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'gesdisc': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',\n 'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\n 'ornldaac': 'https://data.ornldaac.earthdata.nasa.gov/s3credentials',\n 'ghrcdaac': 'https://data.ghrc.earthdata.nasa.gov/s3credentials'\n}\n\nCreate a function to make a request to an endpoint for temporary credentials. Remember, each DAAC has their own endpoint and credentials are not usable for cloud data from other DAACs.\n\ndef get_temp_creds(provider):\n return requests.get(s3_cred_endpoint[provider]).json()\n\n\ntemp_creds_req = get_temp_creds('podaac')\n#temp_creds_req"
+ "objectID": "notebooks/Tutorials_TEMPLATE.html#get-temporary-aws-credentials-optional-section-for-cloud-use",
+ "href": "notebooks/Tutorials_TEMPLATE.html#get-temporary-aws-credentials-optional-section-for-cloud-use",
+ "title": "Tutorial Title",
+ "section": "Get Temporary AWS Credentials (Optional section for cloud use)",
+ "text": "Get Temporary AWS Credentials (Optional section for cloud use)\nDirect S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. For now, each NASA DAAC has different AWS credentials endpoints. Below are some of the credential endpoints to various DAACs:\n\ns3_cred_endpoint = {\n 'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'gesdisc': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',\n 'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\n 'ornldaac': 'https://data.ornldaac.earthdata.nasa.gov/s3credentials',\n 'ghrcdaac': 'https://data.ghrc.earthdata.nasa.gov/s3credentials'\n}\n\nCreate a function to make a request to an endpoint for temporary credentials. Remember, each DAAC has their own endpoint and credentials are not usable for cloud data from other DAACs.\n\ndef get_temp_creds(provider):\n return requests.get(s3_cred_endpoint[provider]).json()\n\n\ntemp_creds_req = get_temp_creds('podaac')\n#temp_creds_req"
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- "objectID": "external/Direct_S3_Access_NetCDF.html#set-up-an-s3fs-session-for-direct-access",
- "href": "external/Direct_S3_Access_NetCDF.html#set-up-an-s3fs-session-for-direct-access",
- "title": "How to Access Data Directly in Cloud (netCDF)",
- "section": "Set up an s3fs session for Direct Access",
- "text": "Set up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint.\n\nfs_s3 = s3fs.S3FileSystem(anon=False, \n key=temp_creds_req['accessKeyId'], \n secret=temp_creds_req['secretAccessKey'], \n token=temp_creds_req['sessionToken'],\n client_kwargs={'region_name':'us-west-2'})\n\nIn this example we’re interested in the ECCO data collection from NASA’s PO.DAAC in Earthdata Cloud. In this case it’s the following string that unique identifies the collection of monthly, 0.5-degree sea surface height data (ECCO_L4_SSH_05DEG_MONTHLY_V4R4).\n\nshort_name = 'ECCO_L4_SSH_05DEG_MONTHLY_V4R4'\n\n\nbucket = os.path.join('podaac-ops-cumulus-protected/', short_name, '*2015*.nc')\nbucket\n\nGet a list of netCDF files located at the S3 path corresponding to the ECCO V4r4 monthly sea surface height dataset on the 0.5-degree latitude/longitude grid, for year 2015.\n\nssh_files = fs_s3.glob(bucket)\nssh_files"
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+ "href": "notebooks/sentinel-6/Access_Sentinel6_NRT.html",
+ "title": "Access Sentinel-6 NRT Data",
+ "section": "",
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.\nThis notebook shows a simple way to maintain a local time series of Sentinel-6 NRT data using the CMR Search API. It downloads granules the ingested since the previous run to a designated data folder and overwrites a hidden file inside with the timestamp of the CMR Search request on success."
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- "href": "external/Direct_S3_Access_NetCDF.html#direct-in-region-access",
- "title": "How to Access Data Directly in Cloud (netCDF)",
- "section": "Direct In-region Access",
- "text": "Direct In-region Access\nOpen with the netCDF files using the s3fs package, then load them all at once into a concatenated xarray dataset.\n\nfileset = [fs_s3.open(file) for file in ssh_files]\n\nCreate an xarray dataset using the open_mfdataset() function to “read in” all of the netCDF4 files in one call.\n\nssh_ds = xr.open_mfdataset(fileset,\n combine='by_coords',\n mask_and_scale=True,\n decode_cf=True,\n chunks='auto')\nssh_ds\n\nGet the SSH variable as an xarray dataarray\n\nssh_da = ssh_ds.SSH\nssh_da\n\nPlot the SSH time series using hvplot\n\nssh_da.hvplot.image(y='latitude', x='longitude', cmap='Viridis',).opts(clim=(ssh_da.attrs['valid_min'][0],ssh_da.attrs['valid_max'][0]))"
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+ "href": "notebooks/sentinel-6/Access_Sentinel6_NRT.html#before-you-start",
+ "title": "Access Sentinel-6 NRT Data",
+ "section": "Before you start",
+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an Earthdata account: https://urs.earthdata.nasa.gov for the operations envionrment (most common) or https://uat.urs.earthdata.nasa.gov for the UAT environment.\nAccounts are free to create and take just a moment to set up."
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- "href": "external/Direct_S3_Access_NetCDF.html#resources",
- "title": "How to Access Data Directly in Cloud (netCDF)",
- "section": "Resources",
- "text": "Resources\nDirect access to ECCO data in S3 (from us-west-2)\nData_Access__Direct_S3_Access__PODAAC_ECCO_SSH using CMR-STAC API to retrieve S3 links"
+ "objectID": "notebooks/sentinel-6/Access_Sentinel6_NRT.html#authentication-setup",
+ "href": "notebooks/sentinel-6/Access_Sentinel6_NRT.html#authentication-setup",
+ "title": "Access Sentinel-6 NRT Data",
+ "section": "Authentication setup",
+ "text": "Authentication setup\nWe need some boilerplate up front to log in to Earthdata Login. The function below will allow Python scripts to log into any Earthdata Login application programmatically. To avoid being prompted for credentials every time you run and also allow clients such as curl to log in, you can add the following to a .netrc (_netrc on Windows) file in your home directory:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nMake sure that this file is only readable by the current user or you will receive an error stating “netrc access too permissive.”\n$ chmod 0600 ~/.netrc\nYou’ll need to authenticate using the netrc method when running from command line with papermill. You can log in manually by executing the cell below when running in the notebook client in your browser.\n\nfrom urllib import request\nfrom http.cookiejar import CookieJar\nimport getpass\nimport netrc\n\n\ndef setup_earthdata_login_auth(endpoint):\n \"\"\"\n Set up the request library so that it authenticates against the given Earthdata Login\n endpoint and is able to track cookies between requests. This looks in the .netrc file \n first and if no credentials are found, it prompts for them.\n\n Valid endpoints include:\n urs.earthdata.nasa.gov - Earthdata Login production\n \"\"\"\n try:\n username, _, password = netrc.netrc().authenticators(endpoint)\n except (FileNotFoundError, TypeError):\n # FileNotFound = There's no .netrc file\n # TypeError = The endpoint isn't in the netrc file, causing the above to try unpacking None\n print('Please provide your Earthdata Login credentials to allow data access')\n print('Your credentials will only be passed to %s and will not be exposed in Jupyter' % (endpoint))\n username = input('Username:')\n password = getpass.getpass()\n\n manager = request.HTTPPasswordMgrWithDefaultRealm()\n manager.add_password(None, endpoint, username, password)\n auth = request.HTTPBasicAuthHandler(manager)\n\n jar = CookieJar()\n processor = request.HTTPCookieProcessor(jar)\n opener = request.build_opener(auth, processor)\n request.install_opener(opener)\n\n\nsetup_earthdata_login_auth('urs.earthdata.nasa.gov')\n\nPlease provide your Earthdata Login credentials to allow data access\nYour credentials will only be passed to urs.earthdata.nasa.gov and will not be exposed in Jupyter\n\n\nUsername: nickles\n ···········\n\n\n\nimport requests\nfrom os import makedirs\nfrom os.path import isdir, basename\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen, urlretrieve\nfrom datetime import datetime, timedelta\nfrom json import dumps, loads"
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- "href": "external/DownloadDopplerScattData.html",
- "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
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+ "href": "notebooks/sentinel-6/Access_Sentinel6_NRT.html#hands-off-workflow",
+ "title": "Access Sentinel-6 NRT Data",
+ "section": "Hands-off workflow",
+ "text": "Hands-off workflow\nThis workflow/notebook can be run routinely to maintain a time series of NRT data, downloading new granules as they become available in CMR.\nThe notebook writes/overwrites a file .update to the target data directory with each successful run. The file tracks to date and time of the most recent update to the time series of NRT granules using a timestamp in the format yyyy-mm-ddThh:mm:ssZ.\nThe timestamp matches the value used for the created_at parameter in the last successful run. This parameter finds the granules created within a range of datetimes. This workflow leverages the created_at parameter to search backwards in time for new granules ingested between the time of our timestamp and now.\nThe variables in the cell below determine the workflow behavior on its initial run:\n\nmins: Initialize a new local time series by starting with the granules ingested since ___ minutes ago.\ncmr: The domain of the target CMR instance, either cmr.earthdata.nasa.gov.\nccid: The unique CMR concept-id of the desired collection.\ndata: The path to a local directory in which to download/maintain a copy of the NRT granule time series.\n\n\ncmr = \"cmr.earthdata.nasa.gov\"\n\n# this function returns a concept id for a particular dataset\ndef get_collection(url: str=f\"https://{cmr}/search/collections.umm_json\", **params):\n return requests.get(url, params).json().get(\"items\")[0]\n\n#\n# This cell accepts parameters from command line with papermill: \n# https://papermill.readthedocs.io\n#\n# These variables should be set before the first run, then they \n# should be left alone. All subsequent runs expect the values \n# for cmr, ccid, data to be unchanged. The mins value has no \n# impact on subsequent runs.\n#\n\nmins = 20\n\nname = \"JASON_CS_S6A_L2_ALT_LR_RED_OST_NRT_F\"\n\nccid = get_collection(ShortName=name).get(\"meta\").get(\"concept-id\")\n\ndata = \"resources/nrt\"\n\nThe variable data is pointed at a nearby folder resources/nrt by default. You should change data to a suitable download path on your file system. An unlucky sequence of git commands could disappear that folder and its downloads if your not careful. Just change it.\nThe Python imports relevant to the workflow\nThe search retrieves granules ingested during the last n minutes. A file in your local data dir file that tracks updates to your data directory, if one file exists. The CMR Search falls back on the ten minute window if not.\n\ntimestamp = (datetime.utcnow()-timedelta(minutes=mins)).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\ntimestamp\n\n'2022-11-08T00:15:46Z'\n\n\nThis cell will replace the timestamp above with the one read from the .update file in the data directory, if it exists.\n\nif not isdir(data):\n print(f\"NOTE: Making new data directory at '{data}'. (This is the first run.)\")\n makedirs(data)\nelse:\n try:\n with open(f\"{data}/.update\", \"r\") as f:\n timestamp = f.read()\n except FileNotFoundError:\n print(\"WARN: No .update in the data directory. (Is this the first run?)\")\n else:\n print(f\"NOTE: .update found in the data directory. (The last run was at {timestamp}.)\")\n\nNOTE: Making new data directory at 'resources/nrt'. (This is the first run.)\n\n\nThere are several ways to query for CMR updates that occured during a given timeframe. Read on in the CMR Search documentation:\n\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-new-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#c-with-revised-granules (Collections)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-production-date (Granules)\nhttps://cmr.earthdata.nasa.gov/search/site/docs/search/api.html#g-created-at (Granules)\n\nThe created_at parameter works for our purposes. It’s a granule search parameter that returns the records ingested since the input timestamp.\n\nparams = {\n 'scroll': \"true\",\n 'page_size': 2000,\n 'sort_key': \"-start_date\",\n 'collection_concept_id': ccid, \n 'created_at': timestamp,\n # Limit results to coverage for .5deg bbox in Gulf of Alaska:\n 'bounding_box': \"-146.5,57.5,-146,58\",\n}\n\nparams\n\n{'scroll': 'true',\n 'page_size': 2000,\n 'sort_key': '-start_date',\n 'collection_concept_id': 'C1968980576-POCLOUD',\n 'created_at': '2022-11-08T00:15:46Z',\n 'bounding_box': '-146.5,57.5,-146,58'}\n\n\nGet the query parameters as a string and then the complete search url:\n\nquery = urlencode(params)\nurl = f\"https://{cmr}/search/granules.umm_json?{query}\"\nprint(url)\n\nhttps://cmr.earthdata.nasa.gov/search/granules.umm_json?scroll=true&page_size=2000&sort_key=-start_date&collection_concept_id=C1968980576-POCLOUD&created_at=2022-11-08T00%3A15%3A46Z&bounding_box=-146.5%2C57.5%2C-146%2C58\n\n\nGet a new timestamp that represents the UTC time of the search. Then download the records in umm_json format for granules that match our search parameters:\n\nwith urlopen(url) as f:\n results = loads(f.read().decode())\n\nprint(f\"{results['hits']} new granules ingested for '{ccid}' since '{timestamp}'.\")\n\ntimestamp = datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n\n0 new granules ingested for 'C1968980576-POCLOUD' since '2022-11-08T00:15:46Z'.\n\n\nNeatly print the first granule record (if one was returned):\n\nif len(results['items'])>0:\n print(dumps(results['items'][0], indent=2))\n\nThe link for http access can be retrieved from each granule record’s RelatedUrls field. The download link is identified by \"Type\": \"GET DATA\" .\nSelect the download URL for each of the granule records:\n\ndownloads = [[u['URL'] for u in r['umm']['RelatedUrls'] if u['Type']==\"GET DATA\"][0] for r in results['items']]\ndownloads\n\n[]\n\n\nFinish by downloading the files to the data directory in a loop. Overwrite .update with a new timestamp on success.\n\nfor f in downloads:\n try:\n urlretrieve(f, f\"{data}/{basename(f)}\")\n except Exception as e:\n print(f\"[{datetime.now()}] FAILURE: {f}\\n\\n{e}\\n\")\n raise e\n else:\n print(f\"[{datetime.now()}] SUCCESS: {f}\")\n\nIf there were updates to the local time series during this run and no exceptions were raised during the download loop, then overwrite the timestamp file that tracks updates to the data folder (resources/nrt/.update):\n\nif len(results['items'])>0:\n with open(f\"{data}/.update\", \"w\") as f:\n f.write(timestamp)"
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+ "href": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html",
+ "title": "Harmony Concise + L2SS-Py Demo",
"section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop"
+ "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
},
{
- "objectID": "external/DownloadDopplerScattData.html#create-a-netrc-file-if-non-existent.",
- "href": "external/DownloadDopplerScattData.html#create-a-netrc-file-if-non-existent.",
- "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
- "section": "Create a netrc file, if non-existent.",
- "text": "Create a netrc file, if non-existent.\nPrior to doing this, you must obtain an account with NASA Earthdata.\n\nnetrc_file = setup_netrc_file()\n\nnetrc file not found, please login into NASA Earthdata:\nnterc file written to /Users/erodrigu/.netrc\n\n\nEnter NASA Earthdata Login Username: ········\nEnter NASA Earthdata Login Password: ········"
+ "objectID": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#what-is-concise",
+ "href": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#what-is-concise",
+ "title": "Harmony Concise + L2SS-Py Demo",
+ "section": "",
+ "text": "Concise is a Harmony service developed by PODAAC that allows users to concatenate multiple L2 granules together into a single granule. This concatenation is done by adding a new subset_index dimension to the resulting granule."
},
{
- "objectID": "external/DownloadDopplerScattData.html#install-the-podaac-download-ustility-using-pip",
- "href": "external/DownloadDopplerScattData.html#install-the-podaac-download-ustility-using-pip",
- "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
- "section": "Install the PODAAC download ustility using pip",
- "text": "Install the PODAAC download ustility using pip\n\n!pip install podaac-data-subscriber > pip.log"
+ "objectID": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#what-is-l2ss-py-concise",
+ "href": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#what-is-l2ss-py-concise",
+ "title": "Harmony Concise + L2SS-Py Demo",
+ "section": "What is L2SS-Py + Concise?",
+ "text": "What is L2SS-Py + Concise?\nHarmony supports chaining multiple services together. The L2SS-Py + Concise chain allows users to combine spatial, temporal, and variable subsetting with granule concatenation."
},
{
- "objectID": "external/DownloadDopplerScattData.html#use-the-dopplerscatt-utility-program-to-download-the-data",
- "href": "external/DownloadDopplerScattData.html#use-the-dopplerscatt-utility-program-to-download-the-data",
- "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
- "section": "Use the DopplerScatt utility program to download the data",
- "text": "Use the DopplerScatt utility program to download the data\nModify the destination directory, starting and ending dates (using the same format shown), and the download utility path (although this should not need to be modified).\n\ndownload_dopplerscatt_data(data_dir = '../data/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1',\n start_date = '2021-11-03T00:00:00Z',\n end_date = '2021-11-04T00:00:00Z',\n downloader='podaac-data-downloader')\n\n[2022-11-27 10:44:34,150] {podaac_data_downloader.py:155} INFO - NOTE: Making new data directory at ../data/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1(This is the first run.)\n[2022-11-27 10:44:35,376] {podaac_data_downloader.py:243} INFO - Found 1 total files to download\n[2022-11-27 10:44:58,149] {podaac_data_downloader.py:276} INFO - 2022-11-27 10:44:58.149797 SUCCESS: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1/dopplerscatt_20211103_125259.tomoL2CF.nc\n[2022-11-27 10:44:58,150] {podaac_data_downloader.py:287} INFO - Downloaded Files: 1\n[2022-11-27 10:44:58,150] {podaac_data_downloader.py:288} INFO - Failed Files: 0\n[2022-11-27 10:44:58,150] {podaac_data_downloader.py:289} INFO - Skipped Files: 0\n[2022-11-27 10:44:59,026] {podaac_access.py:122} INFO - CMR token successfully deleted\n[2022-11-27 10:44:59,026] {podaac_data_downloader.py:299} INFO - END\n\n\nSuccesfully downloaded desired DopplerScatt data."
+ "objectID": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#before-you-start",
+ "href": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#before-you-start",
+ "title": "Harmony Concise + L2SS-Py Demo",
+ "section": "Before you start",
+ "text": "Before you start\nBefore you beginning this tutorial, make sure you have an account in the Earthdata Login, which is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register for an Earthdata Login account. It is free to create and only takes a moment to set up.\nYou will also need a netrc file containing your NASA Earthdata Login credentials in order to execute this notebook. A netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata."
},
{
- "objectID": "external/DownloadDopplerScattData.html#cleanup-auxiliary-files-if-desired",
- "href": "external/DownloadDopplerScattData.html#cleanup-auxiliary-files-if-desired",
- "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
- "section": "Cleanup auxiliary files, if desired",
- "text": "Cleanup auxiliary files, if desired\nThe netrc file is not secure, so you may want to remove it if you are concerned about the security of your Earthdata credentials. WARNING This will remove your existing netrc file, which may already contain other information in addition to your Earthdata credentials.\n\n!rm pip.log $netrc_file"
+ "objectID": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#running-the-demo",
+ "href": "notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html#running-the-demo",
+ "title": "Harmony Concise + L2SS-Py Demo",
+ "section": "Running the Demo",
+ "text": "Running the Demo\nThe remaining notebook walks through constructing a request that first subsets multiple files from a collection and then concatenates the results together into a single output file. This is accomplished using the Harmony coverages API through the use of the harmony-py python library.\nThe collection being used in the demonstration is the ASCATB-L2-25km collection which contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 25 km sampling resolution.\nThe first step is to import the libraries needed to run the demo.\n\nimport xarray as xr\nimport tempfile\nfrom IPython.display import display, JSON\nfrom datetime import datetime, timedelta, time\nfrom harmony import BBox, Client, Collection, Request, Environment, LinkType\n\nfrom mpl_toolkits.basemap import Basemap\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport s3fs\n\nimport warnings\nwarnings.filterwarnings('ignore')\n%matplotlib inline\n\nCreate Harmony-py client.\n\nharmony_client = Client(env=Environment.PROD)\n\nWith the client created, we can contruct and validate the request. As this is a subsetting + concatenation request, we specify options on the request that define spatial bounds, variables we are interested in, temporal bounds, and indicated the result should be concatenated. Since this is a near real time dataset, we will request the data from yesterday.\n\ncollection = Collection(id='C2075141559-POCLOUD')\n\nyesterday = datetime.today() - timedelta(days=1)\n\nrequest = Request(\n collection=collection,\n spatial=BBox(-180, -30, 180, 30),\n variables=[\n 'wind_speed', \n 'wind_dir'\n ],\n temporal={\n 'start': datetime.combine(yesterday, time.min),\n 'stop': datetime.combine(yesterday, time.max)\n },\n concatenate=True\n)\n\nrequest.is_valid()\n\nTrue\n\n\nNow that we have a valid request we simply need to call the submit function using the client we created earlier and pass in the request as a parameter.\nTip: if you want to see the request before submitting it, use the request_as_curl function on the client to get an equivalent curl command for the request that will be submitted.\n\nprint(harmony_client.request_as_curl(request))\njob_id = harmony_client.submit(request)\nprint(f'Job ID: {job_id}')\n\ncurl -X GET -H 'Accept: */*' -H 'Accept-Encoding: gzip, deflate' -H 'Connection: keep-alive' -H 'Cookie: urs_user_already_logged=yes; token=*****; _urs-gui_session=046f3430c6ca2f9e3e00d94c0bee2f70' -H 'User-Agent: Windows/10 harmony-py/0.4.2 CPython/3.8.12 python-requests/2.25.1' 'https://harmony.earthdata.nasa.gov/C2075141559-POCLOUD/ogc-api-coverages/1.0.0/collections/wind_speed,wind_dir/coverage/rangeset?forceAsync=true&subset=lat%28-30%3A30%29&subset=lon%28-180%3A180%29&subset=time%28%222022-10-19T00%3A00%3A00%22%3A%222022-10-19T23%3A59%3A59.999999%22%29&concatenate=true'\nJob ID: 87ec4775-7949-482c-96b2-11f5e6941d15\n\n\nAfter submitting the request it is possible to retrieve the current processing status by using the job ID returned from the submission.\n\nharmony_client.status(job_id)\n\n{'status': 'running',\n 'message': 'The job is being processed',\n 'progress': 0,\n 'created_at': datetime.datetime(2022, 10, 20, 22, 45, 28, 721000, tzinfo=tzutc()),\n 'updated_at': datetime.datetime(2022, 10, 20, 22, 45, 29, 72000, tzinfo=tzutc()),\n 'created_at_local': '2022-10-20T15:45:28-07:00',\n 'updated_at_local': '2022-10-20T15:45:29-07:00',\n 'data_expiration': datetime.datetime(2022, 11, 19, 22, 45, 28, 721000, tzinfo=tzutc()),\n 'data_expiration_local': '2022-11-19T14:45:28-08:00',\n 'request': 'https://harmony.earthdata.nasa.gov/C2075141559-POCLOUD/ogc-api-coverages/1.0.0/collections/wind_speed,wind_dir/coverage/rangeset?forceAsync=true&subset=lat(-30%3A30)&subset=lon(-180%3A180)&subset=time(%222022-10-19T00%3A00%3A00%22%3A%222022-10-19T23%3A59%3A59.999999%22)&concatenate=true',\n 'num_input_granules': 16}\n\n\nIf the request is still running, we can wait until the Harmony request has finished processing. This cell will wait until the request has finised.\n\nharmony_client.wait_for_processing(job_id, show_progress=True)\n\n [ Processing: 100% ] |###################################################| [|]\n\n\nNow that the request has completed we can inspect the results using xarray and matplotlib.\nFirst, let’s download the result into a temporary directory\n\ntemp_dir = tempfile.mkdtemp()\nfutures = harmony_client.download_all(job_id, directory=temp_dir, overwrite=True)\nfile_names = [f.result() for f in futures]\nfile_names\n\n['C:\\\\Users\\\\nickles\\\\AppData\\\\Local\\\\Temp\\\\tmpqzco2nld\\\\C2075141559-POCLOUD_merged.nc4']\n\n\nWith the output file downloaded, now we can open concatenated granule using xarray to inspect some of the metadata.\nNotice the variable subset has been successfully executed – only wind_dir and wind_speed vars are present. In addition, there is a new dimension subset_index added to each variable in the dataset. The index of this dimension corresponds to the original file named in the subset_files variable that contained the data at that index.\n\nds = xr.open_dataset(file_names[0], decode_times=False)\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset>\nDimensions: (subset_index: 16, NUMROWS: 596, NUMCELLS: 42)\nCoordinates:\n lat (subset_index, NUMROWS, NUMCELLS) float32 ...\n lon (subset_index, NUMROWS, NUMCELLS) float32 ...\nDimensions without coordinates: subset_index, NUMROWS, NUMCELLS\nData variables:\n subset_files (subset_index) object 'ascat_20221018_222700_metopb_52328_e...\n time (subset_index, NUMROWS, NUMCELLS) float64 ...\n wind_speed (subset_index, NUMROWS, NUMCELLS) float32 ...\n wind_dir (subset_index, NUMROWS, NUMCELLS) float32 ...\nAttributes: (12/18)\n title: MetOp-B ASCAT Level 2 25.0 km Ocean Sur...\n title_short_name: ASCATB-L2-25km\n Conventions: CF-1.6\n institution: EUMETSAT/OSI SAF/KNMI\n source: MetOp-B ASCAT\n software_identification_level_1: 1000\n ... ...\n processing_level: L2\n rev_orbit_period: 6081.7\n orbit_inclination: 98.7\n references: ASCAT Wind Product User Manual, https:/...\n comment: Orbit period and inclination are consta...\n history_json: [{\"date_time\": \"2022-10-20T22:45:37.904...xarray.DatasetDimensions:subset_index: 16NUMROWS: 596NUMCELLS: 42Coordinates: (2)lat(subset_index, NUMROWS, NUMCELLS)float32...valid_min :-9000000valid_max :9000000standard_name :latitudelong_name :latitudeunits :degrees_north[400512 values with dtype=float32]lon(subset_index, NUMROWS, NUMCELLS)float32...valid_min :0valid_max :36000000standard_name :longitudelong_name :longitudeunits :degrees_east[400512 values with dtype=float32]Data variables: (4)subset_files(subset_index)object...long_name :List of subsetted files used to create this merge product.array(['ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_170300_metopb_52339_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_000900_metopb_52329_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_184500_metopb_52340_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_220600_metopb_52342_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_202700_metopb_52341_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_234800_metopb_52343_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_015100_metopb_52330_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_051200_metopb_52332_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_033300_metopb_52331_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_065400_metopb_52333_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_083600_metopb_52334_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_101800_metopb_52335_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_133900_metopb_52337_eps_o_250_3301_ovw.l2_subsetted.nc4',\n 'ascat_20221019_120000_metopb_52336_eps_o_250_3301_ovw.l2_subsetted.nc4'],\n dtype=object)time(subset_index, NUMROWS, NUMCELLS)float64...valid_min :0valid_max :2147483647standard_name :timelong_name :timeunits :seconds since 1990-01-01calendar :proleptic_gregorian[400512 values with dtype=float64]wind_speed(subset_index, NUMROWS, NUMCELLS)float32...valid_min :0valid_max :5000standard_name :wind_speedlong_name :wind speed at 10 munits :m s-1[400512 values with dtype=float32]wind_dir(subset_index, NUMROWS, NUMCELLS)float32...valid_min :0valid_max :3600standard_name :wind_to_directionlong_name :wind direction at 10 munits :degree[400512 values with dtype=float32]Attributes: (18)title :MetOp-B ASCAT Level 2 25.0 km Ocean Surface Wind Vector Producttitle_short_name :ASCATB-L2-25kmConventions :CF-1.6institution :EUMETSAT/OSI SAF/KNMIsource :MetOp-B ASCATsoftware_identification_level_1 :1000instrument_calibration_version :0software_identification_wind :3301pixel_size_on_horizontal :25.0 kmservice_type :epsprocessing_type :Ocontents :ovwprocessing_level :L2rev_orbit_period :6081.7orbit_inclination :98.7references :ASCAT Wind Product User Manual, https://osi-saf.eumetsat.int/, https://scatterometer.knmi.nl/comment :Orbit period and inclination are constant values. All wind directions in oceanographic convention (0 deg. flowing North)history_json :[{\"date_time\": \"2022-10-20T22:45:37.904685+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:40.891502+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": 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180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:43.732829+00:00\", \"derived_from\": \"https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/ASCATB-L2-25km/ascat_20221019_133900_metopb_52337_eps_o_250_3301_ovw.l2.nc\", \"program\": \"l2ss-py\", \"version\": \"2.2.0\", \"parameters\": \"bbox=[[-180, 180], [-30, 30]] cut=True\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}, {\"date_time\": \"2022-10-20T22:45:48.424799+00:00\", \"derived_from\": [\"ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2_subsetted.nc4\", \"ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2_subsetted.nc4\", 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\"ascat_20221019_120000_metopb_52336_eps_o_250_3301_ovw.l2_subsetted.nc4\"], \"program\": \"concise\", \"version\": \"0.5.0\", \"parameters\": \"input_files=[PosixPath('/tmp/tmp6qevy37z/ascat_20221018_222700_metopb_52328_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_152100_metopb_52338_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_170300_metopb_52339_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_000900_metopb_52329_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_184500_metopb_52340_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_220600_metopb_52342_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_202700_metopb_52341_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_234800_metopb_52343_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_015100_metopb_52330_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_051200_metopb_52332_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_033300_metopb_52331_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_065400_metopb_52333_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_083600_metopb_52334_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_101800_metopb_52335_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_133900_metopb_52337_eps_o_250_3301_ovw.l2_subsetted.nc4'), PosixPath('/tmp/tmp6qevy37z/ascat_20221019_120000_metopb_52336_eps_o_250_3301_ovw.l2_subsetted.nc4')]\", \"program_ref\": \"https://cmr.earthdata.nasa.gov:443/search/concepts/S2153799015-POCLOUD\", \"$schema\": \"https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json\"}]\n\n\nUsing matplotlib, we can genearte a plot for each granule that makes up this concatenated granule. Plot wind_speed for each granule using subset_index dimension.\n\nfig = plt.figure(figsize=(20, 40))\n\nfor index in range(0, len(ds.subset_index)): \n ax = fig.add_subplot((len(ds.subset_index)+1)//2, 2, index+1, projection=ccrs.PlateCarree())\n\n p = ds.isel(subset_index=index).plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=\"wind_speed\",\n s=1,\n levels=9,\n cmap=\"jet\",\n ax=ax\n )\n \n ax.set_global()\n ax.coastlines()\n\nplt.show()\n\n\n\n\nPlot wind_speed for all data in this concatenated granule on a single map. Notice that the data is within the spatial bounds we provided earlier.\n\nplt.figure(figsize=(12, 6))\nax = plt.axes(projection=ccrs.PlateCarree())\n\np = ds.plot.scatter(\n y=\"lat\",\n x=\"lon\",\n hue=\"wind_speed\",\n s=1,\n levels=9,\n cmap=\"jet\",\n ax=ax\n)\n\nax.set_global()\nax.coastlines()\nplt.show()"
},
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- "objectID": "external/cof-zarr-reformat.html",
- "href": "external/cof-zarr-reformat.html",
- "title": "COF Zarr Access via Reformat",
- "section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
+ "objectID": "notebooks/datasets/OPERA_GIS_Cloud.html#summary-learning-objectives",
+ "href": "notebooks/datasets/OPERA_GIS_Cloud.html#summary-learning-objectives",
+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Summary & Learning Objectives",
+ "text": "Summary & Learning Objectives\n\nNotebook showcasing how to work with OPERA DSWx data in the cloud\n\nUtilizing the earthaccess Python package. For more information visit: https://nsidc.github.io/earthaccess/\nOption to query the new dataset based on users choice; either by classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’), etc.\nVisualizing the dataset based on its classified layer values.\nMosaicking multiple layers into a single layer."
