Robust ensemble feature extraction for solving conditional nonlinear optimal perturbation. (2015) - Wen, Shicheng; Yuan, Shijin; Li, Hongyu; Mu, Bin
Conditional nonlinear optimal perturbation (CNOP) has been widely applied to predictability and sensitivity studies of nonlinear models. The popular methods of solving CNOP can be divided into two categories: adjoint-based and ensemble-based. Although the adjoint-based method is very accurate, it requires the development of adjoint models. The ensemble-based method is an adjoint-free technique, but either its robustness is weak or its filtering process is dependent on observation or experience to a great extent. In this paper, we propose a robust ensemble-based method to solve CNOP. This method does not need the filtering process and can extract robust features. To demonstrate the validity, the proposed method is applied to the Zebiak-Cane (ZC) model and compared with the adjoint-based method and other ensemble-based methods. To improve the computational efficiency, we design an OpenMP-based parallelising scheme for the proposed method. Experimental results show that the proposed method can outperform other ensemble-based methods in robustness and the corresponding solution of CNOP significantly approximates the one obtained with the adjoint-based method.