基于密度峰值聚类的中尺度涡轨迹自动追踪方法

王辉赞; 郭芃; 倪钦彪; 李佳讯

doi:10.3969/j.issn.0253-4193.2018.08.001

基于密度峰值聚类的中尺度涡轨迹自动追踪方法

doi: 10.3969/j.issn.0253-4193.2018.08.001

1.
国防科技大学气象海洋学院, 湖南长沙 410073;国家海洋局第二海洋研究所卫星海洋环境动力学国家重点实验室, 浙江杭州 310012
2.
94587部队, 江苏连云港 222345
3.
国家海洋局第二海洋研究所卫星海洋环境动力学国家重点实验室, 浙江杭州 310012;厦门大学近海海洋环境科学国家重点实验室, 福建厦门 361005
4.
海军海洋测绘研究所, 天津 300061

基金项目: 中国科学院战略性先导科技专项（A类）资助（XDA11010103）；国家自然科学基金（41706021，41775053，41206002）；国家海洋局第二海洋研究所专项资助（JG1416）；中国博士后科学基金（2014M551711）；江苏省自然科学基金（BK20151447）。

计量
- 文章访问数: 966
- HTML全文浏览量: 30
- PDF下载量: 407
- 被引次数: 0
出版历程
- 收稿日期: 2017-06-10
- 修回日期: 2017-10-30

A CFSFDP clustering-based eddy trajectory tracking method

1.
Institute of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China;State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
2.
Unit 94587, Lianyungang 222345, China
3.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, China
4.
Naval Institute of Hydrographic Surveying and Charting, Tianjin 300061, China

摘要

摘要: 中尺度涡信息的提取包括涡旋的识别和轨迹追踪，其自动识别与追踪对于基于海量数据的中尺度涡分析十分重要。传统涡旋轨迹自动追踪方法一般需要预先设定搜索半径的阈值，存在一定的主观性。针对传统中尺度涡轨迹追踪方法存在的问题，论文从聚类的角度出发，提出基于密度峰值聚类算法实现对涡旋轨迹的自动追踪，并以南海中尺度涡追踪为例，将基于聚类的追踪算法与传统的相似度追踪算法进行比较分析。结果表明：（1）基于密度峰值聚类算法，可实现对海洋中尺度涡的自动追踪，该算法涡旋追踪准确率优于传统相似度算法；（2）该涡旋追踪算法对资料的完整性依赖度较低，特别是对于存在部分缺损数据的情况仍能较准确追踪；（3）该追踪算法克服了传统涡旋追踪算法需要预先设定搜索半径阈值的问题，自适应性更强。
- 中尺度涡 /
- 轨迹追踪 /
- 密度峰值聚类算法 /
- 南海
Abstract: Information extraction of ocean mesoscale eddies includes the eddy identification and its trajectory tracking, both of which are very important for the research on mesoscale eddies based on the massive data. The traditional methods of trajectory tracking generally need to be set the threshold of the search radius beforehand, which could introduce a certain degree of subjectivity. To improve the existing problem of the traditional methods, an automatic tracking method of the mesoscale eddies is proposed in this study based on the Clustering by Fast Search and Find of Density Peaks (CFSFDP) from clustering point of view. Then it was compared with the traditional similarity algorithm by taking the South China Sea as a testbed. Our results show that:(1) Based on the CFSFDP algorithm, the automatic tracking of mesoscale eddies is realized, and the accuracy is better than the traditional similarity method; (2) The proposed tracking algorithm is less dependent on the data integrity especially for the presence of partial missing data; (3) Our proposed tracking method has stronger adaptability, which overcomes the problem that the search radius need to be set beforehand in the traditional method.
- mesoscale eddies /
- trajectory tracking /
- Clustering by Fast search and Find of Density peaks(CFSFDP) /
- South China Sea

HTML全文

参考文献(20)

Wang Guihua, Su Jilian, Chu P C. Mesoscale eddies in the South China Sea observed with altimeter data[J]. Geophysical Research Letters, 2003, 30(21):2121.

Chelton D B, Schlax M G, Samelson R M. Global observations of nonlinear mesoscale eddies[J]. Progress in Oceanography, 2011, 91(2):167-216.

