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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Bibliographic Details
Main Authors: Obakrim, Said, Ailliot, Pierre, Monbet, Valérie, Raillard, Nicolas
Other Authors: 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
Language:English
Published: HAL CCSD 2022
Subjects:
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
North Atlantic
Online Access:https://hal.science/hal-03825413
https://doi.org/10.1002/essoar.10510147.2
Description
Summary: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.

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