Statistical Models for Tornado Climatology: Long and Short-Term Views

This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineat...

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Published in:PLOS ONE
Main Authors: Elsner, James B., Jagger, Thomas H., Fricker, Tyler
Format: Text
Language:English
Published: Public Library of Science 2016
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119788/
http://www.ncbi.nlm.nih.gov/pubmed/27875581
https://doi.org/10.1371/journal.pone.0166895
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spelling ftpubmed:oai:pubmedcentral.nih.gov:5119788 2023-05-15T17:34:11+02:00 Statistical Models for Tornado Climatology: Long and Short-Term Views Elsner, James B. Jagger, Thomas H. Fricker, Tyler 2016-11-22 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119788/ http://www.ncbi.nlm.nih.gov/pubmed/27875581 https://doi.org/10.1371/journal.pone.0166895 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119788/ http://www.ncbi.nlm.nih.gov/pubmed/27875581 http://dx.doi.org/10.1371/journal.pone.0166895 © 2016 Elsner et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CC-BY Research Article Text 2016 ftpubmed https://doi.org/10.1371/journal.pone.0166895 2016-12-18T01:02:11Z This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public. Text North Atlantic North Atlantic oscillation PubMed Central (PMC) PLOS ONE 11 11 e0166895
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Research Article
spellingShingle Research Article
Elsner, James B.
Jagger, Thomas H.
Fricker, Tyler
Statistical Models for Tornado Climatology: Long and Short-Term Views
topic_facet Research Article
description This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public.
format Text
author Elsner, James B.
Jagger, Thomas H.
Fricker, Tyler
author_facet Elsner, James B.
Jagger, Thomas H.
Fricker, Tyler
author_sort Elsner, James B.
title Statistical Models for Tornado Climatology: Long and Short-Term Views
title_short Statistical Models for Tornado Climatology: Long and Short-Term Views
title_full Statistical Models for Tornado Climatology: Long and Short-Term Views
title_fullStr Statistical Models for Tornado Climatology: Long and Short-Term Views
title_full_unstemmed Statistical Models for Tornado Climatology: Long and Short-Term Views
title_sort statistical models for tornado climatology: long and short-term views
publisher Public Library of Science
publishDate 2016
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119788/
http://www.ncbi.nlm.nih.gov/pubmed/27875581
https://doi.org/10.1371/journal.pone.0166895
genre North Atlantic
North Atlantic oscillation
genre_facet North Atlantic
North Atlantic oscillation
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119788/
http://www.ncbi.nlm.nih.gov/pubmed/27875581
http://dx.doi.org/10.1371/journal.pone.0166895
op_rights © 2016 Elsner et al
http://creativecommons.org/licenses/by/4.0/
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
op_rightsnorm CC-BY
op_doi https://doi.org/10.1371/journal.pone.0166895
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