Severe Weather Prediction Using Storm Surrogates from an Ensemble Forecasting System

AbstractProbabilistic 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 o...

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