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

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Listed:
  • Fernanda Valente

    (FEARP-USP, Av. Bandeirantes, 3900-Vila Monte Alegre, Ribeirão Preto SP 14040-905, Brazil
    These authors contributed equally to this work.
    We thank Guest Editor Claudio Morana and two anonymous referees for their valuable comments and criticisms.)

  • Márcio Laurini

    (FEARP-USP, Av. Bandeirantes, 3900-Vila Monte Alegre, Ribeirão Preto SP 14040-905, Brazil
    These authors contributed equally to this work.
    We thank Guest Editor Claudio Morana and two anonymous referees for their valuable comments and criticisms.)

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.

Suggested Citation

  • Fernanda Valente & Márcio Laurini, 2020. "Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach," Econometrics, MDPI, vol. 8(2), pages 1-26, June.
    • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:2:p:25-:d:369918
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    References listed on IDEAS

    as
    1. D. Simpson & J. B. Illian & F. Lindgren & S. H. Sørbye & H. Rue, 2016. "Going off grid: computationally efficient inference for log-Gaussian Cox processes," Biometrika, Biometrika Trust, vol. 103(1), pages 49-70.
    2. Cameron Lee, 2012. "Utilizing synoptic climatological methods to assess the impacts of climate change on future tornado-favorable environments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(2), pages 325-343, June.
    3. M. P. Laurini, 2019. "A spatio‐temporal approach to estimate patterns of climate change," Environmetrics, John Wiley & Sons, Ltd., vol. 30(1), February.
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    5. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    6. Morana, Claudio & Sbrana, Giacomo, 2019. "Climate change implications for the catastrophe bonds market: An empirical analysis," Economic Modelling, Elsevier, vol. 81(C), pages 274-294.
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