Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data
Eddy identification and tracking are essential for understanding ocean dynamics. This study employed the elliptical Gaussian function (EGF) simulations and the py-eddy-tracker (PET) algorithm, validated by Surface Velocity Program (SVP) drifter data, to track eddies in the western North Pacific Ocea...
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2025-05-01
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| author | Lan Zhang Cheinway Hwang Han-Yang Liu Emmy T. Y. Chang Daocheng Yu |
| author_facet | Lan Zhang Cheinway Hwang Han-Yang Liu Emmy T. Y. Chang Daocheng Yu |
| author_sort | Lan Zhang |
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| description | Eddy identification and tracking are essential for understanding ocean dynamics. This study employed the elliptical Gaussian function (EGF) simulations and the py-eddy-tracker (PET) algorithm, validated by Surface Velocity Program (SVP) drifter data, to track eddies in the western North Pacific Ocean. The PET method effectively identified large- and mesoscale eddies but struggled with submesoscale features, indicating areas for improvement. Simulated satellite altimetry by EGF, mirroring Surface Water and Ocean Topography (SWOT)’s high-resolution observations, confirmed PET’s capability in processing fine-scale data, though accuracy declined for submesoscale eddies. Over 22 years, 1,188,649 eddies were identified, mainly concentrated east of Taiwan. Temporal analysis showed interannual variability, more cyclonic than anticyclonic eddies, and a seasonal peak in spring, likely influenced by marine conditions. Short-lived eddies were uniformly distributed, while long-lived ones followed major currents, validating PET’s robustness with SVP drifters. The launch of the SWOT satellite in 2022 has enhanced fine-scale ocean studies, enabling the detection of submesoscale eddies previously unresolved by conventional altimetry. SWOT observations reveal intricate eddy structures, including small cyclonic features in the northwestern Pacific, demonstrating its potential for improving eddy tracking. Future work should refine the PET algorithm for SWOT’s swath altimetry, addressing data gaps and unclosed contours. Integrating SWOT with in situ drifters, numerical models, and machine learning will further enhance eddy classification, benefiting ocean circulation studies and climate modeling. |
| format | Article |
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| institution | OA Journals |
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| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-6b994789de9b4f2e99e4358d2a6408a22025-08-20T02:33:55ZengMDPI AGRemote Sensing2072-42922025-05-011710166510.3390/rs17101665Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT DataLan Zhang0Cheinway Hwang1Han-Yang Liu2Emmy T. Y. Chang3Daocheng Yu4Institute of Earthquake Forecasting, China Earthquake Administration, No. 63, Fuxing Road, Beijing 100036, ChinaDepartment of Civil Engineering, National Yang Ming Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, TaiwanDepartment of Civil Engineering, National Yang Ming Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, TaiwanInstitute of Oceanography, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, TaiwanSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaEddy identification and tracking are essential for understanding ocean dynamics. This study employed the elliptical Gaussian function (EGF) simulations and the py-eddy-tracker (PET) algorithm, validated by Surface Velocity Program (SVP) drifter data, to track eddies in the western North Pacific Ocean. The PET method effectively identified large- and mesoscale eddies but struggled with submesoscale features, indicating areas for improvement. Simulated satellite altimetry by EGF, mirroring Surface Water and Ocean Topography (SWOT)’s high-resolution observations, confirmed PET’s capability in processing fine-scale data, though accuracy declined for submesoscale eddies. Over 22 years, 1,188,649 eddies were identified, mainly concentrated east of Taiwan. Temporal analysis showed interannual variability, more cyclonic than anticyclonic eddies, and a seasonal peak in spring, likely influenced by marine conditions. Short-lived eddies were uniformly distributed, while long-lived ones followed major currents, validating PET’s robustness with SVP drifters. The launch of the SWOT satellite in 2022 has enhanced fine-scale ocean studies, enabling the detection of submesoscale eddies previously unresolved by conventional altimetry. SWOT observations reveal intricate eddy structures, including small cyclonic features in the northwestern Pacific, demonstrating its potential for improving eddy tracking. Future work should refine the PET algorithm for SWOT’s swath altimetry, addressing data gaps and unclosed contours. Integrating SWOT with in situ drifters, numerical models, and machine learning will further enhance eddy classification, benefiting ocean circulation studies and climate modeling.https://www.mdpi.com/2072-4292/17/10/1665drifterKuroshio currentocean eddyPy_Eddy_Tracking algorithmsatellite altimetrysurface water and ocean topography (SWOT) |
| spellingShingle | Lan Zhang Cheinway Hwang Han-Yang Liu Emmy T. Y. Chang Daocheng Yu Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data Remote Sensing drifter Kuroshio current ocean eddy Py_Eddy_Tracking algorithm satellite altimetry surface water and ocean topography (SWOT) |
| title | Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data |
| title_full | Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data |
| title_fullStr | Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data |
| title_full_unstemmed | Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data |
| title_short | Automated Eddy Identification and Tracking in the Northwest Pacific Based on Conventional Altimeter and SWOT Data |
| title_sort | automated eddy identification and tracking in the northwest pacific based on conventional altimeter and swot data |
| topic | drifter Kuroshio current ocean eddy Py_Eddy_Tracking algorithm satellite altimetry surface water and ocean topography (SWOT) |
| url | https://www.mdpi.com/2072-4292/17/10/1665 |
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