Observations from NOAA’s geostationary (GOES-R) and polar-orbiting (JPSS) satellites provide vital information for a myriad of research and operational applications in Atmospheric and Oceanic Sciences. NOAA satellite data are distributed in netCDF4 (.nc) format, however, and the process of accessing the files and processing the contents correctly can be challenging. This short course will break down these barriers by teaching participants how to use Python to perform the basic steps necessary to work with NOAA netCDF4 satellite data files, with the end goal of making professional-quality imagery suitable for use in scientific presentations and journal articles, or in social media.
|Registration close date:||December 29, 2022|
|Participant cap:||100 (50 in-person, 50 remote)|
The full-day course will consist of sequential hands-on Python tutorials. Participants will run provided Python code in Jupyter Notebook, learning best practices for using popular Python packages such as netCDF4, NumPy, Matplotlib, Cartopy, MetPy, and SharpPy to:
Examples will focus on specific GOES-R (ABI) and JPSS (VIIRS, NUCAPS) datasets, such as dust RGB, aerosol optical depth, and temperature and water vapor profiles, for relevant events/hazards including fires, smoke, blowing dust, and winter storms. But the presented workflow and Python skills will be applicable to any NOAA netCDF4 satellite data. At the end of the course, participants will have the opportunity to visualize a data file of their choosing; participants can bring a data file to work with, or instructors can help them download a file of interest from one of the NOAA online archives.
STC at NOAA/NESDIS Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction (PGRR) program
IMSG at the NOAA/NESDIS Center for Satellite Applications and Research (STAR)