Interactive comment on “ Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems ” by Qin Zheng

The authors applied the particle swarm optimization (PSO) algorithm to solve the conditional nonlinear optimal perturbation (CNOP) and the lower bound of maximum predictable time (LBMPT). The results obtained by the PSO algorithm were compared to those by the traditional optimization algorithm (such as, a gradient descent algorithm based on the adjoint model, ADJ). The authors found that the PSO algorithm had advantage to compute the CNOP when the initial perturbation was large or the prediction time was long for the strong nonlinearity of the dynamical model on the prediction variable. Authors attempted to obtain the CNOP using the PSO algorithm. Considering the applications of CNOP, it is an interesting work.