Complex Network Modeling and Visualization Analysis for Ocean Observation Data

doi:10.16182/j.issn1004731x.joss.201807002

Abstract

Abstract: Ocean data analysis is one of the important foundations in marine science research. Analysis on the sea surface temperature based on complex network theory helps explore the marine dynamics in a new perspective. The ocean is divided into grids, and the annual average of the sea surface temperature is calculated to reflect the properties of the corresponding grid area. The mutual information and the Pearson correlation coefficient are used to measure the similarity between different areas. The nonlinear and linear complex network models which reflect the station of the global marine climate can be built. Finally some popular measures including degree distribution, clustering coefficient and betweenness are introduced to discover the ocean phenomena, such as energy transfer of ocean, and the system robust and seasonal variation of the ocean dynamics are analyzed.

Key words: complex networks, visualization, time series, mutual information, Pearson correlation coefficient, sea surface temperature, topological simulation

CLC Number:

TP391

Sun Xin, Li Zhenhua, Dong Junyu, LuoXinyan, Yang Yuting. Complex Network Modeling and Visualization Analysis for Ocean Observation Data[J]. Journal of System Simulation, 2018, 30(7): 2445-2452.

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