SEIA: a scale-selective eddy identification algorithm for the global ocean
Automatic eddy identification algorithms are crucial for global eddy research. This study presents a scale-selective eddy identification algorithm (SEIA; https://github.com/Yk-Yang/SEIA ) for the global ocean based on closed sea level anomalies (SLAs) that features two improvements in the detection...
| Main Authors: | , , |
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| Format: | Text |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/essd-2022-77 https://essd.copernicus.org/preprints/essd-2022-77/ |
| Summary: | Automatic eddy identification algorithms are crucial for global eddy research. This study presents a scale-selective eddy identification algorithm (SEIA; https://github.com/Yk-Yang/SEIA ) for the global ocean based on closed sea level anomalies (SLAs) that features two improvements in the detection and tracking processes. First, the scale-selective scheme replaces the previously used threshold for defining the eddy boundary and restricts the numbers of upper and lower grid points based on the data resolution and eddy spatial scale. Under such conditions, the eddy boundary is as large as possible, while the eddy region is not overestimated. Furthermore, a novel and effective overlap scheme is used to track eddies by calculating the intersecting ratio of time-step-successive eddies. SEIA generates 278,630 anticyclonic eddies and 274,351 cyclonic eddies from AVISO’s SLA dataset over a five-year period (2015–2019; http://www.doi.org/10.11922/sciencedb.o00035.00004 Yang et al., 2022). The global distribution of eddies, propagation speed, and eddy path characteristics in the Southern Ocean verify the validity of SEIA. |
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