diff --git a/.nojekyll b/.nojekyll
index a8dee90e..7edf1b40 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-72398bcf
\ No newline at end of file
+03d0513e
\ No newline at end of file
diff --git a/external/DownloadDopplerScattData.html b/external/DownloadDopplerScattData.html
index f83ff179..f912897b 100644
--- a/external/DownloadDopplerScattData.html
+++ b/external/DownloadDopplerScattData.html
@@ -1111,7 +1111,7 @@ S-MODE Workshop: Science Case Study Airborne Part 1
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop
diff --git a/external/ECCO_cloud_direct_access_s3.html b/external/ECCO_cloud_direct_access_s3.html
index 4286fce4..130b894d 100644
--- a/external/ECCO_cloud_direct_access_s3.html
+++ b/external/ECCO_cloud_direct_access_s3.html
@@ -1120,7 +1120,7 @@ Direct Access to ECCO V4r4 Datasets in the Cloud
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, ECCO.
diff --git a/external/ECCO_download_data.html b/external/ECCO_download_data.html
index 8a0196cd..ddb34b78 100644
--- a/external/ECCO_download_data.html
+++ b/external/ECCO_download_data.html
@@ -1115,7 +1115,7 @@ Access to ECCO V4r4 Datasets on a Local Machine
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, ECCO.
diff --git a/external/Introduction_to_xarray.html b/external/Introduction_to_xarray.html
index 96e9dc49..cc75f86e 100644
--- a/external/Introduction_to_xarray.html
+++ b/external/Introduction_to_xarray.html
@@ -1114,7 +1114,7 @@ Xarray
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from NASA Openscapes 2021 Cloud Hackathon Repository
diff --git a/external/July_2022_Earthdata_Webinar.html b/external/July_2022_Earthdata_Webinar.html
index bb0e4e25..80da02fe 100644
--- a/external/July_2022_Earthdata_Webinar.html
+++ b/external/July_2022_Earthdata_Webinar.html
@@ -1171,7 +1171,7 @@ Earthdata Webinar
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, the-coding-club
diff --git a/external/SWOT_SSH_dashboard.html b/external/SWOT_SSH_dashboard.html
index 3cb5952b..7d1115be 100644
--- a/external/SWOT_SSH_dashboard.html
+++ b/external/SWOT_SSH_dashboard.html
@@ -1131,7 +1131,7 @@ Integrating Dask, Kerchunk, Zarr and Xarray
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, the-coding-club.
diff --git a/external/SWOT_to_kerchunk.html b/external/SWOT_to_kerchunk.html
index 808cf65e..643d47a0 100644
--- a/external/SWOT_to_kerchunk.html
+++ b/external/SWOT_to_kerchunk.html
@@ -1133,7 +1133,7 @@ Kerchunk JSON Generation
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, the-coding-club.
diff --git a/external/VisualizeDopplerScattData.html b/external/VisualizeDopplerScattData.html
index 068bdfbf..d508e0ad 100644
--- a/external/VisualizeDopplerScattData.html
+++ b/external/VisualizeDopplerScattData.html
@@ -1112,7 +1112,7 @@ S-MODE Workshop: Science Case Study Airborne Part 2
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop
diff --git a/external/cof-zarr-reformat.html b/external/cof-zarr-reformat.html
index 3512c291..77c28f88 100644
--- a/external/cof-zarr-reformat.html
+++ b/external/cof-zarr-reformat.html
@@ -1114,7 +1114,7 @@ COF Zarr Access via Reformat
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, ECCO.