Li Jiaxun, Wang Guihua, Zhai Xiaoming. Observed cold filaments associated with mesoscale eddies in the South China Sea[J]. Journal of Geophysical Research:Oceans, 2017, 122(1):762-770.

Okubo A. Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences[J]. Deep Sea Research and Oceanographic Abstracts, 1970, 17(3):445-454.

Weiss J. The dynamics of enstrophy transfer in two-dimensional hydrodynamics[J]. Physica D:Nonlinear Phenomena, 1991, 48(2/3):273-294.

Chaigneau A, Gizolme A, Grados C. Mesoscale eddies off Peru in altimeter records:Identification algorithms and eddy spatio-temporal patterns[J]. Progress in Oceanography, 2008, 79(2/4):106-119.

Sadarjoen I A, Post F H. Detection, quantification, and tracking of vortices using streamline geometry[J]. Computers & Graphics, 2000, 24(3):333-341.

Nencioli F, Dong C M, Dickey T, et al. A vector geometry-based eddy detection algorithm and its application to a high-resolution numerical model product and high-frequency radar surface velocities in the Southern California Bight[J]. Journal of Atmospheric and Oceanic Technology, 2010, 27(3):564-579.

Chen Gengxin, Hou Yijun, Chu Xiaoqing. Mesoscale eddies in the South China Sea:Mean properties, spatiotemporal variability, and impact on thermohaline structure[J]. Journal of Geophysical Research:Oceans, 2011, 116(C6):C06018.

Li Jiaxun, Zhang Ren, Jin Baogang. Eddy characteristics in the northern South China Sea as inferred from Lagrangian drifter data[J]. Ocean Science, 2011, 7(5):661-669.

Dong Changming, Lin Xiayan, Liu Yu, et al. Three-dimensional oceanic eddy analysis in the Southern California Bight from a numerical product[J]. Journal of Geophysical Research:Oceans, 2012, 117(C7):C00H14.

刘宇. 基于拉格朗日数据和欧拉数据的中尺度涡旋识别与分析[D]. 广州:中国科学院南海海洋研究所, 2012. Liu Yu. Mesoscale eddy detection and analysis from Lagrangian data and Eulerian data[D]. Guangzhou:South China Sea Institute of Oceanology, Chinese Academy of Sciences, 2012.

倪钦彪. 吕宋海峡附近中尺度涡的统计特征和复合三维结构[D]. 厦门:厦门大学, 2013. Ni Qinbiao. Statistical characteristics and composite three-dimensional structures of mesoscale eddies near the Luzon Strait[D]. Xiamen:Xiamen University, 2013.

Henson S A, Thomas A C. A census of oceanic anticyclonic eddies in the Gulf of Alaska[J]. Deep-Sea Research Part Ⅰ:Oceanographic Research Papers, 2008, 55(2):163-176.

吴笛. 南海中尺度涡移动轨迹聚类分析[D]. 北京:中国科学院大学, 2015. Wu Di. Trajectory clustering analysis of ocean eddies in the South China Sea[D]. Beijing:University of Chinese Academy of Sciences, 2015.

王桂华, 苏纪兰, 齐义泉. 南海中尺度涡研究进展[J]. 地球科学进展, 2005, 20(8):882-886. Wang Guihua, Su Jilan, Qi Yiquan. Advances in studying mesoscale eddies in South China Sea[J]. Advances in Earth Science, 2005, 20(8):882-886.

Xiu Peng, Chai Fei, Shi Lei, et al. A census of eddy activities in the South China Sea during 1993-2007[J]. Journal of Geophysical Research:Oceans, 2010, 115(C3):C03012.

郑全安, 谢玲玲, 郑志文, 等. 南海中尺度涡研究进展[J]. 海洋科学进展, 2017, 35(2):131-158. Zheng Quanan, Xie Lingling, Zheng Zhiwen, et al. Progress in research of mesoscale eddies in the South China Sea[J]. Advances in Marine Science, 2017, 35(2):131-158.

Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496.

Yi Jiawei, Du Y, He Z, et al. Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly[J]. Ocean Science, 2014, 10(1):39-48.

施引文献

资源附件(0)

访问统计