Noaa probsevere v2.0—probhail, probwind, and probtor

Abstract

Severe convective storms are hazardous to both life and property and thus their accurate and timely prediction is imperative. In response to this critical need to help fulfill the mission of the National Oceanic and Atmospheric Administration (NOAA), NOAA and the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin (UW) have developed NOAA ProbSevere—an operational short-term forecasting subsystem within the Multi-Radar Multi-Sensor (MRMS) system, providing storm-based probabilistic guidance to severe convective hazards. ProbSevere extracts and integrates pertinent data from a variety of meteorological sources via multiplatform multiscale storm identification and tracking in order to compute severe hazard probabilities in a statistical framework, using naïve Bayesian classifiers. Version 1 of ProbSevere (PSv1) employed one model—the ‘‘probability of any severe hazard’’ trained on the U.S. National Weather Service (NWS) criteria. Version 2 of ProbSevere (PSv2) implements four models, three naïve Bayesian classifiers trained to specific hazards: 1) severe hail, 2) severe straight-line wind gusts, 3) tornadoes; and a combined model for any of the aforementioned hazards, which takes the maximum probability of the three classifiers. This paper overviews the ProbSevere system and details the construction and selection of predictors for the models. An evaluation of the four models demonstrated that v2 is more skillful than v1 for each severe hazard with higher critical success index scores and that the optimal probability threshold varies by region of the United States. The discussion highlights PSv2 in NOAA’s Hazardous Weather Testbed (HWT) and current and future research for convective nowcasting.