AMS short course
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Description
This project accompanies a presentation within the AMS short course, "GOES-R and JPSS Satellite Data and Tools Available through Cloud Service Providers." It is divided into two sections: course_materials and take_home_materials.
The course_materials folder walks the user chronologically through 1) a Jupyter Notebook aimed to help users learn to access data on the AWS, Azure, and Google clouds, 2) an interactive Jupyter Notebook that allows users to request GOES-R data from the AWS cloud, then visualize the results, and 3) an interactive Jupyter Notebook that allows for the visualization of JPSS data from the AWS cloud. The course_materials scripts are meant to be used as a foundation for cloud access and satellite studies, with no coding experience required and the extensive use of Widgets, or drop-down menus to make any selections.
The take_home_materials folder provides scripts to facilitate access of GOES-R and JPSS data from each of the Cloud Service Providers (CSPs), and is intended for scientists to use as a starting point in their research using satellite datasets in the cloud. There are no Widgets in these scripts, so some familiarity with Python code and cloud access is required to produce meaningful results.
After walking through the course_materials and take_home_materials scripts, the participant should be able to download and begin altering the take_home_materials scripts to perform cloud-based analysis specific to their own research.
How-to
A guide for visualizing satellite data.
course_materials:
- Select a GOES or JPSS notebook.
- Under "Importing": Run the first cell to install all required packages.
- Under "Defining": Run all of the definition cells, so your code knows how to access and plot the data.
- Under "Choosing your variables!": Run one cell, then make your selection from the drop-down menu that appears -> run the following cell, then make your selection from the drop-down menu that appears -> et cetera. Note: if you re-run the cell above the drop-down menu, the your selection will be reset. Always make sure to run the cell after the drop-down menu.
take_home_materials (satellite visualization):
- Select a GOES or JPSS notebook.
- Under "Importing": Run the first cell to install all required packages.
- Under "Defining": Run all of the definition cells, so your code knows how to access and plot the data.
- Under "Satellite parameters!": Change the input variables (satellite, product, date/time, etc.) to reflect the information you would like to visualize. If you don't know which options are available for input variables, refer to the CSP's data page (via the NODD website) and the finding-products notebook in the in-class folder for guidance.
For reference
The organizations supporting this work include the North Carolina Institute for Climate Studies (NCICS), which operates under Cooperative Institute for Satellite Earth System Studies (CISESS) with the National Oceanic and Atmopsheric Administration (NOAA), and the NOAA Open Data Dissemination (NODD) program.
Support
If you have any questions about this short course, please contact Mya Sears at mjsears@ncsu.edu.
If you have questions about NOAA datasets in the cloud, please reach out to the NODD team at nodd@noaa.gov.
Authors and acknowledgment
Thank you to Denis Willett, Jenny Dissen, Nicholas Shanahan, Liz Cox, and Kate Szura for their assistance in developing the ideas and content within this course. Many additional acknowledgements to the scientists who work with JPSS and GOES-R data, who were instrumental in providing the context required to create this course.
Further coding acknowledgements are located at the foot of all Jupyter Notebooks within this repo.