Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach

Abstract

In this paper, we analyze the tornado occurrences in the Unites States. To perform inference procedures for the spatio-temporal point process we adopt a dynamic representation of Log-Gaussian Cox Process. This representation is based on the decomposition of intensity function in components of trend, cycles, and spatial effects. In this model, spatial effects are also represented by a dynamic functional structure, which allows analyzing the possible changes in the spatio-temporal distribution of the occurrence of tornadoes due to possible changes in climate patterns. The model was estimated using Bayesian inference through the Integrated Nested Laplace Approximations. We use data from the Storm Prediction Center’s Severe Weather Database between 1954 and 2018, and the results provided evidence, from new perspectives, that trends in annual tornado occurrences in the United States have remained relatively constant, supporting previously reported findings.

Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960–2019)

“…Other studies note database deficiencies and tornado reporting bias [32,33,45,56,57]. Untrained witnesses [45] and improved detection methods [58] certainly present limitations, particularly in the early time period of this study.…”

The dynamics of fire activity in Brazilian Pantanal: A LGCP-based structural decomposition

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Spatial Heterogeneity in Climate Change: Evidence from Brazilian Biomes

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