Statistical modeling of the space–time relation between wind and significant wave height
Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea-state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established...
Published in: | Advances in Statistical Climatology, Meteorology and Oceanography |
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00066852 2023-07-02T03:33:07+02:00 Statistical modeling of the space–time relation between wind and significant wave height Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas 2023-06 electronic https://doi.org/10.5194/ascmo-9-67-2023 https://noa.gwlb.de/receive/cop_mods_00066852 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00065322/ascmo-9-67-2023.pdf https://ascmo.copernicus.org/articles/9/67/2023/ascmo-9-67-2023.pdf eng eng Copernicus Publications Advances in Statistical Climatology, Meteorology and Oceanography -- http://advances-statistical-climatology-meteorology-oceanography.net/ -- https://www.adv-stat-clim-meteorol-oceanogr.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2840620 -- 2364-3587 https://doi.org/10.5194/ascmo-9-67-2023 https://noa.gwlb.de/receive/cop_mods_00066852 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00065322/ascmo-9-67-2023.pdf https://ascmo.copernicus.org/articles/9/67/2023/ascmo-9-67-2023.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2023 ftnonlinearchiv https://doi.org/10.5194/ascmo-9-67-2023 2023-06-11T23:19:05Z Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea-state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established between wind fields over the North Atlantic (predictors) and the significant wave height (predictand) at three locations: southwest of the French coast (Gironde), the English Channel, and the Gulf of Maine. The developed method considers both wind seas and swells by including local and global predictors. Using a fully data-driven approach, the global predictors' spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves. Weather types are constructed using a regression-guided clustering method, and the resulting clusters correspond to different wave systems (swells and wind seas). Then, in each weather type, a penalized linear regression model is fitted between the predictor and the predictand. The validation analysis proves the models skill in predicting the significant wave height, with a root mean square error of approximately 0.3 m in the three considered locations. Additionally, the study discusses the physical insights underlying the proposed method. Article in Journal/Newspaper North Atlantic Niedersächsisches Online-Archiv NOA Advances in Statistical Climatology, Meteorology and Oceanography 9 1 67 81 |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas Statistical modeling of the space–time relation between wind and significant wave height |
topic_facet |
article Verlagsveröffentlichung |
description |
Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea-state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established between wind fields over the North Atlantic (predictors) and the significant wave height (predictand) at three locations: southwest of the French coast (Gironde), the English Channel, and the Gulf of Maine. The developed method considers both wind seas and swells by including local and global predictors. Using a fully data-driven approach, the global predictors' spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves. Weather types are constructed using a regression-guided clustering method, and the resulting clusters correspond to different wave systems (swells and wind seas). Then, in each weather type, a penalized linear regression model is fitted between the predictor and the predictand. The validation analysis proves the models skill in predicting the significant wave height, with a root mean square error of approximately 0.3 m in the three considered locations. Additionally, the study discusses the physical insights underlying the proposed method. |
format |
Article in Journal/Newspaper |
author |
Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas |
author_facet |
Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas |
author_sort |
Obakrim, Said |
title |
Statistical modeling of the space–time relation between wind and significant wave height |
title_short |
Statistical modeling of the space–time relation between wind and significant wave height |
title_full |
Statistical modeling of the space–time relation between wind and significant wave height |
title_fullStr |
Statistical modeling of the space–time relation between wind and significant wave height |
title_full_unstemmed |
Statistical modeling of the space–time relation between wind and significant wave height |
title_sort |
statistical modeling of the space–time relation between wind and significant wave height |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/ascmo-9-67-2023 https://noa.gwlb.de/receive/cop_mods_00066852 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00065322/ascmo-9-67-2023.pdf https://ascmo.copernicus.org/articles/9/67/2023/ascmo-9-67-2023.pdf |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
Advances in Statistical Climatology, Meteorology and Oceanography -- http://advances-statistical-climatology-meteorology-oceanography.net/ -- https://www.adv-stat-clim-meteorol-oceanogr.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2840620 -- 2364-3587 https://doi.org/10.5194/ascmo-9-67-2023 https://noa.gwlb.de/receive/cop_mods_00066852 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00065322/ascmo-9-67-2023.pdf https://ascmo.copernicus.org/articles/9/67/2023/ascmo-9-67-2023.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/ascmo-9-67-2023 |
container_title |
Advances in Statistical Climatology, Meteorology and Oceanography |
container_volume |
9 |
container_issue |
1 |
container_start_page |
67 |
op_container_end_page |
81 |
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