MetPy for Quantitative Analysis of Meteorological Data

This virtual short course will teach participants how to use MetPy unit-aware calculation functions on real-world datasets.

March 22-23, 2022, at 11:00 AM - 3:00 PM Eastern Time (Virtual)

Registration close date: Tuesday, March 15, 2022, at 11:59 PM Eastern
Participant cap: 40


Registration rates:

$32 for student members
$64 for members
$204 for non-members

Registration policy:

AMS requires a valid payment to be made within 5 days of the start of a course or sooner if registration has reached capacity. You will be contacted by AMS staff if payment is required. Refunds will not be issued to attendees within 7 days of the start of a course. Registrations are not transferable or exchangeable.

Course Description:

The use of the Python programming language has grown immensely over the past decade and has become an essential tool within education, research, and industry within the atmospheric sciences. MetPy, which is a Python library for meteorological applications, aims to make Python more readily applicable by providing domain-specific functionality on top of the extensive set of general scientific Python tools. This functionality includes reading files, plotting, and an extensive set of calculations; these calculations range in complexity from the basic (e.g. potential temperature, dewpoint) to more complicated (e.g. isentropic interpolation, potential vorticity).

The goal of the course is to have attendees learn how to use MetPy unit-aware calculation functions on real-world datasets taken from realtime and archive remote data servers. This also includes learning about the data manipulation needed to make real-world data ready for use with these tools. Some basic plotting functionality will be covered, but the focus is on attendees getting hands-on experience using calculations to analyze data--this includes gaining practical experience learning how to interpret error messages and correct their root causes. Prior knowledge of Python and NumPy is required.

This course is extensively hands-on through the use of Jupyter notebooks and will consist of one day of interactive lecture sessions with incorporated exercises that will be completed during the short course.

Participants will need access to Zoom through either the web or desktop application.


Ryan May headshot
Ryan M. May

Software Engineer, UCAR/Unidata

Kevin Goebbert headshot
Kevin H. Goebbert

Associate Professor of Meteorology, Valparaiso University

Drew Camron headshot
Drew Camron

Python Developer, UCAR/Unidata