Statistical modeling of the space–time relation between wind and significant wave height

Type Article
Date 2023-06-05
Language English
Author(s) Obakrim SaidORCID1, 2, Ailliot Pierre3, Monbet Valerie1, Raillard NicolasORCID2
Affiliation(s) 1 : Univ. Rennes CNRS, IRMAR – UMR 6625, 35000 Rennes, France
2 : Ifremer, RDT, 29280 Plouzané, France
3 : Laboratoire de Mathématiques de Bretagne Atlantique, Univ. Brest CNRS, UMR 6205, 29200 Brest, France
Source Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO) (2364-3587) (Copernicus Publications), 2023-06-05 , Vol. 9 , N. 1 , P. 67-81
DOI 10.5194/ascmo-9-67-2023

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.

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