Are you tired of wrestling with satellite-based datasets? In this tutorial, you will get a crash course on using Python and other modern tools to analyze your satellite datasets which can speed up the learning curve and improve research efficiency. We’ll cover the basics of the NetCDF and HDF self-describing data formats, display data FAST using Panoply, and demo basic analysis using Python. Additionally, we share some of the best practices such as using version control and clean coding guidelines. This session will be beneficial for both experienced programmers who want overview of modern tools and also those just getting started with scientific programming. While examples will focus on GOES-R and Suomi-NPP aerosol datasets, the presented tools and techniques can be applied to other NASA and NOAA scientific datasets across any discipline. No prior experience required.
This course is designed to introduce earth scientists to modern programming tools and techniques to view and analyze data. The primary goal is for attendees to:
|7:30am||Meet and greet/computing environment set-up|
|8:00am||Importing and displaying data with Python and Jupyter Notebooks|
|9:30am||Understanding and viewing to scientific data formats|
|10:00am||Performing common remote sensing tasks with Python|
|12:00am||Version control and best coding practices|
Please download and/or install the following before the session begins: