Segmented Drifter Trajectory-Based Eddy Identification Validated by SWOT Altimetry - Liu, Tong; Chen, Xiaoyan; Chen, Ge (IEEE geoscience and remote sensing letters, 2025)
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.