論文

2019年6月

Event detection and visualization of ocean eddies simulated by ocean general circulation model

INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING
  • Daisuke Matsuoka
  • ,
  • Fumiaki Araki
  • ,
  • Hideharu Sasaki

10
3
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1142/S1793962319500181
出版者・発行元
WORLD SCIENTIFIC PUBL CO PTE LTD

Numerical study of ocean eddies has been carried out by using high-resolution ocean general circulation models. In order to understand ocean eddies from the large volume data produced by simulations, visualizing only eddy distribution at each time step is insufficient; time-variations in eddy events and phenomena must also be considered. However, existing methods cannot precisely find and track eddy events such as amalgamation and bifurcation. In this study, we propose an original approach for eddy detection, tracking, and event visualization based on an eddy classification system. The proposed method detects streams and currents as well as eddies, and it classifies discovered eddies into several categories using the additional stream and current information. By tracking how the classified eddies vary over time, detecting events such as eddy amalgamation and bifurcation as well as the interaction between eddies and ocean currents becomes achievable. We adopt the proposed method for two ocean areas in which strong ocean currents exist as case studies. We visualize the detected eddies and events in a time series of images, allowing us to acquire an intuitive understanding of a region of interest concealed in a high-resolution data set. Furthermore, our proposed method succeeded in clarifying the occurrence place and seasonality of each type of eddy event.

リンク情報
DOI
https://doi.org/10.1142/S1793962319500181
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000484177200010&DestApp=WOS_CPL
ID情報
  • DOI : 10.1142/S1793962319500181
  • ISSN : 1793-9623
  • eISSN : 1793-9615
  • Web of Science ID : WOS:000484177200010

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