Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies
Assessing hurricane landfall risk is of immense public utility, yet extant methods of diagnosing annual tropical cyclone (TC) activity demonstrate no skill in diagnosing U.S. hurricane landfalls. Atlantic TC count itself has limited skill, explaining less than 20% of interannual variance in landfall...
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ftfloridasu:oai:diginole.lib.fsu.edu:fsu_405700 2024-06-09T07:48:13+00:00 Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies Staehling, Erica M. (authoraut) Truchelut, Ryan E. (authoraut) 2016-08-28 1 online resource computer https://diginole.lib.fsu.edu/islandora/object/fsu%3A405700/datastream/TN/view/Diagnosing%20United%20States%20hurricane%20landfall%20risk.jpg English eng eng Geophysical Research Letters--0094-8276 fsu:405700 (IID) FSU_libsubv1_wos_000384443800056 (DOI) 10.1002/2016GL070117 (URL) http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000384443800056 https://diginole.lib.fsu.edu/islandora/object/fsu%3A405700/datastream/TN/view/Diagnosing%20United%20States%20hurricane%20landfall%20risk.jpg Text 2016 ftfloridasu 2024-05-10T08:08:12Z Assessing hurricane landfall risk is of immense public utility, yet extant methods of diagnosing annual tropical cyclone (TC) activity demonstrate no skill in diagnosing U.S. hurricane landfalls. Atlantic TC count itself has limited skill, explaining less than 20% of interannual variance in landfall incidence. Using extended landfall activity and reanalysis data sets, we employed empirical Poisson modeling to produce a landfall diagnostic index (LDI), incorporating spatially and temporally averaged upper level divergence, relative sea surface temperature, meridional wind, and zonal shear vorticity. LDI captures 31% of interannual variability of U.S. hurricane landfalls and offers physical insight into why indices that successfully capture TC activity fail to diagnose landfalls: there is inherent tension between conditions likely to steer hurricanes toward the U.S. and conditions favorable for TC development. Given this tension, attempting to diagnose, predict, or understand TC count is inadequate for quantifying societal impacts due to landfalling hurricanes. Keywords: climate, climates, cyclogenesis, hurricanes, landfall, models, natural hazard, north-atlantic basin, oscillation, potential index, seasonal prediction, tropical cyclone genesis, tropical cyclones, tropical meteorology, us, Validation Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1002/2016GL070117 Text North Atlantic Florida State University: DigiNole Commons |
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Florida State University: DigiNole Commons |
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ftfloridasu |
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English |
description |
Assessing hurricane landfall risk is of immense public utility, yet extant methods of diagnosing annual tropical cyclone (TC) activity demonstrate no skill in diagnosing U.S. hurricane landfalls. Atlantic TC count itself has limited skill, explaining less than 20% of interannual variance in landfall incidence. Using extended landfall activity and reanalysis data sets, we employed empirical Poisson modeling to produce a landfall diagnostic index (LDI), incorporating spatially and temporally averaged upper level divergence, relative sea surface temperature, meridional wind, and zonal shear vorticity. LDI captures 31% of interannual variability of U.S. hurricane landfalls and offers physical insight into why indices that successfully capture TC activity fail to diagnose landfalls: there is inherent tension between conditions likely to steer hurricanes toward the U.S. and conditions favorable for TC development. Given this tension, attempting to diagnose, predict, or understand TC count is inadequate for quantifying societal impacts due to landfalling hurricanes. Keywords: climate, climates, cyclogenesis, hurricanes, landfall, models, natural hazard, north-atlantic basin, oscillation, potential index, seasonal prediction, tropical cyclone genesis, tropical cyclones, tropical meteorology, us, Validation Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1002/2016GL070117 |
author2 |
Staehling, Erica M. (authoraut) Truchelut, Ryan E. (authoraut) |
format |
Text |
title |
Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies |
spellingShingle |
Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies |
title_short |
Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies |
title_full |
Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies |
title_fullStr |
Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies |
title_full_unstemmed |
Diagnosing United States hurricane landfall risk: An alternative to count-based methodologies |
title_sort |
diagnosing united states hurricane landfall risk: an alternative to count-based methodologies |
publishDate |
2016 |
url |
https://diginole.lib.fsu.edu/islandora/object/fsu%3A405700/datastream/TN/view/Diagnosing%20United%20States%20hurricane%20landfall%20risk.jpg |
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North Atlantic |
genre_facet |
North Atlantic |
op_relation |
Geophysical Research Letters--0094-8276 fsu:405700 (IID) FSU_libsubv1_wos_000384443800056 (DOI) 10.1002/2016GL070117 (URL) http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000384443800056 https://diginole.lib.fsu.edu/islandora/object/fsu%3A405700/datastream/TN/view/Diagnosing%20United%20States%20hurricane%20landfall%20risk.jpg |
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1801379855349055488 |