diff --git a/external/insitu_dataviz_demo.html b/external/insitu_dataviz_demo.html
index bee350c6..70a9d6e4 100644
--- a/external/insitu_dataviz_demo.html
+++ b/external/insitu_dataviz_demo.html
@@ -1116,7 +1116,7 @@ S-MODE Workshop: Science Case Study In Situ
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop
diff --git a/external/zarr-eosdis-store.html b/external/zarr-eosdis-store.html
index dd24ebc0..3357b8b2 100644
--- a/external/zarr-eosdis-store.html
+++ b/external/zarr-eosdis-store.html
@@ -1105,7 +1105,7 @@ Zarr Example
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from NASA’s Zarr EOSDIS store notebook
diff --git a/external/zarr_access.html b/external/zarr_access.html
index 2e12fa9e..32133ee5 100644
--- a/external/zarr_access.html
+++ b/external/zarr_access.html
@@ -1124,7 +1124,7 @@ Zarr Access for NetCDF4 files
-imported on: 2024-07-29
+imported on: 2024-07-30
This notebook is from NASA Openscapes 2021 Cloud Hackathon Repository
diff --git a/quarto_text/SWOT.html b/quarto_text/SWOT.html
index d5ccd1dd..c09aa0f6 100644
--- a/quarto_text/SWOT.html
+++ b/quarto_text/SWOT.html
@@ -1271,8 +1271,8 @@
-
-SWOT Pixel Cloud (PIXC) Area Aggregration locally
+
+SWOT Pixel Cloud (PIXC) Area Aggregation locally
SWOT Pixel Cloud (PIXC) Phase Unwrapping Error Fix locally
diff --git a/search.json b/search.json
index e5e71937..fe17985b 100644
--- a/search.json
+++ b/search.json
@@ -2669,7 +2669,7 @@
"href": "external/DownloadDopplerScattData.html",
"title": "S-MODE Workshop: Science Case Study Airborne Part 1",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop",
+ "text": "imported on: 2024-07-30\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop",
"crumbs": [
"Tutorials",
"Dataset Specific",
@@ -2807,7 +2807,7 @@
"href": "external/July_2022_Earthdata_Webinar.html",
"title": "Earthdata Webinar",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club",
+ "text": "imported on: 2024-07-30\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club",
"crumbs": [
"Webinars",
"July 2022 Earthdata Webinar Notebook"
@@ -2972,7 +2972,7 @@
"href": "external/SWOT_to_kerchunk.html",
"title": "Kerchunk JSON Generation",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club.",
+ "text": "imported on: 2024-07-30\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club.",
"crumbs": [
"Advanced Cloud",
"Kerchunk"
@@ -3159,7 +3159,7 @@
"href": "external/SWOT_SSH_dashboard.html",
"title": "Integrating Dask, Kerchunk, Zarr and Xarray",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club.",
+ "text": "imported on: 2024-07-30\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club.",
"crumbs": [
"Advanced Cloud",
"Dask, Kerchunk, & Zarr"
@@ -3357,7 +3357,7 @@
"href": "external/cof-zarr-reformat.html",
"title": "COF Zarr Access via Reformat",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.",
+ "text": "imported on: 2024-07-30\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.",
"crumbs": [
"Tutorials",
"Cloud Optimized Examples",
@@ -3381,7 +3381,7 @@
"href": "external/VisualizeDopplerScattData.html",
"title": "S-MODE Workshop: Science Case Study Airborne Part 2",
"section": "",
- "text": "imported on: 2024-07-29\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')",
+ "text": "imported on: 2024-07-30\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')",
"crumbs": [
"Tutorials",
"Dataset Specific",
@@ -3407,7 +3407,7 @@
"href": "external/Introduction_to_xarray.html",
"title": "Xarray",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
+ "text": "imported on: 2024-07-30\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
},
{
"objectID": "external/Introduction_to_xarray.html#why-do-we-need-xarray",
@@ -3555,7 +3555,7 @@
"href": "external/insitu_dataviz_demo.html",
"title": "S-MODE Workshop: Science Case Study In Situ",
"section": "",
- "text": "imported on: 2024-07-29\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",
+ "text": "imported on: 2024-07-30\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",
"crumbs": [
"Tutorials",
"Dataset Specific",
@@ -3581,7 +3581,7 @@
"href": "external/zarr-eosdis-store.html",
"title": "Zarr Example",
"section": "",
- "text": "imported on: 2024-07-29\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\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",
+ "text": "imported on: 2024-07-30\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\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",
