Predicting spring tornado activity in the central great plains by 1 march

Abstract

The authors illustrate a statistical model for predicting tornado activity in the central Great Plains by 1 March. The model predicts the number of tornado reports during April-June using February sea surface temperature (SST) data from the Gulf of Alaska (GAK) and the western Caribbean Sea (WCA). The model uses a Bayesian formulation where the likelihood on the counts is a negative binomial distribution and where the nonstationarity in tornado reporting is included as a trend term plus first-order autocorrelation. Posterior densities for the model parameters are generated using the method of integrated nested Laplacian approximation (INLA). The model yields a 51% increase in the number of tornado reports per degree Celsius increase in SST over the WCA and a 15% decrease in the number of reports per degree Celsius increase in SST over the GAK. These significant relationships are broadly consistent with a physical understanding of large-scale atmospheric patterns conducive to severe convective storms across the Great Plains. The SST covariates explain 11% of the out-of-sample variability in observed F1-F5 tornado reports. The paper demonstrates the utility of INLA for fitting Bayesian models to tornado climate data. © 2014 American Meteorological Society.