Multimodal learning–based reconstruction of high-resolution spatial wind speed fields
Wind speed at the sea surface is a key quantity for a variety of scientific applications and human activities. For its importance, many observation techniques exist, ranging from in situ to satellite observations. However, none of such techniques can capture the spatiotemporal variability of the phe...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460224000347/type/journal_article |
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| author | Matteo Zambra Nicolas Farrugia Dorian Cazau Alexandre Gensse Ronan Fablet |
| author_facet | Matteo Zambra Nicolas Farrugia Dorian Cazau Alexandre Gensse Ronan Fablet |
| author_sort | Matteo Zambra |
| collection | DOAJ |
| description | Wind speed at the sea surface is a key quantity for a variety of scientific applications and human activities. For its importance, many observation techniques exist, ranging from in situ to satellite observations. However, none of such techniques can capture the spatiotemporal variability of the phenomenon at the same time. Reanalysis products, obtained from data assimilation methods, represent the state-of-the-art for sea-surface wind speed monitoring but may be biased by model errors and their spatial resolution is not competitive with satellite products. In this work, we propose a scheme based on both data assimilation and deep learning concepts to process spatiotemporally heterogeneous input sources to reconstruct high-resolution time series of spatial wind speed fields. This method allows to us make the most of the complementary information conveyed by the different sea-surface information typically available in operational settings. We use synthetic wind speed data to emulate satellite images, in situ time series and reanalyzed wind fields. Starting from these pseudo-observations, we run extensive numerical simulations to assess the impact of each input source on the model reconstruction performance. We show that our proposed framework outperforms a deep learning–based inversion scheme and can successfully exploit the spatiotemporal complementary information of the different input sources. We also show that the model can learn the possible bias in reanalysis products and attenuate it in the output reconstructions. |
| format | Article |
| id | doaj-art-25685bfc07614ac2a03be37559a135de |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-25685bfc07614ac2a03be37559a135de2025-01-16T21:47:23ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2024.34Multimodal learning–based reconstruction of high-resolution spatial wind speed fieldsMatteo Zambra0https://orcid.org/0000-0002-4435-8624Nicolas Farrugia1Dorian Cazau2Alexandre Gensse3Ronan Fablet4IMT Atlantique, Brest, France Lab-STICC, Brest, FranceIMT Atlantique, Brest, France Lab-STICC, Brest, FranceLab-STICC, Brest, France ENSTA Bretagne, Brest, FranceNaval Group, Toulon, FranceIMT Atlantique, Brest, France Lab-STICC, Brest, FranceWind speed at the sea surface is a key quantity for a variety of scientific applications and human activities. For its importance, many observation techniques exist, ranging from in situ to satellite observations. However, none of such techniques can capture the spatiotemporal variability of the phenomenon at the same time. Reanalysis products, obtained from data assimilation methods, represent the state-of-the-art for sea-surface wind speed monitoring but may be biased by model errors and their spatial resolution is not competitive with satellite products. In this work, we propose a scheme based on both data assimilation and deep learning concepts to process spatiotemporally heterogeneous input sources to reconstruct high-resolution time series of spatial wind speed fields. This method allows to us make the most of the complementary information conveyed by the different sea-surface information typically available in operational settings. We use synthetic wind speed data to emulate satellite images, in situ time series and reanalyzed wind fields. Starting from these pseudo-observations, we run extensive numerical simulations to assess the impact of each input source on the model reconstruction performance. We show that our proposed framework outperforms a deep learning–based inversion scheme and can successfully exploit the spatiotemporal complementary information of the different input sources. We also show that the model can learn the possible bias in reanalysis products and attenuate it in the output reconstructions.https://www.cambridge.org/core/product/identifier/S2634460224000347/type/journal_articledeep learningmultimodal learningsea-surface wind speed reconstructionvariational data assimilation |
| spellingShingle | Matteo Zambra Nicolas Farrugia Dorian Cazau Alexandre Gensse Ronan Fablet Multimodal learning–based reconstruction of high-resolution spatial wind speed fields Environmental Data Science deep learning multimodal learning sea-surface wind speed reconstruction variational data assimilation |
| title | Multimodal learning–based reconstruction of high-resolution spatial wind speed fields |
| title_full | Multimodal learning–based reconstruction of high-resolution spatial wind speed fields |
| title_fullStr | Multimodal learning–based reconstruction of high-resolution spatial wind speed fields |
| title_full_unstemmed | Multimodal learning–based reconstruction of high-resolution spatial wind speed fields |
| title_short | Multimodal learning–based reconstruction of high-resolution spatial wind speed fields |
| title_sort | multimodal learning based reconstruction of high resolution spatial wind speed fields |
| topic | deep learning multimodal learning sea-surface wind speed reconstruction variational data assimilation |
| url | https://www.cambridge.org/core/product/identifier/S2634460224000347/type/journal_article |
| work_keys_str_mv | AT matteozambra multimodallearningbasedreconstructionofhighresolutionspatialwindspeedfields AT nicolasfarrugia multimodallearningbasedreconstructionofhighresolutionspatialwindspeedfields AT doriancazau multimodallearningbasedreconstructionofhighresolutionspatialwindspeedfields AT alexandregensse multimodallearningbasedreconstructionofhighresolutionspatialwindspeedfields AT ronanfablet multimodallearningbasedreconstructionofhighresolutionspatialwindspeedfields |