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  • Author or Editor: A. McGovern x
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David John Gagne II
,
Amy McGovern
,
Jeffrey B. Basara
, and
Rodger A. Brown

Abstract

Oklahoma Mesonet surface data and North American Regional Reanalysis data were integrated with the tracks of over 900 tornadic and nontornadic supercell thunderstorms in Oklahoma from 1994 to 2003 to observe the evolution of near-storm environments with data currently available to operational forecasters. These data are used to train a complex data-mining algorithm that can analyze the variability of meteorological data in both space and time and produce a probabilistic prediction of tornadogenesis given variables describing the near-storm environment. The algorithm was assessed for utility in four ways. First, its probability forecasts were scored. The algorithm did produce some useful skill in discriminating between tornadic and nontornadic supercells as well as in producing reliable probabilities. Second, its selection of relevant attributes was assessed for physical significance. Surface thermodynamic parameters, instability, and bulk wind shear were among the most significant attributes. Third, the algorithm’s skill was compared with the skill of single variables commonly used for tornado prediction. The algorithm did noticeably outperform all of the single variables, including composite parameters. Fourth, the situational variations of the predictions from the algorithm were shown in case studies. They revealed instances both in which the algorithm excelled and in which the algorithm was limited.

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