Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallowwater model -- Journal of Oceanology and Limnology,2016,34(5):1122-1133

	Cite this paper:
	ZOU Guang'an, WANG Qiang, MU Mu. Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallowwater model[J]. Journal of Oceanology and Limnology, 2016, 34(5): 1122-1133

Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallowwater model

ZOU Guang'an^1,2,3, WANG Qiang¹, MU Mu¹

1 Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 School of Mathematics and Statistics, Henan University, Kaifeng 475004, China

Abstract:

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 model. The 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 forecasts. These results reveal that the sensitive areas determined using the CNOP are more sensitive than those of FSV and other randomly selected areas. In addition, ideal hindcasting experiments were conducted to examine the validity of the sensitive areas. The 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 areas. These results suggest that the CNOP method is suitable for determining sensitive areas in the prediction of the Kuroshio large-meander path.

Key words: Kuroshio large meander|conditional nonlinear optimal perturbation (CNOP)|first singular vector (FSV)|sensitive areas

Received: 2014-10-13 Revised: 2015-02-06

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