Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallow-water model

作     者：邹广安 王强 穆穆 

作者机构：Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences Qingdao 266071 China University of Chinese Academy of Sciences Beijing 100049 China School of Mathematics and Statistics Henan University Kaifeng 475004 China 

出 版 物：《Chinese Journal of Oceanology and Limnology》 (中国海洋湖沼学报（英文版）)

年 卷 期：2016年第34卷第5期

页      面：1122-1133页

核心收录：

学科分类：0710[理学-生物学] 0908[农学-水产] 0707[理学-海洋科学] 08[工学] 0815[工学-水利工程] 0816[工学-测绘科学与技术] 

基　　金：Supported by the National Natural Science Foundation of China(Nos.41230420,41306023) the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA11010303) the NSFC-Shandong Joint Fund for Marine Science Research Centers(No.U1406401) 

主　　题：Kuroshio large meander conditional nonlinear optimal perturbation(CNOP) first singular vector(FSV) sensitive areas 

摘      要：Sensitive areas for prediction of the Kuroshio large meander using a 1.5-layer,shallowwater ocean model were investigated using the conditional nonlinear optimal perturbation(CNOP) and first singular vector(FSV) methods.A series of sensitivity experiments were designed to test the sensitivity of sensitive areas within the numerical *** following results were obtained:(1) the effect of initial CNOP and FSV patterns in their sensitive areas is greater than that of the same patterns in randomly selected areas,with the effect of the initial CNOP patterns in CNOP sensitive areas being the greatest;(2) both CNOP- and FSV-type initial errors grow more quickly than random errors;(3) the effect of random errors superimposed on the sensitive areas is greater than that of random errors introduced into randomly selected areas,and initial errors in the CNOP sensitive areas have greater effects on final *** results reveal that the sensitive areas determined using the CNOP are more sensitive than those of FSV and other randomly selected *** addition,ideal hindcasting experiments were conducted to examine the validity of the sensitive *** results indicate that reduction(or elimination) of CNOP-type errors in CNOP sensitive areas at the initial time has a greater forecast benefit than the reduction(or elimination) of FSVtype errors in FSV sensitive *** results suggest that the CNOP method is suitable for determining sensitive areas in the prediction of the Kuroshio large-meander path.