A Bayesian modelling framework for tornado occurrences in North America
Vincent Y S Cheng1, George B Arhonditsis2, David M L Sills3
11] 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.
Nature Communications
|March 26, 2015
Summary
This study reveals a shift in tornado activity towards Canada, particularly the Canadian Prairies, during summer. It identifies key atmospheric drivers like convective available potential energy, wind shear, and storm relative environmental helicity influencing tornado seasonality.
Area of Science:
- Meteorology
- Atmospheric Science
- Climatology
Background:
- Tornadoes are highly destructive weather events causing significant fatalities.
- Understanding tornado spatiotemporal patterns is crucial for hazard mitigation.
Purpose of the Study:
- To model and elucidate the spatiotemporal patterns of tornado activity in North America.
- To identify seasonal atmospheric drivers of tornado occurrence.
Main Methods:
- Bayesian modeling approach applied to North American tornado data.
- Analysis of monthly-averaged atmospheric variables and their correlation with tornado events.
Main Results:
- Significant increase in tornado activity observed in the Canadian Prairies and Northern Great Plains during summer.
- Identified seasonal influence of atmospheric variables: convective available potential energy (summer), vertical wind shear (winter/summer), and storm relative environmental helicity (spring).
- Demonstrated a transition of tornado activity from the United States to Canada.
Conclusions:
- Probabilistic mapping of tornado likelihood provides valuable inference for specific North American locations and time periods.
- The findings enhance understanding of tornado seasonality and geographical shifts, aiding in improved forecasting and preparedness.
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