Proper evaluation of very short-range, storm-scale ensemble forecasts requires techniques that are tailored to individual storms. Storm-based verification techniques improve not only the evaluation of Warn-on-Forecast (WoF) systems (Flora et al. 2019; Potvin et al. 2020), but also assessments of thunderstorm predictability, for which traditional verification methods can produce misleading results (Flora et al. 2018). In addition, maximizing the value of WoF 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 methods are particularly suited to these objectives.
Research proposals are invited on all aspects of verification and post-processing of WoF output. Several years of warm season forecasts from an experimental WoF ensemble will be made available to the successful applicant.
Flora, M. L., C. K. Potvin, and L. J. Wicker, 2018: Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread. Mon. Wea. Rev., 146, 2361–2379, https://doi.org/10.1175/MWR-D-17-0374.1.
Flora, M. L., P. S. Skinner, C. K. Potvin, A. E. Reinhart, T. A. Jones, N. Yussouf, and K. H. Knopfmeier, 2019: Object-Based Verification of Short-Term, Storm-Scale Probabilistic Mesocyclone Guidance from an Experimental Warn-on-Forecast System. Wea. Forecasting, 34, 1721–1739, https://doi.org/10.1175/WAF-D-19-0094.1.
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.
NWP; ensembles; CAMs; post-processing; machine learning; verification; severe weather