PPSO: PCA based particle swarm optimization for solving conditional nonlinear optimal perturbation

Conditional nonlinear optimal perturbation (CNOP)1 has been widely applied to predictability and sensitivity studies of nonlinear models in meteorology and oceanography. The popular solution of CNOP is based on adjoint models, which is also treated as the benchmark. However, the development of adjoint models is time-consuming, especially for the large-scale air-sea coupled model. Intelligent algorithms are another solution of CNOP, but they are just confined to some simple ideal models due to dimensionality. To avoid adjoint models, this paper proposes a principal component analysis (PCA) based particle swarm optimization method to solve the CNOP of complicated practical models. Through dimension reduction, the original problem can be mapped into a low space so that the particle swarm optimization method can search results in it. To demonstrate the validity, the proposed method is applied to the Zebiak-Cane model and compared with the adjoint based method. Experimental results show that the proposed method can approximately solve the CNOP of complicated models without the need of computing adjoint models, and achieve similar results with the adjoint based method.