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RAP opportunity at National Oceanic and Atmospheric Administration     NOAA

Machine Learning Applications to Warn-on-Forecast

Location

National Severe Storms Laboratory

opportunity location
26.76.00.C0706 Norman, OK 73072

Advisers

name email phone
Corey Keith Potvin corey.potvin@noaa.gov 405.206.0485

Description

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, https://doi.org/10.1175/MWR-D-20-0194.1.

Heinselman, P. L., and Coauthors, 2023: Warn-on-Forecast System: From Vision to Reality. Wea. Forecasting39, 75–95, https://doi.org/10.1175/WAF-D-23-0147.1.

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, https://doi.org/10.1175/AIES-D-22-0077.1.

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

Eligibility

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

Stipend

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

$24,000 Supplement for Doctorates in Electrical Engineering

Experience Supplement

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

Additional Benefits

Relocation

Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.

Health insurance

A group health insurance program is available to awardees and their qualifying dependents in the United States.

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