A new automatic oceanic mesoscale eddy detection method using satellite altimeter data based on density clustering
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A new automatic oceanic mesoscale eddy detection method using satellite altimeter data based on density clustering Jitao Li1, Yongquan Liang1, Jie Zhang2, Jungang Yang2*, Pingjian Song2, Wei Cui2, 3 1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590,
China 2 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China 3 Physical Oceanography Lab, Qingdao Collaborative Innovation Center of Marine Science and Technology, Ocean
University of China, Qingdao 266100, China Received 6 May 2018; accepted 9 July 2018 © Chinese Society for Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature 2019
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. Key words: mesoscale eddy, density clustering, shape discrimination, outermost closed contour Citation: Li Jitao, Liang Yongquan, Zhang Jie, Yang Jungang, Song Pingjian, Cui Wei. 2019. A new automatic oceanic mesoscale eddy detection method using satellite altimeter data based on density clustering. Acta Oceanologica Sinica, 38(5): 134–141, doi: 10.1007/s13131019-1447-x
1 Introduction The mesoscale eddy is an important form of seawater transport having a long-term closed sta
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