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…
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
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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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Super proud to work on the Warn-on-Forecast System!
.@MontePhD developed the 1st machine learning products integrated into WoFS. WoFS-ML-Severe products offer near warning-scale guidance on weather hazards. He also developed the main Python codebase for WoFS post-processing & visualizations. #GoldMedal13 ciwro.ou.edu/goldmedal
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Our paper on the usefulness of the "WoFS-ML-Severe" products examined during the 2022 HWT-SFE is available in EOR. This paper explores subjective verification, objective verification, and qualitative analysis of the participant's feedback. journals.ametsoc.org/view/jo…
A Rose by Any Other Name: On Basic Scores from the 2 × 2 Table and the Plethora of Names Attached to Them is available in final form! doi.org/10.1175/AIES-D-23-01… This was a fun side project with @hebrooks87 and hopefully is a fun read for many.
**Experimental (Not Public Yet)**
The new AI hail size algorithm says 2 in is possible!
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NRC postdoc app deadline is coming up! Lots of cool topics!
The Feb 1 deadline for NRC postdoc apps is coming up! I am looking for someone to work with me on a topic related to our Warn-on-Forecast System. Search "Potvin" at ra.nas.edu/RAPLab10/Opportun…. These project descriptions are flexible. Possible topics in next post....
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Our tutorial paper on ML explainability is available in EOR: journals.ametsoc.org/view/jo… #AIforEarthSystems @AMS_AIES
My masters student, Sam Varga, presenting his work on ML for severe weather at watch-to-warning lead times in the Warn-on-Forecast. #32AMSNWP
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The severe weather ML products on the Warn-on-Forecast webviewer (cbwofs.nssl.noaa.gov/Forecas…) have global and local explainability products. Check them out!
First day of the 2023 HWT Spring Forecasting Experiments! Check out some of the new features of the WoFS-ML-Severe on the cloud-based Warn-on-Forecast System.
WoFS is running today and over two domains! Switch b/t the two domains using the Domain pulldown. Also, check out the new options under the ML products tab!
DISCLAIMER: These products are experimental, and care should be taken when interpreting them.
cbwofs.nssl.noaa.gov/