IOCAS-IR  > 海洋环流与波动重点实验室
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
Source PublicationSCIENCE CHINA-EARTH SCIENCES
Volume60Issue:5Pages:866-875
SubtypeArticle
AbstractUsing 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.
KeywordSensitive Area Adaptive Observation The Upstream Kuroshio Transport Conditional Nonlinear Optimal Perturbation (Cnop)
DOI10.1007/s11430-016-9020-8
Indexed BySCI
Language英语
WOS IDWOS:000400551600004
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Version出版稿
Identifierhttp://ir.qdio.ac.cn/handle/337002/137054
Collection海洋环流与波动重点实验室
Affiliation1.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
First Author AffilicationKey Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
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GB/T 7714
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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