Predicting outbreak-level tornado counts and casualties from environmental variables

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Authors

Zoe Schroder , James B. Elsner 

Abstract

Environmental variables are used routinely in forecasting when and where an outbreak
of tornadoes are likely to occur, but more work is needed to understand how
characteristics of severe weather outbreaks vary with the larger scale environmental
factors. Here the authors demonstrate a method to quantify `outbreak-level tornado
and casualty counts with respect to variations in large-scale environmental factors.
They do this by fitting negative binomial regression models to cluster-level data to
estimate the number of tornadoes and the number of casualties on days with at least
ten tornadoes. Results show that a 1000 J/kg increase in CAPE corresponds to a
5% increase in the number of tornadoes and a 28% increase in the number of
casualties, conditional on at least ten tornadoes, and holding the other variables
constant. Further, results show that a 10 m/s increase in deep-layer bulk shear
corresponds to a 13% increase in tornadoes and a 98% increase in casualties,
conditional on at least ten tornadoes, and holding the other variables constant. The
casualty-count model quantifies the decline in the number of casualties per year and
indicates that outbreaks have a larger impact in the Southeast than elsewhere after
controlling for population and geographic area.

DOI

https://doi.org/10.31223/osf.io/jfk7g

Subjects

Atmospheric Sciences, Earth Sciences, Life Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Keywords

environment, CAPE, casualties, CIN, counts, negative binomial, outbreaks, regression, shear, tornado

Dates

Published: 2020-04-30 00:53

Last Updated: 2020-08-27 17:38

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License

GNU Lesser General Public License (LGPL) 2.1

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