A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT)
Abstract High‐resolution sea surface velocity (SSV) is crucial for advancing our understanding of ocean sub‐mesoscale processes, energy cascades, etc. The recently launched Surface Water and Ocean Topography (SWOT) satellite measures sea surface height with a sub‐mesoscale resolved resolution. Based...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2024GL110731 |
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| author | Shuyi Zhou Jihai Dong Hong Li Guangjun Xu Fanghua Xu |
| author_facet | Shuyi Zhou Jihai Dong Hong Li Guangjun Xu Fanghua Xu |
| author_sort | Shuyi Zhou |
| collection | DOAJ |
| description | Abstract High‐resolution sea surface velocity (SSV) is crucial for advancing our understanding of ocean sub‐mesoscale processes, energy cascades, etc. The recently launched Surface Water and Ocean Topography (SWOT) satellite measures sea surface height with a sub‐mesoscale resolved resolution. Based on geostrophic balance, the so‐called geostrophic velocity in SWOT is estimated. Although the SWOT‐derived velocity is not true geostrophic velocity as it does not consider the separation of balanced and unbalanced motions, it offers valuable insights into both geostrophic and ageostrophic velocities. Here we propose a machine learning‐based model to infer the SSV using SWOT and drifter data. The result demonstrates the error between the geostrophic velocities from SWOT and the total velocities from the drifter are reduced by about 50%. Furthermore, the kinetic energy of inferred velocities aligns more closely with reanalysis data, particularly at low latitudes. This study thus presents a promising approach for inferring global SSV using SWOT data. |
| format | Article |
| id | doaj-art-ed83e33b4afd475697ca783be42c52f0 |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-ed83e33b4afd475697ca783be42c52f02025-08-20T03:12:53ZengWileyGeophysical Research Letters0094-82761944-80072025-05-01529n/an/a10.1029/2024GL110731A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT)Shuyi Zhou0Jihai Dong1Hong Li2Guangjun Xu3Fanghua Xu4Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing ChinaSchool of Marine Sciences Nanjing University of Information Science and Technology Nanjing ChinaTianjin Key Laboratory for Marine Environmental Research and Service School of Marine Science and Technology Tianjin University Tianjin ChinaCollege of Electronic and Information Engineering Guangdong Ocean University Zhanjiang ChinaDepartment of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing ChinaAbstract High‐resolution sea surface velocity (SSV) is crucial for advancing our understanding of ocean sub‐mesoscale processes, energy cascades, etc. The recently launched Surface Water and Ocean Topography (SWOT) satellite measures sea surface height with a sub‐mesoscale resolved resolution. Based on geostrophic balance, the so‐called geostrophic velocity in SWOT is estimated. Although the SWOT‐derived velocity is not true geostrophic velocity as it does not consider the separation of balanced and unbalanced motions, it offers valuable insights into both geostrophic and ageostrophic velocities. Here we propose a machine learning‐based model to infer the SSV using SWOT and drifter data. The result demonstrates the error between the geostrophic velocities from SWOT and the total velocities from the drifter are reduced by about 50%. Furthermore, the kinetic energy of inferred velocities aligns more closely with reanalysis data, particularly at low latitudes. This study thus presents a promising approach for inferring global SSV using SWOT data.https://doi.org/10.1029/2024GL110731SWOTmachine learningcorrection |
| spellingShingle | Shuyi Zhou Jihai Dong Hong Li Guangjun Xu Fanghua Xu A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT) Geophysical Research Letters SWOT machine learning correction |
| title | A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT) |
| title_full | A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT) |
| title_fullStr | A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT) |
| title_full_unstemmed | A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT) |
| title_short | A Machine Learning‐Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT) |
| title_sort | machine learning based model infers the sea surface velocity of surface water and ocean topography swot |
| topic | SWOT machine learning correction |
| url | https://doi.org/10.1029/2024GL110731 |
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