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🚨 Exciting Announcement! 🚨
Introducing WoFSCast: our AI-powered system for predicting thunderstorm evolution at watch-to-warning scales. 🌩️⚡
essopenarchive.org/users/829…
Can we use AI to predict thunderstorm evolution?
Recently published in @theAGU GRL, we refactored @GoogleDeepMind's GraphCast for regional modeling and trained it on @CIWRO_ / @NOAANSSL's archived Warn-on-Forecast System (WoFS) data.
Meteorologists are turning to AI as a quick, efficient alternative to traditional weather prediction algorithms.
Now researchers including @MontePhD & @corey_potvin have extended this tech to cover localized extreme weather as well as global forecasts. eos.org/research-spotlights/…
🚨 New paper! 🚨 Available in EOR is our paper exploring training AI models on MRMS MESH vs. reported hail sizes: journals.ametsoc.org/view/jo…
Super proud of this work! More to come this upcoming year with @corey_potvin and @DopplerChase as we transition to an ensemble-based WoFSCast.
WoFSCast is the first AI system trained and developed to emulate a sub-hourly convection-allowing weather model (CAM). WoFSCast speeds up forecast generation versus WoFS by at least a factor of ten, from minutes to seconds. #AMS2025
Super proud to see that forecasters find WoFS-ML-Severe useful. #AMS31SLS Always keep your end user in mind when developing new products!
Best of all? WoFSCast achieves this in 30-40 seconds on 1 GPU, compared to the 8-12 minutes and 60+ CPUs WoFS needs.
While development is ongoing, WoFSCast matches WoFS's critical success index (CSI) with fewer false alarms and less spurious convection.
Trained on archived Warn-on-Forecast System (WoFS) data and a refactored GraphCast for limited area domains, WoFSCast can predict thunderstorm evolution for up to 6 hours.
Montgomery Flora, Ph.D. retweeted
Our paper combining AI/ML, storm-scale ensemble prediction, and "forecasting forecast skill" is now in final form in @AMS_AIES:
doi.org/10.1175/AIES-D-23-01…
Co-authors: @MontePhD, Patrick Skinner, @tonyreinhart, @BrianWxFLA









