Ensemble prediction experiments using conditional nonlinear optimal perturbation

2009 ◽  
Vol 52 (4) ◽  
pp. 511-518 ◽  
Author(s):  
ZhiNa Jiang ◽  
Mu Mu ◽  
DongHai Wang
Keyword(s):  
Ensemble Prediction ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation

Robust ensemble feature extraction for solving conditional nonlinear optimal perturbation

2015 ◽  
Vol 11 (4) ◽  
pp. 349
Author(s):  
Shicheng Wen ◽  
Shijin Yuan ◽  
Hongyu Li ◽  
Bin Mu
Keyword(s):  
Feature Extraction ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation

A Method for Identifying the Sensitive Areas in Targeted Observations for Tropical Cyclone Prediction: Conditional Nonlinear Optimal Perturbation

2009 ◽  
Vol 137 (5) ◽  
pp. 1623-1639 ◽  
Author(s):  
Mu Mu ◽  
Feifan Zhou ◽  
Hongli Wang
Keyword(s):  
Tropical Cyclone ◽  
Natural Extension ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation ◽  
Sensitive Areas ◽  
Sensitivity Experiments ◽  
Adaptive Observation ◽  
Adaptive Observations ◽  
Cyclone Prediction

Abstract Conditional nonlinear optimal perturbation (CNOP), which is a natural extension of the linear singular vector into the nonlinear regime, is proposed in this study for the determination of sensitive areas in adaptive observations for tropical cyclone prediction. Three tropical cyclone cases, Mindulle (2004), Meari (2004), and Matsa (2005), are investigated. Using the metrics of kinetic and dry energies, CNOPs and the first singular vectors (FSVs) are obtained over a 24-h optimization interval. Their spatial structures, their energies, and their nonlinear evolutions as well as the induced humidity changes are compared. A series of sensitivity experiments are designed to find out what benefit can be obtained by reductions of CNOP-type errors versus FSV-type errors. It is found that the structures of CNOPs may differ much from those of FSVs depending on the constraint, metric, and the basic state. The CNOP-type errors have larger impact on the forecasts in the verification area as well as the tropical cyclones than the FSV-types errors. The results of sensitivity experiments indicate that reductions of CNOP-type errors in the initial states provide more benefits than reductions of FSV-type errors. These results suggest that it is worthwhile to use CNOP as a method to identify the sensitive areas in adaptive observation for tropical cyclone prediction.

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

2010 ◽  
Vol 27 (6) ◽  
pp. 1311-1321 ◽  
Author(s):  
Guodong Sun ◽  
Mu Mu ◽  
Yale Zhang
Keyword(s):  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation

scholarly journals Conditional Nonlinear Optimal Perturbations: Adjoint-Free Calculation Method and Preliminary Test

2010 ◽  
Vol 138 (4) ◽  
pp. 1043-1049 ◽  
Author(s):  
Bin Wang ◽  
Xiaowei Tan
Keyword(s):  
Prediction Models ◽  
Natural Extension ◽  
Preliminary Test ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Growth Ratio ◽  
Optimal Perturbation ◽  
New Approach ◽  
Simple Theoretical Model ◽  
Simplex Search ◽  
Sensitivity Studies

Abstract An ensemble-based approach is proposed to obtain conditional nonlinear optimal perturbation (CNOP), which is a natural extension of linear singular vector to a nonlinear regime. The new approach avoids the use of adjoint technique during maximization and is thus more attractive. Comparisons among CNOPs of a simple theoretical model generated by the ensemble-based, adjoint-based, and simplex-search methods, respectively, not only show potential equivalence of the first two approaches in application according to their very similar spatial structures and time evolutions of the CNOPs, but also reveal the limited performance of the third measure, an existing adjoint-free algorithm, due to its inconsistent spatial distribution and weak net growth ratio of norm square of CNOP comparing with the results of the first two methods. Because of its attractive features, the new approach is likely to make it easier to apply CNOP in predictability or sensitivity studies using operational prediction models.

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