"crumbs": [
"Tutorials",
"Cloud Optimized Examples",
@@ -3593,7 +3593,7 @@
"href": "external/ECCO_download_data.html",
"title": "Access to ECCO V4r4 Datasets on a Local Machine",
"section": "",
- "text": "imported on: 2024-07-29\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.",
+ "text": "imported on: 2024-07-30\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.",
"crumbs": [
"Tutorials",
"Dataset Specific",
@@ -3631,7 +3631,7 @@
"href": "external/ECCO_cloud_direct_access_s3.html",
"title": "Direct Access to ECCO V4r4 Datasets in the Cloud",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.",
+ "text": "imported on: 2024-07-30\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.",
"crumbs": [
"Tutorials",
"Dataset Specific",
@@ -3744,7 +3744,7 @@
"href": "external/zarr_access.html",
"title": "Zarr Access for NetCDF4 files",
"section": "",
- "text": "imported on: 2024-07-29\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository",
+ "text": "imported on: 2024-07-30\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository",
"crumbs": [
"Tutorials",
"Cloud Optimized Examples",
@@ -6465,7 +6465,7 @@
"href": "quarto_text/SWOT.html#swot-data-resources-tutorials",
"title": "SWOT",
"section": "SWOT Data Resources & Tutorials",
- "text": "SWOT Data Resources & Tutorials\n\nSearch & Download\n\nVia Graphical User Interface:\n\nFind/download SWOT data on Earthdata Search\n\n\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\n\n\nVia Command Line - PO.DAAC subscriber/downloader examples:\nHydrology: These examples will download either the river vector files or the raster files for February 2024:\npodaac-data-downloader -c SWOT_L2_HR_RiverSP_2.0 -d ./SWOT_L2_HR_RiverSP_2.0/ --start-date 2024-02-01T00:00:00Z --end-date 2024-02-29T23:59:59Z\nThis only downloads 1 hours worth of data for the globe:\npodaac-data-downloader -c SWOT_L2_HR_Raster_2.0 -d ./SWOT_L2_HR_Raster_2.0/ --start-date 2024-02-01T00:00:00Z --end-date 2024-02-29T00:59:59Z\nOceanography: These examples will download modeled sea surface heights for the whole SSH collection and then the anomalies using the subscriber then downloader and finally, subset the data by bounding box:\npodaac-data-subscriber -c SWOT_L2_LR_SSH_2.0 -d ./SWOT_L2_LR_SSH_2.0/ --start-date 2023-03-29T00:00:00Z \npodaac-data-subscriber -c SWOT_L2_NALT_OGDR_SSHA_2.0 -d ./data/SWOT_L2_NALT_OGDR_SSHA_2.0 --start-date 2023-08-01T00:00:00Z --end-date 2023-08-02T00:00:00Z\npodaac-data-downloader -c SWOT_L2_NALT_OGDR_SSHA_2.0 -d ./data/SWOT_L2_NALT_OGDR_SSHA_2.0 --start-date 2023-06-23T00:00:00Z --end-date 2023-06-23T06:00:00Z\npodaac-data-downloader -c SWOT_L2_LR_SSH_Basic_2.0 -d ./data -sd 2023-11-25T00:00:00Z -ed 2023-12-15T00:00:00Z -b=\"-22.0,-27,6.5,0\" --subset\n\nSee how to Download/Subscribe for more information on how to use the PO.DAAC subscriber/downloader including with spatial queries.\n\n\n\nSearch SWOT Passes over Time\nCNES developed this dedicated visualization tool for a quick look at where SWOT has been, where it is, and where it will be. Once you have selected the area of interest, click the Search button to search for SWOT passes. The results are displayed in a table and the swaths that intersect the area of interest are displayed on the map. Click on the marker to view the pass number.\nTo launch the Binder application, click on this link.\nTo launch jupyterlab in Binder, clink on this link.\nNote: The Binder versions of this are for casual quick looks, but we recommend for extensive use to access the Jupyter Notebook directly here.\n\n\nSWOT Spatial Coverage\nTo identify spatial coverage/search terms for the science 21-day orbit, PO.DAAC has created a KMZ file that has layers of the SWOT passes and tiles, with corresponding scene numbers identified in the pop-up when a location is selected (see screenshot below). Each layer has direct links to Earthdata Search results (the ‘search’ links) for corresponding files. The passes layer has useful information for all SWOT products, but links to the LR products specifically, the tiles layer is useful for HR products (L1B_HR_SLC, L2_HR_PIXC, and L2_HR_PIXCVec products use tile spatial extents while the L2_HR_Raster product uses scenes. L2_HR_RiverSP and L2_HR_LakeSp use continent-level passes).\nTo download the KMZ file, for the science 21-day orbit, click here.\nFor the Beta Pre-validated data KMZ that used the cal/val 1-day orbit, click here.