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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2025.3590395