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...

Full description

Saved in:
Bibliographic Details
Published in:Advances in atmospheric sciences Vol. 27; no. 6; pp. 1311 - 1321
Main Authors: Sun, Guodong, Mu, Mu, Zhang, Yale
Format: Journal Article
Language:English
Published: Heidelberg SP Science Press 01.11.2010
Springer Nature B.V
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0256-1530
1861-9533
DOI:10.1007/s00376-010-9088-1