Self-motivated individual actively pursuing knowledge. Passions include being a loving husband/father, studying severe weather, and playing music.
Cumming, GA
Joined January 2019
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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…
WoFSCast is currently being evaluated in the NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Comparison to the WoFS can be found at hwt.nssl.noaa.gov/sfe_viewer… under AI5. WoFSCast vs. WoFS in the Dataset tab.
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NOAA EPIC blog post: epic.noaa.gov/wofscast-a-gra…
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AGU GRL article: agupubs.onlinelibrary.wiley.…
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“The model yielded accurate predictions of how storms would evolve for up to 2 hours, matching up to 80% of those generated by the WoFS. Generating a prediction takes 30–40 seconds using 1 GPU. That’s at least a factor of 10 faster than using the current WoFS"
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Known as WoFSCast, the AI model “learned the dynamics of key thunderstorm features, including updrafts, which feed thermodynamic energy into storms, and cold air pockets that form beneath storms, influencing how storms move and grow.”
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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/…
The craziest result: Although the training inputs did not include time of day or any social data, the local storm report (LSR)-trained model produced lower probs at night. This suggests that the biases were coming from the physical environment
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🚨 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
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Super proud to see that forecasters find WoFS-ML-Severe useful. #AMS31SLS Always keep your end user in mind when developing new products!
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