Maximizing the value of Warn-on-Forecast (WoF; Potvin et al. 2020) ensemble 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). 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.
Research proposals are invited on all applications of ML to WoF ensembles. Several years of warm season forecasts from the experimental NSSL WoF System will be made available to the successful applicant.
Potvin, C. K., and Coauthors, 2020: Assessing Systematic Impacts of PBL Schemes on Storm Evolution in the NOAA Warn-on-Forecast System. Mon. Wea. Rev., 148, 2567–2590, https://doi.org/10.1175/MWR-D-19-0389.1.
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.