Statistical modeling of the space–time relation between wind and significant wave height
International audience 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 f...
Published in: | Advances in Statistical Climatology, Meteorology and Oceanography |
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Online Access: | https://hal.science/hal-04204079 https://hal.science/hal-04204079/document https://hal.science/hal-04204079/file/ascmo-9-67-2023.pdf https://doi.org/10.5194/ascmo-9-67-2023 |
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ftunibretagnesud:oai:HAL:hal-04204079v1 2024-06-23T07:55:08+00:00 Statistical modeling of the space–time relation between wind and significant wave height Obakrim, Said Ailliot, Pierre Monbet, Valerie Raillard, Nicolas Unité Recherches et Développements Technologiques (RDT) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) Institut de Recherche Mathématique de Rennes (IRMAR) Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut Agro Rennes Angers Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) 2023-06-05 https://hal.science/hal-04204079 https://hal.science/hal-04204079/document https://hal.science/hal-04204079/file/ascmo-9-67-2023.pdf https://doi.org/10.5194/ascmo-9-67-2023 en eng HAL CCSD Copernicus Publications info:eu-repo/semantics/altIdentifier/doi/10.5194/ascmo-9-67-2023 hal-04204079 https://hal.science/hal-04204079 https://hal.science/hal-04204079/document https://hal.science/hal-04204079/file/ascmo-9-67-2023.pdf doi:10.5194/ascmo-9-67-2023 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 2364-3579 EISSN: 2364-3587 Advances in Statistical Climatology, Meteorology and Oceanography https://hal.science/hal-04204079 Advances in Statistical Climatology, Meteorology and Oceanography, 2023, 9 (1), pp.67-81. ⟨10.5194/ascmo-9-67-2023⟩ [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2023 ftunibretagnesud https://doi.org/10.5194/ascmo-9-67-2023 2024-06-03T23:49:57Z International audience 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 Université de Bretagne Sud: HAL Advances in Statistical Climatology, Meteorology and Oceanography 9 1 67 81 |
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Open Polar |
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Université de Bretagne Sud: HAL |
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ftunibretagnesud |
language |
English |
topic |
[SDU]Sciences of the Universe [physics] |
spellingShingle |
[SDU]Sciences of the Universe [physics] Obakrim, Said Ailliot, Pierre Monbet, Valerie Raillard, Nicolas Statistical modeling of the space–time relation between wind and significant wave height |
topic_facet |
[SDU]Sciences of the Universe [physics] |
description |
International audience 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. |
author2 |
Unité Recherches et Développements Technologiques (RDT) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) Institut de Recherche Mathématique de Rennes (IRMAR) Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut Agro Rennes Angers Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) |
format |
Article in Journal/Newspaper |
author |
Obakrim, Said Ailliot, Pierre Monbet, Valerie Raillard, Nicolas |
author_facet |
Obakrim, Said Ailliot, Pierre Monbet, Valerie 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 |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04204079 https://hal.science/hal-04204079/document https://hal.science/hal-04204079/file/ascmo-9-67-2023.pdf https://doi.org/10.5194/ascmo-9-67-2023 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISSN: 2364-3579 EISSN: 2364-3587 Advances in Statistical Climatology, Meteorology and Oceanography https://hal.science/hal-04204079 Advances in Statistical Climatology, Meteorology and Oceanography, 2023, 9 (1), pp.67-81. ⟨10.5194/ascmo-9-67-2023⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/ascmo-9-67-2023 hal-04204079 https://hal.science/hal-04204079 https://hal.science/hal-04204079/document https://hal.science/hal-04204079/file/ascmo-9-67-2023.pdf doi:10.5194/ascmo-9-67-2023 |
op_rights |
http://creativecommons.org/licenses/by/ 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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1802647587135684608 |