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...
Published in: | Advances in Statistical Climatology, Meteorology and Oceanography |
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2023
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Online Access: | https://doi.org/10.5194/ascmo-9-67-2023 https://doaj.org/article/20d33a3a24bc41f2ad68ca614549a189 |
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ftdoajarticles:oai:doaj.org/article:20d33a3a24bc41f2ad68ca614549a189 2023-07-02T03:33:07+02:00 Statistical modeling of the space–time relation between wind and significant wave height S. Obakrim P. Ailliot V. Monbet N. Raillard 2023-06-01T00:00:00Z https://doi.org/10.5194/ascmo-9-67-2023 https://doaj.org/article/20d33a3a24bc41f2ad68ca614549a189 EN eng Copernicus Publications https://ascmo.copernicus.org/articles/9/67/2023/ascmo-9-67-2023.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-9-67-2023 2364-3579 2364-3587 https://doaj.org/article/20d33a3a24bc41f2ad68ca614549a189 Advances in Statistical Climatology, Meteorology and Oceanography, Vol 9, Pp 67-81 (2023) Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 article 2023 ftdoajarticles https://doi.org/10.5194/ascmo-9-67-2023 2023-06-11T00:37:07Z 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 Directory of Open Access Journals: DOAJ Articles Advances in Statistical Climatology, Meteorology and Oceanography 9 1 67 81 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
spellingShingle |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 S. Obakrim P. Ailliot V. Monbet N. Raillard Statistical modeling of the space–time relation between wind and significant wave height |
topic_facet |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
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) 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. |
format |
Article in Journal/Newspaper |
author |
S. Obakrim P. Ailliot V. Monbet N. Raillard |
author_facet |
S. Obakrim P. Ailliot V. Monbet N. Raillard |
author_sort |
S. Obakrim |
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 |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/ascmo-9-67-2023 https://doaj.org/article/20d33a3a24bc41f2ad68ca614549a189 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 9, Pp 67-81 (2023) |
op_relation |
https://ascmo.copernicus.org/articles/9/67/2023/ascmo-9-67-2023.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-9-67-2023 2364-3579 2364-3587 https://doaj.org/article/20d33a3a24bc41f2ad68ca614549a189 |
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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1770272935728119808 |