},
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- "objectID": "external/cof-zarr-reformat.html#getting-started",
- "href": "external/cof-zarr-reformat.html#getting-started",
- "title": "COF Zarr Access via Reformat",
- "section": "Getting Started",
- "text": "Getting Started\nWe will access monthly ocean bottom pressure (OBP) data from ECCO V4r4 (10.5067/ECG5M-OBP44), which are provided as a monthly time series on a 0.5-degree latitude/longitude grid.\nThe data are archived in netCDF format. However, this notebook demonstration will request conversion to Zarr format for files covering the period between 2010 and 2018. Upon receiving our request, Harmony’s backend will convert the files and stage them in S3 for native access in AWS (us-west-2 region, specifically). We will access the new Zarr datasets as an aggregated dataset using xarray, and leverage the S3 native protocols for direct access to the data in an efficient manner.\n\n\nRequirements\n\nAWS\nThis notebook should be running in an EC2 instance in AWS region us-west-2, as previously mentioned. We recommend using an EC2 with at least 8GB of memory available.\nThe notebook was developed and tested using a t2.large instance (2 cpus; 8GB memory).\n\n\nPython 3\nMost of these imports are from the Python standard library. However, you will need to install these packages into your Python 3 environment if you have not already done so:\n\ns3fs\nrequests\npandas\nxarray\nmatplotlib\n\n\n\n\nRequirements\n\nimport matplotlib.pyplot as plt\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport requests\nimport json\nimport time\nimport s3fs\n\nShortName = \"ECCO_L4_OBP_05DEG_MONTHLY_V4R4\"\n\n\n\nStudy period\nSet some “master” inputs to define the time and place contexts for our case studies in the ipynb. This example will be requesting time subsets and receiving global data back from the Harmony API.\n\nstart_date = \"2010-01-01\"\nend_date = \"2018-12-31\"\n\n\n\nData Access\nSome features in the Harmony API require us to identify the target dataset/collection by its concept-id (which uniquely idenfifies it among the other datasets in the Common Metadata Repository). Support for selection by the dataset ShortName will be added in a future release.\n\nCommon Metadata Repository (CMR)\nFor now, we will need to get the concept-id that corresponds to our dataset by accessing its metadata from the CMR. Read more about the CMR at: https://cmr.earthdata.nasa.gov/\nRequest the UMM Collection metadata (i.e. metadata about the dataset) from the CMR and select the concept-id as a new variable ccid.\n\nresponse = requests.get(\n url='https://cmr.earthdata.nasa.gov/search/collections.umm_json', \n params={'provider': \"POCLOUD\",\n 'ShortName': ShortName,\n 'page_size': 1}\n)\n\nummc = response.json()['items'][0]\n\nccid = ummc['meta']['concept-id']\n\nccid\n\n'C1990404791-POCLOUD'\n\n\n\n\nHarmony API\nAnd get the Harmony API endpoint and zarr parameter like we did for SMAP before:\n\nbase = f\"https://harmony.earthdata.nasa.gov/{ccid}\"\nhreq = f\"{base}/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset\"\nrurl = f\"{hreq}?format=application/x-zarr\"\n\nprint(rurl)\n\nhttps://harmony.earthdata.nasa.gov/C1990404791-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?format=application/x-zarr\n\n\nECCO monthly collections have 312 granules in V4r4 (you can confirm with the granule listing from CMR Search API) so we can get the entire time series for 2010 to 2018 with one request to the Harmony API.\nFormat a string of query parameters to limit the processing to the desired time period. Then, append the string of time subset parameters to the variable rurl.\n\nsubs = '&'.join([f'subset=time(\"{start_date}T00:00:00.000Z\":\"{end_date}T23:59:59.999Z\")'])\n\nrurl = f\"{rurl}&{subs}\"\n\nprint(rurl)\n\nhttps://harmony.earthdata.nasa.gov/C1990404791-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?format=application/x-zarr&subset=time(\"2010-01-01T00:00:00.000Z\":\"2018-12-31T23:59:59.999Z\")\n\n\nSubmit the request and monitor the processing status in a while loop, breaking it on completion of the request job:\n\nresponse = requests.get(url=rurl).json()\n\n# Monitor status in a while loop. Wait 10 seconds for each check.\nwait = 10\nwhile True:\n response = requests.get(url=response['links'][0]['href']).json()\n if response['status']!='running':\n break\n print(f\"Job in progress ({response['progress']}%)\")\n time.sleep(wait)\n\nprint(\"DONE!\")\n\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nJob in progress (0%)\nDONE!\n\n\nAccess the staged cloud datasets over native AWS interfaces\nCheck the message field in the response for clues about how to proceed:\n\nprint(response['message'])\n\nThe job has completed successfully. Contains results in AWS S3. Access from AWS us-west-2 with keys from https://harmony.earthdata.nasa.gov/cloud-access.sh\n\n\nThe third item in the list of links contains the shell script from the job status message printed above. Let’s download the same information in JSON format. It should be the fourth item; check to be sure:\n\nlen(response['links'])\n\n102\n\n\nSelect the url and download the json, then load to Python dictionary and print the keys:\n\nwith requests.get(response['links'][3]['href']) as r:\n creds = r.json()\n\nprint(creds.keys())\n\ndict_keys(['AccessKeyId', 'SecretAccessKey', 'SessionToken', 'Expiration'])\n\n\nCheck the expiration timestamp for the temporary credentials:\n\ncreds['Expiration']\n\n'2021-06-11T02:36:29.000Z'\n\n\nOpen zarr datasets with s3fs and xarray\nGet the s3 output directory and list of zarr datasets from the list of links. The s3 directory should be the fifth item; the urls are from item six onward:\n\ns3_dir = response['links'][4]['href']\n\nprint(s3_dir)\n\ns3://harmony-prod-staging/public/harmony/netcdf-to-zarr/2295236b-8086-4543-9482-f524a9f2d0c3/\n\n\nNow select the URLs for the staged files and print the first one:\n\ns3_urls = [u['href'] for u in response['links'][5:]]\n\nprint(s3_urls[0])\n\ns3://harmony-prod-staging/public/harmony/netcdf-to-zarr/2295236b-8086-4543-9482-f524a9f2d0c3/OCEAN_BOTTOM_PRESSURE_mon_mean_2009-12_ECCO_V4r4_latlon_0p50deg.zarr\n\n\nUse the AWS s3fs package and your temporary aws_creds to open the zarr directory storage:\n\ns3 = s3fs.S3FileSystem(\n key=creds['AccessKeyId'],\n secret=creds['SecretAccessKey'],\n token=creds['SessionToken'],\n client_kwargs={'region_name':'us-west-2'},\n)\n\nlen(s3.ls(s3_dir))\n\n97\n\n\nPlot the first Ocean Bottom Pressure dataset\nCheck out the documentation for xarray’s open_zarr method at this link. Open the first dataset and plot the OBP variable:\n\nds0 = xr.open_zarr(s3.get_mapper(s3_urls[0]), decode_cf=True, mask_and_scale=True)\n\n# Mask the dataset where OBP is not within the bounds of the variable's valid min/max:\nds0_masked = ds0.where((ds0.OBP>=ds0.OBP.valid_min) & (ds0.OBP<=ds0.OBP.valid_max))\n\n# Plot the masked dataset\nds0_masked.OBP.isel(time=0).plot.imshow(size=10)\n\n<matplotlib.image.AxesImage at 0x7f28ed2ba4c0>\n\n\n\n\n\nLoad the zarr datasets into one large xarray dataset\nLoad all the datasets in a loop and concatenate them:\n\nzds = xr.concat([xr.open_zarr(s3.get_mapper(u)) for u in s3_urls], dim=\"time\")\n\nprint(zds)\n\n<xarray.Dataset>\nDimensions: (latitude: 360, longitude: 720, nv: 2, time: 97)\nCoordinates:\n * latitude (latitude) float64 -89.75 -89.25 -88.75 ... 89.25 89.75\n latitude_bnds (latitude, nv) float64 -90.0 -89.5 -89.5 ... 89.5 89.5 90.0\n * longitude (longitude) float64 -179.8 -179.2 -178.8 ... 179.2 179.8\n longitude_bnds (longitude, nv) float64 -180.0 -179.5 -179.5 ... 179.5 180.0\n * time (time) datetime64[ns] 2009-12-16T12:00:00 ... 2017-12-16T...\n time_bnds (time, nv) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>\nDimensions without coordinates: nv\nData variables:\n OBP (time, latitude, longitude) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\n OBPGMAP (time, latitude, longitude) float64 dask.array<chunksize=(1, 360, 720), meta=np.ndarray>\nAttributes: (12/57)\n Conventions: CF-1.8, ACDD-1.3\n acknowledgement: This research was carried out by the Jet Pr...\n author: Ian Fenty and Ou Wang\n cdm_data_type: Grid\n comment: Fields provided on a regular lat-lon grid. ...\n coordinates_comment: Note: the global 'coordinates' attribute de...\n ... ...\n time_coverage_duration: P1M\n time_coverage_end: 2010-01-01T00:00:00\n time_coverage_resolution: P1M\n time_coverage_start: 2009-12-01T00:00:00\n title: ECCO Ocean Bottom Pressure - Monthly Mean 0...\n uuid: 297c8df0-4158-11eb-b208-0cc47a3f687b\n\n\nReference OBP and mask the dataset according to the valid minimum and maximum:\n\nobp = zds.OBP\n\nprint(obp)\n\n<xarray.DataArray 'OBP' (time: 97, latitude: 360, longitude: 720)>\ndask.array<concatenate, shape=(97, 360, 720), dtype=float64, chunksize=(1, 360, 720), chunktype=numpy.ndarray>\nCoordinates:\n * latitude (latitude) float64 -89.75 -89.25 -88.75 ... 88.75 89.25 89.75\n * longitude (longitude) float64 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\n * time (time) datetime64[ns] 2009-12-16T12:00:00 ... 2017-12-16T06:00:00\nAttributes:\n comment: OBP excludes the contribution from global mean at...\n coverage_content_type: modelResult\n long_name: Ocean bottom pressure given as equivalent water t...\n units: m\n valid_max: 72.07011413574219\n valid_min: -1.7899188995361328\n\n\nGet the valid min and max from the corresponding CF attributes:\n\nobp_vmin, obp_vmax = obp.valid_min, obp.valid_max\n\nobp_vmin, obp_vmax\n\n(-1.7899188995361328, 72.07011413574219)\n\n\nMask the dataset according to the OBP min and max and plot a series:\n\n# Mask dataset where not inside OBP variable valid min/max:\nzds_masked = zds.where((obp>=obp_vmin)&(obp<=obp_vmax))\n\n# Plot SSH again for the first 12 time slices:\nobpp = zds_masked.OBP.isel(time=slice(0, 6)).plot(\n x=\"longitude\", \n y=\"latitude\", \n col=\"time\",\n levels=8,\n col_wrap=3, \n add_colorbar=False,\n figsize=(14, 8)\n)\n\n# Plot a colorbar on a secondary axis\nmappable = obpp.axes[0][0].collections[0]\ncax = plt.axes([0.05, -0.04, 0.95, 0.04])\ncbar1 = plt.colorbar(mappable, cax=cax, orientation='horizontal')"
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+ "text": "Requirements\n\n1. Compute environment\nThis tutorial can only be run in the following environment: - AWS instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via and s3fs session; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nImport Packages\n\nimport os\nimport requests \nimport json\nimport boto3\nimport s3fs\nfrom osgeo import gdal\nimport rasterio as rio\nfrom rasterio.plot import show\nfrom rasterio.merge import merge\nfrom rasterio.io import MemoryFile\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes \nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\nimport numpy as np\nfrom pathlib import Path\nfrom urllib.request import urlretrieve\nfrom json import dumps\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store"
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- "title": "Data Downloader: Bulk or one-time Scripted Access to PODAAC data",
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- "text": "The PO.DAAC Data downloader is a python-based tool for bulk and one-off (or non-often) downloading of data from the PO.DAAC archive. Use this script if you want to download data based on a space or time every once and a while.\nFor installation and dependency information, please see the top-level README.\n$> podaac-data-downloader -h\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--limit LIMIT] [--dry-run]\n\noptional arguments:\n -h, --help show this help message and exit\n -c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n -d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\n --cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles\n -sd STARTDATE, --start-date STARTDATE\n The ISO date time after which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n -ed ENDDATE, --end-date ENDDATE\n The ISO date time before which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n -f, --force Flag to force downloading files that are listed in CMR query, even if the file exists and checksum matches\n -b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from the command line.\n Default: \"-180,-90,180,90\".\n -dc Flag to use cycle number for directory where data products will be downloaded.\n -dydoy Flag to use start time (Year/DOY) of downloaded data for directory where data products will be downloaded.\n -dymd Flag to use start time (Year/Month/Day) of downloaded data for directory where data products will be downloaded.\n -dy Flag to use start time (Year) of downloaded data for directory where data products will be downloaded.\n --offset OFFSET Flag used to shift timestamp. Units are in hours, e.g. 10 or -10.\n -e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\n -gr GRANULENAME, --granule-name GRANULENAME\n Flag to download specific granule from a collection. This parameter can only be used if you know the granule name. Only one granule name can be supplied\n --process PROCESS_CMD\n Processing command to run on each downloaded file (e.g., compression). Can be specified multiple times.\n --version Display script version information and exit.\n --verbose Verbose mode.\n -p PROVIDER, --provider PROVIDER\n Specify a provider for collection search. Default is POCLOUD.\n --limit LIMIT Integer limit for number of granules to download. Useful in testing. Defaults to no limit.\n --dry-run Search and identify files to download, but do not actually download them\n\n\n\nUsage:\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose]\n [-p PROVIDER] [--limit LIMIT] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n--cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --cycle must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the downloader.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nUnlike the ‘subscriber’, no ‘state’ is maintained for the downloader. if you re-run the downloader you’ll re-download all of the files again, unlike the subscriber which will download newly ingested data since the last run.\n\n\n\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token\n\n\n\n\n\nIf you’re aware of a file you want to download, you can use the -gr option to download by a filename. The -c (COLLECTION) and -d (directory) options are still required.\nThe -gr option works by taking the file name, removing the suffix and searching for a CMR entry called the granuleUR. Some examples of this include:\n\n\n\n\n\n\n\n\nCollection\nFilename\nCMR GranuleUR\n\n\n\n\nMUR25-JPL-L4-GLOB-v04.2\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2\n\n\nJASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07.nc\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07\n\n\n\nBecause of this behavior, granules without data suffixes and granules where the the UR does not directly follow this convention may not work as anticipated. We will be adding the ability to download by granuleUR in a future enhancement.\n\n\n\nSome PO.DAAC datasets are better suited for cycles based search instead of start and end times. To enabled this, we’ve added ‘cycle’ based downloading to the data-downloader. The following example will download data from cycle 42:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42\nThe cycle parameter can be repeated to specify multiple cycles:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42 --cycle 43 --cycle 44\n\n\n\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z -ed 2014-07-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the downloader is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the downloader will skip downloading that file.\nThis can drastically reduce the time for the downloader to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the downloader to always download matching files, by using –force/-f.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -f\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-downloader -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\" -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Authentication with earthaccess",
+ "text": "Authentication with earthaccess\nIn this notebook, we will be calling the authentication in the below cell.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)"
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- "text": "Usage:\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose]\n [-p PROVIDER] [--limit LIMIT] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n--cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --cycle must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the downloader.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nUnlike the ‘subscriber’, no ‘state’ is maintained for the downloader. if you re-run the downloader you’ll re-download all of the files again, unlike the subscriber which will download newly ingested data since the last run."
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+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Set up an s3fs session for Direct Access",
+ "text": "Set up an s3fs session for Direct Access\ns3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data provider.\n\nfs_s3 = Store(auth).get_s3fs_session(daac=\"PODAAC\", provider=\"POCLOUD\")"
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- "text": "The netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Search Using earthaccess for OPERA DSWx",
+ "text": "Search Using earthaccess for OPERA DSWx\nEach dataset has it’s own unique collection ID. For the OPERA_L3_DSWX-HLS_PROVISIONAL_V1 dataset, we can find the collection ID here.\nFor this tutorial, we are looking at the Lake Powell Reservoir.\nWe used bbox finder to get the exact coordinates for our area of interest.\nWe want to look at two different times for comparison: 04/11/2023 and 05/02/2023. To find these dates, let’s search for all the data granules between the two.\n\nQuery = DataGranules().concept_id(\"C2617126679-POCLOUD\").bounding_box(-111.144811,36.980121,-110.250799,37.915625).temporal(\"2023-04-11T00:00:00\",\"2023-05-02T23:59:59\")\nprint(f\"Granule hits: {Query.hits()}\")\ncloud_granules = Query.get()\n# is this a cloud hosted data granule?\ncloud_granules[0].cloud_hosted\n\nGranule hits: 50\n\n\nTrue\n\n\n\n# Let's pretty print this\ncloud_granules[0]\n\n\n\n \n \n \n \n \n \n \n Data: OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B01_WTR.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B02_BWTR.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B03_CONF.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B04_DIAG.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B05_WTR-1.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B06_WTR-2.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B07_LAND.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B08_SHAD.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B09_CLOUD.tifOPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B10_DEM.tif\n Size: 0 MB\n Spatial: {'HorizontalSpatialDomain': {'Geometry': {'BoundingRectangles': [{'WestBoundingCoordinate': -111, 'SouthBoundingCoordinate': 37.04, 'EastBoundingCoordinate': -109.749, 'NorthBoundingCoordinate': 38.036}]}}}\n \n \n \n \n \n \n \n \n\n\n\nGet S3 Bucket links from search results\nBecause we are working within the AWS cloud, let’s get the S3 bucket links for the 8 desired files. We want to query the dataset based on a specific classified layer ‘B01’ or sensor (‘L8_30_v1.0_B01_WTR’).\nOPERA has 10 different available layers. We will look at ‘B01_WTR’ which is the Water Classification (WTR) layer of the OPERA DSWx dataset. Details on each available layer and the data product can be found here.\n\n#extract S3 bucket links\ndata_links = [g.data_links(access=\"direct\") for g in cloud_granules]\ndata_links[0]\n\n['s3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B01_WTR.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B02_BWTR.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B03_CONF.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B04_DIAG.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B05_WTR-1.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B06_WTR-2.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B07_LAND.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B08_SHAD.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B09_CLOUD.tif',\n 's3://podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_PROVISIONAL_V1/OPERA_L3_DSWx-HLS_T12SWG_20230411T180222Z_20230414T030954Z_L8_30_v1.0_B10_DEM.tif']\n\n\n\n#add the S3 bucket links to a list, here we are looking for B01_WTR layer and two dates specified earlier\ns3 = []\nfor r in data_links:\n for l in r:\n if 'B01_WTR' in l: \n if '20230411' in l:\n s3.append(l)\n if '20230502' in l:\n s3.append(l)\n\nprint(len(s3))\n\n8\n\n\n\napril = []\nmay = []\nfor s in s3:\n if '20230411' in s:\n april.append(s)\n if '20230502' in s:\n may.append(s)\n\nSince we are looking at two seperate times, we can create two lists, one for each date, which will be used to mosaic based on its respective time range later."
},
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- "objectID": "external/Downloader.html#advanced-usage",
- "href": "external/Downloader.html#advanced-usage",
- "title": "Data Downloader: Bulk or one-time Scripted Access to PODAAC data",
- "section": "",
- "text": "If you’re aware of a file you want to download, you can use the -gr option to download by a filename. The -c (COLLECTION) and -d (directory) options are still required.\nThe -gr option works by taking the file name, removing the suffix and searching for a CMR entry called the granuleUR. Some examples of this include:\n\n\n\n\n\n\n\n\nCollection\nFilename\nCMR GranuleUR\n\n\n\n\nMUR25-JPL-L4-GLOB-v04.2\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2\n\n\nJASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07.nc\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07\n\n\n\nBecause of this behavior, granules without data suffixes and granules where the the UR does not directly follow this convention may not work as anticipated. We will be adding the ability to download by granuleUR in a future enhancement.\n\n\n\nSome PO.DAAC datasets are better suited for cycles based search instead of start and end times. To enabled this, we’ve added ‘cycle’ based downloading to the data-downloader. The following example will download data from cycle 42:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42\nThe cycle parameter can be repeated to specify multiple cycles:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42 --cycle 43 --cycle 44\n\n\n\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z -ed 2014-07-01T00:46:02Z\n\n\n\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\n\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\n\nBy default, when the downloader is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the downloader will skip downloading that file.\nThis can drastically reduce the time for the downloader to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the downloader to always download matching files, by using –force/-f.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -f\n\n\n\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-downloader -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\" -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\n\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\n\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
+ "objectID": "notebooks/datasets/OPERA_GIS_Cloud.html#visualizing-the-dataset",
+ "href": "notebooks/datasets/OPERA_GIS_Cloud.html#visualizing-the-dataset",
+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Visualizing the Dataset",
+ "text": "Visualizing the Dataset\nLet’s now visualize an individual layer for a single file that was downloaded using Rasterio to read the GeoTIFF image.\n\ns3_url = s3[2]\n\n\ns3_file_obj1 = fs_s3.open(s3_url, mode='rb')\n\n\ndsw = rio.open(s3_file_obj1)\ndsw\n\n<open DatasetReader name='/vsipythonfilelike/068314bf-c361-41a7-85ac-9bb1356e63b2/068314bf-c361-41a7-85ac-9bb1356e63b2' mode='r'>\n\n\nOPERA is a single band image with specific classified rgb values.\nThis requires to read the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\nimage = dsw.read(1)\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw2 = color_array[image]\n\n\nfig, ax = plt.subplots(figsize=(15,10))\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nax.legend(handles=patches,\n bbox_to_anchor=(1.28, 1),\n facecolor=\"gainsboro\")\n\nplt.imshow(dsw2)\nplt.show()"
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- "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html",
- "href": "external/Direct_Access_SWOT_sim_Oceanography.html",
- "title": "Access Sample SWOT Oceanography Data in the Cloud",
- "section": "",
- "text": "imported on: 2023-07-05\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SWOT-OCEAN-Cloud-Workshop"
+ "objectID": "notebooks/datasets/OPERA_GIS_Cloud.html#mosaic-multiple-opera-layers",
+ "href": "notebooks/datasets/OPERA_GIS_Cloud.html#mosaic-multiple-opera-layers",
+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Mosaic Multiple OPERA Layers",
+ "text": "Mosaic Multiple OPERA Layers\nWhen creating a mosaic, make sure the temporal range is correct/matching.\nThe mosaic is being created because we have 4 results from the bounding box area provided. If you receive more than 1 result and would like to see a single raster image of all the results, mosaicking is the solution. We define the function below to merge the tiff files for each date and return the composite raster into memory.\n\ndef raster2mosaic(date):\n datasets = []\n for file in date:\n file_path = f\"{file}\"\n file_obj = fs_s3.open(file_path)\n dataset = rio.open(file_obj)\n datasets.append(dataset)\n mosaic, output = merge(datasets) #the merge function will mosaic the raster images\n \n #Saving the output of the mosaicked raster image to memory\n memfile = MemoryFile()\n with memfile.open(driver='GTiff', count = 1, width= mosaic.shape[1], height=mosaic.shape[2] , dtype=np.uint8, transform=output) as dst:\n dst.write(mosaic)\n mosaic_bytes = memfile.read()\n with MemoryFile(mosaic_bytes) as memfile:\n dataset1 = memfile.open()\n raster = dataset1.read(1)\n return raster\n\n\naprilmos = raster2mosaic(april)\nmaymos = raster2mosaic(may)"
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- "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#getting-started",
- "href": "external/Direct_Access_SWOT_sim_Oceanography.html#getting-started",
- "title": "Access Sample SWOT Oceanography Data in the Cloud",
- "section": "Getting Started",
- "text": "Getting Started\nIn this notebook will show direct access of PO.DAAC archived products in the Earthdata Cloud in AWS Simple Storage Service (S3). In this demo, we will showcase the usage of SWOT Simulated Level-2 KaRIn SSH from GLORYS for Science Version 1. More information on the datasets can be found at https://podaac.jpl.nasa.gov/dataset/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1.\nWe will access the data from inside the AWS cloud (us-west-2 region, specifically) and load a time series made of multiple netCDF files into a single xarray dataset. This approach leverages S3 native protocols for efficient access to the data.\nIn the future, if you want to use this notebook as a reference, please note that we are not doing collection discovery here - we assume the collection of interest has been determined.\n\nRequirements\nThis can run in the Small openscapes instance, that is, it only needs 8GB of memory and ~2 CPU.\nIf you want to run this in your own AWS account, you can use a t2.large instance, which also has 2 CPU and 8GB memory. It’s improtant to note that all instances using direct S3 access to PO.DAAC or Earthdata data are required to run in us-west-2, or the Oregon region.\nThis instance will cost approximately $0.0832 per hour. The entire demo can run in considerably less time.\n\n\nImports\nMost of these imports are from the Python standard library. However, you will need to install these packages into your Python 3 environment if you have not already done so:\nboto3\ns3fs\nxarray\nmatplotlib\ncartopy"
+ "objectID": "notebooks/datasets/OPERA_GIS_Cloud.html#visualizing-the-mosaic",
+ "href": "notebooks/datasets/OPERA_GIS_Cloud.html#visualizing-the-mosaic",
+ "title": "Working with OPERA Dynamic Surface Water Extent (DSWx) Data:",
+ "section": "Visualizing the Mosaic",
+ "text": "Visualizing the Mosaic\nTo visualize the mosaic, you must utilize the single layer colormap.\nThis will be the ‘dsw’ variable used earlier to visualize a single layer. Similarly reading the single band, then creating a numpy array of the specified rgb values. e.g. ‘variable’.colormap\n\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw3 = color_array[aprilmos]\n\n\ncolor_array = np.asarray(\n [dsw.colormap(1)[i] for i in range(256)], dtype=np.uint8)\ndsw4 = color_array[maymos]\n\n\nfig = plt.figure(figsize=(20, 15))\n\nrows = 1\ncolumns = 2\n\n# Lake Powell 04/11/2023\nfig.add_subplot(rows, columns, 1)\nplt.title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\nplt.imshow(dsw3)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\n# Lake Powell 05/02/2023\nfig.add_subplot(rows, columns, 2)\nplt.title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\nplt.imshow(dsw4)\n\n#Legend based on specifed classified layer.\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nplt.show()\n\n\n\n\n\nTo take a closer look at a specific area of the image, we can create an inset map of a specified area.\n\nfig, ax = plt.subplots(1, 2, figsize=(20, 15))\n\nax[0].imshow(dsw3)\nax[0].set_title(\"OPERA DSWx - Lake Powell: 04/11/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.47,0.35),\n facecolor=\"gainsboro\")\n\nax_ins1 = ax[0].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins1.imshow(dsw3)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins1.set_xlim(x1, x2)\nax_ins1.set_ylim(y1, y2)\nax_ins1.set_xticklabels('')\nax_ins1.set_yticklabels('')\n\nax[0].indicate_inset_zoom(ax_ins1, edgecolor='black')\n\nax[1].imshow(dsw4)\nax[1].set_title(\"OPERA DSWx - Lake Powell: 05/02/2023\")\n\nlegend_labels = {\"white\":\"Not Water\", \"blue\":\"Open Water\", \"lightskyblue\":\"Partial Surface Water\", \"cyan\":\"Snow/Ice\", \"grey\":\"Cloud/Cloud Shadow\"}\npatches = [Patch(color=color, label=label)\n for color, label in legend_labels.items()]\nfig.legend(handles=patches,\n bbox_to_anchor=(0.9, 0.35),\n facecolor=\"gainsboro\")\n\nax_ins2 = ax[1].inset_axes([0.5, 0.5, 0.45, 0.45])\nax_ins2.imshow(dsw4)\n\nx1, x2, y1, y2 = 2200, 2700, 3500, 3000 #Extent set for aoi of inset map.\nax_ins2.set_xlim(x1, x2)\nax_ins2.set_ylim(y1, y2)\nax_ins2.set_xticklabels('')\nax_ins2.set_yticklabels('')\n\nax[1].indicate_inset_zoom(ax_ins2, edgecolor='black')\n\nplt.show()"
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- "href": "external/Direct_Access_SWOT_sim_Oceanography.html#learning-objectives",
- "title": "Access Sample SWOT Oceanography Data in the Cloud",
- "section": "Learning Objectives",
- "text": "Learning Objectives\n\nimport needed libraries\nauthenticate for NASA Earthdata archive (Earthdata Login) (here this takes place as part of obtaining the AWS credentials step)\nobtain AWS credentials for Earthdata DAAC archive in AWS S3\naccess DAAC data by downloading directly into your cloud workspace from S3 within US-west 2 and operating on those files.\naccess DAAC data directly from the in-region S3 bucket without moving or downloading any files to your local (cloud) workspace\nplot the first time step in the data\n\nNote: no files are being donwloaded off the cloud, rather, we are working with the data in the AWS cloud.\n\nimport boto3\nimport json\nimport xarray as xr\nimport s3fs\nimport os\nimport requests\nimport cartopy.crs as ccrs\nfrom matplotlib import pyplot as plt\nfrom os import path\n%matplotlib inline"
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+ "href": "notebooks/datasets/smap_imerg_tutorial.html#summary",
+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "Summary:",
+ "text": "Summary:\nThis tutorial uses the Earthdata Search (https://search.earthdata.nasa.gov/) to download the data on your local machine. You will need to create an account in order to download the data."
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- "title": "Access Sample SWOT Oceanography Data in the Cloud",
- "section": "Get a temporary AWS Access Key based on your Earthdata Login user ID",
- "text": "Get a temporary AWS Access Key based on your Earthdata Login user ID\nDirect S3 access is achieved by passing NASA supplied temporary credentials to AWS so we can interact with S3 objects (i.e. data) from applicable Earthdata Cloud buckets (storage space). For now, each NASA DAAC has different AWS credentials endpoints. Below are some of the credential endpoints to various DAACs.\nThe below methods (get_temp_dreds) requires the user to have a ‘netrc’ file in the users home directory.\n\ns3_cred_endpoint = {\n 'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',\n 'gesdisc': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',\n 'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\n 'ornldaac': 'https://data.ornldaac.earthdata.nasa.gov/s3credentials',\n 'ghrcdaac': 'https://data.ghrc.earthdata.nasa.gov/s3credentials'\n}\n\ndef get_temp_creds(provider):\n return requests.get(s3_cred_endpoint[provider]).json()\n\nWe will now get a credential for the ‘PO.DAAC’ provider and set up our environment to use those values.\nNOTE if you see an error like ‘HTTP Basic: Access denied.’ It means the username/password you’ve entered is incorrect.\nNOTE2 If you get what looks like a long HTML page in your error message (e.g. \n\n…), the right netrc ‘machine’ might be missing.\n\ncreds = get_temp_creds('podaac')\n\nos.environ[\"AWS_ACCESS_KEY_ID\"] = creds[\"accessKeyId\"]\nos.environ[\"AWS_SECRET_ACCESS_KEY\"] = creds[\"secretAccessKey\"]\nos.environ[\"AWS_SESSION_TOKEN\"] = creds[\"sessionToken\"]\n\ns3 = s3fs.S3FileSystem(anon=False)"
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+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "Datasets:",
+ "text": "Datasets:\n\nJPL SMAP L3 Dataset: https://podaac.jpl.nasa.gov/dataset/SMAP_JPL_L3_SSS_CAP_8DAY-RUNNINGMEAN_V5?ids=&values=&search=Smap Level 3&provider=POCLOUD\nGPM IMERG Late Precipitation: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDL_06/summary?keywords=gpm imerg"
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- "title": "Access Sample SWOT Oceanography Data in the Cloud",
- "section": "Location of data in the PO.DAAC S3 Archive",
- "text": "Location of data in the PO.DAAC S3 Archive\nWe need to determine the path for our products of interest. We can do this through several mechanisms. Those are described in the Finding_collection_concept_ids.ipynb notebook, or the Pre-Workshop material, https://podaac.github.io/2022-SWOT-Ocean-Cloud-Workshop/prerequisites/01_Earthdata_Search.html.\nAfter using the Finding_collection_concept_ids.ipynb guide to find our S3 location, we end up with:\n{\n ...\n \"DirectDistributionInformation\": {\n \"Region\": \"us-west-2\",\n \"S3BucketAndObjectPrefixNames\": [\n \"podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/\",\n \"podaac-ops-cumulus-public/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/\"\n ],\n \"S3CredentialsAPIEndpoint\": \"https://archive.podaac.earthdata.nasa.gov/s3credentials\",\n \"S3CredentialsAPIDocumentationURL\": \"https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME\"\n },\n ...\n}"
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+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "Learning Objectives:",
+ "text": "Learning Objectives:\nUses python to plot the SMAP sea surface salinity anomalies over the ocean and the IMERG precipitation over the land."
},
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- "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#now-that-we-have-the-s3-bucket-location-for-the-data-of-interest",
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- "title": "Access Sample SWOT Oceanography Data in the Cloud",
- "section": "Now that we have the S3 bucket location for the data of interest…",
- "text": "Now that we have the S3 bucket location for the data of interest…\nIt’s time to find our data! Below we are using a glob to find file names matching a pattern. Here, we want any files matching the pattern used below; here this equates, in science, terms, to Cycle 001 and the first 10 passes. This information can be gleaned from product description documents. Another way of finding specific data files would be to search on cycle/pass from CMR or Earthdata Search GUI and use the S3 links provided in the resulting metadata or access links, respectively, directly instead of doing a glob (essentially an ‘ls’).\nThe files we are looking at are about 11-13 MB each. So the 10 we’re looking to access are about ~100 MB total.\n\ns3path = 's3://podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_00*.nc'\nremote_files = s3.glob(s3path)\n\n\nremote_files\n\n['podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_001_20140412T120000_20140412T125126_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_002_20140412T125126_20140412T134253_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_003_20140412T134253_20140412T143420_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_004_20140412T143420_20140412T152546_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_005_20140412T152547_20140412T161713_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_006_20140412T161714_20140412T170840_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_007_20140412T170840_20140412T180007_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_008_20140412T180008_20140412T185134_DG10_01.nc',\n 'podaac-ops-cumulus-protected/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_SCIENCE_V1/SWOT_L2_LR_SSH_Expert_001_009_20140412T185134_20140412T194301_DG10_01.nc']"
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+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "Import needed packages",
+ "text": "Import needed packages\n\nimport glob\nimport numpy as np\nimport xarray as xr\nimport hvplot.xarray\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nfrom datetime import datetime\nimport os\n\n/opt/anaconda3/envs/plotting/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\""
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- "href": "external/Direct_Access_SWOT_sim_Oceanography.html#a-final-word",
- "title": "Access Sample SWOT Oceanography Data in the Cloud",
- "section": "A final word…",
- "text": "A final word…\nAccessing data completely from S3 and in memory are affected by various things.\n\nThe format of the data - archive formats like NetCDF, GEOTIFF, HDF vs cloud optimized data structures (Zarr, kerchunk, COG). Cloud formats are made for accessing only the pieces of data of interest needed at the time of the request (e.g. a subset, timestep, etc).\nThe internal structure of the data. Tools like xarray make a lot of assumptions about how to open and read a file. Sometimes the internals don’t fit the xarray ‘mould’ and we need to continue to work with data providers and software providers to make these two sides work together. Level 2 data (non-gridded), specifically, suffers from some of the assumptions made."
+ "objectID": "notebooks/datasets/smap_imerg_tutorial.html#load-directories",
+ "href": "notebooks/datasets/smap_imerg_tutorial.html#load-directories",
+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "Load directories",
+ "text": "Load directories\n\n#Replace with your directory \nbase_directory = \"/Users/username\" \n\n#Replace with your directory. This will be the output for plots\noutputpath = (os.path.join(base_directory, 'Desktop/plots/'))"
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- "section": "",
- "text": "The Sentinel-6 Michael Freilich satellite measures the height of the ocean. In addition, an instrument on board the satellite uses the Global Navigation Satellite System Radio-Occultation sounding technique, which analyses changes in signals from international global navigation system satellites to determine atmospheric temperature and humidity. More information can be found on PO.DAAC’s Sentinel-6 Michael Freilich webpage."
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+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "Open subset_location the SMAP SSS data for a specified subset",
+ "text": "Open subset_location the SMAP SSS data for a specified subset\n\n#the subset used to determine which values are plotted\nsubset_bounds = ['20221226', '20230116'] \n\n#calculates the mean over the same time period as the plotted values\nsubset_mean_bounds = subset_bounds \n\n#Replace with your directory of SMAP files\nallsubset_files = xr.open_mfdataset(os.path.join(base_directory, 'Desktop/JPL_SMAP_L3/SMAP*.nc*'))\n\n\n#grabs smap data from 12-26-2015 to 01-16-2023\nallsubset_files = allsubset_files.sel(time=(allsubset_files['time'].dt.strftime('%m%d') >= subset_mean_bounds[0][4:8]) | (allsubset_files['time'].dt.strftime('%m%d') <= subset_mean_bounds[1][4:8]))\nallsubset_files['time'].values\n\narray(['2015-12-26T12:00:00.000000000', '2015-12-27T12:00:00.000000000',\n '2015-12-28T12:00:00.000000000', '2015-12-29T12:00:00.000000000',\n '2015-12-30T12:00:00.000000000', '2015-12-31T12:00:00.000000000',\n '2016-01-01T12:00:00.000000000', '2016-01-02T12:00:00.000000000',\n 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+ "href": "notebooks/datasets/smap_imerg_tutorial.html#calculates-the-mean",
+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "Calculates the mean",
+ "text": "Calculates the mean\n\n#subsets to the location of the pacific coast and California \nsubset_location = allsubset_files.where((allsubset_files.latitude>20)&(allsubset_files.latitude<50)&(allsubset_files.longitude>-140)&(allsubset_files.longitude<-100), drop=True)\n\n#calculates the mean for the 'smap_sss' variable\nsubset_mean_values = np.nanmean(subset_location['smap_sss'], axis=0)\n\n#gets rid of past mean value if you were to run this code again with different dates\nif 'backup_subset_mean_values' in globals():\n del backup_subset_mean_values\n \n#plots the figure and saves it to your output path\nfig = plt.figure(figsize= (16,10))\nax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\ns = plt.pcolormesh(subset_location.longitude, subset_location.latitude, subset_mean_values, vmin = 33, vmax= 35, cmap = 'rainbow', transform = ccrs.PlateCarree())\ncb = plt.colorbar(s)\ncb.set_label('psu')\nax.set_title(f'SSS mean over 12/26 to 1/16 (2015-2023)', size = 24)\nax.grid()\nax.add_feature(cfeature.OCEAN)\nax.add_feature(cfeature.LAND)\nax.add_feature(cfeature.LAKES)\nax.add_feature(cfeature.RIVERS)\nax.add_feature(cfeature.STATES)\nax.coastlines()\nax.set_xlim(-130, -115)\nax.set_ylim(32, 42.5)\n\n#saves figure to output path \nplt.savefig(outputpath+datetime.now().strftime(\"%Y%m%d-%H%M%S\")+'.png',dpi=400, facecolor='w', transparent=False, bbox_inches='tight')\n\n/var/folders/f0/dgnqgvtx46513by9cdh6fnjw0000gq/T/ipykernel_66947/1610522150.py:5: RuntimeWarning: Mean of empty slice\n subset_mean_values = np.nanmean(subset_location['smap_sss'], axis=0)"
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+ "title": "SMAP Sea Surface Salinity and IMERG Precipitation Tutorial",
+ "section": "plot the anomalies for subset bounds",
+ "text": "plot the anomalies for subset bounds\n\n#Plots precip data only over land, using a colorscale\nmode_choice = 2 \n\n#currently, the line below is commented but you can uncomment this line and it will display raw SSS values (doesnt calculate anomalies) \n#calc_anomaly = 0 \n\n#calculate anomalies relative to the mean\ncalc_anomaly = 1 \n\n#Replace with your dictory for IMERG precip data\ndirectory = os.path.join(base_directory, 'Desktop/imerg_precip')\nif 'backup_subset_mean_values' not in globals():\n backup_subset_mean_values = subset_mean_values\n\n#raw sss values\nif calc_anomaly == 0:\n subset_mean_values = 0\n smin = 31\n smax = 34.5 \n smap = 'rainbow'\n\n#anomaly values\nif calc_anomaly != 0:\n subset_mean_values = backup_subset_mean_values \n smin = -1\n smax = +1 \n smap = 'seismic'\n\n#Replace with your directory \nsss_subset = sorted(glob.glob(os.path.join(base_directory, 'Desktop/jpl_smap_l3/SMAP*.nc*')))\n\n#grabs the sss file date for the subset location and plots anomalies\nfor filename in sss_subset:\n file_date = filename.split('_')[-3].replace('.nc', '')\n updated_file_date = file_date[0:4]+'-'+file_date[4:6]+'-'+file_date[6:8]\n if int(file_date) < int(subset_bounds[0]) or int(file_date) > int(subset_bounds[1]):\n continue\n print(file_date)\n sss_ds = xr.open_dataset(filename)\n try:\n sss_ds = sss_ds.where((sss_ds.latitude>20)&(sss_ds.latitude<50)&(sss_ds.longitude>-140)&(sss_ds.longitude<-100), drop=True)- subset_mean_values\n if sss_ds.smap_sss.size == 0:\n continue\n except Exception as e:\n print(e)\n continue\n plt.rcParams.update({\"font.size\": 24})\n fig = plt.figure(figsize= (16,10))\n ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n\n #grabs the precip file date for the subset location and plots \n substring = file_date\n precip_counter = 0\n for file_path in glob.glob(os.path.join(directory, f'*{substring}*')):\n precip_counter += 1\n if precip_counter >1:\n crashnow\n print(file_path)\n precip = xr.open_dataset(file_path)\n precip = precip.where((precip.lat>20)&(precip.lat<50)&(precip.lon>-140)&(precip.lon<-100),drop=True)\n val = 24*precip.HQprecipitation[0,:,:]/(0.5*precip.HQprecipitation_cnt[0,:,:])\n if mode_choice == 2: \n p = plt.pcolormesh(precip.lon,precip.lat,val.transpose(), cmap = 'viridis', vmax = 20, vmin = 0, transform = ccrs.PlateCarree())\n cb = plt.colorbar(p, fraction=0.046, pad=0.04)\n cb.set_label('mm/day')\n\n #the line below plots the raw sss values if its uncommented\n s = plt.pcolormesh(sss_ds.longitude, sss_ds.latitude, sss_ds.smap_sss, vmin = smin, vmax = smax, cmap = smap, transform = ccrs.PlateCarree())\n \n #adds the colorbar and features to the plot\n cb = plt.colorbar(s, location = 'left', fraction=0.046, pad=0.04)\n if calc_anomaly == 1:\n cb.set_label(f'psu anomalies wrt mean of 12/26 to 1/16 (2015-23)')\n ax.set_title(f'SSS Anomaly over ocean, Precipitation on land\\n{updated_file_date}', size = 24)\n if calc_anomaly != 1:\n cb.set_label('psu')\n ax.set_title(f'SSS over ocean, Precipitation on land\\n{updated_file_date}', size = 24)\n ax.grid()\n ax.add_feature(cfeature.OCEAN)\n ax.add_feature(cfeature.LAND)\n ax.add_feature(cfeature.LAKES)\n ax.add_feature(cfeature.RIVERS)\n ax.add_feature(cfeature.STATES)\n ax.coastlines()\n ax.set_xlim(-130, -115)\n ax.set_ylim(32, 42.5)\n\n #saves figure to output path \n plt.savefig(outputpath+datetime.now().strftime(\"%Y%m%d-%H%M%S\")+'.png',dpi=400, facecolor='w', transparent=False, bbox_inches='tight')\n\n/var/folders/f0/dgnqgvtx46513by9cdh6fnjw0000gq/T/ipykernel_66947/576375500.py:48: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n fig = plt.figure(figsize= (16,10))"
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- "text": "The Sentinel-6 Michael Freilich satellite measures the height of the ocean. In addition, an instrument on board the satellite uses the Global Navigation Satellite System Radio-Occultation sounding technique, which analyses changes in signals from international global navigation system satellites to determine atmospheric temperature and humidity. More information can be found on PO.DAAC’s Sentinel-6 Michael Freilich webpage."
+ "text": "Author: Julie Sanchez, NASA JPL PO.DAAC"
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- "section": "Data Resources & Tutorials",
- "text": "Data Resources & Tutorials\n\nData Access\n\nAccess by Cycle/Pass\n\nAccess Near Real-Time Data\n\nOPeNDAP Access"
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+ "href": "notebooks/datasets/enso_MUR_tutorial_final.html#summary",
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+ "section": "Summary",
+ "text": "Summary\n\nEl Niño-Southern Oscillation (ENSO) is a climate pattern in the Pacific Ocean that has two phases: El Niño (warm/wet phase) and La Niña (cold/dry phase). ENSO has global impacts on wildfires, weather, and ecosystems. We have been experiencing La Niña conditions for the last 2 and a half years. The last El Niño event occurred in 2015/2016 and a weak El Niño event was also experienced during the winter of 2018/2019.\nThis tutorial uses the SST anomaly variable derived from a MUR climatology dataset - MUR25-JPL-L4-GLOB-v04.2 (average between 2003 and 2014). This tutorial uses the PO.DAAC Downloader which downloads data to your local computer and uses the data to run a notebook using python. The following code produces the sea surface temperature anomalies (SSTA) over the Pacific Ocean."
