Severe Weather Prediction Using Storm Surrogates from an Ensemble Forecasting System

Abstract

Abstract Probabilistic severe weather forecasts for days 1 and 2 were produced using 30-member convection-allowing ensemble forecasts initialized by an ensemble Kalman filter data assimilation system during a 32-day period coinciding with the Mesoscale Predictability Experiment. The forecasts were generated by smoothing the locations where model output indicated extreme values of updraft helicity, a surrogate for rotating thunderstorms in model output. The day 1 surrogate severe probability forecasts (SSPFs) produced skillful and reliable predictions of severe weather during this period, after an appropriate calibration of the smoothing kernel. The ensemble SSPFs exceeded the skill of SSPFs derived from two benchmark deterministic forecasts, with the largest differences occurring on the mesoscale, while all SSPFs produced similar forecasts on synoptic scales. While the deterministic SSPFs often overforecasted high probabilities, the ensemble improved the reliability of these probabilities, at the expense of producing fewer high-probability values. For the day 2 period, the SSPFs provided competitive guidance compared to the day 1 forecasts, although additional smoothing was needed to produce the same level of skill, reducing the forecast sharpness. Results were similar using 10 ensemble members, suggesting value exists when running a smaller ensemble if computational resources are limited. Finally, the SSPFs were compared to severe weather risk areas identified in Storm Prediction Center (SPC) convective outlooks. The SSPF skill was comparable to the SPC outlook skill in identifying regions where severe weather would occur, although performance varied on a day-to-day basis.