Abstract
Conditional nonlinear optimal perturbation (CNOP) has been widely used in the predictability and sensitivity studies of the weather or climate models. The popular solution to the CNOP is the adjoint-based method. However, many numerical models have no adjoint models, thus bringing about a limitation to the CNOP applications. To avoid the adjoint models, we propose the robust PCA-based genetic algorithm for solving the CNOP (RGA_CNOP). To demonstrate the validity of the proposed method, it is applied to the CNOP of the Zebiak-Cane (ZC) model, and compared with the adjoint-based method. Experimental results show the RGA_CNOP can obtain approximate results to the adjoint-based method.
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