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Ocean Science An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 1
Ocean Sci., 10, 39–48, 2014
https://doi.org/10.5194/os-10-39-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ocean Sci., 10, 39–48, 2014
https://doi.org/10.5194/os-10-39-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Feb 2014

Research article | 10 Feb 2014

Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly

J. Yi1, Y. Du1, Z. He2,3, and C. Zhou1 J. Yi et al.
  • 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2State Key Laboratory of Tropical Oceanography (LTO), South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, Guangdong, China
  • 3College of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China

Abstract. Automated methods are important for automatically detecting mesoscale eddies in large volumes of altimeter data. While many algorithms have been proposed in the past, this paper presents a new method, called hybrid detection (HD), to enhance the eddy detection accuracy and the capability of recognizing eddy multi-core structures from maps of sea level anomaly (SLA). The HD method has integrated the criteria of the Okubo–Weiss (OW) method and the sea surface height-based (SSH-based) method, two commonly used eddy detection algorithms. Evaluation of the detection accuracy shows that the successful detection rate of HD is ~ 96.6% and the excessive detection rate is ~ 14.2%, which outperforms the OW and those methods using SLA extrema to identify eddies. The capability of recognizing multi-core structures and its significance in tracking eddy splitting or merging events have been illustrated by comparing with the detection results of different algorithms and observations in previous literature.

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