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- "text": "Additional Resources\nNASA Mission Page"
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+ "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
+ "section": "Requirements",
+ "text": "Requirements\n\n1. Earthdata Login\n\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\n\n2. netrc File\n\nYou will need a .netrc file containing your NASA Earthdata Login credentials in order to execute the notebooks. A .netrc file can be created manually within text editor and saved to your home directory. For additional information see: Authentication for NASA Earthdata tutorial.\n\n\n\n3. PO.DAAC Data Downloader\n\nTo download the data via command line, this tutorial uses PO.DAAC’s Data Downloader. The downloader can be installed using these instructions The Downloader is useful if you need to download PO.DAAC data once in a while or prefer to do it “on-demand”. The Downloader makes no assumptions about the last time run or what is new in the archive, it simply uses the provided requests and downloads all matching data."
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- "text": "The Downloader is useful if you need to download PO.DAAC data once in a while or prefer to do it “on-demand”. The Downloader makes no assumptions about the last time run or what is new in the archive, it simply uses the provided requests and downloads all matching data.\n\n\n\nThe subscriber is useful for users who need to continuously pull the latest data from the PO.DAAC archive. If you feed data into a model or real time process, the subscriber allows you to repeatedly run the script and only download the latest data.\n\n\n\nBoth subscriber and downloader require Python >= 3.7.\nThe subscriber and downloader scripts are available in the pypi python repository, it can be installed via pip:\npip install podaac-data-subscriber\nyou should now have access to the downloader and subscriber Command line interfaces.\n\nNote: If after installation, the podaac-data-subscriber or podaac-data-downloader commands are not available, you may need to add the script location to the PATH. This could be due to a User Install of the python package, which is common on shared systems where python packages are installed for the user (not the system). See Installing to the User Site and User Installs for more information on finding the location of installed scripts and adding them to the PATH.\n\nTo use the Subscriber or Downloader, you will need to have an Earthdata login account. You will also need a netrc file with your Earthdata Login credentials to access the data. Follow these authentication instructions to create your netrc if you do not have one already."
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+ "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
+ "section": "Learning Objectives",
+ "text": "Learning Objectives\n\nIntroduction to the PO.DAAC Data Downloader\nLearn how to plot the SSTA for the ENSO 3.4 Region and a timeseries"
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- "objectID": "quarto_text/DataSubscriberDownloader.html#introduction",
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+ "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
+ "section": "Download Data in the Command Line using the PO.DAAC Data Downloader",
+ "text": "Download Data in the Command Line using the PO.DAAC Data Downloader\nIn your terminal, go to the folder you want to download the files to – this will be important to remember. You will need to put your path name in the code below. Copy and paste each line (below) into your terminal. If you have all the prerequisites, the files will download to your folder:\npodaac-data-downloader -h\n\npodaac-data-subscriber -c MUR25-JPL-L4-GLOB-v04.2 -d ./data/MUR25-JPL-L4-GLOB-v04.2 --start-date 2022-12-1T00:00:00Z -ed 2023-04-24T23:59:00Z -d ."
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+ "href": "notebooks/datasets/enso_MUR_tutorial_final.html#import-packages",
+ "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
+ "section": "Import Packages",
+ "text": "Import Packages\n\n# Import packages \nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport xarray as xr\nimport matplotlib.ticker as mticker\nimport netCDF4 as nc\nimport numpy as np\nimport datetime as dt\nimport glob\nimport hvplot.xarray\nimport pandas as pd\n\n\n\n\n\n\n\n\n\n\n\nInput your folder directory where you used the Downloader to store the data\n\ndir = '/Users/your_user_name/folder_name/'"
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+ "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
+ "section": "Open and Plot Sea Surface Temperature Anomalies",
+ "text": "Open and Plot Sea Surface Temperature Anomalies\n\n# Read the April 24 2023 NetCDF file\nds = xr.open_dataset(dir +'20230424090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc')\n\n# Extract the required variables\nlon = ds['lon']\nlat = ds['lat']\nsst_anomaly = ds['sst_anomaly']\n\n\n# Create the figure\nfig = plt.figure(figsize=(10, 10))\nax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(-150, 10))\n\n\n# Plot the sst_anomaly data with vmin and vmax\npcm = ax.pcolormesh(ds.lon, ds.lat, ds.sst_anomaly[0], transform=ccrs.PlateCarree(), cmap='rainbow', vmin=-2, vmax=2)\n\n\n# Plot the equator line\nax.plot(np.arange(360), np.zeros((360)), transform=ccrs.PlateCarree(), color='black')\n\n\n# Define the El Niño 1 + 2 region\nenso_bounds_lon = [-90, -80, -80, -90, -90]\nenso_bounds_lat = [-10, -10, 0, 0, -10]\n# Plot the Enso region box\nax.plot(enso_bounds_lon, enso_bounds_lat, transform=ccrs.PlateCarree(), color='black', linewidth=2)\n\n\n# Define the El Niño 3 region\nenso_bounds_lon2 = [-150, -90, -90, -150, -150]\nenso_bounds_lat2 = [-5, -5, 5, 5, -5]\n# Plot great circle equations for Enso region 3 (accounts for the curve)\nfor i in range(4):\n circle_lon = np.linspace(enso_bounds_lon2[i], enso_bounds_lon2[i+1], 100)\n circle_lat = np.linspace(enso_bounds_lat2[i], enso_bounds_lat2[i+1], 100)\n ax.plot(circle_lon, circle_lat, transform=ccrs.PlateCarree(), color='brown', linestyle='--', linewidth=2)\n\n\n# Add coastlines and gridlines\nax.add_feature(cfeature.COASTLINE)\nax.add_feature(cfeature.LAND, facecolor='gray')\nax.add_feature(cfeature.LAKES)\nax.add_feature(cfeature.RIVERS)\n\n\n#Set tick locations and labels for the colorbar\ncbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, fraction=0.04)\ncbar.set_label('SST Anomaly', color = 'white')\ncbar.set_ticks([-2, -1, 0, 1, 2])\ncbar.set_ticklabels([-2, -1, 0, 1, 2]) \ncbar.ax.tick_params(color='white')\ncbar.ax.xaxis.set_ticklabels(cbar.ax.get_xticks(), color='white')\ncbar.ax.yaxis.set_ticklabels(cbar.ax.get_yticks(), color='white')\n\n\n# Add white text on top left and right\nfig.text(0.02, 0.95, 'APR 24 2023', color='white', fontsize=20, ha='left', va='top')\nfig.text(0.98, 0.95, 'MUR SSTA', color='white', fontsize=20, ha='right', va='top')\n\n\n# #Background set to black\nfig.set_facecolor('black')\n\n\nplt.show()"
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+ "title": "Mapping Sea Surface Temperature Anomalies to Observe Potential El Niño Conditions",
+ "section": "Plot a Time Series of MUR SSTA",
+ "text": "Plot a Time Series of MUR SSTA\n\n# Open data from December to April\nds2 = xr.open_mfdataset(dir + '20*.nc*', combine='by_coords')\n\n# Grab the time values\ntimes = ds2.time.values\n\n# Select the El Niño 1+2 region\nsubset_ds = ds2.sel(lat=slice(-10, 0)).sel(lon=slice(-90, -80))\n\n# Select ssta for El Niño 1+2 region\ndata = subset_ds.sst_anomaly.values\ndata_means = [np.nanmean(step) for step in data]\n\n# Select the El Niño 3 region\nsubset_ds2 = ds2.sel(lat=slice(-5, 5)).sel(lon=slice(-150, -90)) \ndata2 = subset_ds2.sst_anomaly.values\ndata_means2 = [np.nanmean(step) for step in data2]\n\n# Plot the figure with labels\nfig = plt.figure(figsize=(20,6))\nplt.title('MUR SST Anomaly in El Niño 1+2 and El Niño 3 Regions', fontsize=20)\nplt.ylabel('Anomaly in Degrees C', fontsize=16)\nplt.tick_params(labelsize=12) \nplt.grid(True)\n\nplt.plot(times, data_means, color='black', linewidth=4, label='Niño 1+2')\nplt.plot(times, data_means2[:len(times)], color='brown', linewidth=4, linestyle='--', label='Niño 3')\n\nplt.ylim(-4, 4)\n\n# Add legend with labels\nplt.legend(fontsize=16) \n\n# Increase label size\nplt.xticks(fontsize=16)\nplt.yticks(fontsize=16)\n\nplt.show()"
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- "text": "The Downloader is useful if you need to download PO.DAAC data once in a while or prefer to do it “on-demand”. The Downloader makes no assumptions about the last time run or what is new in the archive, it simply uses the provided requests and downloads all matching data.\n\n\n\nThe subscriber is useful for users who need to continuously pull the latest data from the PO.DAAC archive. If you feed data into a model or real time process, the subscriber allows you to repeatedly run the script and only download the latest data.\n\n\n\nBoth subscriber and downloader require Python >= 3.7.\nThe subscriber and downloader scripts are available in the pypi python repository, it can be installed via pip:\npip install podaac-data-subscriber\nyou should now have access to the downloader and subscriber Command line interfaces.\n\nNote: If after installation, the podaac-data-subscriber or podaac-data-downloader commands are not available, you may need to add the script location to the PATH. This could be due to a User Install of the python package, which is common on shared systems where python packages are installed for the user (not the system). See Installing to the User Site and User Installs for more information on finding the location of installed scripts and adding them to the PATH.\n\nTo use the Subscriber or Downloader, you will need to have an Earthdata login account. You will also need a netrc file with your Earthdata Login credentials to access the data. Follow these authentication instructions to create your netrc if you do not have one already."
+ "text": "From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link."
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- "section": "Command (cmd) Line Examples",
- "text": "Command (cmd) Line Examples\nThe dataset listing pages on the PO.DAAC Portal provide cmd line examples for each dataset respectively. For example, on a MUR SST dataset landing page, if you click the Download icon under Capabilities on the right side, the following script for the subscriber should be visible:\npodaac-data-subscriber -c MUR25-JPL-L4-GLOB-v04.2 -d ./data/MUR25-JPL-L4-GLOB-v04.2 --start-date 2002-08-31T21:00:00Z\nDownloading simulated SWOT Raster data over a specified region and time:\npodaac-data-downloader -c SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 -d ./SWOT_SIMULATED_NA_CONTINENT_L2_HR_Raster_V1 --start-date 2022-08-02T00:00:00Z --end-date 2022-08-22T00:00:00Z -b=\"-97,32.5,-96.5,33\"\nSubscribing to the [GRACE-FO Monthly Ocean Bottom Pressure Anomaly Dataset]:(https://podaac.jpl.nasa.gov/dataset/TELLUS_GRFO_L3_CSR_RL06_OCN_v04)\npodaac-data-subscriber -c TELLUS_GRFO_L3_CSR_RL06_OCN_v04 -d ./data/TELLUS_GRFO_L3_CSR_RL06_OCN_v04 --start-date 2018-05-22T00:00:00Z"
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+ "href": "notebooks/SearchDownload_SWOTviaCMR.html#summary",
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+ "section": "Summary",
+ "text": "Summary\nThis notebook will find and download simulated SWOT data programmatically via earthaccess. For more information about earthaccess visit: https://nsidc.github.io/earthaccess/"
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- "text": "Tutorial Examples Utilizing the PO.DAAC Subscriber/Downloader:\n\nSWOT NetCDF to Geotiff Conversion"
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+ "text": "Requirements\n\n1. Compute environment\nThis tutorial can be run in the following environments: - Local compute environment e.g. laptop, server: this tutorial can be run on your local machine\n\n\n2. Earthdata Login\nAn Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.\n\n\nImport libraries\n\nimport requests\nimport json\nimport geopandas as gpd\nimport glob\nfrom pathlib import Path\nimport pandas as pd\nimport os\nimport zipfile\nfrom urllib.request import urlretrieve\nfrom json import dumps\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\nIn this notebook, we will be calling the authentication in the below cell.\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)\n\n\n\nSearch for SWOT sample data links\nWe want to find the SWOT sample files that will cross over our region of interest, in the case, a bounding box of the United States.\nEach dataset has it’s own unique collection ID. For the SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1 dataset, we find the collection ID here.\nSample SWOT Hydrology Datasets and Associated Collection IDs: 1. River Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1 - C2263384307-POCLOUD\n\nLake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1 - C2263384453-POCLOUD\nRaster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1 - C2263383790-POCLOUD\nWater Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1 - C2263383386-POCLOUD\nWater Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1 - C2263383657-POCLOUD\n\n\n#earthaccess data search\nQuery = DataGranules().concept_id(\"C2263384307-POCLOUD\").bounding_box(-124.848974,24.396308,-66.885444,49.384358)\nprint(f\"Granule hits: {Query.hits()}\")\n\nGranule hits: 46\n\n\n\ngranules = Query.get()\n\n\n#extract the data links from the granules\ndata_links = [g.data_links(access=\"on_prem\") for g in granules]\n\n\ndata_links[0]\n\n['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1/SWOT_L2_HR_RiverSP_Node_007_022_NA_20220804T224145_20220804T224402_PGA0_01.zip']\n\n\n\n\nGet Download links from earthaccess search results\n\n#add desired links to a list\n#if the link has \"Reach\" instead of \"Node\" in the name, we want to download it for the swath use case\ndownloads = []\nfor r in data_links:\n for l in r:\n if 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/' in l:\n if 'Reach' in l:\n downloads.append(l)\n \nprint(len(downloads))\n\n23\n\n\nThis leaves us with half of the original links from our search.\n\n\nDownload the Data into a folder\n\n#Create folder to house downloaded data \nfolder = Path(\"SWOT_sample_files\")\n#newpath = r'SWOT_sample_files' \nif not os.path.exists(folder):\n os.makedirs(folder)\n\n\n#download data\nStore(auth).get(downloads, \"./SWOT_sample_files\")\n\n\n\n\n\n\n\n\n\n\n['SWOT_L2_HR_RiverSP_Reach_007_022_NA_20220804T224145_20220804T224402_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_104_NA_20220807T205936_20220807T210016_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_132_NA_20220808T210018_20220808T210252_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_162_NA_20220809T224722_20220809T225058_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_175_NA_20220810T101607_20220810T101940_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_177_NA_20220810T120102_20220810T120420_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_203_NA_20220811T101614_20220811T102211_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_287_NA_20220814T101759_20220814T102333_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_343_NA_20220816T101844_20220816T102323_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_371_NA_20220817T101846_20220817T102530_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_427_NA_20220819T101956_20220819T102559_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_440_NA_20220819T210905_20220819T211311_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_453_NA_20220820T083815_20220820T084053_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_468_NA_20220820T211105_20220820T211330_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip']\n\n\n\n\nShapefiles come in a .zip format, and need to be unzipped in the existing folder\n\nfor item in os.listdir(folder): # loop through items in dir\n if item.endswith(\".zip\"): # check for \".zip\" extension\n zip_ref = zipfile.ZipFile(f\"{folder}/{item}\") # create zipfile object\n zip_ref.extractall(folder) # extract file to dir\n zip_ref.close() # close file\n\n\nos.listdir(folder)\n\n['SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_175_NA_20220810T101607_20220810T101940_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_440_NA_20220819T210905_20220819T211311_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_177_NA_20220810T120102_20220810T120420_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_427_NA_20220819T101956_20220819T102559_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_104_NA_20220807T205936_20220807T210016_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_162_NA_20220809T224722_20220809T225058_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_287_NA_20220814T101759_20220814T102333_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_104_NA_20220807T205936_20220807T210016_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_132_NA_20220808T210018_20220808T210252_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_343_NA_20220816T101844_20220816T102323_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_104_NA_20220807T205936_20220807T210016_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_287_NA_20220814T101759_20220814T102333_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_203_NA_20220811T101614_20220811T102211_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_440_NA_20220819T210905_20220819T211311_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_177_NA_20220810T120102_20220810T120420_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_427_NA_20220819T101956_20220819T102559_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_175_NA_20220810T101607_20220810T101940_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_453_NA_20220820T083815_20220820T084053_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_371_NA_20220817T101846_20220817T102530_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_343_NA_20220816T101844_20220816T102323_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_453_NA_20220820T083815_20220820T084053_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_132_NA_20220808T210018_20220808T210252_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_343_NA_20220816T101844_20220816T102323_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_468_NA_20220820T211105_20220820T211330_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_203_NA_20220811T101614_20220811T102211_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_022_NA_20220804T224145_20220804T224402_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_343_NA_20220816T101844_20220816T102323_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_132_NA_20220808T210018_20220808T210252_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_162_NA_20220809T224722_20220809T225058_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_468_NA_20220820T211105_20220820T211330_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_203_NA_20220811T101614_20220811T102211_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_022_NA_20220804T224145_20220804T224402_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_453_NA_20220820T083815_20220820T084053_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_371_NA_20220817T101846_20220817T102530_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_162_NA_20220809T224722_20220809T225058_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_468_NA_20220820T211105_20220820T211330_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_177_NA_20220810T120102_20220810T120420_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_132_NA_20220808T210018_20220808T210252_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_037_NA_20220805T115553_20220805T120212_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_287_NA_20220814T101759_20220814T102333_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_177_NA_20220810T120102_20220810T120420_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_440_NA_20220819T210905_20220819T211311_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_315_NA_20220815T101757_20220815T102414_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_104_NA_20220807T205936_20220807T210016_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_065_NA_20220806T115630_20220806T120114_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_427_NA_20220819T101956_20220819T102559_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_121_NA_20220808T115628_20220808T120311_PGA0_01.prj',\n 'SWOT_L2_HR_RiverSP_Reach_007_022_NA_20220804T224145_20220804T224402_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_177_NA_20220810T120102_20220810T120420_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_175_NA_20220810T101607_20220810T101940_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_287_NA_20220814T101759_20220814T102333_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_205_NA_20220811T120350_20220811T120457_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_147_NA_20220809T101525_20220809T101639_PGA0_01.zip',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_300_NA_20220814T210504_20220814T210907_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_440_NA_20220819T210905_20220819T211311_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_427_NA_20220819T101956_20220819T102559_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_453_NA_20220820T083815_20220820T084053_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_104_NA_20220807T205936_20220807T210016_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_022_NA_20220804T224145_20220804T224402_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_203_NA_20220811T101614_20220811T102211_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_175_NA_20220810T101607_20220810T101940_PGA0_01.shp',\n 'SWOT_L2_HR_RiverSP_Reach_007_287_NA_20220814T101759_20220814T102333_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_162_NA_20220809T224722_20220809T225058_PGA0_01.shp.xml',\n 'SWOT_L2_HR_RiverSP_Reach_007_371_NA_20220817T101846_20220817T102530_PGA0_01.dbf',\n 'SWOT_L2_HR_RiverSP_Reach_007_483_NA_20220821T102527_20220821T102706_PGA0_01.shx',\n 'SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shx',\n 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+ "text": "This notebook will introduce you to programmatic Common Metadata Repository (CMR) search in python, using PO.DAAC Data as the example of data we’re interested in. While these tutorials focus on PO.DAAC data, the same strategies and code snippets can be used for other earthdata collections."
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- "section": "Step 2: Run the Script",
- "text": "Step 2: Run the Script\nUsage:\nusage: PO.DAAC bulk-data downloader [-h] -c COLLECTION -d OUTPUTDIRECTORY [--cycle SEARCH_CYCLES] [-sd STARTDATE] [-ed ENDDATE] [-f] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-e EXTENSIONS] [-gr GRANULENAME] [--process PROCESS_CMD] [--version] [--verbose]\n [-p PROVIDER] [--limit LIMIT] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n--cycle SEARCH_CYCLES\n Cycle number for determining downloads. can be repeated for multiple cycles \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --cycle must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the downloader.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nUnlike the ‘subscriber’, no ‘state’ is maintained for the downloader. if you re-run the downloader you’ll re-download all of the files again, unlike the subscriber which will download newly ingested data since the last run."
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+ "text": "API Documentation\nThis tutorial is not meant to be a replacement for the official CMR documentation. Its features are well documented and that should be the first place to go for information. It can be found at https://cmr.earthdata.nasa.gov/search. Some users may find it easier to navigate the Earthdata Search interface, find data of interest, and then automate the results using scripts. We’d suggest visiting https://search.earthdata.nasa.gov/"
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- "section": "Note: netrc file",
- "text": "Note: netrc file\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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+ "section": "CMR Background information",
+ "text": "CMR Background information\nCMR houses metadata for the 12 different DAACs. These come in the following forms:\n\nCollections\nGranules\nVariables\nServices\nVisualizations\nTools\n\nThis tutorial will focus on Collections and Granules. for more information, see the https://earthdata.nasa.gov/learn/user-resources/glossary"
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- "text": "Advanced Usage\n\nDownload data by filename\nIf you’re aware of a file you want to download, you can use the -gr option to download by a filename. The -c (COLLECTION) and -d (directory) options are still required.\nThe -gr option works by taking the file name, removing the suffix and searching for a CMR entry called the granuleUR. Some examples of this include:\n\n\n\n\n\n\n\n\nCollection\nFilename\nCMR GranuleUR\n\n\n\n\nMUR25-JPL-L4-GLOB-v04.2\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc\n20221206090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2\n\n\nJASON_CS_S6A_L2_ALT_HR_STD_OST_NRT_F\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07.nc\nS6A_P4_2__HR_STD__NR_077_039_20221212T181728_20221212T182728_F07\n\n\n\nBecause of this behavior, granules without data suffixes and granules where the the UR does not directly follow this convention may not work as anticipated. We will be adding the ability to download by granuleUR in a future enhancement.\n\n\nDownload data by cycle\nSome PO.DAAC datasets are better suited for cycles based search instead of start and end times. To enabled this, we’ve added ‘cycle’ based downloading to the data-downloader. The following example will download data from cycle 42:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42\nThe cycle parameter can be repeated to specify multiple cycles:\npodaac-data-downloader -c JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -d ./JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F -dc -b=\"-20,-20,20,20\" --cycle 42 --cycle 43 --cycle 44\n\n\nRequest data from another DAAC…\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z -ed 2014-07-01T00:46:02Z\n\n\nLogging\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\nControlling output directories\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\nDownloader behavior when a file already exists\nBy default, when the downloader is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the downloader will skip downloading that file.\nThis can drastically reduce the time for the downloader to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the downloader to always download matching files, by using –force/-f.\npodaac-data-downloader -c SENTINEL-1A_SLC -d myData -f\n\n\nSetting a bounding rectangle for filtering results\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-downloader -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\" -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\nSetting extensions\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\nrun a post download process\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\nIn need of Help?\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
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+ "title": "Introduction to Programmatic Common Metadata Repository Search",
+ "section": "Collection / Dataset Series",
+ "text": "Collection / Dataset Series\nCollection of datasets sharing the same product specification. They are synonym of EO collections. They are named dataset series as they may be mapped to ‘dataset series’ according to the terminology defined in ISO 19113, ISO 19114 and ISO 19115."
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- "text": "Run the Script\nUsage:\nusage: PO.DAAC data subscriber [-h] -c COLLECTION -d OUTPUTDIRECTORY [-f] [-sd STARTDATE] [-ed ENDDATE] [-b BBOX] [-dc] [-dydoy] [-dymd] [-dy] [--offset OFFSET] [-m MINUTES] [-e EXTENSIONS] [--process PROCESS_CMD] [--version] [--verbose] [-p PROVIDER] [--dry-run]\nTo run the script, the following parameters are required:\n-c COLLECTION, --collection-shortname COLLECTION\n The collection shortname for which you want to retrieve data.\n-d OUTPUTDIRECTORY, --data-dir OUTPUTDIRECTORY\n The directory where data products will be downloaded.\nAnd one of\n-sd STARTDATE, --start-date STARTDATE\n The ISO date time before which data should be retrieved. For Example, --start-date 2021-01-14T00:00:00Z\n-ed ENDDATE, --end-date ENDDATE\n The ISO date time after which data should be retrieved. For Example, --end-date 2021-01-14T00:00:00Z\n-m MINUTES, --minutes MINUTES\n How far back in time, in minutes, should the script look for data. If running this script as a cron, this value should be equal to or greater than how often your cron runs. \nCOLLECTION is collection shortname of interest. This can be found from the PO.DAAC Portal, CMR, or earthdata search. Please see the included Finding_shortname.pdf document on how to find a collection shortname.\nOUTPUTDIRECTORY is the directory in which files will be downloaded. It’s customary to set this to a data directory and include the collection shortname as part of the path so if you run multiple subscribers, the data are not dumped into the same directory.\nOne last required item is a time entry, one of --start-date, --end-date, or --minutes must be specified. This is done so that a time is explicitly requested, and fewer assumptions are made about how the users is running the subscriber.\nThe Script will login to CMR and the PO.DAAC Archive using a netrc file. See Note 1 for more information on setting this up.\nEvery time the script runs successfully (that is, no errors), a .update__<collectionname> file is created in your download directory with the last run timestamp. This timestamp will be used the next time the script is run. It will look for data between the timestamp in that file and the current time to determine new files to download."
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+ "section": "Granule",
+ "text": "Granule\nThe smallest aggregation of data which is independently managed (i. e. described, inventoried, retrievable). Granules may be managed as logical granules and/or physical granules. See also Scene.\nNote that granule is often equivalent to Data Set."
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- "section": "Note: CMR times",
- "text": "Note: CMR times\nThere are numerous ‘times’ available to query on in CMR. For the default subscriber, we look at the ‘created at’ field, which will look for when a granule file was ingested into the archive. This means as PO.DAAC gets data, your subscriber will also get data, regardless of the time range within the granule itself."
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+ "section": "Data Set",
+ "text": "Data Set\nA logically meaningful grouping or collection of similar or related data. Data having all of the same characteristics (source or class of source, processing level, resolution, etc.) but different independent variable ranges and/or responding to a specific need are normally considered part of a single data set. A data set is typically composed by products from several missions, gathered together to respond to the overall coverage or revisit requirements from a specific group of users.\nIn the context of EO data preservation a data set consists of the data records of one mission, sensor, and product type and the associated knowledge(information, tools). See collection."
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- "section": "Note: netrc file",
- "text": "Note: netrc file\nThe netrc used within the script will allow Python scripts to log into any Earthdata Login without being prompted for credentials every time you run. The netrc file should be placed in your HOME directory. To find the location of your HOME directory\nOn UNIX you can use\necho $HOME\nOn Windows you can use\necho %HOMEDRIVE%%HOMEPATH%\nThe output location from the command above should be the location of the .netrc (_netrc on Windows) file.\nThe format of the netrc file is as follows:\nmachine urs.earthdata.nasa.gov\n login <your username>\n password <your password>\nfor example:\nmachine urs.earthdata.nasa.gov\n login podaacUser\n password podaacIsAwesome\nIf the script cannot find the netrc file, you will be prompted to enter the username and password and the script wont be able to generate the CMR token"
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+ "href": "notebooks/podaac_cmr_tutorial.html#what-does-all-of-this-mean",
+ "title": "Introduction to Programmatic Common Metadata Repository Search",
+ "section": "What does all of this mean?",
+ "text": "What does all of this mean?\nFor the most part, users want to discover collections of interest to them, usually defined by parameter (Sea Surface Temperature, Ocean Winds, Sea Surface Height, etc), Level, spatial and temporal coverage, etc. Lets show an example."
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- "text": "Advanced Usage\n\nRequest data from another DAAC…\nUse the ‘provider’ flag to point at a non-PO.DAAC provider. Be aware, the default data types (–extensions) may need to be specified if the desired data are not in the defaults.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -p ASF -sd 2014-06-01T00:46:02Z\n\n\nLogging\nFor error troubleshooting, one can set an environment variable to gain more insight into errors:\nexport PODAAC_LOGLEVEL=DEBUG\nAnd then run the script. This should give you more verbose output on URL requests to CMR, tokens, etc.\n\n\nControlling output directories\nThe subscriber allows the placement of downloaded files into one of several directory structures based on the flags used to run the subscriber.\n\n-d - required, specifies the directory to which data is downloaded. If this is the only flag specified, all files will be downloaded to this single directory.\n-dc - optional, if ‘cycle’ information exists in the product metadata, download it to the data directory and use a relative c path to store granules. The relative path is 0 padded to 4 total digits (e.g. c0001)\n-dydoy - optional, relative paths use the start time of a granule to layout data in a YEAR/DAY-OF-YEAR path\n-dymd - optional, relative paths use the start time of a granule to layout data in a YEAR/MONTH/DAY path\n\n\n\nSubscriber behavior when a file already exists\nBy default, when the subscriber is about to download a file, it first: - Checks if the file already exists in the target location - Creates a checksum for the file and sees if it matches the checksum for that file in CMR\nIf the file already exists AND the checksum matches, the subscriber will skip downloading that file.\nThis can drastically reduce the time for the subscriber to complete. Also, since the checksum is verified, files will still be re-downloaded if for some reason the file has changed (or the file already on disk is corrupted).\nYou can override this default behavior - forcing the subscriber to always download matching files, by using –force/-f.\npodaac-data-subscriber -c SENTINEL-1A_SLC -d myData -f\n\n\nRunning as a Cron job\nTo automatically run and update a local file system with data files from a collection, one can use a syntax like the following:\n10 * * * * podaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d /path/to/data/VIIRS_N20-OSPO-L2P-v2.61 -e .nc -e .h5 -m 60 -b=\"-180,-90,180,90\" --verbose >> ~/.subscriber.log\n\nThis will run every hour at ten minutes passed, and output will be appended to a local file called ~/.subscriber.log\n\n\nSetting a bounding rectangle for filtering results\nIf you’re interested in a specific region, you can set the bounds parameter on your request to filter data that passes through a certain area. This is useful in particular for non-global datasets (such as swath datasets) with non-global coverage per file.\nNote: This does not subset the data, it just uses file metadata to see if any part of the datafile passes through your region. This will download the entire file, including data outside of the region specified.\n-b BBOX, --bounds BBOX\n The bounding rectangle to filter result in. Format is W Longitude,S Latitude,E Longitude,N Latitude without spaces. Due to an issue with parsing arguments, to use this command, please use the -b=\"-180,-90,180,90\" syntax when calling from\n the command line. Default: \"-180,-90,180,90\\.\n\nAn example of the -b usage:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -b=\"-180,-90,180,90\"\n\n\nSetting extensions\nSome collections have many files. To download a specific set of files, you can set the extensions on which downloads are filtered. By default, “.nc”, “.h5”, and “.zip” files are downloaded by default. The -e option is a regular expression check so you can do advanced things like -e PTM_\\\\d+ to match PTM_ followed by one or more digits- useful when the ending of a file has no suffix and has a number (1-12 for PTM, in this example)\n-e EXTENSIONS, --extensions EXTENSIONS\n Regexps of extensions of products to download. Default is [.nc, .h5, .zip, .tar.gz, .tiff]\nAn example of the -e usage- note the -e option is additive:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e .nc -e .h5\nOne may also specify a regular expression to select files. For example, the following are equivalent:\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_1, -e PTM_2, ..., -e PMT_10 -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\nand\npodaac-data-subscriber -c VIIRS_N20-OSPO-L2P-v2.61 -d ./data -e PTM_\\\\d+ -sd 2020-06-01T00:46:02Z -ed 2020-07-01T00:46:02Z\n\n\nrun a post download process\nUsing the --process option, you can run a simple command agaisnt the “just” downloaded file. This will take the format of “ ”. This means you can run a command like --process gzip to gzip all downloaded files. We do not support more advanced processes at this time (piping, running a process on a directory, etc).\n\n\nIn need of Help?\nThe PO.DAAC User Services Office is the primary point of contact for answering your questions concerning data and information held by the PO.DAAC. User Services staff members are knowledgeable about both the data ordering system and the data products themselves. We answer questions about data, route requests to other DAACs, and direct questions we cannot answer to the appropriate information source.\nPlease contact us via email at podaac@podaac.jpl.nasa.gov"
+ "objectID": "notebooks/podaac_cmr_tutorial.html#find-collections-by-parameter",
+ "href": "notebooks/podaac_cmr_tutorial.html#find-collections-by-parameter",
+ "title": "Introduction to Programmatic Common Metadata Repository Search",
+ "section": "Find collections by parameter",
