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Article Type:
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Predicting the Climatology of Tornado Occurrences in North America with a Bayesian Hierarchical Modeling Framework

Vincent Y. S. Cheng Ecological Modeling Laboratory, Department of Physical and Environmental Sciences, University of Toronto, Toronto, and Risk Sciences International, Ottawa, Ontario, Canada

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George B. Arhonditsis Ecological Modeling Laboratory, Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, Canada

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David M. L. Sills Cloud Physics and Severe Weather Research Section, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment and Climate Change Canada, Toronto, Ontario, Canada

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William A. Gough Climate Laboratory, Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, Canada

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Heather Auld Risk Sciences International, Ottawa, Ontario, Canada

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Print Publication:
01 Mar 2016
DOI:
https://doi.org/10.1175/JCLI-D-15-0404.1
Page(s):
1899–1917
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Abstract

Destruction and fatalities from recent tornado outbreaks in North America have raised considerable concerns regarding their climatic and geographic variability. However, regional characterization of tornado activity in relation to large-scale climatic processes remains highly uncertain. Here, a novel Bayesian hierarchical framework is developed for elucidating the spatiotemporal variability of the factors underlying tornado occurrence in North America. It is demonstrated that regional variability of tornado activity can be characterized using a hierarchical parameterization of convective available potential energy, storm relative helicity, and vertical wind shear quantities. It is shown that the spatial variability of tornado occurrence during the warm summer season can be explained by convective available potential energy and storm relative helicity alone, while vertical wind shear is clearly better at capturing the spatial variability of the cool season tornado activity. The results suggest that the Bayesian hierarchical modeling approach is effective for understanding the regional tornadic environment and in forming the basis for establishing tornado prognostic tools in North America.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0404.s1.

Corresponding author address: George B. Arhonditsis, Dept. of Physical and Environmental Sciences, University of Toronto, 1265 Military Trail, Toronto ON M1C 1A4, Canada. E-mail: georgea@utsc.utoronto.ca

Abstract

Destruction and fatalities from recent tornado outbreaks in North America have raised considerable concerns regarding their climatic and geographic variability. However, regional characterization of tornado activity in relation to large-scale climatic processes remains highly uncertain. Here, a novel Bayesian hierarchical framework is developed for elucidating the spatiotemporal variability of the factors underlying tornado occurrence in North America. It is demonstrated that regional variability of tornado activity can be characterized using a hierarchical parameterization of convective available potential energy, storm relative helicity, and vertical wind shear quantities. It is shown that the spatial variability of tornado occurrence during the warm summer season can be explained by convective available potential energy and storm relative helicity alone, while vertical wind shear is clearly better at capturing the spatial variability of the cool season tornado activity. The results suggest that the Bayesian hierarchical modeling approach is effective for understanding the regional tornadic environment and in forming the basis for establishing tornado prognostic tools in North America.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0404.s1.

Corresponding author address: George B. Arhonditsis, Dept. of Physical and Environmental Sciences, University of Toronto, 1265 Military Trail, Toronto ON M1C 1A4, Canada. E-mail: georgea@utsc.utoronto.ca

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