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	A fast algorithm for solving cnop and associated target observation tests
	Wang Bin1; Tan Xiaowei1,2
刊名	Acta meteorologica sinica
	2009-08-01
卷号	23期号:4页码:387-402
关键词	Fast algorithm Cnop (conditional nonlinear optimal perturbation) Target observation Osse (observing system simulation experiment)
ISSN号	0894-0525
通讯作者	Wang bin(wab@lasg.iap.ac.cn)
英文摘要	Conditional nonlinear optimal perturbation (cnop) is a new method proposed by mu et al. in 2003, which generalizes the linear singular vector (lsv) to include nonlinearity. it has become a powerful tool for studying predictability and sensitivity among other issues in nonlinear systems. this is because the cnop is able to represent, while the lsv is unable to deal with, the fastest developing perturbation in a nonlinear system. the wide application of this new method, however, has been limited due to its large computational cost related to the use of an adjoint technique. in order to greatly reduce the computational cost, we hereby propose a fast algorithm for solving the cnop based on the empirical orthogonal function (eof). the algorithm is tested in target observation experiments of typhoon matsa using the global/regional assimilation and prediction system (grapes), an operational regional forecast model of china. the effectivity and feasibility of the algorithm to determine the sensitivity (target) area is evaluated through two observing system simulation experiments (osses). the results, as expected, show that the energy of the cnop solved by the new algorithm develops quickly and nonlinearly. the sensitivity area is effectively identified with the cnop from the new algorithm, using 24 h as the prediction time window. the 24-h accumulated rainfall prediction errors (arpes) in the verification region are reduced significantly compared with the "true state," when the initial conditions (ics) in the sensitivity area are replaced with the "observations." the decrease of the arpes can be achieved for even longer prediction times (e.g., 72 h). further analyses reveal that the decrease of the 24-h arpes in the verification region is attributable to improved simulations of the typhoon's initial warm-core, upper level relative vorticity, water vapor conditions, etc., as a result of the updated ics in the sensitivity area.
WOS关键词	NONLINEAR OPTIMAL PERTURBATION ; ADAPTIVE OBSERVATIONS ; MESOSCALE PREDICTABILITY ; INITIAL CONDITIONS ; SINGULAR VECTORS ; KALMAN FILTER ; GROWTH ; ENSO ; SENSITIVITY ; CONVECTION
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
语种	英语
出版者	ACTA METEOROLOGICA SINICA PRESS
WOS记录号	WOS:000270209200001
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2402420
专题	中国科学院大学
通讯作者	Wang Bin
作者单位	1.Chinese Acad Sci, Inst Atmospher Phys, LASG, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Grad Sch, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wang Bin,Tan Xiaowei. A fast algorithm for solving cnop and associated target observation tests[J]. Acta meteorologica sinica,2009,23(4):387-402.
APA	Wang Bin,&Tan Xiaowei.(2009).A fast algorithm for solving cnop and associated target observation tests.Acta meteorologica sinica,23(4),387-402.
MLA	Wang Bin,et al."A fast algorithm for solving cnop and associated target observation tests".Acta meteorologica sinica 23.4(2009):387-402.

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