+ "text": "Find collections by parameter\n\nfrom urllib import request\nimport json\nimport pprint\n\ncmr_url = \"https://cmr.earthdata.nasa.gov/search/\"\n\nwith request.urlopen(cmr_url+\"collections.umm_json?science_keywords[0][topic]=OCEANS\") as response:\n data = response.read()\n encoding = response.info().get_content_charset('utf-8')\n JSON_object = json.loads(data.decode(encoding))\n pp = pprint.PrettyPrinter(indent=2)\n pp.pprint(JSON_object)\n\n{ 'hits': 10904,\n 'items': [ { 'meta': { 'concept-id': 'C1214305813-AU_AADC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'ASAC_2201_HCL_0.5',\n 'provider-id': 'AU_AADC',\n 'revision-date': '2019-12-12T16:00:14Z',\n 'revision-id': 9,\n 'user-id': 'sritz'},\n 'umm': { 'Abstract': 'These results are for the 0.5 hour '\n 'extraction of HCl.\\n'\n '\\n'\n 'See also the metadata records for the 4 '\n 'hour extraction of HCl, and the time '\n 'trial data for 1 M HCl extractions.\\n'\n '\\n'\n 'A regional survey of potential '\n 'contaminants in marine or estuarine '\n 'sediments is often one of the first steps '\n 'in a post-disturbance environmental '\n 'impact assessment. Of the many different '\n 'chemical extraction or digestion '\n 'procedures that have been proposed to '\n 'quantify metal contamination, partial '\n 'acid extractions are probably the best '\n 'overall compromise between selectivity, '\n 'sensitivity, precision, cost and '\n 'expediency. The extent to which measured '\n 'metal concentrations relate to the '\n 'anthropogenic fraction that is '\n 'bioavailable is contentious, but is one '\n 'of the desired outcomes of an assessment '\n 'or prediction of biological impact. As '\n 'part of a regional survey of metal '\n \"contamination associated with Australia's \"\n 'past waste management activities in '\n 'Antarctica, we wanted to identify an acid '\n 'type and extraction protocol that would '\n 'allow a reasonable definition of the '\n 'anthropogenic bioavailable fraction for a '\n 'large number of samples. From a kinetic '\n 'study of the 1 M HCl extraction of two '\n 'certified Certified Reference Materials '\n '(MESS-2 and PACS-2) and two Antarctic '\n 'marine sediments, we concluded that a 4 '\n 'hour extraction time allows the '\n 'equilibrium dissolution of relatively '\n 'labile metal contaminants, but does not '\n 'favour the extraction of natural geogenic '\n 'metals. In a regional survey of 88 '\n 'marine samples from the Casey Station '\n 'area of East Antarctica, the 4 h '\n 'extraction procedure correlated best with '\n 'biological data, and most clearly '\n 'identified those sediments thought to be '\n 'contaminated by runoff from abandoned '\n 'waste disposal sites. Most importantly '\n 'the 4 hour extraction provided better '\n 'definition of the low to moderately '\n 'contaminated locations by picking up '\n 'small differences in anthropogenic metal '\n 'concentrations. For the purposes of '\n 'inter-regional comparison, we recommend a '\n '4 hour 1 M HCl acid extraction as a '\n 'standard method for assessing metal '\n 'contamination in Antarctica.\\n'\n '\\n'\n 'The fields in this dataset are\\n'\n '\\n'\n 'Location\\n'\n 'Site\\n'\n 'Replicate\\n'\n 'Antimony\\n'\n 'Arsenic\\n'\n 'Cadmium\\n'\n 'Chromium\\n'\n 'Copper\\n'\n 'Iron\\n'\n 'Lead\\n'\n 'Manganese\\n'\n 'Nickel\\n'\n 'Silver\\n'\n 'Tin\\n'\n 'Zinc',\n 'AccessConstraints': { 'Description': 'The data are '\n 'available for '\n 'download from '\n 'the url given '\n 'below.'},\n 'AncillaryKeywords': [ 'ANTIMONY',\n 'ARSENIC',\n 'BIOAVAILABLE METALS',\n 'CADMIUM',\n 'CHROMIUM',\n 'COPPER',\n 'IRON',\n 'KINETICS',\n 'LEAD',\n 'LOCATION',\n 'MANGANESE',\n 'MESS',\n 'MULTIVARIATE ANALYSIS',\n 'NICKEL',\n 'PACS',\n 'REPLICATE',\n 'SILVER',\n 'SITE',\n 'TIN',\n 'WINDMILL ISLANDS',\n 'ZINC'],\n 'CollectionCitations': [ { 'Creator': 'Snape, I., '\n 'Riddle, M.J., '\n 'Gore, D., '\n 'Stark, J.S., '\n 'Scouller, R. '\n 'and Stark, S.C.',\n 'OnlineResource': { 'Linkage': 'https://data.aad.gov.au/metadata/records/ASAC_2201_HCL_0.5'},\n 'Publisher': 'Australian '\n 'Antarctic '\n 'Data Centre',\n 'ReleaseDate': '2004-08-02T00:00:00.000Z',\n 'SeriesName': 'CAASM '\n 'Metadata',\n 'Title': '0.5 hour 1 M HCl '\n 'extraction data '\n 'for the Windmill '\n 'Islands marine '\n 'sediments',\n 'Version': '1'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'ian.snape@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3158'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3591'}]},\n 'FirstName': 'IAN',\n 'LastName': 'SNAPE',\n 'Roles': [ 'Investigator',\n 'Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'martin.riddle@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3573'}]},\n 'FirstName': 'MARTIN',\n 'LastName': 'RIDDLE',\n 'MiddleName': 'J.',\n 'Roles': ['Investigator']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'dave.connell@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3244'}]},\n 'FirstName': 'DAVE',\n 'LastName': 'CONNELL',\n 'MiddleName': 'J.',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Marsfield',\n 'Country': 'Australia',\n 'PostalCode': '2109',\n 'StateProvince': 'New '\n 'South '\n 'Wales',\n 'StreetAddresses': [ 'Department '\n 'of '\n 'Physical '\n 'Geography',\n 'Macquarie '\n 'University']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'damian.gore@mq.edu.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '2 '\n '9850 '\n '8420'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '2 '\n '9850 '\n '8391'}]},\n 'FirstName': 'DAMIAN',\n 'LastName': 'GORE',\n 'Roles': ['Investigator']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'jonny.stark@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3158'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3589'}]},\n 'FirstName': 'JONATHAN',\n 'LastName': 'STARK',\n 'MiddleName': 'SEAN',\n 'Roles': ['Investigator']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'beck.scouller@aad.gov.au'}]},\n 'FirstName': 'REBECCA',\n 'LastName': 'SCOULLER',\n 'Roles': [ 'Investigator',\n 'Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'scott.stark@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3169'}]},\n 'FirstName': 'SCOTT',\n 'LastName': 'STARK',\n 'MiddleName': 'CHARLES',\n 'Roles': ['Investigator']}],\n 'DOI': {'DOI': 'doi:10.4225/15/5747A30D1F767'},\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://data.aad.gov.au',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Kingston',\n 'Country': 'Australia',\n 'PostalCode': '7050',\n 'StateProvince': 'Tasmania',\n 'StreetAddresses': [ 'Australian '\n 'Antarctic '\n 'Division',\n '203 '\n 'Channel '\n 'Highway']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'metadata@aad.gov.au'},\n { 'Type': 'Fax',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3351'},\n { 'Type': 'Telephone',\n 'Value': '+61 '\n '3 '\n '6232 '\n '3244'}]},\n 'FirstName': 'DATA '\n 'OFFICER',\n 'LastName': 'AADC',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Australian Antarctic '\n 'Data Centre, Australia',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'AU/AADC'}],\n 'DataLanguage': 'eng',\n 'DirectoryNames': [ {'ShortName': 'AMD/AU'},\n {'ShortName': 'CEOS'},\n {'ShortName': 'AMD'}],\n 'EntryTitle': '0.5 hour 1 M HCl extraction data for '\n 'the Windmill Islands marine sediments',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION',\n 'OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Windmill '\n 'Islands',\n 'Type': 'ANTARCTICA'},\n { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'POLAR'}],\n 'MetadataDates': [ { 'Date': '2004-07-30T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-26T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ShortName': 'SEDIMENT '\n 'CORERS'}],\n 'ShortName': 'Not provided'},\n {'ShortName': 'LABORATORY'},\n {'ShortName': 'FIELD SURVEYS'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'PublicationReferences': [ { 'Author': 'Snape, I., '\n 'Scouller, '\n 'R.C., Stark, '\n 'S.C., Stark, '\n 'J., Riddle, '\n 'M.J., Gore, '\n 'D.B.',\n 'DOI': { 'DOI': 'doi:10.1016/j.chemosphere.2004.05.042'},\n 'Issue': '6',\n 'Pages': '491-504',\n 'PublicationDate': '2004-01-01T00:00:00.000Z',\n 'Series': 'Chemosphere',\n 'Title': 'Characterisation '\n 'of the dilute '\n 'HCl extraction '\n 'method for the '\n 'identification '\n 'of metal '\n 'contamination '\n 'in Antarctic '\n 'marine '\n 'sediments',\n 'Volume': '57'}],\n 'Quality': 'The dates provided in temporal coverage '\n 'are approximate only. Years are correct.\\n'\n '\\n'\n 'See the referenced paper for full details '\n 'on steps taken to ensure quality of data.\\n'\n '\\n'\n 'To assess extraction efficiency for a '\n 'range of sediment types, four marine '\n 'sediments were analysed in detail. Two '\n 'international certified reference '\n 'materials (CRMs) and two '\n 'well-characterised Antarctic sediments '\n 'were chosen to compare and contrast '\n 'moderately to strongly contaminated '\n 'samples (based on total metal digest), '\n 'with clean samples of similar matrices. '\n 'One CRM was an uncontaminated continental '\n 'shelf mud (MESS-2), and the other a '\n 'contaminated harbour mud (PACS-2) (NRCC, '\n '2002). The two Antarctic sediments were '\n 'collected as part of a regional '\n 'hierarchical survey (Stark et al., 2003). '\n 'One Antarctic sample was from an area of '\n 'known metal pollution in Brown Bay (BB), '\n \"which is adjacent to the 'Old' Casey \"\n 'Station waste disposal site (Snape et al., '\n '2001; Stark et al., 2003). The second '\n 'Antarctic sample was from a non-impacted '\n \"control site from O'Brien Bay (OBB), 3 km \"\n 'south of Casey Station and the disposal '\n 'site (Fig. 1). The Antarctic samples, OBB '\n 'and BB, have similar matrices, proportions '\n 'of mud (less than 63 microns; 19% and 22% '\n 'respectively) and total organic carbon '\n 'contents (1.9% and 2.3% respectively). '\n 'Both MESS and PACS are sieved, homogenised '\n 'and dried CRMs that have been ground to '\n '~50 microns (NRCC, 2002). In contrast, '\n 'OBB and BB were only sieved to less than 2 '\n 'mm, thereby removing only the very largest '\n 'particles (less than or equal to 3%). The '\n 'Antarctic samples were collected using '\n 'acid-washed PVC coring tubes. The samples '\n 'were kept frozen at -20 degrees C until '\n 'wet-sieved with a small amount of clean '\n 'filtered (0.45 microns cellulose nitrate) '\n \"O'Brien Bay seawater through 2 mm nylon \"\n 'mesh held in a plastic sieve unit. The '\n 'sediments were then oven-dried to constant '\n 'weight at 103 degrees C (Loring and '\n 'Rantala 1992), and stored in Nalgene HDPE '\n 'bottles until analysis.',\n 'RelatedUrls': [ { 'Description': 'Download point for '\n 'the data',\n 'Type': 'GET DATA',\n 'URL': 'http://data.aad.gov.au/aadc/portal/download_file.cfm?file_id=1677',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Public information '\n 'for ASAC project '\n '2201',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://secure3.aad.gov.au/proms/public/projects/report_project_public.cfm?project_no=2201',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Citation reference '\n 'for this metadata '\n 'record and dataset',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=ASAC_2201_HCL_0.5',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ENVIRONMENTAL IMPACTS',\n 'Topic': 'HUMAN DIMENSIONS',\n 'VariableLevel1': 'HEAVY METALS '\n 'CONCENTRATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MARINE SEDIMENTS',\n 'Topic': 'OCEANS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MARINE SEDIMENTS',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEDIMENT '\n 'CHEMISTRY'}],\n 'ShortName': 'ASAC_2201_HCL_0.5',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 110.0,\n 'NorthBoundingCoordinate': -66.0,\n 'SouthBoundingCoordinate': -66.0,\n 'WestBoundingCoordinate': 110.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1997-10-01T00:00:00.000Z',\n 'EndingDateTime': '1999-03-31T23:59:59.999Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'This '\n 'data '\n 'set '\n 'conforms '\n 'to '\n 'the '\n 'CCBY '\n 'Attribution '\n 'License\\n'\n '(http://creativecommons.org/licenses/by/4.0/).\\n'\n '\\n'\n 'Please '\n 'follow '\n 'instructions '\n 'listed '\n 'in '\n 'the '\n 'citation '\n 'reference '\n 'provided '\n 'at '\n 'http://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=ASAC_2201_HCL_0.5 '\n 'when '\n 'using '\n 'these '\n 'data.'}},\n 'Version': '1'}},\n { 'meta': { 'concept-id': 'C1214422215-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'OES_CEOB_106_MILE',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-08T12:53:39Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The 106-Mile Dumpsite Oceanographic '\n 'project is response to the Ocean Dumping\\n'\n 'Act '\n '(http://www.epa.gov/history/topics/mprsa/02.htm) '\n 'which directs the National\\n'\n 'Oceanic and Atmospheric Administration '\n '(NOAA), the US Environmental Protection\\n'\n 'Agency (EPA), and the US Coast Guard to '\n 'perform research, monitoring, and\\n'\n 'surveillance until ocean dumping of '\n 'municipal sewage sludge is ended in '\n '1992. \\n'\n 'NOAA is responsible for research and '\n 'monitoring of the far-field and '\n 'long-term\\n'\n 'effects of dumping at the dumpsite, which '\n 'is located in the Mid-Atlantic Bight\\n'\n '(MAB).\\n'\n '\\n'\n 'Four 1991 seasonal deployments of four '\n 'satellite-tracked drifters each and a\\n'\n 'single 1990 deployment of eight drifters '\n 'were conducted across the continental\\n'\n 'shelf break and Dumpsite. The tracking '\n 'and processing of near-surface drifters\\n'\n 'continues. Coupled with EPA weekly '\n 'drifter deployments, the CEOB drifter '\n 'study\\n'\n 'provides information on the relationship '\n 'between suspended sludge dispersal to\\n'\n 'the near-surface circulation and '\n 'interaction of shelf water, slope water, '\n 'and\\n'\n 'Gulf Stream over the continental margin '\n 'in the MAB.\\n'\n '\\n'\n 'Hydrographic studies include '\n 'quasi-synoptic conductivity and '\n 'temperature\\n'\n 'profile and expendable bathythermograph '\n '(XBT) surveys of the MAB and dumpsite.\\n'\n 'These surveys were conducted in support '\n 'of biological and chemical sampling and\\n'\n 'in conjunction with the deployment of '\n 'drifters.\\n'\n '\\n'\n 'Weekly transects were taken across the '\n 'shelf, slope, and northern tip of the\\n'\n 'Dumpsite to the Gulf Stream from the ship '\n 'of opportunity, M/V OLEANDER; this\\n'\n 'was an expansion of the monthly program '\n 'managed by National Marine Fisheries\\n'\n \"Service's Northeast Fisheries Center, in \"\n 'Narragansett, Rhode Island.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-01 '\n '12:48:25'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'AncillaryKeywords': [ 'OES/CEOB_106_MILE',\n 'CONSERVATION'],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3281',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'NOAA/National '\n 'Ocean '\n 'Service',\n '1305 '\n 'East-West '\n 'Highway, '\n 'Room '\n '6543']}],\n 'ContactMechanisms': [ { 'Type': 'Fax',\n 'Value': '301-713-4501'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-2809'}]},\n 'FirstName': 'FRANK',\n 'LastName': 'AIKMAN',\n 'Roles': ['Investigator']}],\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://coastalscience.noaa.gov/about/centers/ccma',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3281',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'National '\n 'Centers '\n 'for '\n 'Coastal '\n 'Ocean '\n 'Science',\n 'Center '\n 'for '\n 'Coastal '\n 'Monitoring '\n 'and '\n 'Assessment',\n 'NOAA/National '\n 'Ocean '\n 'Survey',\n '1305 '\n 'East-West '\n 'Highway, '\n 'SSMC4']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'harris.white@noaa.gov'},\n { 'Type': 'Fax',\n 'Value': '301-713-4338'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-3034'}]},\n 'FirstName': 'HARRIS',\n 'LastName': 'WHITE',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Center for Coastal '\n 'Monitoring and '\n 'Assessment, National '\n 'Ocean Service, NOAA, '\n 'U.S. Department of '\n 'Commerce',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'DOC/NOAA/NOS/NCCOS/CCMA'}],\n 'DirectoryNames': [ {'ShortName': 'USA/NOAA'},\n {'ShortName': 'CEOS'}],\n 'EntryTitle': '106-Mile Dumpsite Oceanographic Project '\n '(Mid Atlantic Bight); Surface Drifters '\n 'and Hydrographic Measurements; NOAA/NOS',\n 'ISOTopicCategories': [ 'ELEVATION',\n 'ENVIRONMENT',\n 'OCEANS'],\n 'LocationKeywords': [ { 'Category': 'OCEAN',\n 'Type': 'ATLANTIC OCEAN'},\n { 'Category': 'CONTINENT',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Mid-Atlantic '\n 'Bight',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'NEW YORK',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Narragansett',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'RHODE ISLAND',\n 'Type': 'NORTH AMERICA'}],\n 'MetadataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-20T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ShortName': 'BATHYTHERMOGRAPHS'},\n { 'LongName': 'Conductivity, '\n 'Temperature, '\n 'Depth',\n 'ShortName': 'CTD'},\n { 'ShortName': 'DRIFTING '\n 'BUOYS'},\n { 'LongName': 'Salinity, '\n 'Temperature, '\n 'Depth',\n 'ShortName': 'STD'},\n { 'LongName': 'Expendable '\n 'Bathythermographs',\n 'ShortName': 'XBT'}],\n 'ShortName': 'BUOYS'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'RelatedUrls': [ { 'Description': 'Information on the '\n '106-mile dumpsite',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.oar.noaa.gov/spotlite/archive/spot_oceandumping.html',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SLUDGE '\n 'DISPERSAL',\n 'Term': 'ENVIRONMENTAL IMPACTS',\n 'Topic': 'HUMAN DIMENSIONS',\n 'VariableLevel1': 'SEWAGE '\n 'DISPOSAL'},\n { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'CONTINENTAL '\n 'RISES/SLOPES',\n 'Term': 'BATHYMETRY/SEAFLOOR '\n 'TOPOGRAPHY',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'CONTINENTAL '\n 'MARGINS'},\n { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'CONTINENTAL '\n 'SHELVES',\n 'Term': 'BATHYMETRY/SEAFLOOR '\n 'TOPOGRAPHY',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'CONTINENTAL '\n 'MARGINS'},\n { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'GULF '\n 'STREAM',\n 'Term': 'OCEAN CIRCULATION',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'OCEAN '\n 'CURRENTS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN CIRCULATION',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WIND-DRIVEN '\n 'CIRCULATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SALINITY/DENSITY',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'CONDUCTIVITY'}],\n 'ShortName': 'OES_CEOB_106_MILE',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -70.0,\n 'NorthBoundingCoordinate': 41.0,\n 'SouthBoundingCoordinate': 37.0,\n 'WestBoundingCoordinate': -74.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1970-01-01T00:00:00.000Z'}]}],\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214422266-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'CS0005',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-08T13:12:29Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The database includes 1951-88 monthly '\n 'cumulative streamflow (mouth of\\n'\n 'Chesapeake Bay) in cubic feet per '\n 'second. The data were digitized\\n'\n 'from data provided by the U.S. Geological '\n 'Survey.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-01 '\n '12:48:33'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'AncillaryKeywords': [ 'CHESAPEAKE BAY',\n 'ESTUARY',\n 'RUNOFF'],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3282',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'NOAA/NOS '\n 'N/SCI2',\n '1315 '\n 'East-West '\n 'Hwy']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'Michael.Dowgiallo@noaa.gov'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-3338 '\n 'x129'}]},\n 'FirstName': 'MICHAEL',\n 'LastName': 'DOWGIALLO',\n 'MiddleName': 'J.',\n 'Roles': [ 'Investigator',\n 'Metadata Author']}],\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.cop.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Silver '\n 'Spring',\n 'Country': 'USA',\n 'PostalCode': '20910-3282',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'NOAA/NOS '\n 'N/SCI2',\n '1315 '\n 'East-West '\n 'Hwy']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'Michael.Dowgiallo@noaa.gov'},\n { 'Type': 'Telephone',\n 'Value': '(301) '\n '713-3338 '\n 'x129'}]},\n 'FirstName': 'MICHAEL',\n 'LastName': 'DOWGIALLO',\n 'MiddleName': 'J.',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Center for Sponsored '\n 'Coastal Ocean '\n 'Research, National '\n 'Centers for Coastal '\n 'Ocean Science, '\n 'National Ocean '\n 'Service, NOAA, U.S. '\n 'Department of Commerce',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'DOC/NOAA/NOS/NCCOS/CSCOR'}],\n 'DirectoryNames': [ {'ShortName': 'USA/NOAA'},\n {'ShortName': 'CEOS'}],\n 'EntryTitle': '1951-88 Monthly Cumulative Streamflow '\n 'at the Mouth of the Chesapeake Bay',\n 'ISOTopicCategories': [ 'GEOSCIENTIFIC INFORMATION',\n 'INLAND WATERS',\n 'OCEANS'],\n 'LocationKeywords': [ { 'Category': 'OCEAN',\n 'DetailedLocation': 'CHESAPEAKE '\n 'BAY',\n 'Type': 'ATLANTIC OCEAN'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'CHESAPEAKE '\n 'BAY',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Chesapeake '\n 'Bay',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'MARYLAND',\n 'Type': 'NORTH AMERICA'}],\n 'MetadataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-20T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [{'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Projects': [ { 'LongName': 'Coastal Ocean Program',\n 'ShortName': 'COP'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE WATER',\n 'Topic': 'TERRESTRIAL '\n 'HYDROSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WATER '\n 'FEATURES',\n 'VariableLevel2': 'RIVERS/STREAMS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE WATER',\n 'Topic': 'TERRESTRIAL '\n 'HYDROSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WATER '\n 'PROCESSES/MEASUREMENTS',\n 'VariableLevel2': 'DISCHARGE/FLOW'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'COASTAL PROCESSES',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ESTUARIES'}],\n 'ShortName': 'CS0005',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -75.0,\n 'NorthBoundingCoordinate': 40.0,\n 'SouthBoundingCoordinate': 36.0,\n 'WestBoundingCoordinate': -78.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1951-01-01T00:00:00.000Z',\n 'EndingDateTime': '1988-12-31T23:59:59.999Z'}]}],\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214621676-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'darling_sst_82-93',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-12T15:31:27Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'Seawater Surface Temperature Data '\n 'Collected between the years 1982-1989 '\n 'and\\n'\n '1993 off the dock at the Darling Marine '\n 'Center, Walpole, Maine',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-02 '\n '13:18:42'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://dmc.umaine.edu/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Walpole',\n 'Country': 'USA',\n 'PostalCode': '04573',\n 'StateProvince': 'ME',\n 'StreetAddresses': [ '193 '\n \"Clark's \"\n 'Cove '\n 'Road']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'darling@maine.edu'},\n { 'Type': 'Fax',\n 'Value': '207-563-3119'},\n { 'Type': 'Telephone',\n 'Value': '207-563-3146'}]},\n 'FirstName': 'KEVIN',\n 'LastName': 'ECKELBARGER',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Darling Marine Center, '\n 'School of Marine '\n 'Science, University of '\n 'Maine',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'UMAINE/SMS/DMC'}],\n 'DataLanguage': 'eng',\n 'DirectoryNames': [{'ShortName': 'USA/NASA'}],\n 'EntryTitle': '1982-1989 and 1993 Seawater '\n 'Temperatures at the Darling Marine '\n 'Center',\n 'ISOTopicCategories': ['OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'MAINE',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'OCEAN',\n 'Subregion1': 'NORTH ATLANTIC '\n 'OCEAN',\n 'Subregion2': 'GULF OF MAINE',\n 'Type': 'ATLANTIC OCEAN'}],\n 'MetadataDates': [ { 'Date': '2002-11-07T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-24T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [{'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Projects': [ { 'LongName': 'Gulf of Maine Ocean Data '\n 'Partnership',\n 'ShortName': 'GOMODP'}],\n 'RelatedUrls': [ { 'Description': 'Seawater Surface '\n 'Temperature Data',\n 'Type': 'GET DATA',\n 'URL': 'http://server.dmc.maine.edu/dmctemps1980s.html',\n 'URLContentType': 'DistributionURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE'}],\n 'ShortName': 'darling_sst_82-93',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -66.74,\n 'NorthBoundingCoordinate': 47.67,\n 'SouthBoundingCoordinate': 42.85,\n 'WestBoundingCoordinate': -71.31}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1982-03-01T00:00:00.000Z',\n 'EndingDateTime': '1989-09-14T23:59:59.999Z'},\n { 'BeginningDateTime': '1993-01-29T00:00:00.000Z',\n 'EndingDateTime': '1993-12-31T23:59:59.999Z'}]}],\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214609006-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'PASSCAL_FOGO',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-07-30T15:20:13Z',\n 'revision-id': 3,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The data were acquired during July 1991 '\n 'in conjunction with LITHOPROBE\\n'\n 'East. Three marine airgun lines were shot '\n 'on the northeast\\n'\n 'Newfoundland shelf and recorded on Fogo '\n 'Island off the north coast of\\n'\n 'Newfoundland. The source was an untuned '\n 'array of five 1000\\n'\n 'cu. in. airguns.\\n'\n '\\n'\n 'The data were recorded with a 13 element '\n 'array of 3-component\\n'\n 'receivers. 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'CEOS'},\n {'ShortName': 'AMD'},\n {'ShortName': 'ARCTIC'}],\n 'EntryTitle': '1998-1999 Raw data of CTD in Prydz Bay '\n 'region of the southern Indian Ocean, '\n 'CHINARE-15',\n 'ISOTopicCategories': ['OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'DetailedLocation': 'Prydz Bay',\n 'Type': 'ANTARCTICA'},\n { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'ARCTIC'},\n { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'POLAR'}],\n 'MetadataDates': [ { 'Date': '2007-08-16T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-20T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Conductivity, '\n 'Temperature, '\n 'Depth',\n 'ShortName': 'CTD'}],\n 'ShortName': 'SHIPS',\n 'Type': 'In Situ Ocean-based '\n 'Platforms'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Quality': '\\n'\n ' This instrument is used as '\n 'routine observation, and the data set is '\n 'in good quality in general.\\n'\n ' ',\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'BATHYMETRY/SEAFLOOR '\n 'TOPOGRAPHY',\n 'Topic': 'OCEANS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SALINITY/DENSITY',\n 'Topic': 'OCEANS'}],\n 'ShortName': '1998-1999_15-15_S_OC_OC05_LO_O011301_000_R0_Y',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 77.0,\n 'NorthBoundingCoordinate': -62.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': 70.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL_VERTICAL',\n 'VerticalSpatialDomains': [ { 'Type': 'Minimum '\n 'Depth',\n 'Value': '0M'},\n { 'Type': 'Maximum '\n 'Depth',\n 'Value': '3500'}]},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1999-01-01T00:00:00.000Z',\n 'EndingDateTime': '1999-02-01T23:59:59.999Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': '\\n'\n ' '\n 'Data '\n 'may '\n 'not '\n 'be '\n 'used '\n 'for '\n 'commercial '\n 'applications\\n'\n ' '}},\n 'Version': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1214612327-SCIOPS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'darling_sst_99',\n 'provider-id': 'SCIOPS',\n 'revision-date': '2018-11-12T15:31:29Z',\n 'revision-id': 4,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': '1999 Seawater Surface Temperature Data '\n 'collected off the dock at the Darling\\n'\n 'Marine Center Walpole, Maine.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.extraction_date',\n 'Value': '2015-12-02 '\n '12:54:13'},\n { 'DataType': 'FLOAT',\n 'Description': 'Not '\n 'provided',\n 'Name': 'metadata.keyword_version',\n 'Value': '8.1'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DataCenters': [ { 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://dmc.umaine.edu/',\n 'URLContentType': 'DataCenterURL'}]},\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Walpole',\n 'Country': 'USA',\n 'PostalCode': '04573',\n 'StateProvince': 'ME',\n 'StreetAddresses': [ '193 '\n \"Clark's \"\n 'Cove '\n 'Road']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'darling@maine.edu'},\n { 'Type': 'Fax',\n 'Value': '207-563-3119'},\n { 'Type': 'Telephone',\n 'Value': '207-563-3146'}]},\n 'FirstName': 'KEVIN',\n 'LastName': 'ECKELBARGER',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'LongName': 'Darling Marine Center, '\n 'School of Marine '\n 'Science, University of '\n 'Maine',\n 'Roles': ['ARCHIVER', 'DISTRIBUTOR'],\n 'ShortName': 'UMAINE/SMS/DMC'}],\n 'DataLanguage': 'eng',\n 'DirectoryNames': [{'ShortName': 'USA/NASA'}],\n 'EntryTitle': '1999 Seawater Temperatures at the '\n 'Darling Marine Center',\n 'ISOTopicCategories': ['OCEANS'],\n 'LocationKeywords': [ { 'Category': 'CONTINENT',\n 'Subregion1': 'UNITED STATES '\n 'OF AMERICA',\n 'Subregion2': 'MAINE',\n 'Type': 'NORTH AMERICA'},\n { 'Category': 'OCEAN',\n 'Subregion1': 'NORTH ATLANTIC '\n 'OCEAN',\n 'Subregion2': 'GULF OF MAINE',\n 'Type': 'ATLANTIC OCEAN'}],\n 'MetadataDates': [ { 'Date': '2002-11-07T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [{'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Projects': [ { 'LongName': 'Gulf of Maine Ocean Data '\n 'Partnership',\n 'ShortName': 'GOMODP'}],\n 'RelatedUrls': [ { 'Description': '1999 Seawater '\n 'Surface Temperature '\n 'Data',\n 'Type': 'GET DATA',\n 'URL': 'http://server.dmc.maine.edu/dmctemp99.html',\n 'URLContentType': 'DistributionURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE'}],\n 'ShortName': 'darling_sst_99',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -66.74,\n 'NorthBoundingCoordinate': 47.67,\n 'SouthBoundingCoordinate': 42.85,\n 'WestBoundingCoordinate': -71.31}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '1999-01-01T00:00:00.000Z',\n 'EndingDateTime': '1999-12-31T23:59:59.999Z'}]}],\n 'Version': 'Not provided'}}],\n 'took': 17}\n\n\nThere’s a lot going on here. First off, the url:\nhttps://cmr.earthdata.nasa.gov/search/collections.umm_json?science_keywords[0][topic]=OCEANS\nThe basic premise is this: We are asking for all collections (../search/collections) that fall under the ‘OCEANS’ science topic as defined by GCMD. We are requesting this in the umm_json format (.umm_json). What we get back is a listing of those collections matching this. When last run, this was over 10900 collections! that’s a lot. Let’s get that down a bit…\n\nwith request.urlopen(cmr_url+\"collections.umm_json?science_keywords[0][topic]=OCEANS&science_keywords[0][term]=Ocean%20Temperature&has_granules_or_cwic=true&page_size=50\") as response:\n data = response.read()\n encoding = response.info().get_content_charset('utf-8')\n JSON_object = json.loads(data.decode(encoding))\n pp = pprint.PrettyPrinter(indent=2)\n pp.pprint(JSON_object)\n\n{ 'hits': 483,\n 'items': [ { 'meta': { 'concept-id': 'C1597928934-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-VIIRS_N20-OSPO-L2P',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:50:50Z',\n 'revision-id': 2,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'NOAA-20 (N20/JPSS-1/J1) is the second '\n 'satellite in the US NOAA latest '\n 'generation Joint Polar Satellite System '\n '(JPSS). N20 was launched on November 18, '\n '2017. In conjunction with the first US '\n 'satellite in JPSS series, Suomi National '\n 'Polar-orbiting Partnership (S-NPP) '\n 'satellite launched on October 28, 2011, '\n 'N20 form the new NOAA polar '\n 'constellation. NOAA is responsible for '\n 'all JPSS products, including SST from the '\n 'Visible Infrared Imaging Radiometer Suite '\n '(VIIRS). VIIRS is a whiskbroom scanning '\n 'radiometer, which takes measurements in '\n 'the cross-track direction within a field '\n 'of view of 112.56-deg using 16 detectors '\n 'and a double-sided mirror assembly. At a '\n 'nominal altitude of 829 km, the swath '\n 'width is 3,060 km, providing global daily '\n 'coverage for both day and night passes. '\n 'VIIRS has 22 spectral bands, covering the '\n 'spectrum from 0.4-12 um, including 16 '\n 'moderate resolution bands (M-bands). \\n'\n '\\n'\n 'The L2P SST product is derived at the '\n 'native sensor resolution (~0.75 km at '\n 'nadir, ~1.5 km at swath edge) using '\n \"NOAA's Advanced Clear-Sky Processor for \"\n 'Ocean (ACSPO) system, and reported in 10 '\n 'minute granules in netCDF4 format, '\n 'compliant with the GHRSST Data '\n 'Specification version 2 (GDS2). There are '\n '144 granules per 24hr interval, with a '\n 'total data volume of 27GB/day. In '\n 'addition to pixel-level earth locations, '\n 'Sun-sensor geometry, and ancillary data '\n 'from the NCEP global weather forecast, '\n 'ACSPO outputs include four brightness '\n 'temperatures (BTs) in M12 (3.7um), M14 '\n '(8.6um), M15 (11um), and M16 (12um) '\n 'bands, and two reflectances in M5 '\n '(0.67um) and M7 (0.87um) bands. The '\n 'reflectances are used for cloud '\n 'identification. Beginning with ACSPO '\n 'v2.60, all BTs and reflectances are '\n 'destriped (Bouali and Ignatov, 2014) and '\n 'resampled (Gladkova et al., 2016), to '\n 'minimize the effect of bow-tie '\n 'distortions and deletions. SSTs are '\n 'retrieved from destriped BTs. \\n'\n '\\n'\n 'SSTs are derived from BTs using the '\n 'Multi-Channel SST (MCSST; night) and '\n 'Non-Linear SST (NLSST; day) algorithms '\n '(Petrenko et al., 2014). ACSPO clear-sky '\n 'mask (ACSM) is provided in each pixel as '\n 'part of variable l2p_flags, which also '\n 'includes day/night, land, ice, twilight, '\n 'and glint flags (Petrenko et al., 2010). '\n 'Fill values are reported in all pixels '\n 'with >5 km inland. For each valid water '\n 'pixel (defined as ocean, sea, lake or '\n 'river, and up to 5 km inland), four BTs '\n 'in M12/14/15/16 (included for those users '\n 'interested in direct \"radiance '\n 'assimilation\", e.g., NOAA NCEP, NASA '\n 'GMAO, ECMWF) and two refelctances in M5/7 '\n 'are reported, along with derived SST. '\n 'Other variables include NCEP wind speed '\n 'and ACSPO SST minus reference SST '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). '\n 'Only ACSM confidently clear pixels are '\n 'recommended (equivalent to GDS2 quality '\n 'level=5). Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with QL=5. Note that users of ACSPO data '\n 'have the flexibility to ignore the ACSM '\n 'and derive their own clear-sky mask, and '\n 'apply it to BTs and SSTs. They may also '\n 'ignore ACSPO SSTs, and derive their own '\n 'SSTs from the original BTs. \\n'\n '\\n'\n 'The L2P product is monitored and '\n 'validated against quality controlled in '\n 'situ data provided by NOAA in situ SST '\n 'Quality Monitor system (iQuam; Xu and '\n 'Ignatov, 2014), using another NOAA '\n 'system, SST Quality Monitor (SQUAM; Dash '\n 'et al, 2010). Corresponding clear-sky BTs '\n 'are validated against RTM simulation in '\n 'the Monitoring IR Clear-sky Radiances '\n 'over Ocean for SST system (MICROS; Liang '\n 'and Ignatov, 2011). A reduced size '\n '(1GB/day), equal-angle gridded '\n '(0.02-deg), ACSPO L3U product is also '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-OSPO-L3U-v2.61, '\n 'where gridded L2P SSTs with QL=5 only are '\n 'reported, and BT layers omitted.',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': '',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'Name': 'Office '\n 'of '\n 'Satellite '\n 'and '\n 'Product '\n 'Operations '\n 'website',\n 'Protocol': 'HTTP'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 2P '\n 'OSPO dataset '\n 'v2.61 from VIIRS '\n 'on the NOAA-20 '\n 'satellite (GDS '\n 'v2) (GDS version '\n '2)',\n 'Version': '2.61'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}],\n 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov',\n 'URLContentType': 'DataContactURL'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'NonDataCenterAffiliation': 'Technical '\n 'Contact',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Kihai,',\n 'LastName': 'Yury',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Petrenko,',\n 'LastName': 'Boris',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Jonasson,',\n 'LastName': 'Olafur',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Pennybacker,',\n 'LastName': 'Matthew',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Gladkova,',\n 'LastName': 'Irina',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Asheville',\n 'Country': 'USA',\n 'PostalCode': '28801-5001',\n 'StateProvince': 'NC',\n 'StreetAddresses': [ 'Federal '\n 'Building '\n '151 '\n 'Patton '\n 'Avenue']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-713-3277'},\n { 'Type': 'Fax',\n 'Value': '301-713-3300'},\n { 'Type': 'Email',\n 'Value': 'NODC.DataOfficer@noaa.gov'}],\n 'RelatedUrls': [ { 'Description': 'Main '\n 'NCEI '\n 'website '\n 'providing '\n 'links '\n 'to '\n 'access '\n 'data '\n 'and '\n 'data '\n 'services.',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.ncei.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'National Centers for '\n 'Environmental '\n 'Information, NESDIS, '\n 'NOAA, U.S. Department '\n 'of Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-08-07T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P OSPO dataset v2.61 from '\n 'VIIRS on the NOAA-20 satellite (GDS v2) '\n '(GDS version 2)',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'OCEANS',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'BIOTA',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL OCEAN'}],\n 'MetadataAssociations': [ { 'EntryId': 'GHRSST Level '\n '2P OSPO '\n 'dataset v2.61 '\n 'from VIIRS on '\n 'the NOAA-20 '\n 'satellite (GDS '\n 'v2)',\n 'Type': 'COLLECTION',\n 'Version': '2.61'}],\n 'Platforms': [ { 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-20',\n 'ShortName': 'NOAA-20'},\n { 'Instruments': [ { 'LongName': 'Visible '\n 'Infrared '\n 'Imaging '\n 'Radiometer '\n 'Suite',\n 'ShortName': 'VIIRS'}],\n 'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Purpose': 'This dataset is available to the public '\n 'for a wide variety of uses including '\n 'scientific research and analysis.',\n 'RelatedUrls': [ { 'Description': 'Navigate directly '\n 'to the URL for a '\n 'descriptive web '\n 'page with download '\n 'links.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://doi.org/10.25921/sfs7-9688',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Search for data '\n 'granules belonging '\n 'to this collection '\n '(a granule is the '\n 'smallest '\n 'aggregation of data '\n 'that can be '\n 'independently '\n 'described and '\n 'retrieved).',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://www.nodc.noaa.gov/search/granule/rest/find/document?searchText=fileIdentifier%3AGHRSST-VIIRS_N20-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_N20-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_N20-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_N20-OSPO-L2P*&start=1&max=100&f=searchPage',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through a '\n 'variety of services '\n 'via a THREDDS '\n '(Thematic Real-time '\n 'Environmental '\n 'Distributed Data '\n 'Services) Data '\n 'Server (TDS). '\n 'Depending on the '\n 'dataset, the TDS '\n 'can provide WMS, '\n 'WCS, DAP, HTTP, and '\n 'other data access '\n 'and metadata '\n 'services as well. '\n 'For more '\n 'information on the '\n 'TDS, see '\n 'http://www.unidata.ucar.edu/software/thredds/current/tds/.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/thredds/catalog/ghrsst/L2P/VIIRS_N20/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Navigate directly '\n 'to the URL for data '\n 'access and direct '\n 'download.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/L2P/VIIRS_N20/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through '\n 'the File Transfer '\n 'Protocol (FTP). You '\n 'may use any FTP '\n 'client to download '\n 'these data.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/L2P/VIIRS_N20/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Preview graphic',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://data.nodc.noaa.gov/cgi-bin/gfx?id=gov.noaa.nodc:GHRSST-VIIRS_N20-OSPO-L2P',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Samples, Interface '\n 'Control Document '\n 'describing file '\n 'contents, '\n 'background ppt and '\n 'other info',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/ACSPO/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'and A. Heidinger, '\n '2010: Clear-Sky '\n 'Mask for ACSPO. '\n 'JTech, 27, '\n '1609-1623',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://journals.ametsoc.org/doi/abs/10.1175/2010JTECHA1413.1',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'J. Stroup, P. Dash, '\n '2014: Evaluation '\n 'and Selection of '\n 'SST Regression '\n 'Algorithms for JPSS '\n 'VIIRS. JGR, 119, '\n '4580-4599, doi: '\n '10.1002/2013JD020637',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://onlinelibrary.wiley.com/doi/10.1002/2013JD020637/abstract',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': '(Search Granule)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHV20-2PO61',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Xu, F.',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/iquam/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Liang, X.',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/micros/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dash, P., A. '\n 'Ignatov, Y. Kihai',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'oceanography',\n 'Term': 'Not provided',\n 'Topic': 'Not provided'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS',\n 'VariableLevel2': 'WIND SPEED'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SEA ICE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ICE EXTENT'}],\n 'ShortName': '10.25921/sfs7-9688',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2018-11-07T00:00:00.000Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'Distribution '\n 'liability: '\n 'NOAA '\n 'and '\n 'NCEI '\n 'make '\n 'no '\n 'warranty, '\n 'expressed '\n 'or '\n 'implied, '\n 'regarding '\n 'these '\n 'data, '\n 'nor '\n 'does '\n 'the '\n 'fact '\n 'of '\n 'distribution '\n 'constitute '\n 'such '\n 'a '\n 'warranty. '\n 'NOAA '\n 'and '\n 'NCEI '\n 'cannot '\n 'assume '\n 'liability '\n 'for '\n 'any '\n 'damages '\n 'caused '\n 'by '\n 'any '\n 'errors '\n 'or '\n 'omissions '\n 'in '\n 'these '\n 'data. '\n 'If '\n 'appropriate, '\n 'NCEI '\n 'can '\n 'only '\n 'certify '\n 'that '\n 'the '\n 'data '\n 'it '\n 'distributes '\n 'are '\n 'an '\n 'authentic '\n 'copy '\n 'of '\n 'the '\n 'records '\n 'that '\n 'were '\n 'accepted '\n 'for '\n 'inclusion '\n 'in '\n 'the '\n 'NCEI '\n 'archives.'}},\n 'Version': '2.61'}},\n { 'meta': { 'concept-id': 'C1597928333-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-MSG03-OSPO-L2P',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:38:50Z',\n 'revision-id': 2,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The Meteosat Second Generation (MSG-3) '\n 'satellites are spin stabilized '\n 'geostationary satellites operated by the '\n 'European Organization for the '\n 'Exploitation of Meteorological Satellites '\n '(EUMETSAT) to provide accurate weather '\n 'monitoring data through its primary '\n 'instrument the Spinning Enhanced Visible '\n 'and InfraRed Imager (SEVIRI), which has '\n 'the capacity to observe the Earth in 12 '\n 'spectral channels. Eight of these '\n 'channels are in the thermal infrared, '\n 'providing among other information, '\n 'observations of the temperatures of '\n 'clouds, land and sea surfaces at '\n 'approximately 5 km resolution with a 15 '\n 'minute duty cycle. This Group for High '\n 'Resolution Sea Surface Temperature '\n '(GHRSST) dataset produced by the US '\n 'National Oceanic and Atmospheric '\n 'Administration (NOAA) National '\n 'Environmental Satellite, Data, and '\n 'Information Service (NESDIS) is derived '\n 'from the SEVIRI instrument on the second '\n 'MSG satellite (also known as Meteosat-9) '\n 'that was launched on 22 December 2005. '\n 'Skin sea surface temperature (SST) data '\n 'are calculated from the infrared channels '\n 'of SEVIRI at full resolution every 15 '\n 'minutes. L2P data products with Single '\n 'Sensor Error Statistics (SSES) are then '\n 'derived following the GHRSST-PP Data '\n 'Processing Specification (GDS) version '\n '2.0.',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': 'Koner, Prabhat',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'https://podaac.jpl.nasa.gov/',\n 'Name': 'NASA '\n 'JPL '\n 'PO.DAAC '\n 'website',\n 'Protocol': 'HTTPS'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 2P '\n 'Atlantic Regional '\n 'Skin Sea Surface '\n 'Temperature from '\n 'the Spinning '\n 'Enhanced Visible '\n 'and InfraRed '\n 'Imager (SEVIRI) '\n 'on the Meteosat '\n 'Second Generation '\n '(MSG-3) satellite '\n '(GDS version 2)',\n 'Version': '1.0'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-763-8102 '\n 'x172'},\n { 'Type': 'Fax',\n 'Value': '301-763-8572'},\n { 'Type': 'Email',\n 'Value': 'Eileen.Maturi@noaa.gov'}]},\n 'FirstName': 'Eileen',\n 'LastName': 'Maturi',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-763-8102 '\n 'x172'},\n { 'Type': 'Fax',\n 'Value': '301-763-8572'},\n { 'Type': 'Email',\n 'Value': 'Eileen.Maturi@noaa.gov'}]},\n 'FirstName': 'Eileen',\n 'LastName': 'Maturi',\n 'NonDataCenterAffiliation': 'Technical '\n 'Contact',\n 'Roles': ['Metadata Author']},\n { 'FirstName': 'Maturi,',\n 'LastName': 'Eileen',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'SUITLAND',\n 'Country': 'USA',\n 'PostalCode': '20746',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'E/SP '\n 'NSOF, '\n '4231 '\n 'SUITLAND '\n 'ROAD']}]},\n 'FirstName': 'Sapper,',\n 'LastName': 'John',\n 'Roles': ['Technical Contact']},\n { 'FirstName': 'Harris,',\n 'LastName': 'Andy',\n 'Roles': ['Technical Contact']},\n { 'FirstName': 'Mittaz,',\n 'LastName': 'Jonathan',\n 'Roles': ['Technical Contact']},\n { 'FirstName': 'Koner,',\n 'LastName': 'Prabhat',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Asheville',\n 'Country': 'USA',\n 'PostalCode': '28801-5001',\n 'StateProvince': 'NC',\n 'StreetAddresses': [ 'Federal '\n 'Building '\n '151 '\n 'Patton '\n 'Avenue']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-713-3277'},\n { 'Type': 'Fax',\n 'Value': '301-713-3300'},\n { 'Type': 'Email',\n 'Value': 'NODC.DataOfficer@noaa.gov'}],\n 'RelatedUrls': [ { 'Description': 'Main '\n 'NCEI '\n 'website '\n 'providing '\n 'links '\n 'to '\n 'access '\n 'data '\n 'and '\n 'data '\n 'services.',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.ncei.