\nThese files can be opened in the Google Earth desktop application and viewed like the following:\n\n\n\n\n\nScreenshot of pass and tile layer in spatial coverage KMZ file viewed in the Google Earth Desktop application\n\n\n\nThe KaRIN HR Masks true/false text pop up for tiles comes from the two different masks used for different parts of the year. The ‘Seasonal’ mask is used from Dec 1st to March 1st and removes part of the Canadian archipelago coverage to collect additional data over sea ice instead, indicated by true/false statements.\n\n\nTips for SWOT Spatial Search\nTo support spatial search of SWOT data products, the following naming conventions may be of help. Tip: use these shortname identifiers below when searching for SWOT data in the NASA Earthdata Search portal or programmatically using the CMR API and/or earthaccess python library.\nSWOT HR data products use Tiles, Scenes, or Continent-level Swaths IDs depending on the product, which define the spatial extent of what is in each file, as follows in the chart below. Along-track scene and tile numbers are numbered sequentially following the spacecraft flight direction, so the numbers increase from south to north for ascending passes and from north to south for descending passes. SWOT LR products use global swaths and nadir tracks that use pass numbers. See SWOT Spatial Coverage Section above for information to find the pass, tile or scene numbers.\n\n\n\n\n\n\n\n\n\n\nProduct (organized by…)\nFile Naming Convention\nNotes\n\n\n\n\n\n\nL2_HR_RiverSP L2_HR_LakeSP (continent-level swaths)\nPPP_CC\nPPP = pass number (valid range: 001-584) CC = continent code (options listed below) AF - Africa EU - Europe and Middle East SI - Siberia AS - Central and Southeast Asia AU - Australia and Oceania SA - South America NA - North America and Caribbean AR - North American Arctic GR - Greenland Ex: 013_NA = pass 013, North America\n\n\n\n\nL2_HR_PIXC L2_HR_PIXCVec L1B_HR_SLC (tiles)\nPPP_TTTC\nPPP = pass number (valid range: 001-584) TTT = tile number (valid range: 001-308) C = character L or R corresponding to left or right swaths Ex: 001_120R = pass 001, right swath, tile 120\n\n\n\n\nL2_HR_Raster (scenes)\nPPP_SSS\nPPP = pass number (valid range: 001-584) SSS = scene number (valid range: 001-154) Scenes correspond to 2 x 2 sets of tiles scene number x 2 = tile number Ex: 001_060 = pass 001, scene 60, corresponding to the same location as the PIXC/PIXCVec tile example above.\n\n\n\n\nL2_RAD_(O/I)GDR L2_NALT_(O/I)GDR(nadir) L2_LR_SSH (swath)\nPPP_\nPPP = pass number (valid range: 001-584) Ex: 013_ = pass 013\n\n\n\n\n\nIn Earthdata Search GUI:\n\nUse the top left Search Box and search with keywords, e.g. SWOT L2 HR\nSelect a collection of interest\nA Filter Granule filtering capability will show up on the left hand side of the GUI. Recall naming convention is _cycle_pass_spatialIdentifier_.\n\nUse wildcards to narrow down spatially, using one of the codes from the table above depending on your use case. Tip: use underscores ( _ ) with your wildcard key words for a more specific search.\nExample: *_NA_* will filter the RiverSP or LakeSP collection selected to only return those granules (files) that are part of the North America collection\nExample: *_004_256_* will filter the RiverSP or LakeSP collection selected to only return those granules (files) that correspond to cycle 004, pass 256\nExample: *_004_253_128* will filter the Raster collection selected to only return those granules (files) that correspond to cycle 004, pass 253, scene 128\n\nIn addition, you can also draw a region of interest (ROI) on the map, using the Spatial Search Filter icon or the Advanced Search under the main search box. These will help to filter what is returned for the spatial search. Tip: It is recommended that ROI searches are used together with wildcards described above for a more accurate search.