Statistical Assessment of Spatial Tornado Occurrences in Canada: Modeling and Estimation

Abstract

Abstract Canada experiences a relatively large number of tornadoes, which can cause a significant amount of damage and fatalities. In this study, a preferred prediction model for the spatially varying tornado occurrence rate is developed for Canada. The development takes into account the most commonly used spatial stochastic models and the underreporting that is due to low population density. It incorporates the annual average cloud-to-ground lightning flash (ACGLF) density and annual average thunderstorm days (ATD) as covariates in the prediction model. The model parameters estimation is carried out by using both the maximum likelihood method and the Bayesian inference. The analysis results indicate that the negative binomial model is preferable to the zero-inflated Poisson model and the Poisson model. The results show that tornado occurrence in Canada is associated with large overdispersion. Also, the statistical analysis indicates that the prediction model for the tornado occurrence rate developed on the basis of Bayesian inference is relatively insensitive to the assumed “noninformative” prior distributions. A prediction model is suggested for the spatially varying tornado occurrence rate based on the negative binomial model with the ACGLF density and ATD as covariates.