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

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