readme updates in prep for new is2-cs2 tutorial

Author: asteiker

README.md

@@ -45,12 +45,22 @@ The Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) proj
 
 The notebooks on this project demonstrate how to search and access ITS_LIVE velocity pairs and provide a simple example on how to build a data cube.
 
-### [ICESat-2_Cloud_Access](./notebooks/ICESat-2_Cloud_Access)
+### [ICESat-2_Cloud_Access Notebooks](./notebooks/ICESat-2_Cloud_Access)
+
+These notebooks demonstrate how to search and access ICESat-2 from the NASA Earthdata Cloud:
 
 **Accessing and working with ICESat-2 Data in the Cloud**
 
 Originally presented to the UWG (User Working Group) in May 2022, this tutorial demonstrates how to search for ICESat-2 data hosted in the Earthdata Cloud and how to directly access it from an Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instance using the `earthaccess` package.
 
+**UPDATE: IS2-CS2 NOTEBOOK TITLE**
+
+UPDATE: Description
+
+**Processing Large-scale Time Series of ICESat-2 Sea Ice Height in the Cloud**
+
+This notebook utilizes several libraries to performantly search, access, read, and grid ATL10 data over the Ross Sea, Antarctica including `earthaccess`, `h5coro`, and `geopandas`. The notebook provides further guidance on how to scale this analysis to the entire continent, running the same workflow from a script that can be run from your laptop using [Coiled](https://www.coiled.io/).
+
 ### [MEaSUREs](./notebooks/measures)
 
 **Download, crop, resample, and plot multiple GeoTIFFs**

notebooks/ICESat-2_Cloud_Access/README.md

@@ -1,24 +1,49 @@
 # ICESat-2 Cloud Access 
 
 ## Summary
-This notebook demonstrates searching for cloud-hosted ICESat-2 data and directly accessing Land Ice Height (ATL06) granules from an Amazon Compute Cloud (EC2) instance using the `earthaccess` package. NASA data "in the cloud" are stored in Amazon Web Services (AWS) Simple Storage Service (S3) Buckets. **Direct Access** is an efficient way to work with data stored in an S3 Bucket when you are working in the cloud. Cloud-hosted granules can be opened and loaded into memory without the need to download them first. This allows you take advantage of the scalability and power of cloud computing.
+We provide several notebooks showcasing how to search and access ICESat-2 from the NASA Earthdata Cloud. NASA data "in the cloud" are stored in Amazon Web Services (AWS) Simple Storage Service (S3) Buckets. **Direct Access** is an efficient way to work with data stored in an S3 Bucket when you are working in the cloud. Cloud-hosted granules can be opened and loaded into memory without the need to download them first. This allows you take advantage of the scalability and power of cloud computing.
+
+### [Accessing and working with ICESat-2 data in the cloud](./ATL06-direct-access_rendered.ipynb)
+This notebook demonstrates searching for cloud-hosted ICESat-2 data and directly accessing Land Ice Height (ATL06) granules from an Amazon Compute Cloud (EC2) instance using the `earthaccess` package. 
+
+#### Key Learning Objectives 
+1. Use `earthaccess` to search for ICESat-2 data using spatial and temporal filters and explore search results;
+2. Open data granules using direct access to the ICESat-2 S3 bucket;
+3. Load a HDF5 group into an `xarray.Dataset`;
+4. Visualize the land ice heights using `hvplot`.
+
+### [UPDATE:IS2-CS2](./UPDATE-NOTEBOOK-FILENAME.ipynb)
+UPDATE: This notebook demonstrates...
+
+#### Key Learning Objectives 
+UPDATE
+1. Objective 1
+2. Objective 2
+3. Objective 3
+4. Objective 4
+
+### [Processing Large-scale Time Series of ICESat-2 Sea Ice Height in the Cloud](./ATL10-h5coro_rendered.ipynb)
+This notebook utilizes several libraries to performantly search, access, read, and grid ATL10 data over the Ross Sea, Antarctica including `earthaccess`, `h5coro`, and `geopandas`. The notebook provides further guidance on how to scale this analysis to the entire continent, running the same workflow from a script that can be run from your laptop using [Coiled](https://www.coiled.io/).
+
+#### Key Learning Objectives 
+1. Use earthaccess to authenticate with Earthdata Login, search for ICESat-2 data using spatial and temporal filters, and directly access files in the cloud.
+2. Open data granules using h5coro to efficiently read HDF5 data from the NSIDC DAAC S3 bucket.
+3. Load data into a geopandas.DataFrame containing geodetic coordinates, ancillary variables, and date/time converted from ATLAS Epoch.
+4. Grid track data to EASE-Grid v2 6.25 km projected grid using drop-in-the-bucket resampling.
+5. Calculate mean statistics and assign aggregated data to grid cells.
+6. Visualize aggregated sea ice height data on a map.
 
 ## Set up
-To run the notebook provided in this folder in the Amazon Web Services (AWS) cloud, there are a couple of options:
-* An EC2 instance already set up with the necessary software installed to run a Jupyter notebook, and the environment set up using the provided environment.yml file. **Note:** If you are running this notebook on your own AWS EC2 instance using the environment set up using the environment.yml file in the NSIDC-Data-Tutorials/notebooks/ICESat-2_Cloud_Access/environment folder, you may need to run the following command before running the notebook to ensure the notebook executes properly:
+To run the notebooks provided in this folder in the Amazon Web Services (AWS) cloud, there are a couple of options:
+* An EC2 instance already set up with the necessary software installed to run a Jupyter notebook, and the environment set up using the provided environment.yml file. **Note:** If you are running these notebooks on your own AWS EC2 instance using the environment set up using the environment.yml file in the NSIDC-Data-Tutorials/notebooks/ICESat-2_Cloud_Access/environment folder, you may need to run the following command before running the notebook to ensure the notebook executes properly:
 
 `jupyter nbextension enable --py widgetsnbextension`
 
     You do NOT need to do this if you are using the environment set up using the environment.yml file from the NSIDC-Data-Tutorials/binder folder.
 
-* Alternatively, if you have access to one, it can be run in a managed cloud-based Jupyter hub. Just make sure all the necessary libraries are installed (`earthaccess`,`xarray`, and `hvplot`). 
+* Alternatively, if you have access to one, it can be run in a managed cloud-based Jupyter hub. Just make sure all the necessary libraries are installed (e.g. `earthaccess`,`xarray`,`hvplot`, etc.). 
 
-For further details on the prerequisites, see the 'Prerequisites' section in the notebook. 
+For further details on the prerequisites, see the 'Prerequisites' section in each notebook. 
 
-## Key Learning Objectives 
 
-1. Use `earthaccess` to search for ICESat-2 data using spatial and temporal filters and explore search results;
-2. Open data granules using direct access to the ICESat-2 S3 bucket;
-3. Load a HDF5 group into an `xarray.Dataset`;
-4. Visualize the land ice heights using `hvplot`.