\n\n\n\n\nAccess & Visualization\n\n\n\n\n\nBasic Access SWOT Hydrology data in the cloud | locally\n\n\nBasic Access SWOT Oceanography data in the cloud | locally\n\n\nSWOT Raster Multifile Access & Quality Flag Application in the cloud | locally\n\n\nHydrocron: Time series API Multi-reach tutorial - See Hydrocron documentation and more description under tools below. \n\n\nSWOT Pixel Cloud (PIXC) Area Aggregration locally\n\n\nSWOT Pixel Cloud (PIXC) Phase Unwrapping Error Fix locally\n\n\nQuality Flag Tutorial - Quality Flag Tips for all products, specifically demonstrates SSHA 8-bit quality flag application\n\n\n\nData Story\n\nSWOT Hydrology Science Workflow in the Cloud or on a local machine - Retrieving SWOT attributes (WSE, width, slope) and plotting a longitudinal profile along a river or over a basin\n\n\n\nGIS workflows\n\nSWOT: Through a GIS Lens StoryMap\n\n\nShapefile exploration\n\n\nTransform SWOT Datetime field for use in GIS Software\n\n\n\nTransform\n\nHiTIDE subsetter for Sea Surface Height Products - select KaRIn instrument in sensors, see video tutorial here\n\n\nHydrocron: Time series API - Currently for rivers, see Hydrocron documentation and more description under tools below. \n\n\nTransform SWOT Hydrology lake shapefiles into time series - work around for lake time series while Hydrocron is under development to include lakes.\n\n\nNetCDF to Geotiff Conversion - mac or Linux | Windows\n\n\n\nTools\nHydrocron - an API that repackages the river shapefile dataset (L2_HR_RiverSP) into csv or GeoJSON formats that make time-series analysis easier. SWOT data is archived as individually timestamped shapefiles, which would otherwise require users to perform potentially thousands of file operations per river feature to view the data as a timeseries. Hydrocron makes this possible with a single API call.\nSWODLR - a system for generating on demand raster products from SWOT L2 raster data with custom resolutions, projections, and extents. -in development\nHiTIDE subsetter for Sea Surface Height Products - select KaRIn instrument in sensors, see video tutorial here\n\n\nSWORD of Science\nThe SWORD of Science (SoS) is a community-driven dataset produced for and from the execution of the Confluence workflow which is a cloud-based workflow that executes on SWOT observations to produce river discharge parameter estimates. Data granules contain two files, priors and results. The priors file contains prior information, such as in-situ gauge data and model output that is used to generate the discharge products. The results file contains the resulting river discharge data products.\n\nExplore river discharge\nExplore river discharge with gauge data\nPlot ALL river discharge algorithms\nVisualize river discharge",
+ "text": "SWOT Data Resources & Tutorials\n\nSearch & Download\n\nVia Graphical User Interface:\n\nFind/download SWOT data on Earthdata Search\n\n\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\n\n\nVia Command Line - PO.DAAC subscriber/downloader examples:\nHydrology: These examples will download either the river vector files or the raster files for February 2024:\npodaac-data-downloader -c SWOT_L2_HR_RiverSP_2.0 -d ./SWOT_L2_HR_RiverSP_2.0/ --start-date 2024-02-01T00:00:00Z --end-date 2024-02-29T23:59:59Z\nThis only downloads 1 hours worth of data for the globe:\npodaac-data-downloader -c SWOT_L2_HR_Raster_2.0 -d ./SWOT_L2_HR_Raster_2.0/ --start-date 2024-02-01T00:00:00Z --end-date 2024-02-29T00:59:59Z\nOceanography: These examples will download modeled sea surface heights for the whole SSH collection and then the anomalies using the subscriber then downloader and finally, subset the data by bounding box:\npodaac-data-subscriber -c SWOT_L2_LR_SSH_2.0 -d ./SWOT_L2_LR_SSH_2.0/ --start-date 2023-03-29T00:00:00Z \npodaac-data-subscriber -c SWOT_L2_NALT_OGDR_SSHA_2.0 -d ./data/SWOT_L2_NALT_OGDR_SSHA_2.0 --start-date 2023-08-01T00:00:00Z --end-date 2023-08-02T00:00:00Z\npodaac-data-downloader -c SWOT_L2_NALT_OGDR_SSHA_2.0 -d ./data/SWOT_L2_NALT_OGDR_SSHA_2.0 --start-date 2023-06-23T00:00:00Z --end-date 2023-06-23T06:00:00Z\npodaac-data-downloader -c SWOT_L2_LR_SSH_Basic_2.0 -d ./data -sd 2023-11-25T00:00:00Z -ed 2023-12-15T00:00:00Z -b=\"-22.0,-27,6.5,0\" --subset\n\nSee how to Download/Subscribe for more information on how to use the PO.DAAC subscriber/downloader including with spatial queries.\n\n\n\nSearch SWOT Passes over Time\nCNES developed this dedicated visualization tool for a quick look at where SWOT has been, where it is, and where it will be. Once you have selected the area of interest, click the Search button to search for SWOT passes. The results are displayed in a table and the swaths that intersect the area of interest are displayed on the map. Click on the marker to view the pass number.\nTo launch the Binder application, click on this link.\nTo launch jupyterlab in Binder, clink on this link.\nNote: The Binder versions of this are for casual quick looks, but we recommend for extensive use to access the Jupyter Notebook directly here.