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'National Centers for '\n 'Environmental '\n 'Information, NESDIS, '\n 'NOAA, U.S. Department '\n 'of Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-01-03T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P Atlantic Regional Skin '\n 'Sea Surface Temperature from the '\n 'Spinning Enhanced Visible and InfraRed '\n 'Imager (SEVIRI) on the Meteosat Second '\n 'Generation (MSG-3) satellite (GDS '\n 'version 2)',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'OCEANS',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'BIOTA',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'OCEAN',\n 'Subregion1': 'NORTH ATLANTIC '\n 'OCEAN',\n 'Type': 'ATLANTIC OCEAN'},\n { 'Category': 'OCEAN',\n 'Subregion1': 'NORTH ATLANTIC '\n 'OCEAN',\n 'Subregion2': 'MEDITERRANEAN '\n 'SEA',\n 'Type': 'ATLANTIC OCEAN'},\n { 'Category': 'OCEAN',\n 'Subregion1': 'SOUTH ATLANTIC '\n 'OCEAN',\n 'Type': 'ATLANTIC OCEAN'},\n { 'Category': 'OCEAN',\n 'Type': 'INDIAN OCEAN'},\n { 'Category': 'OCEAN',\n 'Subregion1': 'RED SEA',\n 'Type': 'INDIAN OCEAN'}],\n 'MetadataAssociations': [ { 'EntryId': 'GHRSST Level '\n '2P Atlantic '\n 'Regional Skin '\n 'Sea Surface '\n 'Temperature '\n 'from the '\n 'Spinning '\n 'Enhanced '\n 'Visible and '\n 'InfraRed '\n 'Imager '\n '(SEVIRI) on '\n 'the Meteosat '\n 'Second '\n 'Generation '\n '(MSG-3) '\n 'satellite (GDS '\n 'version 2)',\n 'Type': 'COLLECTION',\n 'Version': '1.0'}],\n 'Platforms': [ { 'LongName': 'Meteosat Second '\n 'Generation 2',\n 'ShortName': 'MSG2'},\n { 'Instruments': [ { 'LongName': 'Spinning '\n 'Enhanced '\n 'Visible '\n 'and '\n 'Infrared '\n 'Imager',\n 'ShortName': 'SEVIRI'}],\n 'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Purpose': 'This dataset is available to the public '\n 'for a wide variety of uses including '\n 'scientific research and analysis.',\n 'RelatedUrls': [ { 'Description': 'Navigate directly '\n 'to the URL for a '\n 'descriptive web '\n 'page with download '\n 'links.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://doi.org/10.7289/v5j67dz9',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Search for data '\n 'granules belonging '\n 'to this collection '\n '(a granule is the '\n 'smallest '\n 'aggregation of data '\n 'that can be '\n 'independently '\n 'described and '\n 'retrieved).',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://www.nodc.noaa.gov/search/granule/rest/find/document?searchText=fileIdentifier%3AGHRSST-MSG03-OSPO-L2P*%20OR%20fileIdentifier%3AMSG03-OSPO-L2P*&start=1&max=100&f=searchPage',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through a '\n 'variety of services '\n 'via a THREDDS '\n '(Thematic Real-time '\n 'Environmental '\n 'Distributed Data '\n 'Services) Data '\n 'Server (TDS). '\n 'Depending on the '\n 'dataset, the TDS '\n 'can provide WMS, '\n 'WCS, DAP, HTTP, and '\n 'other data access '\n 'and metadata '\n 'services as well. '\n 'For more '\n 'information on the '\n 'TDS, see '\n 'http://www.unidata.ucar.edu/software/thredds/current/tds/.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/thredds/catalog/ghrsst/L2P/MSG03/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Navigate directly '\n 'to the URL for data '\n 'access and direct '\n 'download.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/L2P/MSG03/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through '\n 'the File Transfer '\n 'Protocol (FTP). You '\n 'may use any FTP '\n 'client to download '\n 'these data.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/L2P/MSG03/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Preview graphic',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://data.nodc.noaa.gov/cgi-bin/gfx?id=gov.noaa.nodc:GHRSST-MSG03-OSPO-L2P',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/miscellaneous-documents/GHRSSTUserGuidev91.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/governance-documents/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dissemination '\n 'reports log',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.nodc.noaa.gov/SatelliteData/ghrsst/logs/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Journal Article',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://dx.doi.org/10.1016/j.rse.2012.12.019',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Electronic diagram '\n 'plotting tool',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://gsics.tools.eumetsat.int/plotter/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.eumetsat.int/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'GHRSST Project Home '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Read software',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/sw/IDL/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': '(Search Granule)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHMG3-2PO02',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'oceanography',\n 'Term': 'Not provided',\n 'Topic': 'Not provided'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AEROSOLS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'AEROSOL '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS',\n 'VariableLevel2': 'WIND SPEED'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SEA ICE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ICE EXTENT'}],\n 'ShortName': '10.7289/v5j67dz9',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 81.0,\n 'NorthBoundingCoordinate': 73.0,\n 'SouthBoundingCoordinate': -73.0,\n 'WestBoundingCoordinate': -81.0}],\n 'CoordinateSystem': 'CARTESIAN'}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2013-08-01T00:00:00.000Z',\n 'EndingDateTime': '2018-02-20T00:00:00.000Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'Distribution '\n 'liability: '\n 'NOAA '\n 'and '\n 'NCEI '\n 'make '\n 'no '\n 'warranty, '\n 'expressed '\n 'or '\n 'implied, '\n 'regarding '\n 'these '\n 'data, '\n 'nor '\n 'does '\n 'the '\n 'fact '\n 'of '\n 'distribution '\n 'constitute '\n 'such '\n 'a '\n 'warranty. '\n 'NOAA '\n 'and '\n 'NCEI '\n 'cannot '\n 'assume '\n 'liability '\n 'for '\n 'any '\n 'damages '\n 'caused '\n 'by '\n 'any '\n 'errors '\n 'or '\n 'omissions '\n 'in '\n 'these '\n 'data. '\n 'If '\n 'appropriate, '\n 'NCEI '\n 'can '\n 'only '\n 'certify '\n 'that '\n 'the '\n 'data '\n 'it '\n 'distributes '\n 'are '\n 'an '\n 'authentic '\n 'copy '\n 'of '\n 'the '\n 'records '\n 'that '\n 'were '\n 'accepted '\n 'for '\n 'inclusion '\n 'in '\n 'the '\n 'NCEI '\n 'archives.'}},\n 'Version': '1.0'}},\n { 'meta': { 'concept-id': 'C1597990368-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-VIIRS_NPP-OSPO-L3U',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:52:23Z',\n 'revision-id': 2,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The Joint Polar Satellite System (JPSS), '\n 'starting with S-NPP launched on 28 '\n 'October 2011, is the new generation of '\n 'the US Polar Operational Environmental '\n 'Satellites (POES). The Suomi National '\n 'Polar-orbiting Partnership (S-NPP) is a '\n 'collaboration between NASA and NOAA. \\n'\n '\\n'\n 'The ACSPO SNPP/VIIRS L3U (Level 3 '\n 'Uncollated) product is a gridded version '\n 'of the ACSPO SNPP/VIIRS L2P product '\n 'available here '\n 'https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-OSPO-L2P-v2.61. '\n 'The L3U output files are 10-minute '\n 'granules in netCDF4 format, compliant '\n 'with the GHRSST Data Specification '\n 'version 2 (GDS2). There are 144 granules '\n 'per 24hr interval, with a total data '\n 'volume of 500MB/day. Fill values are '\n 'reported at all invalid pixels, including '\n 'pixels with >5 km inland. For each valid '\n 'water pixel (defined as ocean, sea, lake '\n 'or river, and up to 5 km inland), the '\n 'following layers are reported: SSTs, '\n 'ACSPO clear-sky mask (ACSM; provided in '\n 'each grid as part of l2p_flags, which '\n 'also includes day/night, land, ice, '\n 'twilight, and glint flags), NCEP wind '\n 'speed, and ACSPO SST minus reference '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 '\n '). Only L2P SSTs with QL=5 were gridded, '\n 'so all valid SSTs are recommended for the '\n 'users. Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with valid SST. The ACSPO VIIRS L3U '\n 'product is monitored and validated '\n 'against iQuam in situ data (Xu and '\n 'Ignatov, 2014) in SQUAM (Dash et al, '\n '2010).',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': '',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'Name': 'Office '\n 'of '\n 'Satellite '\n 'and '\n 'Product '\n 'Operations '\n 'website',\n 'Protocol': 'HTTP'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 3U '\n 'OSPO dataset '\n 'v2.61 from VIIRS '\n 'on S-NPP '\n 'Satellite (GDS '\n 'v2) (GDS version '\n '2)',\n 'Version': '2.61'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}],\n 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.star.nesdis.noaa.gov',\n 'URLContentType': 'DataContactURL'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'NonDataCenterAffiliation': 'Technical '\n 'Contact',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Pennybacker,',\n 'LastName': 'Matthew',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Gladkova,',\n 'LastName': 'Irina',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Kihai,',\n 'LastName': 'Yury',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Petrenko,',\n 'LastName': 'Boris',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Jonasson,',\n 'LastName': 'Olafur',\n 'Roles': ['Technical Contact']}],\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Asheville',\n 'Country': 'USA',\n 'PostalCode': '28801-5001',\n 'StateProvince': 'NC',\n 'StreetAddresses': [ 'Federal '\n 'Building '\n '151 '\n 'Patton '\n 'Avenue']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-713-3277'},\n { 'Type': 'Fax',\n 'Value': '301-713-3300'},\n { 'Type': 'Email',\n 'Value': 'NODC.DataOfficer@noaa.gov'}],\n 'RelatedUrls': [ { 'Description': 'Main '\n 'NCEI '\n 'website '\n 'providing '\n 'links '\n 'to '\n 'access '\n 'data '\n 'and '\n 'data '\n 'services.',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://www.ncei.noaa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'National Centers for '\n 'Environmental '\n 'Information, NESDIS, '\n 'NOAA, U.S. Department '\n 'of Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-08-11T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 3U OSPO dataset v2.61 from '\n 'VIIRS on S-NPP Satellite (GDS v2) (GDS '\n 'version 2)',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'OCEANS',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'BIOTA',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL OCEAN'}],\n 'MetadataAssociations': [ { 'EntryId': 'GHRSST Level '\n '3U OSPO '\n 'dataset v2.61 '\n 'from VIIRS on '\n 'S-NPP '\n 'Satellite (GDS '\n 'v2)',\n 'Type': 'COLLECTION',\n 'Version': '2.61'}],\n 'Platforms': [ { 'LongName': 'Suomi National '\n 'Polar-orbiting '\n 'Partnership',\n 'ShortName': 'SUOMI-NPP'},\n { 'Instruments': [ { 'LongName': 'Visible '\n 'Infrared '\n 'Imaging '\n 'Radiometer '\n 'Suite',\n 'ShortName': 'VIIRS'}],\n 'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Purpose': 'This dataset is available to the public '\n 'for a wide variety of uses including '\n 'scientific research and analysis.',\n 'RelatedUrls': [ { 'Description': 'Navigate directly '\n 'to the URL for a '\n 'descriptive web '\n 'page with download '\n 'links.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://doi.org/10.7289/v5kk98s8',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Search for data '\n 'granules belonging '\n 'to this collection '\n '(a granule is the '\n 'smallest '\n 'aggregation of data '\n 'that can be '\n 'independently '\n 'described and '\n 'retrieved).',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://www.nodc.noaa.gov/search/granule/rest/find/document?searchText=fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L3U*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L3U*&start=1&max=100&f=searchPage',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through a '\n 'variety of services '\n 'via a THREDDS '\n '(Thematic Real-time '\n 'Environmental '\n 'Distributed Data '\n 'Services) Data '\n 'Server (TDS). '\n 'Depending on the '\n 'dataset, the TDS '\n 'can provide WMS, '\n 'WCS, DAP, HTTP, and '\n 'other data access '\n 'and metadata '\n 'services as well. '\n 'For more '\n 'information on the '\n 'TDS, see '\n 'http://www.unidata.ucar.edu/software/thredds/current/tds/.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/thredds/catalog/ghrsst/L3U/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Navigate directly '\n 'to the URL for data '\n 'access and direct '\n 'download.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/L3U/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through '\n 'the File Transfer '\n 'Protocol (FTP). You '\n 'may use any FTP '\n 'client to download '\n 'these data.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'ftp://ftp.nodc.noaa.gov/pub/data.nodc/ghrsst/L3U/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Preview graphic',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://data.nodc.noaa.gov/cgi-bin/gfx?id=gov.noaa.nodc:GHRSST-VIIRS_NPP-OSPO-L3U',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/miscellaneous-documents/GHRSSTUserGuidev91.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://accession.nodc.noaa.gov/0123222/data/0-data/governance-documents/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dissemination '\n 'reports log',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.nodc.noaa.gov/SatelliteData/ghrsst/logs/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'online document',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/ACSPO/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Journal Article',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://dx.doi.org/10.1016/j.rse.2015.01.003',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Journal Article',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://dx.doi.org/10.1002/2013JD020637',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Institution web '\n 'page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'SST products '\n 'monitored',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Samples, Interface '\n 'Control Document '\n 'describing file '\n 'contents, '\n 'background ppt and '\n 'other info',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/ACSPO/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'and A. Heidinger, '\n '2010: Clear-Sky '\n 'Mask for ACSPO. '\n 'JTech, 27, '\n '1609-1623',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://journals.ametsoc.org/doi/abs/10.1175/2010JTECHA1413.1',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'J. Stroup, P. Dash, '\n '2014: Evaluation '\n 'and Selection of '\n 'SST Regression '\n 'Algorithms for JPSS '\n 'VIIRS. JGR, 119, '\n '4580-4599, doi: '\n '10.1002/2013JD020637',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://onlinelibrary.wiley.com/doi/10.1002/2013JD020637/abstract',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Read software',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/sw/IDL/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': '(Search Granule)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHVRS-3UO61',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Xu, F.',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/iquam/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Liang, X.',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/micros/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dash, P., A. '\n 'Ignatov, Y. Kihai',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'oceanography',\n 'Term': 'Not provided',\n 'Topic': 'Not provided'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS',\n 'VariableLevel2': 'WIND SPEED'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SEA ICE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'ICE EXTENT'}],\n 'ShortName': '10.7289/v5kk98s8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2015-05-19T00:00:00.000Z'}]}],\n 'UseConstraints': { 'Description': { 'Description': 'Distribution '\n 'liability: '\n 'NOAA '\n 'and '\n 'NCEI '\n 'make '\n 'no '\n 'warranty, '\n 'expressed '\n 'or '\n 'implied, '\n 'regarding '\n 'these '\n 'data, '\n 'nor '\n 'does '\n 'the '\n 'fact '\n 'of '\n 'distribution '\n 'constitute '\n 'such '\n 'a '\n 'warranty. '\n 'NOAA '\n 'and '\n 'NCEI '\n 'cannot '\n 'assume '\n 'liability '\n 'for '\n 'any '\n 'damages '\n 'caused '\n 'by '\n 'any '\n 'errors '\n 'or '\n 'omissions '\n 'in '\n 'these '\n 'data. '\n 'If '\n 'appropriate, '\n 'NCEI '\n 'can '\n 'only '\n 'certify '\n 'that '\n 'the '\n 'data '\n 'it '\n 'distributes '\n 'are '\n 'an '\n 'authentic '\n 'copy '\n 'of '\n 'the '\n 'records '\n 'that '\n 'were '\n 'accepted '\n 'for '\n 'inclusion '\n 'in '\n 'the '\n 'NCEI '\n 'archives.'}},\n 'Version': '2.61'}},\n { 'meta': { 'concept-id': 'C1224519979-NOAA_NCEI',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST-VIIRS_NPP-OSPO-L2P',\n 'provider-id': 'NOAA_NCEI',\n 'revision-date': '2019-08-12T19:51:43Z',\n 'revision-id': 7,\n 'user-id': 'mmorahan'},\n 'umm': { 'Abstract': 'The Joint Polar Satellite System (JPSS), '\n 'starting with S-NPP launched on 28 '\n 'October 2011, is the new generation of '\n 'the US Polar Operational Environmental '\n 'Satellites (POES). The Suomi National '\n 'Polar-orbiting Partnership (S-NPP) is a '\n 'collaboration between NASA and NOAA. NOAA '\n 'is responsible for all JPSS products, '\n 'including SST from the Visible Infrared '\n 'Imaging Radiometer Suite (VIIRS). VIIRS '\n 'is a whiskbroom scanning radiometer, '\n 'which takes measurements in the '\n 'cross-track direction within a field of '\n 'view of 112.56-deg using 16 detectors and '\n 'a double-sided mirror assembly. At a '\n 'nominal altitude of 829 km, the swath '\n 'width is 3,060 km, providing global daily '\n 'coverage for both day and night passes. '\n 'VIIRS has 22 spectral bands covering the '\n 'spectrum from 0.4-12 um, including 16 '\n 'moderate resolution bands (M-bands). \\n'\n '\\n'\n 'The L2P SST product is derived at the '\n 'native sensor resolution (~0.75 km at '\n 'nadir, ~1.5 km at swath edge) using '\n \"NOAA's Advanced Clear-Sky Processor for \"\n 'Ocean (ACSPO) system, and reported in '\n '10-minute granules in netCDF4 format, '\n 'compliant with the GHRSST Data '\n 'Specification version 2 (GDS2). There are '\n '144 granules per 24hr interval, with a '\n 'total data volume of 27GB/day. In '\n 'addition to pixel-level earth locations, '\n 'Sun-sensor geometry, and ancillary data '\n 'from the NCEP global weather forecast, '\n 'ACSPO outputs include four brightness '\n 'temperatures (BTs) in M12 (3.7um), M14 '\n '(8.6um), M15 (11um), and M16 (12um) '\n 'bands, and two reflectances in M5 '\n '(0.67um) and M7 (0.87um) bands. The '\n 'reflectances are used for cloud '\n 'identification. Beginning with ACSPO '\n 'v2.60, all BTs and reflectances are '\n 'destriped (Bouali and Ignatov, 2014) and '\n 'resampled (Gladkova et al., 2016), to '\n 'minimize the effect of bow-tie '\n 'distortions and deletions. SSTs are '\n 'retrieved from destriped BTs.\\n'\n '\\n'\n 'SSTs are derived from BTs using the '\n 'Multi-Channel SST (MCSST; night) and '\n 'Non-Linear SST (NLSST; day) algorithms '\n '(Petrenko et al., 2014). An ACSPO '\n 'clear-sky mask (ACSM) is provided in each '\n 'pixel as part of variable l2p_flags, '\n 'which also includes day/night, land, ice, '\n 'twilight, and glint flags (Petrenko et '\n 'al., 2010). Fill values are reported in '\n 'all invalid pixels, including those with '\n '>5 km inland. For each valid water pixel '\n '(defined as ocean, sea, lake or river, '\n 'and up to 5 km inland), four BTs in '\n 'M12/14/15/16 (included for those users '\n 'interested in direct \"radiance '\n 'assimilation\", e.g., NOAA NCEP, NASA '\n 'GMAO, ECMWF) and two refelctances in M5/7 '\n 'are reported, along with derived SST. '\n 'Other variables include NCEP wind speed '\n 'and ACSPO SST minus reference SST '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). '\n 'Only ACSM confidently clear pixels are '\n 'recommended (equivalent to GDS2 quality '\n 'level=5). Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with QL=5. Note that users of ACSPO data '\n 'have the flexibility to ignore the ACSM '\n 'and derive their own clear-sky mask, and '\n 'apply it to BTs and SSTs. They may also '\n 'ignore ACSPO SSTs, and derive their own '\n 'SSTs from the original BTs.\\n'\n '\\n'\n 'The ACSPO VIIRS L2P product is monitored '\n 'and validated against quality controlled '\n 'in situ data provided by NOAA in situ SST '\n 'Quality Monitor system (iQuam; Xu and '\n 'Ignatov, 2014) using another NOAA system, '\n 'SST Quality Monitor (SQUAM; Dash et al, '\n '2010). Corresponding clear-sky BTs are '\n 'validated against RTM simulations in the '\n 'Monitoring IR Clear-sky Radiances over '\n 'Ocean for SST system (MICROS; Liang and '\n 'Ignatov, 2011). A reduced size (1GB/day), '\n 'equal-angle gridded (0.02-deg '\n 'resolution), ACSPO L3U product is also '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-OSPO-L3U-v2.61, '\n 'where gridded L2P SSTs with QL=5 only are '\n 'reported, and BT layers omitted.',\n 'AncillaryKeywords': [ 'DOC/NOAA/NESDIS/NODC > '\n 'National Oceanographic Data '\n 'Center, NESDIS, NOAA, U.S. '\n 'Department of Commerce',\n 'DOC/NOAA/NESDIS/NCEI > '\n 'National Centers for '\n 'Environmental Information, '\n 'NESDIS, NOAA, U.S. Department '\n 'of Commerce',\n 'DOC/NOAA/NESDIS/OSDPD > Office '\n 'of Satellite Data Processing '\n 'and Distribution, NESDIS, '\n 'NOAA, U.S. Department of '\n 'Commerce',\n 'NASA/JPL/PODAAC > Physical '\n 'Oceanography Distributed '\n 'Active Archive Center, Jet '\n 'Propulsion Laboratory, NASA'],\n 'CollectionCitations': [ { 'Creator': '',\n 'DataPresentationForm': 'tableDigital',\n 'OnlineResource': { 'Description': 'Institution '\n 'web '\n 'page',\n 'Function': 'information',\n 'Linkage': 'http://www.ospo.noaa.gov/Organization/About/contact.html',\n 'Name': 'Office '\n 'of '\n 'Satellite '\n 'and '\n 'Product '\n 'Operations '\n 'website',\n 'Protocol': 'HTTP'},\n 'Publisher': '',\n 'Title': 'GHRSST Level 2P '\n 'OSPO dataset '\n 'v2.61 from VIIRS '\n 'on S-NPP '\n 'Satellite (GDS '\n 'v2) (GDS version '\n '2)',\n 'Version': '2.61'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'ContactPersons': [ { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}],\n 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'http://www.star.nesdis.noaa.gov',\n 'URLContentType': 'DataContactURL'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'ContactInstruction': 'Phone/FAX/E-mail',\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3379'},\n { 'Type': 'Fax',\n 'Value': 'none'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'NonDataCenterAffiliation': 'Technical '\n 'Contact',\n 'Roles': ['Metadata Author']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Kihai,',\n 'LastName': 'Yury',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'College '\n 'Park',\n 'Country': 'USA',\n 'PostalCode': '20740',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ '5830 '\n 'University '\n 'Research '\n 'Court']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '301-683-3485'}]},\n 'FirstName': 'Petrenko,',\n 'LastName': 'Boris',\n 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Commerce',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'DOC/NOAA/NESDIS/NCEI'}],\n 'DataDates': [ { 'Date': '2019-08-06T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P OSPO dataset v2.61 from '\n 'VIIRS on S-NPP Satellite (GDS v2) (GDS '\n 'version 2)',\n 'ISOTopicCategories': [ 'ENVIRONMENT',\n 'OCEANS',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'BIOTA',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL OCEAN'}],\n 'MetadataAssociations': [ { 'EntryId': 'GHRSST Level '\n '2P OSPO '\n 'dataset v2.61 '\n 'from VIIRS on '\n 'S-NPP '\n 'Satellite (GDS '\n 'v2)',\n 'Type': 'COLLECTION',\n 'Version': '2.61'}],\n 'Platforms': [ { 'LongName': 'Suomi National '\n 'Polar-orbiting '\n 'Partnership',\n 'ShortName': 'SUOMI-NPP'},\n { 'Instruments': [ { 'LongName': 'Visible '\n 'Infrared '\n 'Imaging '\n 'Radiometer '\n 'Suite',\n 'ShortName': 'VIIRS'}],\n 'ShortName': 'Not provided'}],\n 'ProcessingLevel': {'Id': 'Not provided'},\n 'Purpose': 'This dataset is available to the public '\n 'for a wide variety of uses including '\n 'scientific research and analysis.',\n 'RelatedUrls': [ { 'Description': 'Navigate directly '\n 'to the URL for a '\n 'descriptive web '\n 'page with download '\n 'links.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://doi.org/10.7289/v5pr7sx5',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Search for data '\n 'granules belonging '\n 'to this collection '\n '(a granule is the '\n 'smallest '\n 'aggregation of data '\n 'that can be '\n 'independently '\n 'described and '\n 'retrieved).',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://www.nodc.noaa.gov/search/granule/rest/find/document?searchText=fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AGHRSST-VIIRS_NPP-OSPO-L2P*%20OR%20fileIdentifier%3AVIIRS_NPP-OSPO-L2P*&start=1&max=100&f=searchPage',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through a '\n 'variety of services '\n 'via a THREDDS '\n '(Thematic Real-time '\n 'Environmental '\n 'Distributed Data '\n 'Services) Data '\n 'Server (TDS). '\n 'Depending on the '\n 'dataset, the TDS '\n 'can provide WMS, '\n 'WCS, DAP, HTTP, and '\n 'other data access '\n 'and metadata '\n 'services as well. '\n 'For more '\n 'information on the '\n 'TDS, see '\n 'http://www.unidata.ucar.edu/software/thredds/current/tds/.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/thredds/catalog/ghrsst/L2P/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Navigate directly '\n 'to the URL for data '\n 'access and direct '\n 'download.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. '\n 'However, fees '\n 'may appl',\n 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/L2P/VIIRS_NPP/OSPO/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'These data are '\n 'available through '\n 'the File Transfer '\n 'Protocol (FTP). You '\n 'may use any FTP '\n 'client to download '\n 'these data.',\n 'GetData': { 'Fees': 'In most '\n 'cases, '\n 'electronic '\n 'downloads of '\n 'the data are '\n 'free. 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'\n 'Ignatov, Y. Kihai, '\n 'and A. Heidinger, '\n '2010: Clear-Sky '\n 'Mask for ACSPO. '\n 'JTech, 27, '\n '1609-1623',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://journals.ametsoc.org/doi/abs/10.1175/2010JTECHA1413.1',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Petrenko, B., A. '\n 'Ignatov, Y. Kihai, '\n 'J. Stroup, P. Dash, '\n '2014: Evaluation '\n 'and Selection of '\n 'SST Regression '\n 'Algorithms for JPSS '\n 'VIIRS. 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'associations': { 'services': [ 'S1571647120-LANCEAMSR2']},\n 'concept-id': 'C1000000000-LANCEAMSR2',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': True,\n 'has-temporal-subsetting': False,\n 'has-transforms': True,\n 'has-variables': True,\n 'native-id': 'NRT AMSR2 L2B GLOBAL SWATH GSFC '\n 'PROFILING ALGORITHM 2010: SURFACE '\n 'PRECIPITATION, WIND SPEED OVER OCEAN, '\n 'WATER VAPOR OVER OCEAN AND CLOUD LIQUID '\n 'WATER OVER OCEAN V0',\n 'provider-id': 'LANCEAMSR2',\n 'revision-date': '2020-03-05T19:34:19Z',\n 'revision-id': 30,\n 'user-id': 'lance_amsr2'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer 2 (AMSR2) instrument on the '\n 'Global Change Observation Mission - Water '\n '1 (GCOM-W1) provides global passive '\n 'microwave measurements of terrestrial, '\n 'oceanic, and atmospheric parameters for '\n 'the investigation of global water and '\n 'energy cycles. Near real-time (NRT) '\n 'products are generated within 3 hours of '\n 'the last observations in the file, by the '\n 'Land Atmosphere Near real-time Capability '\n 'for EOS (LANCE) at the AMSR Science '\n 'Investigator-led Processing System (AMSR '\n 'SIPS), which is collocated with the '\n 'Global Hydrology Resource Center (GHRC) '\n 'DAAC. The GCOM-W1 AMSR2 Level-2B rain '\n 'and ocean products include global '\n 'precipitation and ocean parameters (not '\n 'including Sea Surface Temperatures), '\n 'calculated by the Goddard PROFiling '\n 'algorithm (GPROF) 2010 version using as '\n 'input the resampled brightness '\n 'temperature (Level-1R) data provided by '\n 'the Japanese Aerospace Exploration Agency '\n '(JAXA). Data are stored in HDF-EOS5 '\n 'format and are available via HTTP from '\n 'the EOSDIS LANCE system at '\n 'https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/. '\n 'If data latency is not a primary concern, '\n 'please consider using science quality '\n 'products. Science products are created '\n 'using the best available ancillary, '\n 'calibration and ephemeris information. '\n 'Science quality products are an '\n 'internally consistent, well-calibrated '\n \"record of the Earth's geophysical \"\n 'properties to support science. The AMSR '\n 'SIPS plans to start producing initial '\n 'AMSR2 standard science quality data '\n 'products in late 2015 and they will be '\n 'available from the NSIDC DAAC. Notice: '\n 'All LANCE AMSR2 data should be used with '\n 'the understanding that these are '\n 'preliminary products. Cross calibration '\n 'with AMSR-E products has not been '\n 'performed. As updates are made to the '\n 'L1R data set, those changes will be '\n 'reflected in this higher level product.',\n 'AccessConstraints': { 'Description': 'This product '\n 'has full public '\n 'access.',\n 'Value': 0.0},\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Product '\n 'DOI',\n 'Name': 'identifier_product_doi',\n 'Value': '10.5067/AMSR2/A2_RainOcn_NRT'},\n { 'DataType': 'STRING',\n 'Description': 'DOI '\n 'authority',\n 'Name': 'identifier_product_doi_authority',\n 'Value': 'http://dx.doi.org'},\n { 'DataType': 'STRING',\n 'Description': 'Flag to '\n 'indicate '\n 'ascending '\n 'or '\n 'descending',\n 'Name': 'AscendingDescendingFlg',\n 'Value': 'Ascending and '\n 'Descending'}],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Fees': '0.0',\n 'Format': 'Not '\n 'provided',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Kummerow, '\n 'Christian, '\n 'Ralph '\n 'Ferraro '\n 'and '\n 'David '\n 'Duncan.2015. '\n 'NRT '\n 'AMSR2 '\n 'L2B '\n 'GLOBAL '\n 'SWATH '\n 'GSFC '\n 'PROFILING '\n 'ALGORITHM '\n '2010: '\n 'SURFACE '\n 'PRECIPITATION, '\n 'WIND '\n 'SPEED '\n 'OVER '\n 'OCEAN, '\n 'WATER '\n 'VAPOR '\n 'OVER '\n 'OCEAN '\n 'AND '\n 'CLOUD '\n 'LIQUID '\n 'WATER '\n 'OVER '\n 'OCEAN '\n '[indicate '\n 'subset '\n 'used]. '\n 'Dataset '\n 'available '\n 'online '\n 'from '\n 'the '\n 'NASA '\n 'Global '\n 'Hydrology '\n 'Resource '\n 'Center '\n 'DAAC, '\n 'Huntsville, '\n 'Alabama, '\n 'U.S.A. '\n 'DOI: '\n 'http://dx.doi.org/10.5067/AMSR2/A2_RainOcn_NRT'}],\n 'CollectionDataType': 'NEAR_REAL_TIME',\n 'CollectionProgress': 'ACTIVE',\n 'DOI': {'DOI': '10.5067/AMSR2/A2_RainOcn_NRT'},\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Huntsville',\n 'Country': 'USA',\n 'PostalCode': '35805',\n 'StateProvince': 'Alabama',\n 'StreetAddresses': [ '320 '\n 'Sparkman '\n 'Drive']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '+1 '\n '256-961-7932'},\n { 'Type': 'Fax',\n 'Value': '+1 '\n '256-824-5149'},\n { 'Type': 'Email',\n 'Value': 'support-ghrc@earthdata.nasa.gov'}]},\n 'Roles': ['DISTRIBUTOR'],\n 'ShortName': 'NASA/MSFC/AMSR '\n 'SIPS/LANCE'}],\n 'DataDates': [ { 'Date': '2017-01-24T16:53:49.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2020-03-03T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'NRT AMSR2 L2B GLOBAL SWATH GSFC '\n 'PROFILING ALGORITHM 2010: SURFACE '\n 'PRECIPITATION, WIND SPEED OVER OCEAN, '\n 'WATER VAPOR OVER OCEAN AND CLOUD LIQUID '\n 'WATER OVER OCEAN V0',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'Platforms': [ { 'Instruments': [ { 'ComposedOf': [ { 'ShortName': 'AMSR2'}],\n 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2',\n 'ShortName': 'AMSR2'}],\n 'LongName': 'Global Change '\n 'Observation Mission '\n '1st-Water',\n 'ShortName': 'GCOM-W1',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Land, Atmosphere Near '\n 'real-time Capability for '\n 'EOS',\n 'ShortName': 'LANCE'}],\n 'RelatedUrls': [ { 'Description': 'Files may be '\n 'downloaded directly '\n 'to your workstation '\n 'from this link',\n 'Type': 'GET DATA',\n 'URL': 'https://lance.nsstc.nasa.gov/amsr2-science/data/level2/rainocean/R00/hdfeos5/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_LW.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_WS.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_D_LW.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_D_SP.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_SP.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_SR.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Sample browse image',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/pub/browse_sample/lance/AMSR_2_L2_RainOcean_R00_20160623_A_WV.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Files may be '\n 'downloaded directly '\n 'to your workstation '\n 'from this link',\n 'Subtype': 'LANCE',\n 'Type': 'GET DATA',\n 'URL': 'https://lance.itsc.uah.edu/amsr2-science/data/level2/rainocean/R00/hdfeos5/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The guide document '\n 'contains detailed '\n 'information about '\n 'the dataset',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://lance.nsstc.nasa.gov/amsr2-science/doc/LANCE_A2_RainOcn_NRT_dataset.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Interactively '\n 'browse imagery in '\n 'EOSDIS Worldview',\n 'Subtype': 'WORLDVIEW',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://worldview.earthdata.nasa.gov/?p=geographic&l=MODIS_Terra_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor,OrbitTracks_GCOM-W1_Ascending(hidden),OrbitTracks_GCOM-W1_Descending(hidden),AMSR2_Cloud_Liquid_Water_Day,AMSR2_Cloud_Liquid_Water_Night(hidden),AMSR2_Columnar_Water_Vapor_Day(hidden),AMSR2_Columnar_Water_Vapor_Night(hidden),AMSR2_Surface_Precipitation_Rate_Day(hidden),AMSR2_Surface_Precipitation_Rate_Night(hidden),AMSR2_Surface_Rain_Rate_Day(hidden),AMSR2_Surface_Rain_Rate_Night(hidden),AMSR2_Wind_Speed_Day(hidden),AMSR2_Wind_Speed_Night(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'The home page for '\n 'the project or '\n 'program which '\n 'sponsored the '\n 'dataset',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://earthdata.nasa.gov/earth-observation-data/near-real-time',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'LANCE NRT AMSR2 L2B '\n 'Global Swath Rain '\n 'Ocean Data '\n 'Quickview using '\n 'Python and GIS',\n 'Subtype': 'DATA RECIPE',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/home/data-recipes/lance-nrt-amsr2-l2b-global-swath-rain-ocean-data-quickview-using-python-and-gis',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Instructions for '\n 'citing GHRC data',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://ghrc.nsstc.nasa.gov/home/about-ghrc/citing-ghrc-daac-data',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Water '\n 'Vapor',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Water Vapor '\n 'Profiles'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Water '\n 'Vapor',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Water Vapor '\n 'Indicators',\n 'VariableLevel2': 'Total '\n 'Precipitable '\n 'Water'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Winds',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Surface '\n 'Winds',\n 'VariableLevel2': 'Wind Speed'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Winds',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Surface '\n 'Winds',\n 'VariableLevel2': 'Wind '\n 'Direction'},\n { 'Category': 'Earth Science',\n 'Term': 'Precipitation',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Precipitation '\n 'Rate'},\n { 'Category': 'Earth Science',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'},\n { 'Category': 'Earth Science',\n 'Term': 'Atmospheric Water '\n 'Vapor',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Water Vapor '\n 'Indicators',\n 'VariableLevel2': 'Water Vapor'},\n { 'Category': 'Earth Science',\n 'Term': 'Clouds',\n 'Topic': 'Atmosphere',\n 'VariableLevel1': 'Cloud '\n 'Microphysics',\n 'VariableLevel2': 'Cloud Liquid '\n 'Water/Ice'}],\n 'ShortName': 'A2_RainOcn_NRT',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'NO_SPATIAL',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 89.2,\n 'SouthBoundingCoordinate': -89.3,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2019-01-20T00:00:00.000Z'}]}],\n 'TemporalKeywords': ['1 minute - < 1 hour'],\n 'Version': '0'}},\n { 'meta': { 'concept-id': 'C1653649483-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+NOAA/STAR+GOES-16+ABI+L2P+America+Region+SST+v2.70+dataset+in+GDS2',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-18T18:33:43Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'GOES-16 (G16) is the first satellite in '\n 'the US NOAA third generation of '\n 'Geostationary Operational Environmental '\n 'Satellites (GOES), a.k.a. GOES-R series '\n '(which will also include -S, -T, and -U). '\n 'G16 was launched on 19 Nov 2016 and '\n 'initially placed in an interim position '\n 'at 89.5-deg W, between GOES-East and '\n '-West. Upon completion of Cal/Val in Dec '\n '2018, it was moved to its permanent '\n 'position at 75.2-deg W, and declared NOAA '\n 'operational GOES-East on 18 Dec 2018. \\n'\n 'NOAA is responsible for all GOES-R '\n 'products, including Sea Surface '\n 'Temperature (SST) from the Advanced '\n 'Baseline Imager (ABI). The ABI offers '\n 'vastly enhanced capabilities for SST '\n 'retrievals, over the heritage GOES-I/P '\n 'Imager, including five narrow bands '\n '(centered at 3.9, 8.4, 10.3, 11.2, and '\n '12.3 um) out of 16 that can be used for '\n 'SST, as well as accurate sensor '\n 'calibration, image navigation and '\n 'co-registration, spectral fidelity, and '\n 'sophisticated pre-processing '\n '(geo-rectification, radiance '\n 'equalization, and mapping). From altitude '\n '35,800 km, G16/ABI can accurately map SST '\n 'in a Full Disk (FD) area from 15-135-deg '\n 'W and 60S-60N, with spatial resolution '\n '2km at nadir (degrading to 15km at view '\n 'zenith angle, 67-deg) and temporal '\n 'sampling of 10min (15min prior to 2 Apr '\n '2019). \\n'\n 'The Level 2 Preprocessed (L2P) SST '\n 'product is derived at the native sensor '\n 'resolution using NOAA Advanced Clear-Sky '\n 'Processor for Ocean (ACSPO) system. ACSPO '\n 'first processes every 10min FD data SSTs '\n 'are derived from BTs using the ACSPO '\n 'clear-sky mask (ACSM; Petrenko et al., '\n '2010) and Non-Linear SST (NLSST) '\n 'algorithm (Petrenko et al., 2014). '\n 'Currently, only 4 longwave bands centered '\n 'at 8.4, 10.3, 11.2, and 12.3 um are used '\n '(the 3.9 microns was initially excluded, '\n 'to minimize possible discontinuities in '\n 'the diurnal cycle). The regression is '\n 'tuned against quality controlled in situ '\n 'SSTs from drifting and tropical mooring '\n 'buoys in the NOAA iQuam system (Xu and '\n 'Ignatov, 2014). The 10-min FD data are '\n 'subsequently collated in time, to produce '\n '1-hr L2P product, with improved coverage, '\n 'and reduced cloud leakages and image '\n 'noise, compared to each individual 10min '\n 'image. \\n'\n 'In the collated L2P, SSTs and BTs are '\n 'only reported in clear-sky water pixels '\n '(defined as ocean, sea, lake or river, '\n 'and up to 5 km inland) and fill values '\n 'elsewhere. The L2P is reported in netCDF4 '\n 'GHRSST Data Specification version 2 '\n '(GDS2) format, 24 granules per day, with '\n 'a total data volume of 0.6GB/day. In '\n 'addition to SST, ACSPO files also include '\n 'sun-sensor geometry, four BTs in ABI '\n 'bands 11 (8.4um), 13 (10.3um), 14 '\n '(11.2um), and 15 (12.3um) and two '\n 'reflectances in bands 2 and 3 (0.64um and '\n '0.86um; used for cloud identification). '\n 'The l2p_flags layer includes day/night, '\n 'land, ice, twilight, and glint flags. '\n 'Other variables include NCEP wind speed '\n 'and ACSPO SST minus reference SST '\n '(Canadian Met Centre 0.1deg L4 SST; '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0).\\n'\n 'Pixel-level earth locations are not '\n 'reported in the granules, as they remain '\n 'unchanged from granule to granule. To '\n 'obtain those, user has a choice of using '\n 'a flat lat-lon file, or a Python script, '\n 'both available at '\n 'ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. '\n 'Per GDS2 specifications, two additional '\n 'Sensor-Specific Error Statistics layers '\n '(SSES bias and standard deviation) are '\n 'reported in each pixel. \\n'\n 'The ACSPO VIIRS L2P product is monitored '\n 'and validated against in situ data (Xu '\n 'and Ignatov, 2014) using the Satellite '\n 'Quality Monitor SQUAM (Dash et al, 2010), '\n 'and BTs are validated against RTM '\n 'simulation in MICROS (Liang and Ignatov, '\n '2011). A reduced size (0.2GB/day), '\n 'equal-angle gridded (0.02-deg '\n 'resolution), ACSPO L3C product is also '\n 'available at '\n 'https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L3C-v2.70, '\n 'where gridded L2P SSTs are reported, and '\n 'BT layers omitted.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'NOAA/NESDIS '\n 'USA, '\n '5200 '\n 'Auth '\n 'Rd, '\n 'Camp '\n 'Springs, '\n 'MD, '\n '20746, '\n 'NOAA/NESDIS, '\n '2019-05-15, '\n 'GHRSST '\n 'NOAA/STAR '\n 'GOES-16 '\n 'ABI '\n 'L2P '\n 'America '\n 'Region '\n 'SST '\n 'v2.70 '\n 'dataset '\n 'in '\n 'GDS2, '\n '10.5067/GHG16-2PO27, '}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHG16-2PO27'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'NOAA/NESDIS'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '301-683-3379'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Center for Satellite '\n 'Applications and '\n 'Research'}],\n 'DataDates': [ { 'Date': '2019-03-28T21:34:17.610Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-03-28T21:34:17.610Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST NOAA/STAR GOES-16 ABI L2P '\n 'America Region SST v2.70 dataset in '\n 'GDS2',\n 'LocationKeywords': [ { 'Category': 'OTHER',\n 'Type': 'Western Atlantic'}],\n 'MetadataDates': [ { 'Date': '2019-11-18T18:33:40.123Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '7000.0'}],\n 'LongName': 'Advanced '\n 'Baseline '\n 'Imager '\n '(ABI)',\n 'ShortName': 'ABI'}],\n 'LongName': 'Geostationary '\n 'Operational '\n 'Environmental Satellite '\n '16',\n 'ShortName': 'GOES-16',\n 'Type': 'Geostationary'}],\n 'ProcessingLevel': {'Id': '2P'},\n 'Projects': [{'ShortName': 'GHRSST'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L2P/GOES16/STAR/v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHG16-2PO27',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/docs/geo_nav.py',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Dash, P., A. '\n 'Ignatov, Y. Kihai',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Liang, X.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/iquam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Home Page of the '\n 'GHRSST Project',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/docs/G16_075_0_W.nc',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHG16-2PO27',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/v2.70/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'None',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABI_G16-STAR-L2P-v2.70',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -15.0,\n 'NorthBoundingCoordinate': 59.0,\n 'SouthBoundingCoordinate': -59.0,\n 'WestBoundingCoordinate': -135.