Segmented Drifter Trajectory-Based Eddy Identification Validated by SWOT Altimetry

Lagrangian drifter datasets are widely used in oceanographic research but remain limited in eddy identification. This study proposes an eddy identification method using segmented drifter trajectories. The idea is to take advantage of the cycloidal drifter in a swirling flow as a tracer to determine...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 22; S. 1 - 5
Hauptverfasser:	Liu, Tong, Chen, Xiaoyan, Chen, Ge
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Altimeters Altimetry Clustering Current rings Datasets Deformation Density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm Drifters Eddies eddy identification Geoscience and remote sensing Identification methods Lagrangian drifter Marine technology Noise Oceanographic research Oceans Satellites Spatial distribution Spatial resolution Spinning Surface water Surface Water and Ocean Topography (SWOT) altimetry Swirling Tracers Training Trajectory trajectory segment Vortices
ISSN:	1545-598X, 1558-0571
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Abstract	Lagrangian drifter datasets are widely used in oceanographic research but remain limited in eddy identification. This study proposes an eddy identification method using segmented drifter trajectories. The idea is to take advantage of the cycloidal drifter in a swirling flow as a tracer to determine the eddy's spinning direction. We use the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm on eddy-trapped drifter trajectories to extract drifter spinning channels, which are used to establish criteria based on trajectory lengths and rotation angles, and then applied to drifters outside altimetrically identified eddies. Despite limitations related to the uneven spatial distribution and applicability of drifter datasets, the new approach captures an additional 25.48% of anticyclonic eddies (AEs) and 27.36% of cyclonic eddies (CEs) with the rest remaining outside eddy (OE), with 89.04% smaller than the local Rossby radius of deformation to be defined as submesoscale eddies, demonstrating its complementary capability to altimeter-based eddy identification. Furthermore, the algorithm is independently applied to drifter data in 2023, and the high-resolution Surface Water and Ocean Topography (SWOT) data products are used to validate the drifter-only identified eddies. The overall consistency between drifter-identified eddies and background fields reaches 94%, demonstrating the reliability and effectiveness of the drifter-based eddy identification method.
AbstractList	Lagrangian drifter datasets are widely used in oceanographic research but remain limited in eddy identification. This study proposes an eddy identification method using segmented drifter trajectories. The idea is to take advantage of the cycloidal drifter in a swirling flow as a tracer to determine the eddy's spinning direction. We use the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm on eddy-trapped drifter trajectories to extract drifter spinning channels, which are used to establish criteria based on trajectory lengths and rotation angles, and then applied to drifters outside altimetrically identified eddies. Despite limitations related to the uneven spatial distribution and applicability of drifter datasets, the new approach captures an additional 25.48% of anticyclonic eddies (AEs) and 27.36% of cyclonic eddies (CEs) with the rest remaining outside eddy (OE), with 89.04% smaller than the local Rossby radius of deformation to be defined as submesoscale eddies, demonstrating its complementary capability to altimeter-based eddy identification. Furthermore, the algorithm is independently applied to drifter data in 2023, and the high-resolution Surface Water and Ocean Topography (SWOT) data products are used to validate the drifter-only identified eddies. The overall consistency between drifter-identified eddies and background fields reaches 94%, demonstrating the reliability and effectiveness of the drifter-based eddy identification method.
Author	Chen, Xiaoyan Liu, Tong Chen, Ge
Author_xml	– sequence: 1 givenname: Tong orcidid: 0009-0007-3082-1861 surname: Liu fullname: Liu, Tong email: tongliu@stu.ouc.edu.cn organization: Department of Marine Technology, State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China – sequence: 2 givenname: Xiaoyan orcidid: 0000-0003-1410-0857 surname: Chen fullname: Chen, Xiaoyan email: xiaoyanchen@ouc.edu.cn organization: Department of Marine Technology, State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China – sequence: 3 givenname: Ge orcidid: 0000-0003-4868-5179 surname: Chen fullname: Chen, Ge email: gechen@ouc.edu.cn organization: Department of Marine Technology, State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China
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SubjectTerms	Algorithms Altimeters Altimetry Clustering Current rings Datasets Deformation Density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm Drifters Eddies eddy identification Geoscience and remote sensing Identification methods Lagrangian drifter Marine technology Noise Oceanographic research Oceans Satellites Spatial distribution Spatial resolution Spinning Surface water Surface Water and Ocean Topography (SWOT) altimetry Swirling Tracers Training Trajectory trajectory segment Vortices
Title	Segmented Drifter Trajectory-Based Eddy Identification Validated by SWOT Altimetry
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