A nonlinear statistical ensemble model for short-range rainfall prediction

Abstract

Following the practice of the numerical weather ensemble prediction, a nonlinear statistical ensemble prediction model has been developed based on a neural network technique with a Particle Swarm Optimization (PSO) algorithm. The model is validated by short-range climate forecasts of monthly mean rainfall at 37 stations in Guangxi, China during the first rainy season (April, May, and June). Independent prediction results show that the Particle Swarm Optimization Neural Network ensemble prediction model is clearly better than the traditional linear statistical method, such as the multiple regression method and the stepwise regression method. It is also suggested that by applying multiple ensemble members with each member objectively determined by the PSO algorithm, the generalization capacity of the ensemble prediction model is enhanced, demonstrating a vast range of possibilities for operational short-range climate prediction.