Conditional Nonlinear Optimal Perturbation (CNOP) is a new method proposed by Mu et al. in 2003, which generalizes the linear singular vector (LSV) to include nonlinearity. It has become a powerful tool for studying predictability and sensitivity among other issues in nonlinear systems. This is because the CNOP is able to represent, while the LSV is unable to deal with, the fastest developing perturbation in a nonlinear system. The wide application of this new method, however, has been limited due to its large computational cost related to the use of an adjoint technique. In order to greatly reduce the computational cost, we hereby propose a fast algorithm for solving the CNOP based on the empirical orthogonal function (EOF). The algorithm is tested in target observation experiments of Typhoon Matsa using the Global/Regional Assimilation and PrEdiction System (GRAPES), an operational regional forecast model of China. The effectivity and feasibility of the algorithm to determine the sensitivity (target) area is evaluated through two observing system simulation experiments (OSSEs). The results, as expected, show that the energy of the CNOP solved by the new algorithm develops quickly and nonlinearly. The sensitivity area is effectively identified with the CNOP from the new algorithm, using 24 h as the prediction time window. The 24-h accumulated rainfall prediction errors (ARPEs) in the verification region are reduced significantly compared with the "true state," when the initial conditions (ICs) in the sensitivity area are replaced with the "observations." The decrease of the ARPEs can be achieved for even longer prediction times (e.g., 72 h). Further analyses reveal that the decrease of the 24-h ARPEs in the verification region is attributable to improved simulations of the typhoon's initial warm-core, upper level relative vorticity, water vapor conditions, etc., as a result of the updated ICs in the sensitivity area.
