Algorithm studies on how to obtain a conditional nonlinear optimal perturbation (CNOP)

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 investi...

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Vydáno v:Advances in atmospheric sciences Ročník 27; číslo 6; s. 1311 - 1321
Hlavní autoři: Sun, Guodong, Mu, Mu, Zhang, Yale
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg SP Science Press 01.11.2010
Springer Nature B.V
Témata:
Algorithms
Atmospheric Sciences
Earth and Environmental Science
Earth Sciences
Ecosystem models
El Nino
Geophysics/Geodesy
Grasslands
Meteorology
Nonlinear programming
Nonlinear systems
conditional nonlinear optimal perturbation
constrained optimization problem
unconstrained optimization problem
ISSN:0256-1530, 1861-9533
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Shrnutí: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.
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ISSN:0256-1530
1861-9533
DOI:10.1007/s00376-010-9088-1