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Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

文献类型：期刊论文

作者Sun Guodong1; Mu Mu1,2; Zhang Yale1,3
刊名ADVANCES IN ATMOSPHERIC SCIENCES
出版日期2010-11-01
卷号27期号:6页码:1311-1321
关键词Conditional Nonlinear Optimal Perturbation Constrained Optimization Problem Unconstrained Optimization Problem
ISSN号0256-1530
DOI10.1007/s00376-010-9088-1
文献子类Article
英文摘要The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples.; The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.
学科主题Meteorology & Atmospheric Sciences
URL标识查看原文
语种英语
WOS记录号WOS:000283136900008
公开日期2010-12-24
源URL[http://ir.qdio.ac.cn/handle/337002/5265]  
专题海洋研究所_海洋环流与波动重点实验室
作者单位1.Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Wave, Qingdao 266071, Peoples R China
3.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Sun Guodong,Mu Mu,Zhang Yale. Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)[J]. ADVANCES IN ATMOSPHERIC SCIENCES,2010,27(6):1311-1321.
APA Sun Guodong,Mu Mu,&Zhang Yale.(2010).Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP).ADVANCES IN ATMOSPHERIC SCIENCES,27(6),1311-1321.
MLA Sun Guodong,et al."Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)".ADVANCES IN ATMOSPHERIC SCIENCES 27.6(2010):1311-1321.

入库方式： OAI收割

来源：海洋研究所

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