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...
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ftunivrennes1hal:oai:HAL:hal-03825413v1 2024-04-14T08:15:51+00:00 Statistical modeling of the space-time relation between wind and significant wave height Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas 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) 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) SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART) Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche Mathématique de Rennes (IRMAR) 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)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) 2022-10-22 https://hal.science/hal-03825413 https://doi.org/10.1002/essoar.10510147.2 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.1002/essoar.10510147.2 hal-03825413 https://hal.science/hal-03825413 doi:10.1002/essoar.10510147.2 https://hal.science/hal-03825413 2022 [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] info:eu-repo/semantics/preprint Preprints, Working Papers, . 2022 ftunivrennes1hal https://doi.org/10.1002/essoar.10510147.2 2024-03-21T16:14:27Z 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) in a location in the Bay of Biscay off the French coast. The developed method takes into consideration both wind seas and swells by including local and global predictors. The global predictors’ spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves, using a fully data-driven approach. 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 model’s skill in predicting the significant wave height (RMSE = 0.27m); furthermore, the interpretability of the model is discussed. Report North Atlantic Université de Rennes 1: Publications scientifiques (HAL) |
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Open Polar |
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Université de Rennes 1: Publications scientifiques (HAL) |
op_collection_id |
ftunivrennes1hal |
language |
English |
topic |
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
spellingShingle |
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Obakrim, Said Ailliot, Pierre Monbet, Valérie Raillard, Nicolas Statistical modeling of the space-time relation between wind and significant wave height |
topic_facet |
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
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) in a location in the Bay of Biscay off the French coast. The developed method takes into consideration both wind seas and swells by including local and global predictors. The global predictors’ spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves, using a fully data-driven approach. 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 model’s skill in predicting the significant wave height (RMSE = 0.27m); furthermore, the interpretability of the model is discussed. |
author2 |
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) 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) SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART) Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche Mathématique de Rennes (IRMAR) 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)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) |
format |
Report |
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 |
HAL CCSD |
publishDate |
2022 |
url |
https://hal.science/hal-03825413 https://doi.org/10.1002/essoar.10510147.2 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
https://hal.science/hal-03825413 2022 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1002/essoar.10510147.2 hal-03825413 https://hal.science/hal-03825413 doi:10.1002/essoar.10510147.2 |
op_doi |
https://doi.org/10.1002/essoar.10510147.2 |
_version_ |
1796314331345846272 |