\n\n\nSWOT Spatial Coverage\nTo identify spatial coverage/search terms for the science 21-day orbit, PO.DAAC has created a KMZ file that has layers of the SWOT passes and tiles, with corresponding scene numbers identified in the pop-up when a location is selected (see screenshot below). Each layer has direct links to Earthdata Search results (the ‘search’ links) for corresponding files. The passes layer has useful information for all SWOT products, but links to the LR products specifically, the tiles layer is useful for HR products (L1B_HR_SLC, L2_HR_PIXC, and L2_HR_PIXCVec products use tile spatial extents while the L2_HR_Raster product uses scenes. L2_HR_RiverSP and L2_HR_LakeSp use continent-level passes).\nTo download the KMZ file, for the science 21-day orbit, click here.\nFor the Beta Pre-validated data KMZ that used the cal/val 1-day orbit, click here.\nThese files can be opened in the Google Earth desktop application and viewed like the following:\n\n\n\n\n\nScreenshot of pass and tile layer in spatial coverage KMZ file viewed in the Google Earth Desktop application\n\n\n\nThe KaRIN HR Masks true/false text pop up for tiles comes from the two different masks used for different parts of the year. The ‘Seasonal’ mask is used from Dec 1st to March 1st and removes part of the Canadian archipelago coverage to collect additional data over sea ice instead, indicated by true/false statements.\n\n\nTips for SWOT Spatial Search\nTo support spatial search of SWOT data products, the following naming conventions may be of help. Tip: use these shortname identifiers below when searching for SWOT data in the NASA Earthdata Search portal or programmatically using the CMR API and/or earthaccess python library.\nSWOT HR data products use Tiles, Scenes, or Continent-level Swaths IDs depending on the product, which define the spatial extent of what is in each file, as follows in the chart below. Along-track scene and tile numbers are numbered sequentially following the spacecraft flight direction, so the numbers increase from south to north for ascending passes and from north to south for descending passes. SWOT LR products use global swaths and nadir tracks that use pass numbers. See SWOT Spatial Coverage Section above for information to find the pass, tile or scene numbers.\n\n\n\n\n\n\n\n\n\n\nProduct (organized by…)\nFile Naming Convention\nNotes\n\n\n\n\n\n\nL2_HR_RiverSP L2_HR_LakeSP (continent-level swaths)\nPPP_CC\nPPP = pass number (valid range: 001-584) CC = continent code (options listed below) AF - Africa EU - Europe and Middle East SI - Siberia AS - Central and Southeast Asia AU - Australia and Oceania SA - South America NA - North America and Caribbean AR - North American Arctic GR - Greenland Ex: 013_NA = pass 013, North America\n\n\n\n\nL2_HR_PIXC L2_HR_PIXCVec L1B_HR_SLC (tiles)\nPPP_TTTC\nPPP = pass number (valid range: 001-584) TTT = tile number (valid range: 001-308) C = character L or R corresponding to left or right swaths Ex: 001_120R = pass 001, right swath, tile 120\n\n\n\n\nL2_HR_Raster (scenes)\nPPP_SSS\nPPP = pass number (valid range: 001-584) SSS = scene number (valid range: 001-154) Scenes correspond to 2 x 2 sets of tiles scene number x 2 = tile number Ex: 001_060 = pass 001, scene 60, corresponding to the same location as the PIXC/PIXCVec tile example above.\n\n\n\n\nL2_RAD_(O/I)GDR L2_NALT_(O/I)GDR(nadir) L2_LR_SSH (swath)\nPPP_\nPPP = pass number (valid range: 001-584) Ex: 013_ = pass 013\n\n\n\n\n\nIn Earthdata Search GUI:\n\nUse the top left Search Box and search with keywords, e.g. SWOT L2 HR\nSelect a collection of interest\nA Filter Granule filtering capability will show up on the left hand side of the GUI. Recall naming convention is _cycle_pass_spatialIdentifier_.\n\nUse wildcards to narrow down spatially, using one of the codes from the table above depending on your use case. Tip: use underscores ( _ ) with your wildcard key words for a more specific search.\nExample: *_NA_* will filter the RiverSP or LakeSP collection selected to only return those granules (files) that are part of the North America collection\nExample: *_004_256_* will filter the RiverSP or LakeSP collection selected to only return those granules (files) that correspond to cycle 004, pass 256\nExample: *_004_253_128* will filter the Raster collection selected to only return those granules (files) that correspond to cycle 004, pass 253, scene 128\n\nIn addition, you can also draw a region of interest (ROI) on the map, using the Spatial Search Filter icon or the Advanced Search under the main search box. These will help to filter what is returned for the spatial search. Tip: It is recommended that ROI searches are used together with wildcards described above for a more accurate search.