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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ftcopernicus:oai:publications.copernicus.org:ascmo106859 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 2023-06-05 application/pdf https://doi.org/10.5194/ascmo-9-67-2023 https://ascmo.copernicus.org/articles/9/67/2023/ eng eng doi:10.5194/ascmo-9-67-2023 https://ascmo.copernicus.org/articles/9/67/2023/ eISSN: 2364-3587 Text 2023 ftcopernicus https://doi.org/10.5194/ascmo-9-67-2023 2023-06-12T16:24:19Z 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. Text North Atlantic Copernicus Publications: E-Journals Advances in Statistical Climatology, Meteorology and Oceanography 9 1 67 81 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
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 |
Text |
author |
Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas |
spellingShingle |
Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas Statistical modeling of the space–time relation between wind and significant wave height |
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 |
publishDate |
2023 |
url |
https://doi.org/10.5194/ascmo-9-67-2023 https://ascmo.copernicus.org/articles/9/67/2023/ |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
eISSN: 2364-3587 |
op_relation |
doi:10.5194/ascmo-9-67-2023 https://ascmo.copernicus.org/articles/9/67/2023/ |
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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1770272928171032576 |