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RAP opportunity at National Oceanic and Atmospheric Administration     NOAA

Machine Learning Applications to Warn-on-Forecast


National Severe Storms Laboratory

opportunity location
26.76.00.C0706 Norman, OK 73072


name email phone
Corey Keith Potvin 405.206.0485


Maximizing the value of Warn-on-Forecast System (WoFS; Heinselman et al. 2024) output requires application of post-processing techniques that (1) predict storm hazards that are not explicitly predicted by the ensemble system, (2) calibrate ensemble forecast output based on previous system performance, and (3) provide dynamic estimates of forecast accuracy to improve usability. Machine learning (ML) methods are particularly suited to these objectives (Flora et al. 2021; McGovern et al. 2023). ML is also a promising avenue for improving storm-scale data assimilation, e.g., by replacing current cloud analysis schemes used to accelerate spin-up of thunderstorms in the ensemble. 

A particularly ambitious application of ML, specifically deep learning (DL), is the emulation of NWP models. The ability of DL models to emulate global NWP models with reasonable accuracy has recently been demonstrated by Google DeepMind's Graphcast and other DL models. The emulation of convection-allowing models (like the WoFS) is an emerging and exciting topic that is rapidly growing in priority within NOAA.

Research proposals are invited on all applications of ML/DL to the WoFS. 

Flora, M. L., C. K. Potvin, P. S. Skinner, S. Handler, and A. McGovern, 2021: Using machine learning to generate storm-scale probabilistic guidance of severe weather hazards in the Warn-on-Forecast System. Mon. Wea. Rev., 149, 1535-1557,

Heinselman, P. L., and Coauthors, 2023: Warn-on-Forecast System: From Vision to Reality. Wea. Forecasting39, 75–95,

McGovern, A., R. J. Chase, M. Flora, D. J. Gagne, R. Lagerquist, C. K. Potvin, N. Snook, and E. Loken, 2023: A Review of Machine Learning for Convective Weather. Artif. Intell. Earth Syst.2, e220077,

key words
Machine learning; deep learning; AI; ensembles; CAMs; severe weather


Citizenship:  Open to U.S. citizens, permanent residents and non-U.S. citizens
Level:  Open to Postdoctoral and Senior applicants


Base Stipend Travel Allotment Supplementation
$60,000.00 $3,000.00

$24,000 Supplement for Doctorates in Electrical Engineering

Experience Supplement:
Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the number of years of experience past their PhD.

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