Modeling the Climatology of Tornado Occurrence using Bayesian Inference

Our mechanistic understanding of tornadic environments has significantly improved by the recent technological enhancements in the detection of tornadoes as well as the advances of numerical weather predictive modeling. Nonetheless, despite the decades of active research, prediction of tornado occurrence remains one of the most difficult problems in meteorological and climate science. In our efforts to develop predictive tools for tornado occurrence, there are a number of issues to overcome, such as the treatment of inconsistent tornado records, the consideration of suitable combination of atmospheric predictors, and the selection of appropriate resolution to accommodate the variability in time and space. In this dissertation, I address each of these topics by undertaking three empirical (statistical) modeling studies, where I examine the signature of different atmospheric factors influencing the tornado occurrence, the sampling biases in tornado observations, and the optimal spatiotemporal resolution for studying tornado occurrence. In the first study, I develop a novel Bayesian statistical framework to assess the probability of tornado occurrence in Canada, in which the sampling bias of tornado observations and the linkage between lightning climatology and tornadogenesis are considered. The results produced reasonable probability estimates of tornado occurrence for the under-sampled areas in the model domain. The same study also delineated the geographical variability in the lightning-tornado relationship across Canada. In the second study, I present a novel modeling framework to examine the relative importance of several key atmospheric variables (e.g., convective available potential energy, 0-3 km storm-relative helicity, 0-6 km bulk wind difference, 0-tropopause vertical wind shear) on tornado activity in North America. I found that the variable quantifying the updraft strength is more important during the warm season, whereas the effects of wind-related variables are more uniform across seasons. The residual variability of the same modeling framework (a reflection of the fidelity of the statistical formulation considered) is subsequently used to delineate distinct geographical patterns of tornado activity. This piece of information provides the foundation for the Bayesian hierarchical prognostic model presented in the third chapter of my dissertation. The results of the latter approach reinforce my earlier finding that the spatial variability of the annual and warm seasonal tornado occurrence is well explained by convective available potential energy and storm relative helicity alone, while vertical wind shear is better at reproducing the cool season tornado activity. The Bayesian hierarchical modeling framework offers a promising methodological tool for understanding regional tornado environments and obtaining reliable predictions in North America.

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