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Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model
Zhang Kun1,2; Mu Mu3; Wang Qiang1,4
2017-05-01
发表期刊SCIENCE CHINA-EARTH SCIENCES
卷号60期号:5页码:866-875
文章类型Article
摘要Using the conditional nonlinear optimal perturbation (CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport (UKT) were investigated in the Regional Ocean Modeling System (ROMS). The vertically integrated energy scheme was utilized to identify sensitive areas based on two factors: the specific energy scheme and sensitive area size. Totally 27 sensitive areas, characterized by three energy schemes and nine sensitive area sizes, were evaluated. The results show that the total energy (TE) scheme was the most effective because it includes both the kinetic and potential components of CNOP. Generally, larger sensitive areas led to better predictions. The size of 0.5% of the model domain was chosen after balancing the effectiveness and efficiency of adaptive observation. The optimal sensitive area OSen was determined accordingly. Sensitivity experiments on OSen were then conducted, and the following results were obtained: (1) In OSen, initial errors with CNOP or CNOP-like patterns were more likely to yield worse predictions, and the CNOP pattern was the most unstable. (2) Initial errors in OSen rather than in other regions tended to cause larger prediction errors. Therefore, adaptive observation in OSen can be more beneficial for predicting the seasonal reduction of UKT.
关键词Sensitive Area Adaptive Observation The Upstream Kuroshio Transport Conditional Nonlinear Optimal Perturbation (Cnop)
DOI10.1007/s11430-016-9020-8
收录类别SCI
语种英语
WOS记录号WOS:000400551600004
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文献类型期刊论文
版本出版稿
条目标识符http://ir.qdio.ac.cn/handle/337002/137054
专题海洋环流与波动重点实验室
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Fudan Univ, Inst Atmospher Sci, Shanghai 200433, Peoples R China
4.Qingdao Natl Lab Marine Sci & Technol, Lab Ocean & Climate Dynam, Qingdao 266237, Peoples R China
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Zhang Kun,Mu Mu,Wang Qiang. Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model[J]. SCIENCE CHINA-EARTH SCIENCES,2017,60(5):866-875.
APA Zhang Kun,Mu Mu,&Wang Qiang.(2017).Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model.SCIENCE CHINA-EARTH SCIENCES,60(5),866-875.
MLA Zhang Kun,et al."Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model".SCIENCE CHINA-EARTH SCIENCES 60.5(2017):866-875.
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