\n\n\n\n\nAccess & Visualization\n\n\n\n\n\nBasic Access SWOT Hydrology data in the cloud | locally\n\n\nBasic Access SWOT Oceanography data in the cloud | locally\n\n\nSWOT Raster Multifile Access & Quality Flag Application in the cloud | locally\n\n\nHydrocron: Time series API Multi-reach tutorial - See Hydrocron documentation and more description under tools below. \n\n\nSWOT Pixel Cloud (PIXC) Area Aggregation locally\n\n\nSWOT Pixel Cloud (PIXC) Phase Unwrapping Error Fix locally\n\n\nQuality Flag Tutorial - Quality Flag Tips for all products, specifically demonstrates SSHA 8-bit quality flag application\n\n\n\nData Story\n\nSWOT Hydrology Science Workflow in the Cloud or on a local machine - Retrieving SWOT attributes (WSE, width, slope) and plotting a longitudinal profile along a river or over a basin\n\n\n\nGIS workflows\n\nSWOT: Through a GIS Lens StoryMap\n\n\nShapefile exploration\n\n\nTransform SWOT Datetime field for use in GIS Software\n\n\n\nTransform\n\nHiTIDE subsetter for Sea Surface Height Products - select KaRIn instrument in sensors, see video tutorial here\n\n\nHydrocron: Time series API - Currently for rivers, see Hydrocron documentation and more description under tools below. \n\n\nTransform SWOT Hydrology lake shapefiles into time series - work around for lake time series while Hydrocron is under development to include lakes.\n\n\nNetCDF to Geotiff Conversion - mac or Linux | Windows\n\n\n\nTools\nHydrocron - an API that repackages the river shapefile dataset (L2_HR_RiverSP) into csv or GeoJSON formats that make time-series analysis easier. SWOT data is archived as individually timestamped shapefiles, which would otherwise require users to perform potentially thousands of file operations per river feature to view the data as a timeseries. Hydrocron makes this possible with a single API call.\nSWODLR - a system for generating on demand raster products from SWOT L2 raster data with custom resolutions, projections, and extents. -in development\nHiTIDE subsetter for Sea Surface Height Products - select KaRIn instrument in sensors, see video tutorial here\n\n\nSWORD of Science\nThe SWORD of Science (SoS) is a community-driven dataset produced for and from the execution of the Confluence workflow which is a cloud-based workflow that executes on SWOT observations to produce river discharge parameter estimates. Data granules contain two files, priors and results. The priors file contains prior information, such as in-situ gauge data and model output that is used to generate the discharge products. The results file contains the resulting river discharge data products.\n\nExplore river discharge\nExplore river discharge with gauge data\nPlot ALL river discharge algorithms\nVisualize river discharge",
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diff --git a/sitemap.xml b/sitemap.xml
index f23817c5..821c8f91 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,478 +2,478 @@
https://podaac.github.io/tutorials/quarto_text/SMAP.html
- 2024-07-29T23:16:03.982Z
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https://podaac.github.io/tutorials/quarto_text/Questions.html
- 2024-07-29T23:16:03.982Z
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https://podaac.github.io/tutorials/quarto_text/DataSubscriberDownloader.html
- 2024-07-29T23:16:03.982Z
+ 2024-07-30T20:04:06.363Z
https://podaac.github.io/tutorials/quarto_text/CloudvsLocalWorkflows.html
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https://podaac.github.io/tutorials/notebooks/l2-regridding/reprojection notebook.html
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https://podaac.github.io/tutorials/notebooks/aws_lambda_sst/sst-global-mean-exploratory.html
- 2024-07-29T23:16:03.558Z
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https://podaac.github.io/tutorials/notebooks/opendap/MUR-OPeNDAP.html
- 2024-07-29T23:16:03.974Z
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https://podaac.github.io/tutorials/notebooks/Cloud L2SS subset and plot - JH.html
- 2024-07-29T23:16:03.362Z
+ 2024-07-30T20:04:05.747Z
