Merge pull request #16 from NASA-Openscapes/dek_dev

update subsetting and plotting section

README.md

@@ -1,4 +1,4 @@
 # 2023-Cloud-Workshop-AGU
 NASA Openscapes AGU Workshop: Enabling Analysis in the Cloud Using NASA Earth Science Data
 
-Sunday, December 10 from 13:00-16:30. 
+Sunday, December 10 from 13:00-16:30. [Link to workshop entry](https://agu.confex.com/agu/fm23/meetingapp.cgi/Session/193427) in the AGU schedule.

tutorials/Earthdata_Subset_and_Plot.ipynb

@@ -17,20 +17,20 @@
    "source": [
     "## Summary\n",
     "\n",
-    "Welcome to Part 2 of the In-cloud Science Workflow.\n",
+    "Welcome to Part 2 of the In-cloud Science Workflow workshop.\n",
     "\n",
     "In these examples we will use the [xarray](https://xarray.dev/), [earthaccess](https://nsidc.github.io/earthaccess/), and [harmony-py](https://github.com/nasa/harmony-py) libraries to subset data and make figures using `cartopy`, `matplotlib`, and `geoviews`.\n",
     "\n",
-    "We will go through **three examples of subsetting and plotting data in the Earthdata Cloud:** \n",
+    "We will go through **two examples of subsetting and plotting data in the Earthdata Cloud:** \n",
     "\n",
     "1. Example 1 - `earthaccess` and `xarray` for precipitation estimates from [IMERG, Daily Level 3 data](https://doi.org/10.5067/GPM/IMERGDF/DAY/07)\n",
     "2. Example 2 - `harmony-py` for direct cloud subsetting of precipitable water data from the [DSCOVR EPIC Composite](https://doi.org/10.5067/EPIC/DSCOVR/L2_COMPOSITE_01).\n",
     "3. Appendix 1 - Snow cover data from [MODIS/Terra, Daily Level 3 data](https://doi.org/10.5067/MODIS/MOD10C1.061) with `rioxarray`\n",
     "4. Appendix 2 - Snow mass data from [SMAP, 3-hourly Level 4 data](https://doi.org/10.5067/EVKPQZ4AFC4D)\n",
     "    \n",
-    "In the first example, we will be accessing data directly from Amazon Web Services (AWS), specifically in the us-west-2 region, which is where all cloud-hosted NASA Earthdata reside. This shared compute environment (JupyterHub) is also running in the same location. We will then load the data into Python as an `xarray` dataset.\n",
+    "In the first example, we will be accessing data directly from Amazon Web Services (AWS), specifically in the us-west-2 region, which is where all cloud-hosted NASA Earthdata reside. This shared compute environment (JupyterHub) is also running in the same location. We will then load the data into Python as an `xarray` dataset and use `xarray` to subset.\n",
     "\n",
-    "For `harmony-py`, we will demonstrate an example of pulling data via the cloud from an existing on-premise data server.  Both `xarray` and `harmony-py` can be run outside of an AWS as well.\n",
+    "In the second example, we will demonstrate an example of pulling data via the cloud from an existing on-premise data server.  In this example, the data are subsetted using one of the data transformation services provided in the NASA Earthdata system. Both `xarray` and `harmony-py` can be run outside of AWS as well.\n",
     "\n",
     "See the bottom of the notebook for additional resources, including several tutorials that that served as a foundation for this clinic. Includes: https://github.com/rupesh2/atmospheric_rivers/tree/main\n",
     "\n",
@@ -3943,7 +3943,9 @@
    "id": "da982b31-5fad-4202-8b86-09d98e83859b",
    "metadata": {},
    "source": [
-    "While this processes, we can discuss the harmony job in some more detail. (Futher information and examples can be found in the `harmony-py` repository, such as [this introductory notebook](https://github.com/nasa/harmony-py/blob/main/examples/intro_tutorial.ipynb).\n",
+    "While this processes, we can discuss the harmony job in some more detail. First, note that this request is identical to what can be achieved through NASA's Earthdata Search interface, such as this URL: https://search.earthdata.nasa.gov/search/granules?p=C1576365803-LARC_ASDC!C1576365803-LARC_ASDC&pg[1][a]=1576368528!1576368575!LARC_ASDC&pg[1][v]=t&pg[1][gsk]=-start_date&pg[1][m]=harmony0&pg[1][of]=application/x-netcdf4&pg[1][ets]=t&pg[1][ess]=t&q=C1576365803-LARC_ASDC&sb[0]=-123.99609%2C37.19991%2C-120.44531%2C38.78263&qt=2016-02-24T12%3A00%3A00.000Z%2C2016-02-24T23%3A00%3A00.000Z&tl=1702228562!3!!&lat=37.8270894268111&long=-130.67578125&zoom=4 \n",
+    "\n",
+    "(Futher information and examples can be found in the `harmony-py` repository, such as [this introductory notebook](https://github.com/nasa/harmony-py/blob/main/examples/intro_tutorial.ipynb).)\n",
     "\n",
     "#### Request Parameters\n",
     "\n",