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Tornado Damage Rating Probabilities Derived from WSR-88D Data

Authors :
Ariel Cohen
Andrew R. Dean
Patrick T. Marsh
Aaron M. Gleason
Elizabeth M. Leitman
Jeremy S. Grams
Bryan T. Smith
Joseph C. Picca
Richard L. Thompson
Source :
Weather and Forecasting. 32:1509-1528
Publication Year :
2017
Publisher :
American Meteorological Society, 2017.

Abstract

Previous work with observations from the NEXRAD (WSR-88D) network in the United States has shown that the probability of damage from a tornado, as represented by EF-scale ratings, increases as low-level rotational velocity increases. This work expands on previous studies by including reported tornadoes from 2014 to 2015, as well as a robust sample of nontornadic severe thunderstorms [≥1-in.- (2.54 cm) diameter hail, thunderstorm wind gusts ≥ 50 kt (25 m s−1), or reported wind damage] with low-level cyclonic rotation. The addition of the nontornadic sample allows the computation of tornado damage rating probabilities across a spectrum of organized severe thunderstorms represented by right-moving supercells and quasi-linear convective systems. Dual-polarization variables are used to ensure proper use of velocity data in the identification of tornadic and nontornadic cases. Tornado damage rating probabilities increase as low-level rotational velocity Vrot increases and circulation diameter decreases. The influence of height above radar level (or range from radar) is less obvious, with a muted tendency for tornado damage rating probabilities to increase as rotation (of the same Vrot magnitude) is observed closer to the ground. Consistent with previous work on gate-to-gate shear signatures such as the tornadic vortex signature, easily identifiable rotation poses a greater tornado risk compared to more nebulous areas of cyclonic azimuthal shear. Additionally, tornado probability distributions vary substantially (for similar sample sizes) when comparing the southeast United States, which has a high density of damage indicators, to the Great Plains, where damage indicators are more sparse.

Details

ISSN :
15200434 and 08828156
Volume :
32
Database :
OpenAIRE
Journal :
Weather and Forecasting
Accession number :
edsair.doi...........56f5ac5551ef2109f6889707733c62b3