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2017-12-15T00:00:00.000Z'}]}],\n 'Version': '2.70'}},\n { 'meta': { 'concept-id': 'C1666605372-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+NOAA/STAR+GOES-16+ABI++L3C+America+Region+SST+v2.70+dataset+in+GDS2',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-10T18:30:10Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The ACSPO G16/ABI L3C (Level 3 Collated) '\n 'product is a gridded version of the ACSPO '\n 'G16/ABI L2P product available at '\n 'https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L2P-v2.70. '\n 'The L3C output files are 1hr granules in '\n 'netCDF4 format, compliant with the GHRSST '\n 'Data Specification version 2 (GDS2). '\n 'There are 24 granules per 24hr interval, '\n 'with a total data volume of 0.2GB/day. '\n 'Fill values are reported at all invalid '\n 'pixels, including pixels with 5 km '\n 'inland. For each valid water pixel '\n '(defined as ocean, sea, lake or river, '\n 'and up to 5 km inland), the following '\n 'layers are reported: SSTs, ACSPO '\n 'clear-sky mask (ACSM; provided in each '\n 'grid as part of l2p_flags, which also '\n 'includes day/night, land, ice, twilight, '\n 'and glint flags), NCEP wind speed, and '\n 'ACSPO SST minus reference (Canadian Met '\n 'Centre 0.1deg L4 SST; available at '\n 'https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). '\n 'All valid SSTs in L3C are recommended for '\n 'users. Per GDS2 specifications, two '\n 'additional Sensor-Specific Error '\n 'Statistics layers (SSES bias and standard '\n 'deviation) are reported in each pixel '\n 'with valid SST. The ACSPO VIIRS L3U '\n 'product is monitored and validated '\n 'against iQuam in situ data (Xu and '\n 'Ignatov, 2014) in SQUAM (Dash et al, '\n '2010).',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'NOAA/NESDIS '\n 'USA, '\n '5200 '\n 'Auth '\n 'Rd, '\n 'Camp '\n 'Springs, '\n 'MD, '\n '20746, '\n 'NOAA/NESDIS, '\n '2019-05-30, '\n 'GHRSST '\n 'NOAA/STAR '\n 'GOES-16 '\n 'ABI '\n 'L3C '\n 'America '\n 'Region '\n 'SST '\n 'v2.70 '\n 'dataset '\n 'in '\n 'GDS2, '\n '10.5067/GHG16-3UO27, '}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHG16-3UO27'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'NOAA/NESDIS'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '301-683-3379'},\n { 'Type': 'Email',\n 'Value': 'Alex.Ignatov@noaa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Alexander',\n 'LastName': 'Ignatov',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Center for Satellite '\n 'Applications and '\n 'Research'}],\n 'DataDates': [ { 'Date': '2019-03-28T21:40:22.827Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-03-28T21:40:22.827Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST NOAA/STAR GOES-16 ABI L3C '\n 'America Region SST v2.70 dataset in '\n 'GDS2',\n 'LocationKeywords': [ { 'Category': 'OTHER',\n 'Type': 'Western Atlantic'}],\n 'MetadataDates': [ { 'Date': '2019-12-10T18:30:08.166Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '0.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '0.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '7000.0'}],\n 'LongName': 'Advanced '\n 'Baseline '\n 'Imager '\n '(ABI)',\n 'ShortName': 'ABI'}],\n 'LongName': 'Geostationary '\n 'Operational '\n 'Environmental Satellite '\n '16',\n 'ShortName': 'GOES-16',\n 'Type': 'Geostationary'}],\n 'ProcessingLevel': {'Id': '3C'},\n 'Projects': [{'ShortName': 'GHRSST'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L3C/AMERICAS/GOES16/STAR/v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Home Page of the '\n 'GHRSST Project',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://www.ghrsst.org',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/',\n 'URLContentType': 'DistributionURL'},\n { 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=ABI_G16-STAR-L3C-v2.70',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Dash, P., A. '\n 'Ignatov, Y. Kihai',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/squam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': '(Search Granule)',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHG16-3UO27',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Xu, F.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://www.star.nesdis.noaa.gov/sod/sst/iquam/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHG16-3UO27',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L3C/AMERICAS/GOES16/STAR/v2.70/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L3C/AMERICAS/GOES16/STAR/v2.70/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'None',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABI_G16-STAR-L3C-v2.70',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': -15.0,\n 'NorthBoundingCoordinate': 59.0,\n 'SouthBoundingCoordinate': -59.0,\n 'WestBoundingCoordinate': -135.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2017-12-15T00:00:00.000Z'}]}],\n 'Version': '2.70'}},\n { 'meta': { 'concept-id': 'C1658476070-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+4+RAMSSA+Australian+Regional+Foundation+Sea+Surface+Temperature+Analysis',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:28Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'A Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis at the Australian '\n 'Bureau of Meteorology using optimal '\n 'interpolation (OI) on a regional 1/12 '\n 'degree grid over the Australian region '\n '(20N - 70S, 60E - 170W). This BLUELink '\n 'Regional Australian Multi-Sensor SST '\n 'Analysis (RAMSSA) v1.0 system blends '\n 'satellite SST observations from the '\n 'Advanced Very High Resolution Radiometer '\n '(AVHRR), the Advanced Along Track '\n 'Scanning Radiometer (AATSR), and, the '\n 'Advanced Microwave Scanning '\n 'Radiometer-EOS (AMSRE), and in situ data '\n 'from ships, and drifting and moored buoy '\n 'from the Global Telecommunications System '\n '(GTS). The processing results in daily '\n 'foundation SST estimates that are largely '\n 'free of nocturnal cooling and diurnal '\n 'warming effects.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n '2008-01-24, '\n 'GHRSST '\n 'Level '\n '4 '\n 'RAMSSA '\n 'Australian '\n 'Regional '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis, '\n '10.5067/GHRAM-4FA01, '\n 'http://www.bom.gov.au/jshess/docs/2011/beggs_hres.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHRAM-4FA01'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694394'},\n { 'Type': 'Email',\n 'Value': 'h.beggs@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Helen',\n 'LastName': 'Beggs',\n 'MiddleName': 'none',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694746'},\n { 'Type': 'Email',\n 'Value': 'ghrsst@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Leon',\n 'LastName': 'Majewski',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'}],\n 'DataDates': [ { 'Date': '2008-01-29T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:46.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 RAMSSA Australian '\n 'Regional Foundation Sea Surface '\n 'Temperature Analysis',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'OCEANIA'}],\n 'MetadataDates': [ { 'Date': '2019-11-20T20:39:25.914Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.12'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.74'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-19',\n 'ShortName': 'NOAA-19',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-20',\n 'ShortName': 'NOAA-20',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting '\n 'Buoys',\n 'ShortName': 'InSitu'}],\n 'LongName': 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'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/L4/AUS/ABOM/RAMSSA_09km/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'ABOM-L4HRfnd-AUS-RAMSSA_09km',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': 60.0},\n { 'EastBoundingCoordinate': -170.0,\n 'NorthBoundingCoordinate': 20.0,\n 'SouthBoundingCoordinate': -70.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 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Level 4 sea surface '\n 'temperature analysis produced daily on an '\n 'operational basis at the Australian '\n 'Bureau of Meteorology using optimal '\n 'interpolation (OI) on a global 0.25 '\n 'degree grid. This BLUELink Global '\n 'Australian Multi-Sensor SST Analysis '\n '(GAMSSA) v1.0 system blends satellite SST '\n 'observations from the Advanced Very High '\n 'Resolution Radiometer (AVHRR), the '\n 'Advanced Along Track Scanning Radiometer '\n '(AATSR), and, the Advanced Microwave '\n 'Scanning Radiometer-EOS (AMSRE), and in '\n 'situ data from ships, and drifting and '\n 'moored buoy from the Global '\n 'Telecommunications System (GTS). In order '\n 'to produce a foundation SST estimate, the '\n 'AATSR skin SST data stream is converted '\n 'to foundation SST using the Donlon et al. '\n '(2002) skin to foundation temperature '\n 'conversion algorithms. These '\n 'empirically-derived algorithms apply a '\n 'small correction for the cool-skin effect '\n 'depending on surface wind speed, and '\n 'filter out SST values suspected to be '\n 'affected by diurnal warming by excluding '\n 'cases which have experienced recent '\n 'surface wind speeds of below 6 ms-1 '\n 'during the day and less than 2 ms-1 '\n 'during the night.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n 'Australian '\n 'Bureau '\n 'of '\n 'Meteorology, '\n '2008-07-28, '\n 'GHRSST '\n 'Level '\n '4 '\n 'GAMSSA '\n 'Global '\n 'Foundation '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'Analysis, '\n '10.5067/GHGAM-4FA01, '\n 'http://www.bom.gov.au/australia/charts/bulletins/apob77.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHGAM-4FA01'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694394'},\n { 'Type': 'Email',\n 'Value': 'h.beggs@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Helen',\n 'LastName': 'Beggs',\n 'MiddleName': 'none',\n 'Roles': [ 'Investigator']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '+61-3-96694746'},\n { 'Type': 'Email',\n 'Value': 'ghrsst@bom.gov.au'}]},\n 'ContactPersons': [ { 'FirstName': 'Leon',\n 'LastName': 'Majewski',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Australian Bureau of '\n 'Meteorology'}],\n 'DataDates': [ { 'Date': '2008-08-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:45.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 4 GAMSSA Global Foundation '\n 'Sea Surface Temperature Analysis',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-18T21:27:49.385Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.12'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.74'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 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'Description': 'Direct download via '\n 'HTTPS protocol.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://n5eil01u.ecs.nsidc.org/AMSA/AE_MoOcn.002',\n 'URLContentType': 'DistributionURL'},\n { 'Description': \"NASA's newest \"\n 'search and order '\n 'tool for '\n 'subsetting, '\n 'reprojecting, and '\n 'reformatting data.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search/granules?p=C179014697-NSIDC_ECS&m=-31.078125!0.28125!1!1!0!0%2C2&q=AE_MoOcn',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Provides access to '\n 'data, '\n 'documentation, '\n 'tools, citation '\n 'information, '\n 'support, and other '\n 'resources.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_MOOCN.002',\n 'URLContentType': 'CollectionURL'},\n { 'Description': \"Includes a user's \"\n 'guide, supplemental '\n 'documents like '\n 'ATBDs and academic '\n 'papers, How Tos, '\n 'FAQs, etc.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_MOOCN.002',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'PRECIPITABLE '\n 'WATER '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN WINDS',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICES',\n 'VariableLevel2': 'WATER '\n 'VAPOR'}],\n 'ShortName': 'AE_MoOcn',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 89.24,\n 'SouthBoundingCoordinate': -89.24,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-19T00:00:00.000Z',\n 'EndingDateTime': '2011-10-01T23:59:59.999Z'}]}],\n 'Version': '2'}},\n { 'meta': { 'associations': { 'services': [ 'S1568899363-NSIDC_ECS',\n 'S1613645416-NSIDC_ECS',\n 'S1613689509-NSIDC_ECS']},\n 'concept-id': 'C130038008-NSIDC_ECS',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/iso19115+xml',\n 'granule-count': 0,\n 'has-formats': True,\n 'has-spatial-subsetting': True,\n 'has-temporal-subsetting': False,\n 'has-transforms': True,\n 'has-variables': True,\n 'native-id': 'AMSR-E/Aqua L2B Global Swath Ocean '\n 'Products derived from Wentz Algorithm '\n 'V002',\n 'provider-id': 'NSIDC_ECS',\n 'revision-date': '2020-03-10T19:26:43Z',\n 'revision-id': 105,\n 'user-id': 'cmr_nsidc_ops'},\n 'umm': { 'Abstract': 'This daily Level-2B swath data set '\n 'includes Sea Surface Temperature (SST), '\n 'Near-Surface Wind Speed, Columnar Water '\n 'Vapor, and Cloud liquid Water data '\n 'arrays, and was used as input to generate '\n 'the following daily, weekly, and monthly '\n 'Level-3 gridded ocean products; '\n 'AE_DyOcn, AE_WkOcn, and AE_MoOcn.',\n 'AdditionalAttributes': [ { 'DataType': 'STRING',\n 'Description': 'Satellite '\n 'Direction',\n 'Name': 'AscendingDescendingFlg'},\n { 'DataType': 'INT',\n 'Description': 'The index '\n 'number for '\n 'the last '\n 'polygon '\n 'associated '\n 'with the '\n 'nominal '\n 'pass '\n 'number in '\n 'the '\n 'granule',\n 'Name': 'EndingPolygonNumber'},\n { 'DataType': 'INT',\n 'Description': 'The '\n 'nominal '\n 'pass index '\n 'number for '\n 'the pass '\n 'that best '\n 'describes '\n 'the '\n 'spatial '\n 'location '\n 'of the '\n 'granule, '\n 'where the '\n 'pass is '\n 'either the '\n 'ascending '\n 'or '\n 'descending '\n 'portion of '\n 'an orbit',\n 'Name': 'NominalPassIndex'},\n { 'DataType': 'INT',\n 'Description': 'The index '\n 'number for '\n 'the first '\n 'polygon '\n 'associated '\n 'with the '\n 'nominal '\n 'pass '\n 'number in '\n 'the '\n 'granule.',\n 'Name': 'StartingPolygonNumber'},\n { 'DataType': 'STRING',\n 'Description': 'Digital '\n 'object '\n 'identifier '\n 'that '\n 'uniquely '\n 'identifies '\n 'this data '\n 'product',\n 'Name': 'identifier_product_doi'},\n { 'DataType': 'STRING',\n 'Description': 'URL of the '\n 'digital '\n 'object '\n 'identifier '\n 'resolving '\n 'authority',\n 'Name': 'identifier_product_doi_authority'}],\n 'CollectionCitations': [ { 'Publisher': 'NASA National '\n 'Snow and Ice '\n 'Data Center '\n 'Distributed '\n 'Active '\n 'Archive '\n 'Center',\n 'Title': 'AMSR-E/Aqua L2B '\n 'Global Swath '\n 'Ocean Products '\n 'derived from '\n 'Wentz Algorithm '\n 'V002',\n 'Version': '2'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'StateProvince': 'Colorado'}]},\n 'GroupName': 'NASA National Snow '\n 'and Ice Data Center '\n 'Distributed Active '\n 'Archive Center',\n 'Roles': ['User Services']}],\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'PostalCode': '80309-0449',\n 'StateProvince': 'CO',\n 'StreetAddresses': [ 'CIRES, '\n '449 '\n 'UCB',\n 'University '\n 'of '\n 'Colorado']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '303 '\n '492 '\n '6199'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '303 '\n '492 '\n '2468'},\n { 'Type': 'Email',\n 'Value': 'nsidc@nsidc.org'}]},\n 'FirstName': 'NSIDC',\n 'LastName': 'Services',\n 'MiddleName': 'User',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904 '\n 'x16'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'wentz@remss.com'}]},\n 'FirstName': 'Frank',\n 'LastName': 'Wentz',\n 'MiddleName': 'J.',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'thomas@remss.com'}]},\n 'FirstName': 'Thomas',\n 'LastName': 'Meissner',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904 '\n 'x16'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'wentz@remss.com'}]},\n 'FirstName': 'Frank',\n 'LastName': 'Wentz',\n 'MiddleName': 'J.',\n 'Roles': ['Technical Contact']},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Santa '\n 'Rosa',\n 'Country': 'USA',\n 'PostalCode': '95401',\n 'StateProvince': 'CA',\n 'StreetAddresses': [ 'Remote '\n 'Sensing '\n 'Systems',\n '438 '\n 'First '\n 'Street',\n 'Suite '\n '200']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Fax',\n 'Value': '1 '\n '707 '\n '545 '\n '2906'},\n { 'Type': 'Email',\n 'Value': 'thomas@remss.com'}]},\n 'FirstName': 'Thomas',\n 'LastName': 'Meissner',\n 'Roles': ['Technical Contact']}],\n 'DOI': {'DOI': '10.5067/AMSR-E/AE_OCEAN.002'},\n 'DataCenters': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'PostalCode': '80309-0449',\n 'StateProvince': 'CO',\n 'StreetAddresses': [ 'National '\n 'Snow '\n 'and '\n 'Ice '\n 'Data '\n 'Center',\n 'CIRES, '\n '449 '\n 'UCB',\n 'University '\n 'of '\n 'Colorado']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '303 '\n '492 '\n '6199'},\n { 'Type': 'Email',\n 'Value': 'nsidc@nsidc.org'}],\n 'RelatedUrls': [ { 'Description': 'Archiving '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://nsidc.org/daac',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA NSIDC DAAC'},\n { 'ContactInformation': { 'Addresses': [ { 'City': 'Boulder',\n 'Country': 'USA',\n 'PostalCode': '80309-0449',\n 'StateProvince': 'CO',\n 'StreetAddresses': [ 'National '\n 'Snow '\n 'and '\n 'Ice '\n 'Data '\n 'Center',\n 'CIRES, '\n '449 '\n 'UCB',\n 'University '\n 'of '\n 'Colorado']}],\n 'ContactMechanisms': [ { 'Type': 'Telephone',\n 'Value': '1 '\n '303 '\n '492 '\n '6199'},\n { 'Type': 'Email',\n 'Value': 'nsidc@nsidc.org'}],\n 'RelatedUrls': [ { 'Description': 'Archiving '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://nsidc.org/daac',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['DISTRIBUTOR'],\n 'ShortName': 'NASA NSIDC DAAC'},\n { 'ContactInformation': { 'RelatedUrls': [ { 'Description': 'Originating '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://weather.msfc.nasa.gov/AMSR/',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['PROCESSOR'],\n 'ShortName': 'NASA/MSFC/AMSR-E '\n 'SIPS'},\n { 'ContactInformation': { 'RelatedUrls': [ { 'Description': 'Originating '\n 'Data '\n 'Center',\n 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://weather.msfc.nasa.gov/AMSR/',\n 'URLContentType': 'DataCenterURL'}]},\n 'Roles': ['ORIGINATOR'],\n 'ShortName': 'NASA/MSFC/AMSR-E '\n 'SIPS'}],\n 'DataLanguage': 'eng; usa',\n 'EntryTitle': 'AMSR-E/Aqua L2B Global Swath Ocean '\n 'Products derived from Wentz Algorithm '\n 'V002',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE'],\n 'LocationKeywords': [ { 'Category': 'Geographic Region',\n 'Type': 'Global Ocean'}],\n 'MetadataDates': [ { 'Date': '2004-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2020-03-06T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'ComposedOf': [ { 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer-EOS',\n 'ShortName': 'AMSR-E'}],\n 'LongName': 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer-EOS',\n 'NumberOfInstruments': 1,\n 'ShortName': 'AMSR-E',\n 'Technique': 'instrument'}],\n 'LongName': 'Earth Observing System, '\n 'AQUA',\n 'ShortName': 'AQUA',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': { 'Id': 'Level 2B',\n 'ProcessingLevelDescription': 'Derived '\n 'geophysical '\n 'variables'},\n 'Purpose': 'Scientific Research',\n 'RelatedUrls': [ { 'Description': 'Direct download via '\n 'HTTPS protocol.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://n5eil01u.ecs.nsidc.org/AMSA/AE_Ocean.002',\n 'URLContentType': 'DistributionURL'},\n { 'Description': \"NASA's newest \"\n 'search and order '\n 'tool for '\n 'subsetting, '\n 'reprojecting, and '\n 'reformatting data.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search/granules?p=C130038008-NSIDC_ECS&m=-30.515625!0.5625!1!1!0!0%2C2&q=AE_Ocean',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Provides access to '\n 'data, '\n 'documentation, '\n 'tools, citation '\n 'information, '\n 'support, and other '\n 'resources.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_OCEAN.002',\n 'URLContentType': 'CollectionURL'},\n { 'Description': \"Includes a user's \"\n 'guide, supplemental '\n 'documents like '\n 'ATBDs and academic '\n 'papers, How Tos, '\n 'FAQs, etc.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://doi.org/10.5067/AMSR-E/AE_OCEAN.002',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'PRECIPITABLE '\n 'WATER '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN WINDS',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WINDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'WINDS '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'WATER '\n 'TEMPERATURE '},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER '\n 'VAPOR'}],\n 'ShortName': 'AE_Ocean',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'ORBIT',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 89.24,\n 'SouthBoundingCoordinate': -89.24,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'OrbitParameters': { 'InclinationAngle': 98.15,\n 'NumberOfOrbits': 0.5,\n 'Period': 98.88,\n 'StartCircularLatitude': -90.0,\n 'SwathWidth': 1450.0},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-19T00:00:00.000Z',\n 'EndingDateTime': '2011-10-03T23:59:59.999Z'}]}],\n 'Version': '2'}},\n { 'meta': { 'concept-id': 'C1243477376-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH2RET_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:00Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2RET. However, it contains '\n 'science retrievals that use the HSB. '\n 'Because the HSB instrument lived only '\n 'from September 2002 through January 2003 '\n 'when it terminally failed, the data set '\n 'covers these five months only. The AIRS '\n 'Standard Retrieval Product consists of '\n 'retrieved estimates of cloud and surface '\n 'properties, plus profiles of retrieved '\n 'temperature, water vapor, ozone, carbon '\n 'monoxide and methane. Estimates of the '\n 'errors associated with these quantities '\n 'is also part of the Standard Product. The '\n 'temperature profile vertical resolution '\n 'is 28 levels total between 1100 mb and '\n '0.1 mb, while moisture profile is '\n 'reported at 14 atmospheric layers between '\n '1100 mb and 50 mb. The horizontal '\n 'resolution is 50 km. An AIRS granule has '\n 'been set as 6 minutes of data, 30 '\n 'footprints cross track by 45 lines along '\n 'track. There are 240\\n'\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2RET_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH2RET',\n 'Title': 'AIRS/Aqua L2 '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA203'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'AIRS/Aqua L2 Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) V006 '\n '(AIRH2RET) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'},\n { 'LongName': 'Humidity '\n 'Sounder '\n 'for '\n 'Brazil',\n 'ShortName': 'HSB'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'}],\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH2RET_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2RET_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRH2RET.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRH2RET.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH2RET%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH2RET+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'}],\n 'ShortName': 'AIRH2RET',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2003-02-05T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477377-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH2SUP_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:01Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2SUP. However, it contains '\n 'science retrievals that use the HSB. '\n 'Because the HSB instrument lived only '\n 'from September 2002 through January 2003 '\n 'when it terminally failed, the data set '\n 'covers these five months only. The '\n 'Support Product includes higher vertical '\n 'resolution profiles of the quantities '\n 'found in the Standard Product, plus '\n 'intermediate outputs (e.g., '\n 'microwave-only retrieval), research '\n 'products such as the abundance of trace '\n 'gases, and detailed quality assessment '\n 'information. The Support Product profiles '\n 'contain 100 levels between 1100 and .016 '\n 'mb; this higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than that in the '\n 'Standard Product profiles. The intended '\n 'users of the Support Product are '\n 'researchers interested in generating '\n 'forward radiance or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data with 30 footprints '\n 'cross track by 45 scanlines of AMSU-A '\n 'data or 135 scanlines of AIRS and HSB '\n 'data. There are 240 granules per day, '\n 'with an orbit repeat cycle of '\n 'approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2SUP_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH2SUP',\n 'Title': 'AIRS/Aqua L2 '\n 'Support Retrieval '\n '(AIRS+AMSU+HSB) '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA209'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Support Retrieval '\n '(AIRS+AMSU+HSB) V006 (AIRH2SUP) at GES '\n 'DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'},\n { 'LongName': 'Humidity '\n 'Sounder '\n 'for '\n 'Brazil',\n 'ShortName': 'HSB'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'}],\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS+AMSU+HSB) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH2SUP_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH2SUP_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRH2SUP.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRH2SUP.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH2SUP%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH2SUP+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MICROWAVE',\n 'Topic': 'SPECTRAL/ENGINEERING',\n 'VariableLevel1': 'BRIGHTNESS '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH2SUP',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2003-02-05T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517226-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3SP8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:04Z',\n 'revision-id': 15,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 563.3,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SP8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3SP8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Support '\n 'Multiday Product '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA314'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Support Multiday '\n 'Product (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3SP8) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical '\n 'retrieval\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3SP8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SP8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3SP8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3SP8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3SP8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3SP8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH3SP8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-02-08T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517230-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3SPD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:05Z',\n 'revision-id': 15,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 474.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3SPD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Support '\n 'Daily Product '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA305'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Support Daily '\n 'Product (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3SPD) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3SPD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3SPD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3SPD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3SPD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3SPD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH3SPD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2003-02-06T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517247-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3SPM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:07Z',\n 'revision-id': 15,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 554.5,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3SPM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Support '\n 'Monthly Product '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA323'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Support Product '\n '(AIRS+AMSU+HSB) 1 degree x 1 degree '\n 'V006 (AIRH3SPM) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (With '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3SPM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3SPM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3SPM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3SPM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3SPM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3SPM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRH3SPM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-03-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517250-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3ST8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:08Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX3ST8. However, it contains '\n 'science retrievals that use the Humidity '\n 'Sounder for Brazil (HSB). Because the HSB '\n 'instrument lived only from September 2002 '\n 'through January 2003 when it terminally '\n 'failed, the data set covers these five '\n 'months only. The AIRS Level 3 8-Day '\n 'Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers an 8-day period, or one-half of '\n 'the Aqua orbit repeat cycle. The mean '\n 'values are simply the arithmetic means of '\n 'the daily products, weighted by the '\n 'number of input counts for each day in '\n 'that grid box. The geophysical parameters '\n 'have been averaged and binned into 1 x 1 '\n 'deg grid cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 452.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3ST8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3ST8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA311'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3ST8) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical '\n 'retrieval\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3ST8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3ST8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3ST8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3ST8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3ST8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3ST8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRH3ST8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-02-08T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517253-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3STD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:09Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX3STD. However, it contains '\n 'science retrievals that use the Humidity '\n 'Sounder for Brazil (HSB). Because the HSB '\n 'instrument lived only from September 2002 '\n 'through January 2003 when it terminally '\n 'failed, the data set covers these five '\n 'months only. The AIRS Level 3 Daily '\n 'Gridded Product contains standard '\n 'retrieval means, standard deviations and '\n 'input counts. Each file covers a temporal '\n 'period of 24 hours for either the '\n 'descending (equatorial crossing North to '\n 'South @1:30 AM local time) or ascending '\n '(equatorial crossing South to North @1:30 '\n 'PM local time) orbit. The data starts at '\n 'the international dateline and progresses '\n 'westward (as do the subsequent orbits of '\n 'the satellite) so that neighboring '\n 'gridded cells of data are no more than a '\n 'swath of time apart (about 90 minutes). '\n 'The two parts of a scan line crossing the '\n 'dateline are included in separate L3 '\n 'files, according to the date, so that '\n 'data points in a grid box are always '\n 'coincident in time. The edge of the AIRS '\n 'Level 3 gridded cells is at the date line '\n '(the 180E/W longitude boundary). When '\n 'plotted, this produces a map with 0 '\n 'degrees longitude in the center of the '\n 'image unless the bins are reordered. This '\n 'method is preferred because the left '\n '(West) side of the image and the right '\n '(East) side of the image contain data '\n 'farthest apart in time. The gridding '\n 'scheme used by AIRS is the same as used '\n 'by TOVS Pathfinder to create Level 3 '\n 'products. The daily Level 3 products have '\n 'gores between satellite paths where there '\n 'is no coverage for that day. The '\n 'geophysical parameters have been averaged '\n 'and binned into 1 x 1 grid cells, from '\n '-180.0 to +180.0 deg longitude and from '\n '-90.0 to +90.0 deg latitude. For each '\n 'grid map of 4-byte floating-point mean '\n 'values there is a corresponding 4-byte '\n 'floating-point map of standard deviation '\n 'and a 2-byte integer grid map of counts. '\n 'The counts map provides the user with the '\n 'number of points per bin that were '\n 'included in the mean and can be used to '\n 'generate custom multi-day maps from the '\n 'daily gridded products. The thermodynamic '\n 'parameters are: Skin Temperature (land '\n 'and sea surface), Air Temperature at the '\n 'surface, Profiles of Air Temperature and '\n 'Water Vapor, Tropopause Characteristics, '\n 'Column Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 376.1,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3STD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA302'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3STD) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3STD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3STD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3STD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3STD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3STD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRH3STD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2003-02-06T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517238-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRH3STM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:11Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX3STM. However, it contains '\n 'science retrievals that use the Humidity '\n 'Sounder for Brazil (HSB). Because the HSB '\n 'instrument lived only from September 2002 '\n 'through January 2003 when it terminally '\n 'failed, the data set covers these five '\n 'months only. The AIRS Level 3 Monthly '\n 'Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers a calendar month. The mean values '\n 'are simply the arithmetic means of the '\n 'daily products, weighted by the number of '\n 'input counts for each day in that grid '\n 'box. The geophysical parameters have been '\n 'averaged and binned into 1 x 1 grid '\n 'cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 445.7,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRH3STM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU+HSB) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA320'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Standard Physical '\n 'Retrieval (AIRS+AMSU+HSB) 1 degree x 1 '\n 'degree V006 (AIRH3STM) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2016-01-28T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (With '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRH3STM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRH3STM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRH3STM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRH3STM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRH3STM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRH3STM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRH3STM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2003-03-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477381-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2RET_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:24Z',\n 'revision-id': 18,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2RET. It is a new product '\n 'produced using AIRS IR only because the '\n 'radiometric noise in AMSU channel 4 '\n 'started to increase significantly (since '\n 'June 2007). The AIRS Standard Retrieval '\n 'Product consists of retrieved estimates '\n 'of cloud and surface properties, plus '\n 'profiles of retrieved temperature, water '\n 'vapor, ozone, carbon monoxide and '\n 'methane. Estimates of the errors '\n 'associated with these quantities is also '\n 'part of the Standard Product. The '\n 'temperature profile vertical resolution '\n 'is 28 levels total between 1100 mb and '\n '0.1 mb, while moisture profile is '\n 'reported at 14 atmospheric layers between '\n '1100 mb and 50 mb. The horizontal '\n 'resolution is 50 km. An AIRS granule has '\n 'been set as 6 minutes of data, 30 '\n 'footprints cross track by 45 lines along '\n 'track. There are 240\\n'\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2RET',\n 'Title': 'AIRS/Aqua L2 '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA202'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'AIRS/Aqua L2 Standard Physical '\n 'Retrieval (AIRS-only) V006 (AIRS2RET) '\n 'at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong,',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Edition': '121',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '15'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Edition': '41',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '2'}],\n 'Quality': 'The product is similar to AIRX2RET except '\n 'the processing uses only the AIRS '\n 'channels. No AMSU information was used in '\n 'the generation of this product. The '\n 'quality of data products, described in the '\n 'associated references, provide information '\n 'about numerous validation studies '\n 'conducted and papers written documenting '\n 'the excellence of the products using '\n 'radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitation of '\n 'the AIRS-only Version-6 retrieval '\n 'products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias. Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2RET_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRS2RET.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRS2RET.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS2RET%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2RET+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS '\n 'instrument,algorithms, '\n 'and other '\n 'AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'}],\n 'ShortName': 'AIRS2RET',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1345119345-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2RET_NRT_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:25Z',\n 'revision-id': 22,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'Level 2 Near Real Time (NRT) Standard '\n 'Physical Retrieval (AIRS-only) product '\n '(AIRS2RET_NRT_006) differs from the '\n 'routine product (AIRS2RET_006) in four '\n 'ways to meet the three hour latency '\n 'requirements of the Land Atmosphere NRT '\n 'Capability Earth Observing System '\n '(LANCE): (1) The NRT granules are '\n 'produced without previous or subsequent '\n 'granules if those granules are not '\n 'available within 5 minutes, (2) the '\n 'predictive ephemeris/attitude data are '\n 'used rather than the definitive '\n 'ephemeris/attitude, (3) if the forecast '\n 'surface pressure is unavailable, a '\n 'surface climatology is used, and (4) no '\n 'ice cloud properties retrievals are '\n 'performed. The consequences of these '\n 'differences are described in the AIRS '\n 'Near Real Time (NRT) data products '\n 'document. The Atmospheric Infrared '\n 'Sounder (AIRS) is a grating spectrometer '\n '(R = 1200) aboard the second Earth '\n 'Observing System (EOS) polar-orbiting '\n 'platform, EOS Aqua. In combination with '\n 'the Advanced Microwave Sounding Unit '\n '(AMSU) and the Humidity Sounder for '\n 'Brazil (HSB), AIRS constitutes an '\n 'innovative atmospheric sounding group of '\n 'visible, infrared, and microwave sensors. '\n 'This product is produced using AIRS IR '\n 'only because the radiometric noise in '\n 'AMSU channel 4 started to increase '\n 'significantly (since June 2007). The AIRS '\n 'Standard Retrieval Product consists of '\n 'retrieved estimates of cloud and surface '\n 'properties, plus profiles of retrieved '\n 'temperature, water vapor, ozone, carbon '\n 'monoxide and methane. Estimates of the '\n 'errors associated with these quantities '\n 'is also part of the Standard Product. The '\n 'temperature profile vertical resolution '\n 'is 28 levels total between 1100 mb and '\n '0.1 mb, while moisture profile is '\n 'reported at 14 atmospheric layers between '\n '1100 mb and 50 mb. The horizontal '\n 'resolution is 50 km. An AIRS granule has '\n 'been set as 6 minutes of data, 30 '\n 'footprints cross track by 45 lines along '\n 'track. There are 240\\n'\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': { 'Description': 'You must '\n 'register using '\n 'the EOSDIS User '\n 'Registration '\n 'System in order '\n 'to access LANCE '\n 'NRT AIRS data. '\n 'You can '\n 'register at '\n 'https://urs.eosdis.nasa.gov/users/new'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'LANCE',\n 'NRT',\n 'RELATIVE_START_DATE: -7'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_NRT_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2016-10-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2RET_NRT',\n 'Title': 'AIRS/Aqua L2 Near '\n 'Real Time (NRT) '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionDataType': 'NEAR_REAL_TIME',\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Near Real Time (NRT) '\n 'Standard Physical Retrieval (AIRS-only) '\n 'V006 (AIRS2RET_NRT) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2016-09-26T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'},\n { 'LongName': 'Land, Atmosphere Near '\n 'real-time Capability for '\n 'EOS',\n 'ShortName': 'LANCE'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Edition': '8',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '1'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '2',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Series': 'EEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing,',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '41'},\n { 'Author': 'Thomas Hearty, '\n 'Feng Ding, Ed '\n 'Esfandiari, '\n 'Andrey '\n 'Savtchenko, '\n 'Michael '\n 'Theobald, '\n 'Bruce Vollmer, '\n 'Xin-Min Hua, '\n 'Evan Manning, '\n 'and Edward '\n 'Olsen',\n 'PublicationPlace': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'Title': 'AIRS Near Real '\n 'Time (NRT) data '\n 'products'}],\n 'Purpose': 'The Near Real Time (NRT) product is for '\n 'users whose primary interest is the low '\n 'latency for data availability. While '\n 'standard data products are available '\n 'within 3 days of observation, NRT data are '\n 'usually available within 3 hours of '\n 'observation.',\n 'Quality': 'The product is similar to AIRX2RET except '\n 'the processing uses only the AIRS '\n 'channels. No AMSU information was used in '\n 'the generation of this product. As a Near '\n 'Real Time (NRT) product this differs from '\n 'AIRS2RET.006 AIRS-only in four ways to '\n 'meet the three hour latency requirement of '\n 'the Land Atmosphere NRT Capability Earth '\n 'Observing System (LANCE). For additional '\n 'information about NRT processing see '\n 'either the Related URL section or the '\n 'Publication References section for the V6 '\n 'NRT memo. The quality of data products, '\n 'described in the associated references, '\n 'provide information about numerous '\n 'validation studies conducted and papers '\n 'written documenting the excellence of the '\n 'products using radiosondes, ground truth, '\n 'other satellites, and model analysis '\n 'products. There are however several '\n 'limitation of the AIRS-only Version-6 '\n 'retrieval products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias. Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2RET_NRT_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2RET_NRT_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS. User '\n 'registration is '\n 'required. Register '\n 'for a username and '\n 'password at '\n 'https://urs.eosdis.nasa.gov/users/new',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_NRT/AIRS2RET_NRT.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_NRT/AIRS2RET_NRT.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'datacasting.',\n 'Subtype': 'DATACAST URL',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/datacasting/AIRS2RET_NRT.006.datacast-feed.xml',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2RET_NRT+005',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS '\n 'instrument,algorithms, '\n 'and other '\n 'AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Memo on NRT vs '\n 'Standard Product',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'LAYERED '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AIRS2RET_NRT',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2016-10-15T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477382-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2SUP_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:29Z',\n 'revision-id': 18,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. This product is '\n 'similar to AIRX2SUP. It is a new product '\n 'produced using AIRS IR only because the '\n 'radiometric noise in AMSU channel 4 '\n 'started to increase significantly (since '\n 'June 2007). The Support Product includes '\n 'higher vertical resolution profiles of '\n 'the quantities found in the Standard '\n 'Product, plus intermediate outputs (e.g., '\n 'microwave-only retrieval), research '\n 'products such as the abundance of trace '\n 'gases, and detailed quality assessment '\n 'information. The Support Product profiles '\n 'contain 100 levels between 1100 and .016 '\n 'mb; this higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than that in the '\n 'Standard Product profiles. The intended '\n 'users of the Support Product are '\n 'researchers interested in generating '\n 'forward radiance or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data, 30 footprints cross '\n 'track by 45 lines along track. There are '\n '240 granules per day, with an orbit '\n 'repeat cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2SUP',\n 'Title': 'AIRS/Aqua L2 '\n 'Support Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA208'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'AIRS/Aqua L2 Support Retrieval '\n '(AIRS-only) V006 (AIRS2SUP) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Edition': '41',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '2'}],\n 'Quality': 'The product is similar to AIRX2SUP except '\n 'the processing uses only the AIRS '\n 'channels. No AMSU information was used in '\n 'the generation of this product. The '\n 'quality of data products, described in the '\n 'associated references, provide information '\n 'about numerous validation studies '\n 'conducted and papers written documenting '\n 'the excellence of the products using '\n 'radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitation of '\n 'the AIRS-only Version-6 retrieval '\n 'products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also , the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS-only) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2SUP_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRS2SUP.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRS2SUP.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS2SUP%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2SUP+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS2SUP',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1345119372-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS2SUP_NRT_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:30Z',\n 'revision-id': 22,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'Level 2 Near Real Time (NRT) Support '\n 'Retrieval (AIRS-only) product '\n '(AIRS2SUP_NRT_006) differs from the '\n 'routine product (AIRS2SUP_006) in four '\n 'ways to meet the three hour latency '\n 'requirements of the Land Atmosphere NRT '\n 'Capability Earth Observing System '\n '(LANCE): (1) The NRT granules are '\n 'produced without previous or subsequent '\n 'granules if those granules are not '\n 'available within 5 minutes, (2) the '\n 'predictive ephemeris/attitude data are '\n 'used rather than the definitive '\n 'ephemeris/attitude, (3) if the forecast '\n 'surface pressure is unavailable, a '\n 'surface climatology is used, and (4) no '\n 'ice cloud properties retrievals are '\n 'performed. The consequences of these '\n 'differences are described in the AIRS '\n 'Near Real Time (NRT) data products '\n 'document. The Atmospheric Infrared '\n 'Sounder (AIRS) is a grating spectrometer '\n '(R = 1200) aboard the second Earth '\n 'Observing System (EOS) polar-orbiting '\n 'platform, EOS Aqua. In combination with '\n 'the Advanced Microwave Sounding Unit '\n '(AMSU) and the Humidity Sounder for '\n 'Brazil (HSB), AIRS constitutes an '\n 'innovative atmospheric sounding group of '\n 'visible, infrared, and microwave sensors. '\n 'This product is product produced using '\n 'AIRS IR only because the radiometric '\n 'noise in AMSU channel 4 started to '\n 'increase significantly (since June 2007). '\n 'The Support Product includes higher '\n 'vertical resolution profiles of the '\n 'quantities found in the Standard Product, '\n 'plus intermediate outputs (e.g., '\n 'microwave-only retrieval), research '\n 'products such as the abundance of trace '\n 'gases, and detailed quality assessment '\n 'information. The Support Product profiles '\n 'contain 100 levels between 1100 and .016 '\n 'mb; this higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than that in the '\n 'Standard Product profiles. The intended '\n 'users of the Support Product are '\n 'researchers interested in generating '\n 'forward radiance or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data, 30 footprints cross '\n 'track by 45 scanlines. There are 240 '\n 'granules per day, with an orbit repeat '\n 'cycle of approximately 16 day.',\n 'AccessConstraints': { 'Description': 'You must '\n 'register using '\n 'the EOSDIS User '\n 'Registration '\n 'System in order '\n 'to access LANCE '\n 'NRT AIRS data. '\n 'You can '\n 'register at '\n 'https://urs.eosdis.nasa.gov/users/new'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone',\n 'LANCE',\n 'NRT',\n 'RELATIVE_START_DATE: -7'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_NRT_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2016-10-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS2SUP_NRT',\n 'Title': 'AIRS/Aqua L2 Near '\n 'Real Time (NRT) '\n 'Support Retrieval '\n '(AIRS-only) V006',\n 'Version': '006'}],\n 'CollectionDataType': 'NEAR_REAL_TIME',\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Near Real Time (NRT) '\n 'Support Retrieval (AIRS-only) V006 '\n '(AIRS2SUP_NRT) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2016-09-26T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'},\n { 'LongName': 'Land, Atmosphere Near '\n 'real-time Capability for '\n 'EOS',\n 'ShortName': 'LANCE'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres,',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Issue': '3',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '120'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': '. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '2',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Series': 'IEEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '41'},\n { 'Author': 'Thomas Hearty, '\n 'Feng Ding, Ed '\n 'Esfandiari, '\n 'Andrey '\n 'Savtchenko, '\n 'Michael '\n 'Theobald, '\n 'Bruce Vollmer, '\n 'Xin-Min Hua, '\n 'Evan Manning, '\n 'and Edward '\n 'Olsen',\n 'PublicationPlace': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'Title': 'AIRS Near Real '\n 'Time (NRT) data '\n 'products'}],\n 'Purpose': 'The Near Real Time (NRT) product is for '\n 'users whose primary interest is the low '\n 'latency for data availability. While '\n 'standard data products are available '\n 'within 3 days of observation, NRT data are '\n 'usually available within 3 hours of '\n 'observation.',\n 'Quality': 'The product is similar to AIRS2SUP except '\n 'the processing system that produced these '\n 'radiances is a Near Real Time (NRT) '\n 'system. This product this differs from '\n 'AIRS2SUP.006 AIRSonly in four ways to meet '\n 'the three hour latency requirement of the '\n 'Land Atmosphere NRT Capability Earth '\n 'Observing System (LANCE). For additional '\n 'information about NRT processing see '\n 'either the RelatedURL section or the '\n 'References section for the V6 NRT memo.\\n'\n '\\n'\n 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitation of '\n 'the AIRS-only Version-6 retrieval '\n 'products. The AIRS-only surface '\n 'classification determination is not '\n 'optimal in polar regions. In addition, the '\n 'Version-6 retrievals contain a spurious '\n 'dry daytime moisture bias. Another is the '\n 'thickness of the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.',\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS-only) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS2SUP_NRT_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS2SUP_NRT_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS. User '\n 'registration is '\n 'required. Register '\n 'for a username and '\n 'password at '\n 'https://urs.eosdis.nasa.gov/users/new',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_NRT/AIRS2SUP_NRT.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS Near Real Time '\n 'data products.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_NRT/AIRS2RET_NRT.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'datacasting.',\n 'Subtype': 'DATACAST URL',\n 'Type': 'GET DATA',\n 'URL': 'https://discnrt1.gesdisc.eosdis.nasa.gov/datacasting/AIRS2SUP_NRT.006.datacast-feed.xml',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS2SUP_NRT+005',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Memo on NRT vs '\n 'Standard Product',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.5_ProductQuality/nrt_memo_v6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'AEROSOLS',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TRACE '\n 'GASES/TRACE '\n 'SPECIES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PLANETARY '\n 'BOUNDARY '\n 'LAYER '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'LAYERED '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AIRS2SUP_NRT',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2016-10-15T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517268-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3SP8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:41Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 563.3,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SP8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3SP8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Support '\n 'Product '\n '(AIRS-only) 1 '\n 'degree X 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA315'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Support Product '\n '(AIRS-only) 1 degree X 1 degree V006 '\n '(AIRS3SP8) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multi-day standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3SP8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SP8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3SP8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3SP8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3SP8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3SP8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS3SP8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517272-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3SPD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:42Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 474.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3SPD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Support '\n 'Product '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA306'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Support Product '\n '(AIRS-only) 1 degree x 1 degree V006 '\n '(AIRS3SPD) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3SPD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3SPD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3SPD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3SPD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3SPD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS3SPD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517285-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3SPM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:43Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 554.5,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3SPM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Support '\n 'Product '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA324'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Support Product '\n '(AIRS-only) 1 degree x 1 degree V006 '\n '(AIRS3SPM) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3SPM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3SPM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3SPM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3SPM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3SPM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3SPM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRS3SPM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006',\n 'VersionDescription': 'Not provided'}},\n { 'meta': { 'concept-id': 'C1238517287-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3ST8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:44Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Only Level 3 '\n '8-Day Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers an 8-day period, or one-half of '\n 'the Aqua orbit repeat cycle. The mean '\n 'values are simply the arithmetic means of '\n 'the daily products, weighted by the '\n 'number of input counts for each day in '\n 'that grid box. The geophysical parameters '\n 'have been averaged and binned into 1 x 1 '\n 'deg grid cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 452.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3ST8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3ST8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS-only) 1 '\n 'degree X 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA312'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Standard Physical '\n 'Retrieval (AIRS-only) 1 degree X 1 '\n 'degree V006 (AIRS3ST8) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'mproved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics,',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multi-day standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3ST8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3ST8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3ST8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3ST8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3ST8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3ST8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRS3ST8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517289-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3STD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:46Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Only Level 3 '\n 'Daily Gridded Product contains standard '\n 'retrieval means, standard deviations and '\n 'input counts. Each file covers a temporal '\n 'period of 24 hours for either the '\n 'descending (equatorial crossing North to '\n 'South @1:30 AM local time) or ascending '\n '(equatorial crossing South to North @1:30 '\n 'PM local time) orbit. The data starts at '\n 'the international dateline and progresses '\n 'westward (as do the subsequent orbits of '\n 'the satellite) so that neighboring '\n 'gridded cells of data are no more than a '\n 'swath of time apart (about 90 minutes). '\n 'The two parts of a scan line crossing the '\n 'dateline are included in separate L3 '\n 'files, according to the date, so that '\n 'data points in a grid box are always '\n 'coincident in time. The edge of the AIRS '\n 'Level 3 gridded cells is at the date line '\n '(the 180E/W longitude boundary). When '\n 'plotted, this produces a map with 0 '\n 'degrees longitude in the center of the '\n 'image unless the bins are reordered. This '\n 'method is preferred because the left '\n '(West) side of the image and the right '\n '(East) side of the image contain data '\n 'farthest apart in time. The gridding '\n 'scheme used by AIRS is the same as used '\n 'by TOVS Pathfinder to create Level 3 '\n 'products. The daily Level 3 products have '\n 'gores between satellite paths where there '\n 'is no coverage for that day. The '\n 'geophysical parameters have been averaged '\n 'and binned into 1 x 1 deg grid cells, '\n 'from -180.0 to +180.0 deg longitude and '\n 'from -90.0 to +90.0 deg latitude. For '\n 'each grid map of 4-byte floating-point '\n 'mean values there is a corresponding '\n '4-byte floating-point map of standard '\n 'deviation and a 2-byte integer grid map '\n 'of counts. The counts map provides the '\n 'user with the number of points per bin '\n 'that were included in the mean and can be '\n 'used to generate custom multi-day maps '\n 'from the daily gridded products. The '\n 'thermodynamic parameters are: Skin '\n 'Temperature (land and sea surface), Air '\n 'Temperature at the surface, Profiles of '\n 'Air Temperature and Water Vapor, '\n 'Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 376.1,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3STD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA303'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Standard Physical '\n 'Retrieval (AIRS-only) 1 degree x 1 '\n 'degree V006 (AIRS3STD) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3STD_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3STD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3STD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3STD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3STD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRS3STD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517301-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRS3STM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:21:47Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Only Level 3 '\n 'Monthly Gridded Retrieval Product '\n 'contains standard retrieval means, '\n 'standard deviations and input counts. '\n 'Each file covers a calendar month. The '\n 'mean values are simply the arithmetic '\n 'means of the daily products, weighted by '\n 'the number of input counts for each day '\n 'in that grid box. The geophysical '\n 'parameters have been averaged and binned '\n 'into 1 x 1 deg grid cells, from -180.0 to '\n '+180.0 deg longitude and from -90.0 to '\n '+90.0 deg latitude. For each grid map of '\n '4-byte floating-point mean values there '\n 'is a corresponding 4-byte floating-point '\n 'map of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 445.7,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRS3STM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS-only) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'ACTIVE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA321'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Standard Physical '\n 'Retrieval (AIRS-only) 1 degree x 1 '\n 'degree V006 (AIRS3STM) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'PublicationPlace': 'http://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box.',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (AIRS '\n 'only)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRS3STM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRS3STM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRS3STM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRS3STM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRS3STM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRS3STM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRS3STM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': True,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477383-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX2RET_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2020-02-12T18:12:06Z',\n 'revision-id': 32,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Standard '\n 'Retrieval Product consists of retrieved '\n 'estimates of cloud and surface '\n 'properties, plus profiles of retrieved '\n 'temperature, water vapor, ozone, carbon '\n 'monoxide and methane. Estimates of the '\n 'errors associated with these quantities '\n 'are also be part of the Standard Product. '\n 'The temperature profile vertical '\n 'resolution is 28 levels total between '\n '1100 mb and 0.1 mb, while moisture '\n 'profile is reported at 14 atmospheric '\n 'layers between 1100 mb and 50 mb. The '\n 'horizontal resolution is 50 km. An AIRS '\n 'granule has been set as 6 minutes of '\n 'data, 30 footprints cross track by 45 '\n 'lines along track. There are 240 granules '\n 'per day, with an orbit repeat cycle of '\n 'approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 3.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2RET_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX2RET',\n 'Title': 'AIRS/Aqua L2 '\n 'Standard Physical '\n 'Retrieval '\n '(AIRS+AMSU) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': '10.5067/Aqua/AIRS/DATA201'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Standard Physical '\n 'Retrieval (AIRS+AMSU) V006 (AIRX2RET) '\n 'at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens',\n 'Title': 'mproved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde '\n 'data”',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Edition': '120',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the Arctic',\n 'Volume': '3'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Issue': '4',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '121'},\n { 'Author': 'journal '\n 'Editiors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Special Issue, '\n 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '2',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'Series': 'IEEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds',\n 'Volume': '41'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the Version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias, '\n 'another is that the AIRS atmospheric '\n 'temperature layer structure near the '\n 'surface is not sensitive enough for the '\n 'determination of a consistently accurate '\n 'boundary layer. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'In addition, the AIRS retrieval is not '\n 'sensitive to either constituent near the '\n 'surface. Also, the total column ozone is '\n 'good, but the shape of the profile can be '\n 'incorrect in regions of temperature '\n 'inversion. Occasionally in the tropical '\n 'ocean the algorithm confuses silicates '\n 'from dust storms blowing off the African '\n 'continent toward the Americas for high '\n 'levels of ozone. Each variable contains a '\n 'flag indicating the quality of the value. '\n 'The three options for this quality flag '\n 'are: 0 for best quality, 1 for good '\n 'quality, 2 for do not use.\\n'\n '\\n'\n 'This product stopped after September 24, '\n '2016 as the power to the AMSU-A2 '\n 'instrument on Aqua was lost. For data '\n 'after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX2RET_006.jpeg',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2RET_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRX2RET.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRX2RET.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX2RET%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX2RET+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'}],\n 'ShortName': 'AIRX2RET',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Not '\n 'provided'}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2016-09-24T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1243477317-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX2SUP_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:04Z',\n 'revision-id': 19,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The Support Product '\n 'includes higher vertical resolution '\n 'profiles of the quantities found in the '\n 'Standard Product plus intermediate output '\n '(e.g., microwave-only retrieval), '\n 'research products such as the abundance '\n 'of trace gases, and detailed quality '\n 'assessment information. The Support '\n 'Product profiles contain 100 pressure '\n 'levels between 1100 and .016 mb; this '\n 'higher resolution simplifies the '\n 'generation of radiances using forward '\n 'models, though the vertical information '\n 'content is no greater than in the '\n 'Standard Product profiles. The horizontal '\n 'resolution is 50 km. The intended users '\n 'of the Support Product are researchers '\n 'interested in generating forward '\n 'radiance, or in examining research '\n 'products, and the AIRS algorithm '\n 'development team. The Support Product is '\n 'generated at all locations as Standard '\n 'Products. An AIRS granule has been set as '\n '6 minutes of data, 30 footprints cross '\n 'track by 45 lines along track. There are '\n '240 granules per day, with an orbit '\n 'repeat cycle of approximately 16 day.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Carbon Monoxide',\n 'Cloud Liquid Water',\n 'Methane',\n 'Ozone',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'TEMPERATURE',\n 'WATER VAPOR',\n 'Water Vapor Saturation Mass '\n 'Mixing Ratio',\n 'Water Vapor Mass Mixing Ratio',\n 'Cloud Ice/Water Flag',\n 'Layer Molecular Column Density '\n 'of Carbon Monoxide',\n 'Layer Molecular Column Density '\n 'of Cloud Liquid Water',\n 'Layer Molecular Column Density '\n 'of Methane',\n 'Layer Molecular Column Density '\n 'of Ozone'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 21.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2SUP_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-01-15T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX2SUP',\n 'Title': 'AIRS/Aqua L2 '\n 'Support Retrieval '\n '(AIRS+AMSU) V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA207'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L2 Support Retrieval '\n '(AIRS+AMSU) V006 (AIRX2SUP) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-01-10T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2019-09-05T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '2'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'B. H. Kahn et '\n 'al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'OnlineResource': { 'Linkage': 'https://www.atmos-chem-phys.net/14/399/2014/acp-14-399-2014.html'},\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-01T00:00:00.000Z',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder version '\n '6 cloud '\n 'products'},\n { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'Patrick '\n 'Boylan, '\n 'Junhong Wang, '\n 'Stephen A. '\n 'Cohn, Erik '\n 'Fetzer, Eric '\n 'S. Maddy, and '\n 'Sung Wong',\n 'DOI': { 'DOI': '10.1002/2014JD022551'},\n 'Issue': '3',\n 'Pages': '992-1007',\n 'PublicationDate': '2015-02-10T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research: '\n 'Atmospheres,',\n 'Title': 'Validation of '\n 'AIRS version 6 '\n 'temperature '\n 'profiles '\n 'and '\n 'surface-based '\n 'inversions over '\n 'Antarctica '\n 'using '\n 'Concordiasi '\n 'dropsonde data',\n 'Volume': '120'},\n { 'Author': 'L.N. Boisvert, '\n 'D.L. Wu, T. '\n 'Vihma, '\n 'J.Susskind',\n 'DOI': { 'DOI': '10.1002/2014JD02166'},\n 'Edition': '120',\n 'Pages': '945-963',\n 'PublicationDate': '2015-01-15T00:00:00.000Z',\n 'Title': 'Verificaton of '\n 'air/surface '\n 'humidity '\n 'differences '\n 'from AIRS and '\n 'ERA-Interim in '\n 'support of '\n 'turbulent flux '\n 'estimation in '\n 'the '\n 'Arctic”, '\n 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Volume': '3'},\n { 'Author': 'Jacola Roman, '\n 'Robert '\n 'Knuteson, '\n 'Thomas August, '\n 'Tim Hultberg, '\n 'Steve '\n 'Ackerman, and '\n 'Hank Revercomb',\n 'DOI': { 'DOI': '10.1002/2016JD024806'},\n 'Issue': '15',\n 'Pages': '8925-8948',\n 'PublicationDate': '2016-07-21T00:00:00.000Z',\n 'Series': 'Journal of '\n 'Geophysical '\n 'Research, '\n 'Atmospheres',\n 'Title': 'A global '\n 'assessment of '\n 'NASA AIRS v6 '\n 'and EUMETSAT '\n 'IASI v6 '\n 'precipitable '\n 'water vapor '\n 'using '\n 'ground‐based '\n 'GPS SuomiNet '\n 'stations”',\n 'Volume': '121'},\n { 'Author': 'Adam B. '\n 'Milstein, '\n 'William J. '\n 'Blackwell',\n 'DOI': { 'DOI': '10.1002/2015JD024008'},\n 'Edition': '121',\n 'Pages': '1414-1430',\n 'PublicationDate': '2015-12-20T00:00:00.000Z',\n 'Title': 'Neural network '\n 'temperature and '\n 'moisture '\n 'retrieval '\n 'algorithm '\n 'validation for '\n 'AIRS/AMSU and '\n 'CrIS/ATMS',\n 'Volume': '4'},\n { 'Author': 'Journal '\n 'Editors',\n 'DOI': { 'DOI': '10.1029/2005/JD007020'},\n 'Issue': '9',\n 'OtherReferenceDetails': 'This '\n 'special '\n 'issue '\n 'contains '\n 'several '\n 'relevant '\n 'articles.',\n 'Series': 'J. Geophys, '\n 'Res. '\n 'Atmospheres',\n 'Title': 'Validation of '\n 'Atmospheric '\n 'Infrared '\n 'Sounder '\n 'Observations',\n 'Volume': '111',\n '_errors': { 'PublicationDate': 'Could '\n 'not '\n 'parse '\n 'date-time '\n 'value: '\n 'May '\n '2006'}},\n { 'Author': 'Joel Susskind, '\n 'Christopher D. '\n 'Barnet, and '\n 'John M. '\n 'Blaisdell',\n 'DOI': { 'DOI': '10.1109/TGRS.2002.808236'},\n 'Issue': '41',\n 'Pages': '390-409',\n 'PublicationDate': '2003-04-29T00:00:00.000Z',\n 'ReportNumber': '2',\n 'Series': 'IEEE '\n 'Transactions '\n 'on Geoscience '\n 'and Remote '\n 'Sensing',\n 'Title': 'Retrieval of '\n 'Atmospheric and '\n 'Surface '\n 'Parameters From '\n 'AIRS/AMSU/HSB '\n 'Data in the '\n 'Presence of '\n 'Clouds'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the Version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. '\n 'Another is that the thickness of the AIRS '\n 'atmospheric temperature layer structure '\n 'near the surface is not sensitive enough '\n 'for the determination of a consistently '\n 'accurate boundary layer. For trace gases, '\n 'the total column CO and total column '\n 'methane (CH4) are dominated by the initial '\n 'guess and should not be used for research '\n 'purposes. In addition, the AIRS retrieval '\n 'is not sensitive to either constituent '\n 'near the surface. Also, the total column '\n 'ozone is good, but the shape of the '\n 'profile can be incorrect in regions of '\n 'temperature inversion. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off '\n 'the African continent toward the Americas '\n 'for high levels of ozone. Each variable '\n 'contains a flag indicating the quality of '\n 'the value. The three options for this '\n 'quality flag are: 0 for best quality, 1 '\n 'for good quality, 2 for do not use. \\n'\n '\\n'\n 'This product stopped after September 24, '\n '2016 as the power to the AMSU-A2 '\n 'instrument on Aqua was lost. For data '\n 'after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample plot of AIRS '\n 'Level 2 Support '\n 'Retrieval '\n '(AIRS+AMSU) H2O '\n 'Column Density '\n 'Profile and Cloud '\n 'Fraction.',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX2SUP_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX2SUP_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTPS.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level2/AIRX2SUP.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://airsl2.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level2/AIRX2SUP.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX2SUP%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX2SUP+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS ATBD',\n 'Subtype': 'ALGORITHM THEORETICAL '\n 'BASIS DOCUMENT (ATBD)',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://eospso.gsfc.nasa.gov/sites/default/files/atbd/20070301_L2_ATBD_signed.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'FRACTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SULFUR '\n 'COMPOUNDS',\n 'VariableLevel2': 'SULFUR '\n 'DIOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOSPHERIC '\n 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'MICROWAVE',\n 'Topic': 'SPECTRAL/ENGINEERING',\n 'VariableLevel1': 'BRIGHTNESS '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX2SUP',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Kilometers',\n 'XDimension': 50,\n 'YDimension': 50}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-30T00:00:00.000Z',\n 'EndingDateTime': '2016-09-24T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517314-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3SP8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:11Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 563.3,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SP8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3SP8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Support '\n 'Multiday Product '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA313'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Support Multiday '\n 'Product (AIRS+AMSU) 1 degree x 1 degree '\n 'V006 (AIRX3SP8) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3SP8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SP8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3SP8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3SP8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3SP8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3SP8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX3SP8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517317-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3SPD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:12Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 474.9,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3SPD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Support '\n 'Product '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA304'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Support Product '\n '(AIRS+AMSU) 1 degree x 1 degree V006 '\n '(AIRX3SPD) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics,',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3SPD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3SPD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3SPD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3SPD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3SPD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX3SPD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2016-09-25T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517340-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3SPM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:13Z',\n 'revision-id': 16,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The L3 support '\n 'products are similar to the L3 standard '\n 'products but contain fields which are not '\n 'fully validated, or are inputs or '\n 'intermediary values. Because no quality '\n 'control information is available for some '\n 'of these fields, values from failed '\n 'retrievals may be included.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 554.5,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3SPM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Support '\n 'Product '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA322'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Support Product '\n '(AIRS+AMSU) 1 degree x 1 degree V006 '\n '(AIRX3SPM) at GES DISC',\n 'ISOTopicCategories': [ 'IMAGERY/BASE MAPS/EARTH COVER',\n 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'mproved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3SPM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3SPM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3SPM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3SPM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3SPM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3SPM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'DROPLET '\n 'CONCENTRATION/SIZE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD '\n 'OPTICAL '\n 'DEPTH/THICKNESS'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD TYPES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'PRECIPITATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'PRECIPITATION '\n 'RATE'}],\n 'ShortName': 'AIRX3SPM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517323-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3ST8_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:14Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Level 3 8-Day '\n 'Gridded Retrieval Product contains '\n 'standard retrieval means, standard '\n 'deviations and input counts. Each file '\n 'covers an 8-day period, or one-half of '\n 'the Aqua orbit repeat cycle. The mean '\n 'values are simply the arithmetic means of '\n 'the daily products, weighted by the '\n 'number of input counts for each day in '\n 'that grid box. The geophysical parameters '\n 'have been averaged and binned into 1 x 1 '\n 'deg grid cells, from -180.0 to +180.0 deg '\n 'longitude and from -90.0 to +90.0 deg '\n 'latitude. For each grid map of 4-byte '\n 'floating-point mean values there is a '\n 'corresponding 4-byte floating-point map '\n 'of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 452.2,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3ST8_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3ST8',\n 'Title': 'AIRS/Aqua L3 '\n '8-day Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA310'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 8-day Standard Physical '\n 'Retrieval (AIRS+AMSU) 1 degree x 1 '\n 'degree V006 (AIRX3ST8) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'multiday standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3ST8_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3ST8_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3ST8.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3ST8.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3ST8%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3ST8+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRX3ST8',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517344-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3STD_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:16Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Level 3 Daily '\n 'Gridded Product contains standard '\n 'retrieval means, standard deviations and '\n 'input counts. Each file covers a temporal '\n 'period of 24 hours for either the '\n 'descending (equatorial crossing North to '\n 'South @1:30 AM local time) or ascending '\n '(equatorial crossing South to North @1:30 '\n 'PM local time) orbit. The data starts at '\n 'the international dateline and progresses '\n 'westward (as do the subsequent orbits of '\n 'the satellite) so that neighboring '\n 'gridded cells of data are no more than a '\n 'swath of time apart (about 90 minutes). '\n 'The two parts of a scan line crossing the '\n 'dateline are included in separate L3 '\n 'files, according to the date, so that '\n 'data points in a grid box are always '\n 'coincident in time. The edge of the AIRS '\n 'Level 3 gridded cells is at the date line '\n '(the 180E/W longitude boundary). When '\n 'plotted, this produces a map with 0 '\n 'degrees longitude in the center of the '\n 'image unless the bins are reordered. This '\n 'method is preferred because the left '\n '(West) side of the image and the right '\n '(East) side of the image contain data '\n 'farthest apart in time. The gridding '\n 'scheme used by AIRS is the same as used '\n 'by TOVS Pathfinder to create Level 3 '\n 'products. The daily Level 3 products have '\n 'gores between satellite paths where there '\n 'is no coverage for that day. The '\n 'geophysical parameters have been averaged '\n 'and binned into 1 x 1 deg grid cells, '\n 'from -180.0 to +180.0 deg longitude and '\n 'from -90.0 to +90.0 deg latitude. For '\n 'each grid map of 4-byte floating-point '\n 'mean values there is a corresponding '\n '4-byte floating-point map of standard '\n 'deviation and a 2-byte integer grid map '\n 'of counts. The counts map provides the '\n 'user with the number of points per bin '\n 'that were included in the mean and can be '\n 'used to generate custom multi-day maps '\n 'from the daily gridded products. The '\n 'thermodynamic parameters are: Skin '\n 'Temperature (land and sea surface), Air '\n 'Temperature at the surface, Profiles of '\n 'Air Temperature and Water Vapor, '\n 'Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 376.1,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STD_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3STD',\n 'Title': 'AIRS/Aqua L3 '\n 'Daily Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA301'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Daily Standard Physical '\n 'Retrieval (AIRS+AMSU) 1 degree x 1 '\n 'degree V006 (AIRX3STD) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'daily standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3STD_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STD_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3STD.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3STD.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3STD%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3STD+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&version=1.1.1&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Get map example for '\n 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&VERSION=1.1.1&REQUEST=GetMap&SRS=EPSG:4326&WIDTH=720&HEIGHT=360&LAYERS=AIRX3STD_TOTH2OVAP_A&TRANSPARENT=TRUE&FORMAT=image/png&bbox=-180,-90,180,90',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Coverage Service.