https://podaac.github.io/tutorials/notebooks/harmony_concatenation/Harmony_Subsetting_Concatenation.html
- 2024-07-29T23:16:03.886Z
+ 2024-07-30T20:04:06.267Z
https://podaac.github.io/tutorials/notebooks/PODAAC_CMR_Shapefile_Search_MODIS_UAT.html
- 2024-07-29T23:16:03.542Z
+ 2024-07-30T20:04:05.923Z
https://podaac.github.io/tutorials/notebooks/DataStories/eof_example_ersst.html
- 2024-07-29T23:16:03.506Z
+ 2024-07-30T20:04:05.891Z
https://podaac.github.io/tutorials/notebooks/s3/S3-Access.html
- 2024-07-29T23:16:03.978Z
+ 2024-07-30T20:04:06.359Z
https://podaac.github.io/tutorials/notebooks/harmony subsetting/shapefile_subset.html
- 2024-07-29T23:16:03.882Z
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https://podaac.github.io/tutorials/notebooks/batch_download_podaac_data.html
- 2024-07-29T23:16:03.558Z
+ 2024-07-30T20:04:05.943Z
https://podaac.github.io/tutorials/notebooks/GIS/MUR_SSTA_QGIS.html
- 2024-07-29T23:16:03.506Z
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https://podaac.github.io/tutorials/notebooks/GIS/Subscriber_nc_to_tif_SWOT.html
- 2024-07-29T23:16:03.538Z
+ 2024-07-30T20:04:05.923Z
https://podaac.github.io/tutorials/notebooks/GIS/SWOT_GISshapefiles.html
- 2024-07-29T23:16:03.538Z
+ 2024-07-30T20:04:05.923Z
https://podaac.github.io/tutorials/notebooks/meetings_workshops/swot_ea_hackweek_2022/River_Heights_in_the_Cloud.html
- 2024-07-29T23:16:03.946Z
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https://podaac.github.io/tutorials/notebooks/meetings_workshops/swot_ea_hackweek_2022/HLS-WaterDetection-Cloud.html
- 2024-07-29T23:16:03.894Z
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https://podaac.github.io/tutorials/notebooks/meetings_workshops/workshop_osm_2022/S6_OPeNDAP_Access_Gridding.html
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https://podaac.github.io/tutorials/notebooks/meetings_workshops/workshop_osm_2022/ECCO_ssh_sst_corr.html
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https://podaac.github.io/tutorials/notebooks/meetings_workshops/arctic_2019.html
- 2024-07-29T23:16:03.894Z
+ 2024-07-30T20:04:06.271Z
https://podaac.github.io/tutorials/notebooks/SWOT-EA-2021/Estuary_explore_inCloud_zarr.html
- 2024-07-29T23:16:03.550Z
+ 2024-07-30T20:04:05.935Z
https://podaac.github.io/tutorials/notebooks/SearchDownload_SWOTviaCMR.html
- 2024-07-29T23:16:03.550Z
+ 2024-07-30T20:04:05.935Z
https://podaac.github.io/tutorials/notebooks/datasets/SWOT_quality_flag_demo.html
- 2024-07-29T23:16:03.842Z
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https://podaac.github.io/tutorials/notebooks/datasets/SWOT_PIXC_Area_localmachine.html
- 2024-07-29T23:16:03.670Z
+ 2024-07-30T20:04:06.051Z
https://podaac.github.io/tutorials/notebooks/datasets/SWOTHR_localmachine.html
- 2024-07-29T23:16:03.626Z
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https://podaac.github.io/tutorials/notebooks/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_gauges_plot_all.html
- 2024-07-29T23:16:03.662Z
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https://podaac.github.io/tutorials/notebooks/datasets/enso_MUR_tutorial_final.html
- 2024-07-29T23:16:03.854Z
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https://podaac.github.io/tutorials/notebooks/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_gauges.html
- 2024-07-29T23:16:03.658Z
+ 2024-07-30T20:04:06.043Z
https://podaac.github.io/tutorials/notebooks/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_visualize.html
- 2024-07-29T23:16:03.666Z
+ 2024-07-30T20:04:06.047Z
https://podaac.github.io/tutorials/notebooks/datasets/SWOT_Raster_Notebook_cloud.html
- 2024-07-29T23:16:03.790Z
+ 2024-07-30T20:04:06.171Z
https://podaac.github.io/tutorials/notebooks/datasets/SWOT_PIXC_PhaseUnwrap_localmachine.html
- 2024-07-29T23:16:03.690Z
+ 2024-07-30T20:04:06.075Z
https://podaac.github.io/tutorials/notebooks/datasets/smap_imerg_tutorial.html
- 2024-07-29T23:16:03.878Z
+ 2024-07-30T20:04:06.255Z
https://podaac.github.io/tutorials/notebooks/sentinel-6/Access_Sentinel6_NRT.html
- 2024-07-29T23:16:03.982Z
+ 2024-07-30T20:04:06.359Z
https://podaac.github.io/tutorials/notebooks/sentinel-6/S6_Cloud_Notebook.html
- 2024-07-29T23:16:03.982Z
+ 2024-07-30T20:04:06.363Z
https://podaac.github.io/tutorials/notebooks/Advanced_cloud/basic_dask.html
- 2024-07-29T23:16:03.342Z
+ 2024-07-30T20:04:05.727Z
https://podaac.github.io/tutorials/notebooks/Advanced_cloud/dask_delayed_01.html
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