<strong>Abstract.</strong> 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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