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A Bayesian modelling framework for tornado occurrences in North America

Author

Listed:
  • Vincent Y.S. Cheng

    (Ecological Modeling Laboratory, University of Toronto
    Climate Laboratory, University of Toronto)

  • George B. Arhonditsis

    (Ecological Modeling Laboratory, University of Toronto)

  • David M.L. Sills

    (Cloud Physics and Severe Weather Research Section, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada)

  • William A. Gough

    (Climate Laboratory, University of Toronto)

  • Heather Auld

    (Risk Sciences International)

Abstract

Tornadoes represent one of nature’s most hazardous phenomena that have been responsible for significant destruction and devastating fatalities. Here we present a Bayesian modelling approach for elucidating the spatiotemporal patterns of tornado activity in North America. Our analysis shows a significant increase in the Canadian Prairies and the Northern Great Plains during the summer, indicating a clear transition of tornado activity from the United States to Canada. The linkage between monthly-averaged atmospheric variables and likelihood of tornado events is characterized by distinct seasonality; the convective available potential energy is the predominant factor in the summer; vertical wind shear appears to have a strong signature primarily in the winter and secondarily in the summer; and storm relative environmental helicity is most influential in the spring. The present probabilistic mapping can be used to draw inference on the likelihood of tornado occurrence in any location in North America within a selected time period of the year.

Suggested Citation

  • Vincent Y.S. Cheng & George B. Arhonditsis & David M.L. Sills & William A. Gough & Heather Auld, 2015. "A Bayesian modelling framework for tornado occurrences in North America," Nature Communications, Nature, vol. 6(1), pages 1-12, May.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7599
    DOI: 10.1038/ncomms7599
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    Cited by:

    1. Alessio C. Spassiani & Matthew S. Mason & Vincent Y. S. Cheng, 2023. "An Australian convective wind gust climatology using Bayesian hierarchical modelling," 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. 118(3), pages 2037-2067, September.
    2. Ye Zheng & Yazhou Xie & Xuejiao Long, 2021. "A comprehensive review of Bayesian statistics in natural hazards engineering," 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. 108(1), pages 63-91, August.
    3. Zoe Schroder & Tyler Fricker, 2023. "Expanding the historical "outbreak" climatology between 1880 and 1989," 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. 117(3), pages 3273-3285, July.

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