A new automatic oceanic mesoscale eddy detection method using satellite altimeter data based on density clustering

Abstract

The mesoscale eddy is a typical mesoscale oceanic phenomenon that transfers ocean energy. The detection and extraction of mesoscale eddies is an important aspect of physical oceanography, and automatic mesoscale eddy detection algorithms are the most fundamental tools for detecting and analyzing mesoscale eddies. The main data used in mesoscale eddy detection are sea level anomaly (SLA) data merged by multi-satellite altimeters’ data. These data objectively describe the state of the sea surface height. The mesoscale eddy can be represented by a local equivalent region surrounded by an SLA closed contour, and the detection process requires the extraction of a stable closed contour structure from SLA maps. In consideration of the characteristics of mesoscale eddy detection based on SLA data, this paper proposes a new automatic mesoscale eddy detection algorithm based on clustering. The mesoscale eddy structure can be extracted by separating and filtering SLA data sets to separate a mesoscale eddy region and non-eddy region and then establishing relationships among eddy regions and mapping them on SLA maps. This paper overcomes the problem of the sensitivity of parameter setting that affects the traditional detection algorithm and does not require a sensitivity test. The proposed algorithm is thus more adaptable. An eddy discrimination mechanism is added to the algorithm to ensure the stability of the detected eddy structure and to improve the detection accuracy. On this basis, the paper selects the Northwest Pacific Ocean and the South China Sea to carry out a mesoscale eddy detection experiment. Experimental results show that the proposed algorithm is more efficient than the traditional algorithm and the results of the algorithm remain stable. The proposed algorithm detects not only stable single-core eddies but also stable multi-core eddy structures.