general resources
- Meteorology for Scientists and Engineers, 3rd Edition by Dr. Roland Stull (a fully online textbook!)
- Synoptic Meteorology, EUMETSAT international training project textbook (PDF)
- JetStream - An Online School for Weather by the National Weather Service
- METEO 3: Introductory Meteorology by Penn State University
- NOAA/NWS Warning Decision Training Division courses (includes a course on radar and its applications as well as presentations on individual storms of all types)
synoptic & dynamic meteorology
From faculty and researchers
- Using Worked Examples to Improve Student Understanding of Atmospheric Dynamics by Dr. Casey Davenport, University of North Carolina-Charlotte (contact Dr. Davenport for more information)
- A review of quasi-geostrophic theory from SOO Ted Funk at NWS Louisville (PDF)
- NWS JetStream section on synoptic meteorology
- Python notebooks for Synoptic Meteorology by Dr. Kevin Goebbert, Valparaiso University
- Tutorial on balance of atmospheric flow from Plymouth State University that covers geostrophic, cyclostrophic, gradient, and inertial flow
MetEd
- Quasi-geostrophic vorticity equation
- Quasi-geostrophic omega equation
- Jet streak circulations
- General Principles of Atmospheric Motion from the Introduction to Tropical Meteorology textbook
climatology and climate change
- Managing for a Changing Climate, the University of Oklahoma (YouTube playlist)
- Introduction to Modern Climate Change by Dr. Andrew Dessler, Texas A&M University (list of links to videos)
hazards
- Severe Weather 101 from the National Severe Storms Laboratory
- NOAA Education - hurricanes
- National Hurricane Center - Hurricane Preparedness Videos (YouTube playlist)
- The National Hurricane Center cone of uncertainty (YouTube video)
other topics
- 2020 AMS Short Course - Machine Learning in Python for Environmental Science
- 2019 AMS Short Course - Machine Learning in Python for Environmental Science (NOTE: the notebooks in the 2020 version above are the easiest to run)
- Python notebook from the deep-learning tutorial at the University of Oklahoma's 2019 Artificial Intelligence and Machine Learning Symposium
- Satellite meteorology resources plus links to satellite imagery by Dr. Kim Wood, Mississippi State University