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

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 eddies' multi-core structures from maps of sea level anomaly (SLA) by integrating the ideas of the Okubo–Weiss (OW) method and the sea-surface-height-based (SSH-based) method, two well-known eddy detection algorithms. Detection evaluation using an objective validation protocol shows that the HD method owns ~ 96.6% successful detection rate and ~ 14.2% excessive detection rate, which outperforms the OW method and other methods that identify eddies by SLA extrema and confirms the improvement in detection accuracy. The capability of recognizing multi-core structures and its significance in tracking eddies' splitting or merging events have been well illustrated by comparing with other detection algorithms and historical studies.