Montgomery Flora, Ph.D.

Self-motivated individual actively pursuing knowledge. Passions include being a loving husband/father, studying severe weather, and playing music.

[Montgomery Flora, Ph.D. @MontePhD]

🚨 Exciting Announcement! 🚨
Introducing WoFSCast: our AI-powered system for predicting thunderstorm evolution at watch-to-warning scales. 🌩️⚡
https://t.co/dksbLh1cTY

[Montgomery Flora, Ph.D. @MontePhD]

@corey_potvin Pretty animations comparing WoFS and WoFSCast 😀

[Montgomery Flora, Ph.D. @MontePhD]

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.

[AGU's Eos @AGU_Eos]

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. https://t.co/pcSccHjqJS

[Montgomery Flora, Ph.D. @MontePhD]

@corey_potvin

[Montgomery Flora, Ph.D. @MontePhD]

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

[Montgomery Flora, Ph.D. @MontePhD]

🚨 New paper! 🚨 Available in EOR is our paper exploring training AI models on MRMS MESH vs. reported hail sizes: https://t.co/JqPUAlE3b9

[Montgomery Flora, Ph.D. @MontePhD]

Super proud of this work! More to come this upcoming year with @corey_potvin and @DopplerChase as we transition to an ensemble-based WoFSCast.

[CIWRO @CIWRO_]

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

[Montgomery Flora, Ph.D. @MontePhD]

@BrianWxFLA Waiting for WoFS to better analyze the storms west of us

[Montgomery Flora, Ph.D. @MontePhD]

Super proud to see that forecasters find WoFS-ML-Severe useful. #AMS31SLS Always keep your end user in mind when developing new products!

[Montgomery Flora, Ph.D. @MontePhD]

@DopplerChase

[Mete Civelek @mete_civelek]

“Concepts of a plan” = “Manuscript in preparation”

[Montgomery Flora, Ph.D. @MontePhD]

@shoyer Incorporating convection-allowing model (CAM) guidance into operations (e.g., at the storm prediction center; SPC) has revolutionized severe weather prediction over the last 2 decades.

[Montgomery Flora, Ph.D. @MontePhD]

@shoyer In terms of statistical downscaling, you can't hallucinate the scales that aren't there. Weather is a multi-scale phenomenon, and for current applications (e.g., severe weather forecasting), you have to simulate the mesoscales and storm-scales.

[Montgomery Flora, Ph.D. @MontePhD]

We can also create ensemble predictions using the WoFS members as ICs

[Montgomery Flora, Ph.D. @MontePhD]

🚨 Exciting Announcement! 🚨
Introducing WoFSCast: our AI-powered system for predicting thunderstorm evolution at watch-to-warning scales. 🌩️⚡
https://t.co/dksbLh1cTY

[Montgomery Flora, Ph.D. @MontePhD]

@corey_potvin

[Montgomery Flora, Ph.D. @MontePhD]

Best of all? WoFSCast achieves this in 30-40 seconds on 1 GPU, compared to the 8-12 minutes and 60+ CPUs WoFS needs.