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Nat Commun. 2015 Mar 25;6:6599. doi: 10.1038/ncomms7599.

A Bayesian modelling framework for tornado occurrences in North America.

Author information

1
1] Ecological Modeling Laboratory, Department of Physical &Environmental Sciences, University of Toronto, Toronto, Ontario, Canada M1C 1A4 [2] Climate Laboratory, Department of Physical &Environmental Sciences, University of Toronto, Toronto, Ontario, Canada M1C 1A4.
2
Ecological Modeling Laboratory, Department of Physical &Environmental Sciences, University of Toronto, Toronto, Ontario, Canada M1C 1A4.
3
Cloud Physics and Severe Weather Research Section, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada, Toronto, Ontario, Canada M3H 5T4.
4
Climate Laboratory, Department of Physical &Environmental Sciences, University of Toronto, Toronto, Ontario, Canada M1C 1A4.
5
Risk Sciences International, Ottawa, Ontario, Canada K1N 7G2.

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.

PMID:
25807465
DOI:
10.1038/ncomms7599

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