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Get Coverage data '\n 'example for NASA '\n 'GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Coverage Service.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCoverage&CRS=EPSG:4326&format=netCDF&resx=1.0&resy=1.0&BBOX=-179.5,-89.5,179.5,89.5&Coverage=AIRX3STD:CO_VMR_A&Time=2013-08-11',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRX3STD',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-08-31T00:00:00.000Z',\n 'EndingDateTime': '2016-09-25T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1238517346-GES_DISC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/dif10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AIRX3STM_006',\n 'provider-id': 'GES_DISC',\n 'revision-date': '2019-12-11T19:22:17Z',\n 'revision-id': 17,\n 'user-id': 'cloeser'},\n 'umm': { 'Abstract': 'The Atmospheric Infrared Sounder (AIRS) '\n 'is a grating spectrometer (R = 1200) '\n 'aboard the second Earth Observing System '\n '(EOS) polar-orbiting platform, EOS Aqua. '\n 'In combination with the Advanced '\n 'Microwave Sounding Unit (AMSU) and the '\n 'Humidity Sounder for Brazil (HSB), AIRS '\n 'constitutes an innovative atmospheric '\n 'sounding group of visible, infrared, and '\n 'microwave sensors. The AIRS Level 3 '\n 'Monthly Gridded Retrieval Product '\n 'contains standard retrieval means, '\n 'standard deviations and input counts. '\n 'Each file covers a calendar month. The '\n 'mean values are simply the arithmetic '\n 'means of the daily products, weighted by '\n 'the number of input counts for each day '\n 'in that grid box. The geophysical '\n 'parameters have been averaged and binned '\n 'into 1 x1 deg grid cells, from -180.0 to '\n '+180.0 deg longitude and from -90.0 to '\n '+90.0 deg latitude. For each grid map of '\n '4-byte floating-point mean values there '\n 'is a corresponding 4-byte floating-point '\n 'map of standard deviation and a 2-byte '\n 'integer grid map of counts. The counts '\n 'map provides the user with the number of '\n 'points per bin that were included in the '\n 'mean and can be used to generate custom '\n 'multi-day maps from the daily gridded '\n 'products. The thermodynamic parameters '\n 'are: Skin Temperature (land and sea '\n 'surface), Air Temperature at the surface, '\n 'Profiles of Air Temperature and Water '\n 'Vapor, Tropopause Characteristics, Column '\n 'Precipitable Water, Cloud '\n 'Amount/Frequency, Cloud Height, Cloud Top '\n 'Pressure, Cloud Top Temperature, '\n 'Reflectance, Emissivity, Surface '\n 'Pressure, Cloud Vertical Distribution. '\n 'The trace gases parameters are: Total '\n 'Amounts and Vertical Profiles of Carbon '\n 'Monoxide, Methane, and Ozone. The actual '\n 'names of the variables in the data files '\n 'should be inferred from the Processing '\n 'File Description document.',\n 'AccessConstraints': {'Description': 'None'},\n 'AncillaryKeywords': [ 'ATMOSPHERE',\n 'CALIBRATED',\n 'GEOLOCATED',\n 'TEMPERATURE',\n 'CLOUD',\n 'EOSDIS',\n 'Total Ozone',\n 'Global Gridded',\n 'Total Integrated Column Water '\n 'Vapor Burden',\n 'Total Integrated Column Cloud '\n 'Liquid Water',\n 'Total Integrated Column Carbon '\n 'Monoxide',\n 'Spectral IR Surface '\n 'Emissivities',\n 'Spectral Microwave Surface '\n 'Emissivities',\n 'Total Integrated Column Ozone '\n 'Burden',\n 'Outgoing Longwave Radiation '\n 'Flux',\n 'Clear Sky Outgoing Longwave '\n 'Radiation Flux',\n 'Relative Humidity Profile',\n 'Cloud Layer Pressure At Coarse '\n 'Cloud Resolution',\n 'Cloud Layer Pressure At Fine '\n 'Cloud Resolution',\n 'Water Vapor Mass Mixing Ratio '\n 'Profile',\n 'Tropopause Height',\n 'Tropopause Temperature',\n 'Effective Methane Volume '\n 'Mixing Ratio Profile',\n 'Effective Carbon Monoxide '\n 'Volume Mixing Ratio Profile',\n 'Total Integrated Cloud Liquid '\n 'Water'],\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'AverageFileSize': 445.7,\n 'AverageFileSizeUnit': 'MB',\n 'Fees': 'None',\n 'Format': 'HDF-EOS',\n 'FormatType': 'Native',\n 'Media': [ 'Online '\n 'Archive']}]},\n 'CollectionCitations': [ { 'Creator': 'AIRS Science '\n 'Team/Joao '\n 'Teixeira',\n 'DataPresentationForm': 'Digital '\n 'Science '\n 'Data',\n 'OnlineResource': { 'Linkage': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STM_006.html'},\n 'Publisher': 'Goddard Earth '\n 'Sciences Data '\n 'and '\n 'Information '\n 'Services '\n 'Center (GES '\n 'DISC)',\n 'ReleaseDate': '2013-03-12T00:00:00.000Z',\n 'ReleasePlace': 'Greenbelt, '\n 'MD, USA',\n 'SeriesName': 'AIRX3STM',\n 'Title': 'AIRS/Aqua L3 '\n 'Monthly Standard '\n 'Physical '\n 'Retrieval '\n '(AIRS+AMSU) 1 '\n 'degree x 1 degree '\n 'V006',\n 'Version': '006'}],\n 'CollectionProgress': 'COMPLETE',\n 'ContactPersons': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'asghar.e.esfandiari@nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5960'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'FirstName': 'ED',\n 'LastName': 'ESFANDIARI',\n 'Roles': ['Metadata Author']}],\n 'DOI': {'DOI': 'doi:10.5067/Aqua/AIRS/DATA319'},\n 'DataCenters': [ { 'ContactGroups': [ { 'ContactInformation': { 'Addresses': [ { 'City': 'Greenbelt',\n 'Country': 'USA',\n 'PostalCode': '20771',\n 'StateProvince': 'MD',\n 'StreetAddresses': [ 'Goddard '\n 'Earth '\n 'Sciences '\n 'Data '\n 'and '\n 'Information '\n 'Services '\n 'Center',\n 'Code '\n '610.2',\n 'NASA '\n 'Goddard '\n 'Space '\n 'Flight '\n 'Center']}],\n 'ContactMechanisms': [ { 'Type': 'Email',\n 'Value': 'gsfc-help-disc@lists.nasa.gov'},\n { 'Type': 'Telephone',\n 'Value': '301-614-5224'},\n { 'Type': 'Fax',\n 'Value': '301-614-5268'}]},\n 'GroupName': 'GES '\n 'DISC '\n 'HELP '\n 'DESK '\n 'SUPPORT '\n 'GROUP',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'ContactInformation': { 'RelatedUrls': [ { 'Type': 'HOME '\n 'PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/',\n 'URLContentType': 'DataCenterURL'}]},\n 'LongName': 'Goddard Earth Sciences '\n 'Data and Information '\n 'Services Center '\n '(formerly Goddard '\n 'DAAC), Global Change '\n 'Data Center, Earth '\n 'Sciences Division, '\n 'Science and '\n 'Exploration '\n 'Directorate, Goddard '\n 'Space Flight Center, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/GSFC/SED/ESD/GCDC/GESDISC'}],\n 'DataDates': [ { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '1970-01-01T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'DataLanguage': 'eng',\n 'EntryTitle': 'AIRS/Aqua L3 Monthly Standard Physical '\n 'Retrieval (AIRS+AMSU) 1 degree x 1 '\n 'degree V006 (AIRX3STM) at GES DISC',\n 'ISOTopicCategories': [ 'CLIMATOLOGY/METEOROLOGY/ATMOSPHERE',\n 'IMAGERY/BASE MAPS/EARTH COVER',\n 'ENVIRONMENT',\n 'GEOSCIENTIFIC INFORMATION'],\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2013-02-14T00:00:00.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2018-05-21T00:00:00.000Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Instruments': [ { 'LongName': 'Atmospheric '\n 'Infrared '\n 'Sounder',\n 'ShortName': 'AIRS'},\n { 'LongName': 'Advanced '\n 'Microwave '\n 'Sounding '\n 'Unit-A',\n 'ShortName': 'AMSU-A'}],\n 'LongName': 'Earth Observing System, '\n 'Aqua',\n 'ShortName': 'Aqua',\n 'Type': 'Earth Observation '\n 'Satellites'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [ { 'LongName': 'Earth Observing System '\n '(EOS), Aqua',\n 'ShortName': 'Aqua'}],\n 'PublicationReferences': [ { 'Author': 'Joel Susskind, '\n 'John, M. '\n 'Blaisdell, and '\n 'Lena Iredell',\n 'DOI': { 'DOI': '10.1117/1.JRS.8.084994'},\n 'Issue': '1',\n 'Pages': '34',\n 'PublicationDate': '2014-03-31T00:00:00.000Z',\n 'Series': 'J. Appl. Rem. '\n 'Sens.',\n 'Title': 'Improved '\n 'methodology for '\n 'surface and '\n 'atmospheric '\n 'soundings, '\n 'error '\n 'estimates, and '\n 'quality control '\n 'procedures: the '\n 'atmospheric '\n 'infrared '\n 'sounder science '\n 'team version-6 '\n 'retrieval '\n 'algorithm',\n 'Volume': '8'},\n { 'Author': 'B.H. Kahn, '\n 'et.al.',\n 'DOI': { 'DOI': '10.5194/acp-14-399-2014'},\n 'Issue': '1',\n 'Pages': '399-426',\n 'PublicationDate': '2014-01-10T00:00:00.000Z',\n 'Series': 'Atmospheric '\n 'Chemistry and '\n 'Physics',\n 'Title': 'The Atmospheric '\n 'Infrared '\n 'Sounder Version '\n '6 Cloud '\n 'Products',\n 'Volume': '14'}],\n 'Quality': 'The quality of data products, described in '\n 'the associated references, provide '\n 'information about numerous validation '\n 'studies conducted and papers written '\n 'documenting the excellence of the products '\n 'using radiosondes, ground truth, other '\n 'satellites, and model analysis products. '\n 'There are however several limitations of '\n 'the version-6 retrieval products. One is a '\n 'spurious dry daytime moisture bias. In '\n 'addition, there are some erroneous water '\n 'vapor features in the upper stratosphere '\n 'near the top limit of the AIRS '\n 'determination. For trace gases, the total '\n 'column CO and total column methane (CH4) '\n 'are dominated by the initial guess and '\n 'should not be used for research purposes. '\n 'The total ozone product is good, but has '\n 'some limitations where it is too low over '\n 'the warm oceanic pool and a bit too high '\n 'over most land areas. Occasionally in the '\n 'tropical ocean the algorithm confuses '\n 'silicates from dust storms blowing off the '\n 'African continent toward the Americas for '\n 'high levels of ozone. \\n'\n '\\n'\n 'The value for each grid box is the sum of '\n 'the values that fall within the 1x1 area '\n 'divided by the number of points in the '\n 'box. \\n'\n '\\n'\n 'For AIRS/AMSU: This product stopped after '\n 'September 24, 2016 as the power to the '\n 'AMSU-A2 instrument on Aqua was lost. For '\n 'data after this time use AIRS2RET.006 '\n '(AIRS-only) .',\n 'RelatedUrls': [ { 'Description': 'Sample data of the '\n '\"AIRS/Aqua Level 3 '\n 'monthly standard '\n 'physical retrieval '\n 'product (Without '\n 'HSB)\".',\n 'Type': 'GET RELATED VISUALIZATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/AIRX3STM_006.png',\n 'URLContentType': 'VisualizationURL'},\n { 'Description': 'Access the dataset '\n 'landing page from '\n 'the GES DISC '\n 'website.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://disc.gsfc.nasa.gov/datacollection/AIRX3STM_006.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Access the data via '\n 'HTTP.',\n 'Subtype': 'DATA TREE',\n 'Type': 'GET DATA',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/data/Aqua_AIRS_Level3/AIRX3STM.006/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data via '\n 'the OPeNDAP '\n 'protocol.',\n 'Subtype': 'OPENDAP DATA',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/opendap/Aqua_AIRS_Level3/AIRX3STM.006/contents.html',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Simple '\n 'Subset Wizard (SSW) '\n 'to submit subset '\n 'requests for data '\n 'sets across '\n 'multiple data '\n 'centers from a '\n 'single unified '\n 'interface.',\n 'Subtype': 'SIMPLE SUBSET WIZARD '\n '(SSW)',\n 'Type': 'GOTO WEB TOOL',\n 'URL': 'https://disc.gsfc.nasa.gov/SSW/#keywords=AIRX3STM%20006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Use the Earthdata '\n 'Search to find and '\n 'retrieve data sets '\n 'across multiple '\n 'data centers.',\n 'Subtype': 'Earthdata Search',\n 'Type': 'GET DATA',\n 'URL': 'https://search.earthdata.nasa.gov/search?q=AIRX3STM+006',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&version=1.1.1&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Get map example for '\n 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Map Service.',\n 'Subtype': 'WEB MAP SERVICE (WMS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://disc1.gsfc.nasa.gov/daac-bin/wms_airs?service=WMS&VERSION=1.1.1&REQUEST=GetMap&SRS=EPSG:4326&WIDTH=720&HEIGHT=360&LAYERS=AIRX3STM_TOTH2OVAP_A&TRANSPARENT=TRUE&FORMAT=image/png&bbox=-180,-90,180,90',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA GES DISC AIRS '\n 'Gridded L3 data Web '\n 'Coverage Service.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCapabilities',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Example of getting '\n 'coverage data from '\n 'the NASA GES DISC '\n 'AIRS Gridded L3 '\n 'data Web Coverage '\n 'Service. It '\n 'requests one of the '\n 'variables, '\n 'AIRX3STM:CO_VMR_A '\n 'as listed in the '\n 'GetCapabilities '\n 'response, at given '\n 'TIME and BBOX.',\n 'Subtype': 'WEB COVERAGE SERVICE '\n '(WCS)',\n 'Type': 'USE SERVICE API',\n 'URL': 'https://acdisc.gesdisc.eosdis.nasa.gov/daac-bin/wcsAIRSL3?service=WCS&version=1.0.0&request=GetCoverage&CRS=EPSG:4326&format=netCDF&resx=1.0&resy=1.0&BBOX=-197.5,-89.5,179.5,89.5&Coverage=AIRX3STM:CO_VMR_A&Time=2013-07',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AIRS home page at '\n 'NASA/JPL. General '\n 'information on the '\n 'AIRS instrument, '\n 'algorithms, and '\n 'other AIRS-related '\n 'activities can be '\n 'found.',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://airs.jpl.nasa.gov/index.html',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'AIRS Documentation '\n 'Page',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://disc.gsfc.nasa.gov/information/documents?title=AIRS%20Documentation',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'README Document',\n 'Subtype': 'READ-ME',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/README.AIRS_V6.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AIRS Version 6 '\n 'Processing Files '\n 'Description '\n 'Document.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_Released_Processing_Files_Description.pdf',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'TROPOPAUSE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC PRESSURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'SURFACE '\n 'TEMPERATURE',\n 'VariableLevel2': 'AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC '\n 'TEMPERATURE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'UPPER AIR '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'TOTAL '\n 'PRECIPITABLE '\n 'WATER'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'WATER VAPOR'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'PRESSURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD TOP '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'PROPERTIES',\n 'VariableLevel2': 'CLOUD '\n 'VERTICAL '\n 'DISTRIBUTION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE RADIATIVE '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'EMISSIVITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'SURFACE THERMAL '\n 'PROPERTIES',\n 'Topic': 'LAND SURFACE',\n 'VariableLevel1': 'SKIN '\n 'TEMPERATURE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'AIR QUALITY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON '\n 'MONOXIDE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ALTITUDE',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'GEOPOTENTIAL '\n 'HEIGHT'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'INDICATORS',\n 'VariableLevel2': 'HUMIDITY'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC WATER '\n 'VAPOR',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'WATER VAPOR '\n 'PROFILES'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'CLOUDS',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CLOUD '\n 'MICROPHYSICS',\n 'VariableLevel2': 'CLOUD LIQUID '\n 'WATER/ICE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC RADIATION',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OUTGOING '\n 'LONGWAVE '\n 'RADIATION'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'CARBON AND '\n 'HYDROCARBON '\n 'COMPOUNDS',\n 'VariableLevel2': 'METHANE'},\n { 'Category': 'EARTH SCIENCE',\n 'Term': 'ATMOSPHERIC CHEMISTRY',\n 'Topic': 'ATMOSPHERE',\n 'VariableLevel1': 'OXYGEN '\n 'COMPOUNDS',\n 'VariableLevel2': 'OZONE'}],\n 'ShortName': 'AIRX3STM',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'HorizontalDataResolution': { 'GenericResolutions': [ { 'Unit': 'Decimal '\n 'Degrees',\n 'XDimension': 1,\n 'YDimension': 1}]}}}},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-09-01T00:00:00.000Z',\n 'EndingDateTime': '2016-10-01T23:59:59.999Z'}]}],\n 'Version': '006'}},\n { 'meta': { 'concept-id': 'C1658476139-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+2P+Global+Subskin+Sea+Surface+Temperature+version+8a+from+the+Advanced+Microwave+Scanning+Radiometer+2+on+the+GCOM-W+satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:56Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer 2 (AMSR2) was launched on 18 '\n 'May 2012, onboard the Global Change '\n 'Observation Mission - Water (GCOM-W) '\n 'satellite developed by the Japan '\n 'Aerospace Exploration Agency (JAXA). The '\n 'GCOM-W mission aims to establish the '\n 'global and long-term observation system '\n 'to collect data, which is needed to '\n 'understand mechanisms of climate and '\n 'water cycle variations, and demonstrate '\n 'its utilization. AMSR2 onboard the first '\n 'generation of the GCOM-W satellite will '\n 'continue Aqua/AMSR-E observations of '\n 'water vapor, cloud liquid water, '\n 'precipitation, SST, sea surface wind '\n 'speed, sea ice concentration, snow depth, '\n 'and soil moisture. AMSR2 is a remote '\n 'sensing instrument for measuring weak '\n 'microwave emission from the surface and '\n 'the atmosphere of the Earth. From about '\n '700 km above the Earth, AMSR2 will '\n 'provide us highly accurate measurements '\n 'of the intensity of microwave emission '\n 'and scattering. The antenna of AMSR2 '\n 'rotates once per 1.5 seconds and obtains '\n 'data over a 1450 km swath. This conical '\n 'scan mechanism enables AMSR2 to acquire a '\n 'set of daytime and nighttime data with '\n 'more than 99% coverage of the Earth every '\n '2 days. Remote Sensing Systems (RSS, or '\n 'REMSS), providers of these SST data for '\n 'the Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR2 instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"_rt_\" within the file '\n 'name) which is made as available as soon '\n 'as possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v7.2\" within the file '\n 'name) are processed when RSS receives the '\n 'atmospheric model National Center for '\n 'Environmental Prediction (NCEP) Final '\n 'Analysis (FNL) Operational Global '\n 'Analysis. The NCEP wind directions are '\n 'particularly useful for retrieving more '\n 'accurate SSTs and wind speeds. This '\n 'dataset adheres to the GHRSST Data '\n 'Processing Specification (GDS) version 2 '\n 'format specifications.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'Remote '\n 'Sensing '\n 'Systems, '\n '2017-10-31, '\n 'GHRSST '\n 'Level '\n '2P '\n 'Global '\n 'Subskin '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'version '\n '8a '\n 'from '\n 'the '\n 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2 '\n 'on '\n 'the '\n 'GCOM-W '\n 'satellite, '\n '10.5067/GHAM2-2PR8A, '\n 'http://www.remss.com'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHAM2-2PR8A'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Remote Sensing '\n 'Systems'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2017-09-18T17:57:41.242Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-09-18T17:57:41.242Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P Global Subskin Sea '\n 'Surface Temperature version 8a from the '\n 'Advanced Microwave Scanning Radiometer '\n '2 on the GCOM-W satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-20T20:39:54.386Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 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'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/R/',\n 'URLContentType': 'DistributionURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/IDL/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AMSR2 SSTs: '\n 'algorithm '\n 'description, '\n 'browsing of data, '\n 'and ftp of data',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.remss.com/missions/amsr/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHAM2-2PR8A',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/AMSR2/REMSS/v8a/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Sub-skin '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'AMSR2-REMSS-L2P-v8a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2012-07-02T19:00:44.000Z'}]}],\n 'Version': '8a'}},\n { 'meta': { 'concept-id': 'C1658476016-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+3U+Global+Subskin+Sea+Surface+Temperature+version+8a+from+the+Advanced+Microwave+Scanning+Radiometer+2+on+the+GCOM-W+satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-20T20:39:08Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'GDS2 Version -The Advanced Microwave '\n 'Scanning Radiometer 2 (AMSR2) was '\n 'launched on 18 May 2012, onboard the '\n 'Golbal Change Observation Mission - Water '\n '(GCOM-W) satellite developed by the Japan '\n 'Aerospace Exploration Agency (JAXA). The '\n 'GCOM-W mission aims to establish the '\n 'global and long-term observation system '\n 'to collect data, which is needed to '\n 'understand mechanisms of climate and '\n 'water cycle variations, and demonstrate '\n 'its utilization. AMSR2 onboard the first '\n 'generation of the GCOM-W satellite will '\n 'continue Aqua/AMSR-E observations of '\n 'water vapor, cloud liquid water, '\n 'precipitation, SST, sea surface wind '\n 'speed, sea ice concentration, snow depth, '\n 'and soil moisture. AMSR2 is a remote '\n 'sensing instrument for measuring weak '\n 'microwave emission from the surface and '\n 'the atmosphere of the Earth. From about '\n '700 km above the Earth, AMSR2 will '\n 'provide us highly accurate measurements '\n 'of the intensity of microwave emission '\n 'and scattering. The antenna of AMSR2 '\n 'rotates once per 1.5 seconds and obtains '\n 'data over a 1450 km swath. This conical '\n 'scan mechanism enables AMSR2 to acquire a '\n 'set of daytime and nighttime data with '\n 'more than 99% coverage of the Earth every '\n '2 days. Remote Sensing Systems (RSS, or '\n 'REMSS), providers of these SST data for '\n 'the Group for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR-E instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"rt\" within the file name) '\n 'which is made as available as soon as '\n 'possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v8\" within the file name) '\n 'are processed when RSS receives the '\n 'atmospheric mode NCEP FNL analysis. The '\n 'NCEP wind directions are particularly '\n 'useful for retrieving more accurate SSTs '\n 'and wind speeds. The final \"v8\" products '\n 'will continue to accumulate new swaths '\n '(half orbits) until the maps are full, '\n 'generally within 2 days.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'Remote '\n 'Sensing '\n 'Systems, '\n '2017-11-30, '\n 'GHRSST '\n 'Level '\n '3U '\n 'Global '\n 'Subskin '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'version '\n '8a '\n 'from '\n 'the '\n 'Advanced '\n 'Microwave '\n 'Scanning '\n 'Radiometer '\n '2 '\n 'on '\n 'the '\n 'GCOM-W '\n 'satellite, '\n '10.5067/GHAM2-3UR8A, '\n 'http://www.remss.com'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHAM2-3UR8A'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Remote Sensing '\n 'Systems'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2017-09-08T22:29:02.939Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-09-08T22:29:02.939Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 3U Global Subskin Sea '\n 'Surface Temperature version 8a from the '\n 'Advanced Microwave Scanning Radiometer '\n '2 on the GCOM-W satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-20T20:39:06.338Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 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'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac.jpl.nasa.gov/SeaSurfaceTemperature',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Documentation on '\n 'the GDS version 2 '\n 'format '\n 'specification',\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/ghrsst/docs/GDS20r5.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Full details of the '\n 'AMSR2',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://suzaku.eorc.jaxa.jp/GCOM_W/w_amsr2/whats_amsr2.html',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 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'https://thredds.jpl.nasa.gov/thredds/catalog_ghrsst_gds2.html?dataset=AMSR2-REMSS-L3U-v8a',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Samples, Interface '\n 'Control Document '\n 'describing file '\n 'contents, '\n 'background ppt and '\n 'other info',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.remss.com/missions/amsr/',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHAM2-3UR8A',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L3U/AMSR2/REMSS/v8a/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SUB-SKIN '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AMSR2-REMSS-L3U-v8a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -179.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2012-07-02T23:24:00.000Z'}]}],\n 'Version': '8a'}},\n { 'meta': { 'concept-id': 'C1666605425-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+2P+Global+Subskin+Sea+Surface+Temperature+from+the+Advanced+Scanning+Microwave+Radiometer+-+Earth++Observing+System+(AMSR-E)+on+the+NASA+Aqua+Satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-12-10T18:30:18Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer (AMSR-E) was launched on 4 May '\n \"2002, aboard NASA's Aqua spacecraft. The \"\n 'National Space Development Agency of '\n 'Japan (NASDA) provided AMSR-E to NASA as '\n \"an indispensable part of Aqua's global \"\n 'hydrology mission. Over the oceans, '\n 'AMSR-E is measuring a number of important '\n 'geophysical parameters, including sea '\n 'surface temperature (SST), wind speed, '\n 'atmospheric water vapor, cloud water, and '\n 'rain rate. A key feature of AMSR-E is its '\n 'capability to see through clouds, thereby '\n 'providing an uninterrupted view of global '\n 'SST and surface wind fields. Remote '\n 'Sensing Systems (RSS, or REMSS) is the '\n 'provider of these SST data for the Group '\n 'for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR-E instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"_rt_\" within the file '\n 'name) which is made as available as soon '\n 'as possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v7\" within the file name) '\n 'are processed when RSS receives the '\n 'atmospheric model National Center for '\n 'Environmental Prediction (NCEP) Final '\n 'Analysis (FNL) Operational Global '\n 'Analysis. The NCEP wind directions are '\n 'particularly useful for retrieving more '\n 'accurate SSTs and wind speeds. This '\n 'dataset adheres to the GHRSST Data '\n 'Processing Specification (GDS) version 2 '\n 'format specifications.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'Remote '\n 'Sensing '\n 'Systems, '\n '2014-12-01, '\n 'GHRSST '\n 'Level '\n '2P '\n 'Global '\n 'Subskin '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'from '\n 'the '\n 'Advanced '\n 'Scanning '\n 'Microwave '\n 'Radiometer '\n '- '\n 'Earth '\n 'Observing '\n 'System '\n '(AMSR-E) '\n 'on '\n 'the '\n 'NASA '\n 'Aqua '\n 'Satellite, '\n '10.5067/GHAMS-2GR07, '\n 'http://www.remss.com'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/GHAMS-2GR07'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'Remote Sensing '\n 'Systems'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2014-10-07T22:41:20.867Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-04-28T05:01:45.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 2P Global Subskin Sea '\n 'Surface Temperature from the Advanced '\n 'Scanning Microwave Radiometer - Earth '\n 'Observing System (AMSR-E) on the NASA '\n 'Aqua Satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-12-10T18:30:15.958Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s 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'http://thredds.jpl.nasa.gov/thredds/catalog/ncml_aggregation/OceanTemperature/ghrsst/catalog.html?dataset=ncml_aggregation/OceanTemperature/ghrsst/aggregate__ghrsst_REMSS-L2P_GRIDDED_25-AMSRE.ncml',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'AMSR-E calibration '\n 'description',\n 'Subtype': 'INSTRUMENT/SENSOR '\n 'CALIBRATION '\n 'DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://www.remss.com',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Portal to the '\n 'GHRSST Global Data '\n 'Assembly Center and '\n 'data access',\n 'Type': 'GET DATA',\n 'URL': 'http://ghrsst.jpl.nasa.gov',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'AMSR-E SSTs: '\n 'algorithm '\n 'description, '\n 'browsing of data, '\n 'and ftp of 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INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L2P/AMSRE/REMSS/v7/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SUB-SKIN '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AMSRE-REMSS-L2P-v7a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-01T12:06:00.000Z',\n 'EndingDateTime': '2011-10-04T06:51:45.000Z'}]}],\n 'Version': '7.0'}},\n { 'meta': { 'concept-id': 'C1657548613-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'GHRSST+Level+3U+Global+Subskin+Sea+Surface+Temperature+from+the+Advanced+Scanning+Microwave+Radiometer+-+Earth++Observing+System+(AMSR-E)+on+the+NASA+Aqua+Satellite',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-18T21:27:48Z',\n 'revision-id': 2,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'The Advanced Microwave Scanning '\n 'Radiometer (AMSR-E) was launched on 4 May '\n \"2002, aboard NASA's Aqua spacecraft. The \"\n 'National Space Development Agency of '\n 'Japan (NASDA) provided AMSR-E to NASA as '\n \"an indispensable part of Aqua's global \"\n 'hydrology mission. Over the oceans, '\n 'AMSR-E is measuring a number of important '\n 'geophysical parameters, including sea '\n 'surface temperature (SST), wind speed, '\n 'atmospheric water vapor, cloud water, and '\n 'rain rate. A key feature of AMSR-E is its '\n 'capability to see through clouds, thereby '\n 'providing an uninterrupted view of global '\n 'SST and surface wind fields. Remote '\n 'Sensing Systems (RSS, or REMSS) is the '\n 'provider of these SST data for the Group '\n 'for High Resolution Sea Surface '\n 'Temperature (GHRSST) Project, performs a '\n 'detailed processing of AMSR-E instrument '\n 'data in two stages. The first stage '\n 'produces a near-real-time (NRT) product '\n '(identified by \"_rt_\" within the file '\n 'name) which is made as available as soon '\n 'as possible. This is generally within 3 '\n 'hours of when the data are recorded. '\n 'Although suitable for many timely uses '\n 'the NRT products are not intended to be '\n 'archive quality. \"Final\" data (currently '\n 'identified by \"v7\" within the file name) '\n 'are processed when RSS receives the '\n 'atmospheric model National Center for '\n 'Environmental Prediction (NCEP) Final '\n 'Analysis (FNL) Operational Global '\n 'Analysis. The NCEP wind directions are '\n 'particularly useful for retrieving more '\n 'accurate SSTs and wind speeds. 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'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2017-06-13T22:12:37.481Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-06-13T22:12:37.481Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'GHRSST Level 3U Global Subskin Sea '\n 'Surface Temperature from the Advanced '\n 'Scanning Microwave Radiometer - Earth '\n 'Observing System (AMSR-E) on the NASA '\n 'Aqua Satellite',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-11-18T21:27:45.408Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s 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collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L3U/AMSRE/REMSS/v7a',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://data.nodc.noaa.gov/ghrsst/GDS2/L3U/AMSRE/REMSS/v7a',\n 'URLContentType': 'DistributionURL'},\n { 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'http://aqua.nasa.gov/',\n 'URLContentType': 'PublicationURL'},\n { 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/sw/generic_nc_readers/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 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'Format': 'Not '\n 'provided',\n 'MimeType': 'application/xml',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Subtype': 'DIRECT DOWNLOAD',\n 'Type': 'GET DATA',\n 'URL': 'https://podaac.jpl.nasa.gov/ws/search/granule/?datasetId=PODAAC-GHAMS-3GR7A',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/GHAMS-3GR7A',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/ghrsst/data/GDS2/L3U/AMSRE/REMSS/v7a/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'EARTH SCIENCE',\n 'DetailedVariable': 'SUB-SKIN '\n 'SEA '\n 'SURFACE '\n 'TEMPERATURE',\n 'Term': 'OCEAN TEMPERATURE',\n 'Topic': 'OCEANS',\n 'VariableLevel1': 'SEA SURFACE '\n 'TEMPERATURE'}],\n 'ShortName': 'AMSRE-REMSS-L3U-v7a',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2002-06-01T16:12:00.000Z',\n 'EndingDateTime': '2011-10-04T06:54:00.000Z'}]}],\n 'Version': '7a'}},\n { 'meta': { 'concept-id': 'C1652977738-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats': False,\n 'has-spatial-subsetting': False,\n 'has-temporal-subsetting': False,\n 'has-transforms': False,\n 'has-variables': False,\n 'native-id': 'AMSR-E+Level+3+Sea+Surface+Temperature+for+Climate+Model+Comparison',\n 'provider-id': 'PODAAC',\n 'revision-date': '2019-11-06T18:12:48Z',\n 'revision-id': 1,\n 'user-id': 'cia001'},\n 'umm': { 'Abstract': 'This data set contains sea surface '\n 'temperature (SST) data on a monthly 1 '\n 'degree grid from the Advanced Microwave '\n 'Scanning Radiometer (AMSR-E) aboard '\n \"NASA's Aqua spacecraft. The data were \"\n 'produced by Remote Sensing Systems in '\n 'support of the CMIP5 (Coupled Model '\n 'Intercomparison Project Phase 5) under '\n 'the World Climate Research Program '\n '(WCRP). Along with this dataset, two '\n 'additional ancillary data files are '\n 'included in the same directory which '\n 'contain the number of observations and '\n 'standard error co-located on the same 1 '\n 'degree grids. AMSR-E, a '\n 'passive-microwave radiometer launched on '\n 'the Aqua platform on 4 May 2002, was '\n 'provided by the National Space '\n 'Development Agency (NASDA) of Japan to '\n \"NASA as an indispensable part of Aqua's \"\n 'global hydrology mission. Over the '\n 'oceans, AMSR-E is measuring a number of '\n 'important geophysical parameters, '\n 'including SST, wind speed, atmospheric '\n 'water vapor, cloud water, and rain rate. '\n 'A key feature of AMSR-E is its capability '\n 'to see through clouds, thereby providing '\n 'an uninterrupted view of global SST and '\n 'surface wind fields.',\n 'ArchiveAndDistributionInformation': { 'FileDistributionInformation': [ { 'Format': 'NETCDF',\n 'FormatType': 'Native'}]},\n 'CollectionCitations': [ { 'OtherCitationDetails': 'Remote '\n 'Sensing '\n 'Systems, '\n 'JPL, '\n '2011-03-01, '\n 'AMSR-E '\n 'Level '\n '3 '\n 'Sea '\n 'Surface '\n 'Temperature '\n 'for '\n 'Climate '\n 'Model '\n 'Comparison, '\n '10.5067/SST00-1D1M1, '\n 'https://podaac-tools.jpl.nasa.gov/drive/files/OceanTemperature/amsre/L3/sst_1deg_1mo/docs/tosTechNote_AMSRE_L3_v7_200206-201012.pdf'}],\n 'CollectionProgress': 'NOT PROVIDED',\n 'DOI': {'DOI': '10.5067/SST00-1D1M1'},\n 'DataCenters': [ { 'Roles': ['PROCESSOR'],\n 'ShortName': 'REMSS'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '818-393-6710'},\n { 'Type': 'Email',\n 'Value': 'edward.m.armstrong@jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'Edward',\n 'LastName': 'Armstrong',\n 'MiddleName': 'none',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '001 '\n '707 '\n '545 '\n '2904'},\n { 'Type': 'Email',\n 'Value': 'brewer@remss.com'}]},\n 'ContactPersons': [ { 'FirstName': 'Marty',\n 'LastName': 'Brewer',\n 'MiddleName': 'none',\n 'Roles': [ 'Data '\n 'Center '\n 'Contact']}],\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Remote Sensing '\n 'Systems'}],\n 'DataDates': [ { 'Date': '2012-01-26T22:15:23.132Z',\n 'Type': 'CREATE'},\n { 'Date': 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They are simply the daily '\n 'SSTs from the Reynolds National Climatic '\n 'Data Center (NCDC) 0.25 degree GHRSST '\n 'dataset, gridded and averaged using the '\n 'Aquarius processing L2-L3 processing '\n 'scheme to the same 1 degree spatial '\n 'resolution and daily, 7 day, monthly, '\n 'seasonal, and annual time intervals as '\n 'Aquarius L3 standard salinity and wind '\n 'speed products. 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'NASA/OBPG, Reynolds & '\n 'Smith NOAA/NCDC'},\n { 'Roles': ['ARCHIVER'],\n 'ShortName': 'NASA/JPL/PODAAC'},\n { 'ContactInformation': { 'ContactMechanisms': [ { 'Type': 'Primary',\n 'Value': '(818) '\n '393-7165'},\n { 'Type': 'Email',\n 'Value': 'podaac@podaac.jpl.nasa.gov'}]},\n 'ContactPersons': [ { 'FirstName': 'User',\n 'LastName': 'Services',\n 'MiddleName': 'null',\n 'Roles': [ 'Technical '\n 'Contact']}],\n 'LongName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion Laboratory, '\n 'NASA',\n 'Roles': ['ARCHIVER'],\n 'ShortName': 'Physical Oceanography '\n 'Distributed Active '\n 'Archive Center, Jet '\n 'Propulsion '\n 'Laboratory, N'}],\n 'DataDates': [ { 'Date': '2017-10-21T00:17:01.000Z',\n 'Type': 'CREATE'},\n { 'Date': '2017-12-07T07:31:31.000Z',\n 'Type': 'UPDATE'}],\n 'EntryTitle': 'Aquarius Official Release Level 3 '\n 'Ancillary Reynolds Sea Surface '\n 'Temperature Standard Mapped Image '\n 'Ascending Seasonal Data V5.0',\n 'LocationKeywords': [ { 'Category': 'GEOGRAPHIC REGION',\n 'Type': 'GLOBAL'}],\n 'MetadataDates': [ { 'Date': '2019-10-30T16:57:20.863Z',\n 'Type': 'UPDATE'}],\n 'Platforms': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-11',\n 'ShortName': 'NOAA-11',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 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'Radiometer-2',\n 'ShortName': 'AVHRR-2'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-9',\n 'ShortName': 'NOAA-9',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.1'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '99.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 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'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-17',\n 'ShortName': 'NOAA-17',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '102.12'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '98.74'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '2400.0'}],\n 'LongName': 'Advanced '\n 'Very '\n 'High '\n 'Resolution '\n 'Radiometer-3',\n 'ShortName': 'AVHRR-3'}],\n 'LongName': 'National Oceanic & '\n 'Atmospheric '\n 'Administration-19',\n 'ShortName': 'NOAA-19',\n 'Type': 'SPACECRAFT'},\n { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Nominal '\n 'period '\n 'of '\n 'a '\n 'spacecraft?s '\n 'single '\n 'revolution '\n 'on '\n 'an '\n 'orbital '\n 'plane.',\n 'Name': 'OrbitPeriod',\n 'Unit': 'Minutes',\n 'Value': '-999.0'},\n { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'InclinationAngle',\n 'Unit': 'Degrees',\n 'Value': '-999.0'}],\n 'Instruments': [ { 'Characteristics': [ { 'DataType': 'FLOAT',\n 'Description': 'Spacecraft '\n 'angular '\n 'distance '\n 'from '\n 'orbital '\n 'plane '\n 'relative '\n 'to '\n 'the '\n 'Equator.',\n 'Name': 'SwathWidth',\n 'Unit': 'Meters',\n 'Value': '-999.0'}],\n 'LongName': 'Ships '\n 'and '\n 'Moored '\n 'and '\n 'Drifting 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'National Oceanic & '\n 'Atmospheric '\n 'Administration-7',\n 'ShortName': 'NOAA-7',\n 'Type': 'SPACECRAFT'}],\n 'ProcessingLevel': {'Id': '3'},\n 'Projects': [{'ShortName': 'AQUARIUS SAC-D'}],\n 'RelatedUrls': [ { 'Description': 'The HTTP location '\n 'for the collection.',\n 'GetData': { 'Format': 'Not '\n 'provided',\n 'MimeType': 'text/html',\n 'Size': 0.0,\n 'Unit': 'KB'},\n 'Type': 'GET DATA',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/L3/mapped/V5/3month/SCIA',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'NASA Aquarius/SAC-D '\n 'mission website',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://aquarius.nasa.gov/',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'ATBD, Validation '\n 'Analysis, Product '\n 'Specifications, etc',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/docs/v5/',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Thumbnail image for '\n 'Website',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': '/data/export/web/thumbnails',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'Aquarius Data '\n \"User's Guide\",\n 'Subtype': \"USER'S GUIDE\",\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/docs/v5/AQ-010-UG-0008_AquariusUserGuide_DatasetV5.0.pdf',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'IDL Reader and '\n 'calling routines',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/sw/idl/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Mission and '\n 'Instrument Overview',\n 'Type': 'PROJECT HOME PAGE',\n 'URL': 'https://podaac.jpl.nasa.gov/aquarius',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'Information on '\n 'observatory '\n 'maneuvers, '\n 'anomalies and other '\n 'events',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://oceancolor.gsfc.nasa.gov/sdpscgi/public/aquarius_report.cgi',\n 'URLContentType': 'PublicationURL'},\n { 'Description': 'MATLAB Reader and '\n 'calling routines',\n 'Type': 'DOWNLOAD SOFTWARE',\n 'URL': 'https://podaac-tools.jpl.nasa.gov/drive/files/allData/aquarius/sw/matlab/',\n 'URLContentType': 'DistributionURL'},\n { 'Description': 'Access the data set '\n 'landing page for '\n 'the collection.',\n 'Type': 'DATA SET LANDING PAGE',\n 'URL': 'https://doi.org/10.5067/AQR50-3R3AS',\n 'URLContentType': 'CollectionURL'},\n { 'Description': 'The OPeNDAP base '\n 'directory location '\n 'for the collection.',\n 'Subtype': 'GENERAL DOCUMENTATION',\n 'Type': 'VIEW RELATED INFORMATION',\n 'URL': 'https://podaac-opendap.jpl.nasa.gov/opendap/allData/aquarius/L3/mapped/V5/3month/SCIA/',\n 'URLContentType': 'PublicationURL'}],\n 'ScienceKeywords': [ { 'Category': 'Earth Science',\n 'DetailedVariable': 'Blended '\n 'Sea '\n 'Surface '\n 'Temperature',\n 'Term': 'Ocean Temperature',\n 'Topic': 'Oceans',\n 'VariableLevel1': 'Sea Surface '\n 'Temperature'}],\n 'ShortName': 'AQUARIUS_L3_ANCILLARY_SST_SMIA_3MONTH_V5',\n 'SpatialExtent': { 'GranuleSpatialRepresentation': 'CARTESIAN',\n 'HorizontalSpatialDomain': { 'Geometry': { 'BoundingRectangles': [ { 'EastBoundingCoordinate': 180.0,\n 'NorthBoundingCoordinate': 90.0,\n 'SouthBoundingCoordinate': -90.0,\n 'WestBoundingCoordinate': -180.0}],\n 'CoordinateSystem': 'CARTESIAN'},\n 'ResolutionAndCoordinateSystem': { 'GeodeticModel': { 'DenominatorOfFlatteningRatio': 298.2572236,\n 'EllipsoidName': 'WGS '\n '84',\n 'HorizontalDatumName': 'World '\n 'Geodetic '\n 'System '\n '1984',\n 'SemiMajorAxis': 6378137}}},\n 'SpatialCoverageType': 'HORIZONTAL'},\n 'TemporalExtents': [ { 'EndsAtPresentFlag': False,\n 'RangeDateTimes': [ { 'BeginningDateTime': '2011-08-25T01:55:23.000Z',\n 'EndingDateTime': '2015-06-07T11:41:38.000Z'}]}],\n 'Version': '5.0'}},\n { 'meta': { 'concept-id': 'C1649544930-PODAAC',\n 'concept-type': 'collection',\n 'deleted': False,\n 'format': 'application/echo10+xml',\n 'granule-count': 0,\n 'has-formats':