Simulating Tornado Probability and Tornado Wind Speed Based on Statistical Models

This study presents the development of two multiple-regression-based models that statistically simulate tornado probability and tornado wind speed in a diagnostic manner based on WSR-88D storm-scale circulation attributes observed from radar and environmental information. The output from these models is intended to assist meteorologists with the quantification of ongoing tornado potential and impacts. Based on a robust database, the radar-based storm-scale circulation attributes combine with the effective-layer significant tornado parameter to establish an estimated tornado probability and tornado wind speed. While the fits of these models are considered somewhat modest, their regression coefficients generally offer physical consistency, based on findings from previous research. Furthermore, simulating these models on an independent dataset and other past cases featured in previous research reveals encouraging signals for accurately identifying higher potential for tornadoes. This statistical application using large sample-size datasets can serve as a first step to streamlining the process of reproducibly quantifying tornado threats by service-providing organizations in a diagnostic manner, encouraging consistency in messaging scientifically sound information for the protection of life and property. We will provide examples of the application of this work to real-time tornado threat assessment. Moreover, for a sample of Kansas and Nebraska tornadoes, we investigated the variability of the output from the tornado probability model in the time leading up to tornadogenesis -- to identify WSR-88D-based precursors for anticipating forthcoming tornadogenesis.