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Spatial extreme value analysis to project extremes of large-scale indicators for severe weather

Concurrently high values of the maximum potential wind speed of updrafts (W(max)) and 0–6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the produc...

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Detalles Bibliográficos
Autores principales: Gilleland, Eric, Brown, Barbara G, Ammann, Caspar M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3816326/
https://www.ncbi.nlm.nih.gov/pubmed/24223482
http://dx.doi.org/10.1002/env.2234
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author Gilleland, Eric
Brown, Barbara G
Ammann, Caspar M
author_facet Gilleland, Eric
Brown, Barbara G
Ammann, Caspar M
author_sort Gilleland, Eric
collection PubMed
description Concurrently high values of the maximum potential wind speed of updrafts (W(max)) and 0–6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the product of these variables (WmSh) from the National Center for Atmospheric Research/United States National Center for Environmental Prediction reanalysis over North America conditioned on their having extreme energy in the spatial field in order to project the predominant spatial patterns of WmSh. The approach is based on the Heffernan and Tawn conditional extreme value model. Results suggest that this technique estimates the spatial behavior of WmSh well, which allows for exploring possible changes in the patterns over time. While the model enables a method for inferring the uncertainty in the patterns, such analysis is difficult with the currently available inference approach. A variation of the method is also explored to investigate how this type of model might be used to qualitatively understand how the spatial patterns of WmSh correspond to extreme river flow events. A case study for river flows from three rivers in northwestern Tennessee is studied, and it is found that advection of WmSh from the Gulf of Mexico prevails while elsewhere, WmSh is generally very low during such extreme events. © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd.
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spelling pubmed-38163262013-11-07 Spatial extreme value analysis to project extremes of large-scale indicators for severe weather Gilleland, Eric Brown, Barbara G Ammann, Caspar M Environmetrics Research Articles Concurrently high values of the maximum potential wind speed of updrafts (W(max)) and 0–6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the product of these variables (WmSh) from the National Center for Atmospheric Research/United States National Center for Environmental Prediction reanalysis over North America conditioned on their having extreme energy in the spatial field in order to project the predominant spatial patterns of WmSh. The approach is based on the Heffernan and Tawn conditional extreme value model. Results suggest that this technique estimates the spatial behavior of WmSh well, which allows for exploring possible changes in the patterns over time. While the model enables a method for inferring the uncertainty in the patterns, such analysis is difficult with the currently available inference approach. A variation of the method is also explored to investigate how this type of model might be used to qualitatively understand how the spatial patterns of WmSh correspond to extreme river flow events. A case study for river flows from three rivers in northwestern Tennessee is studied, and it is found that advection of WmSh from the Gulf of Mexico prevails while elsewhere, WmSh is generally very low during such extreme events. © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd. Blackwell Publishing Ltd 2013-09 2013-09-18 /pmc/articles/PMC3816326/ /pubmed/24223482 http://dx.doi.org/10.1002/env.2234 Text en © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
spellingShingle Research Articles
Gilleland, Eric
Brown, Barbara G
Ammann, Caspar M
Spatial extreme value analysis to project extremes of large-scale indicators for severe weather
title Spatial extreme value analysis to project extremes of large-scale indicators for severe weather
title_full Spatial extreme value analysis to project extremes of large-scale indicators for severe weather
title_fullStr Spatial extreme value analysis to project extremes of large-scale indicators for severe weather
title_full_unstemmed Spatial extreme value analysis to project extremes of large-scale indicators for severe weather
title_short Spatial extreme value analysis to project extremes of large-scale indicators for severe weather
title_sort spatial extreme value analysis to project extremes of large-scale indicators for severe weather
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3816326/
https://www.ncbi.nlm.nih.gov/pubmed/24223482
http://dx.doi.org/10.1002/env.2234
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