Statistical modeling of the space–time relation between wind and significant wave height
Abstract. 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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Statistical modeling of the space–time relation between wind and significant wave height ; volume:9 ; number:1 ; year:2023 ; pages:67-81 ; extent:15
Advances in statistical climatology, meteorology and oceanography ; 9, Heft 1 (2023), 67-81 (gesamt 15)
- Creator
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Obakrim, Said
Ailliot, Pierre
Monbet, Valérie
Raillard, Nicolas
- DOI
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10.5194/ascmo-9-67-2023
- URN
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urn:nbn:de:101:1-2023060804282710509646
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:55 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Obakrim, Said
- Ailliot, Pierre
- Monbet, Valérie
- Raillard, Nicolas