Using statistical learning approaches to understand trends and variability of tornadoes across the continental United States
The annual frequency of tornadoes during 1950-2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies. The next phase of the study is focused on the spatial and temporal characteristics of large tornado outbreaks (LTOs) which are rated F2(EF2) or greater on Fujita (Enhanced Fujita) scale and has struck several counties in one day. A statistical assessment of changes in the LTOs clusters for two consecutive 30-year time periods 1950-1980 and 1988-2015 has been performed and the findings show a geographical shift of the central impact locations towards Southeast of the United States. The spatial shift is also accompanied by a reduction in cluster variance which suggests LTOs has become less dispersed between the two period. We investigate changes in tornado inter-arrival rate over time during the period of study using an exponential probability model. Results showed that the arrival rate has changed from 124 days during 1950-1980 to 164 days during 1977-2007, which means LTOs were less frequent in the recent period. The analyses performed in this study support previously reported findings in addition to providing complementary information on LTO clustering behavior and return period.
Statistical Learning, Hierarchical Bayesian Models, Tornado Data, ENSO, Machine Learning, Trend Analysis, Tornado Clusters, Spatio-temporal Analysis, Civil Engineering, Other Civil and Environmental Engineering
City University of New York - City College
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