Predicting outbreak-level tornado counts and casualties from environmental variables

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.