A Nonlinear Artificial Intelligence Ensemble Prediction Model Based on EOF for Typhoon Track

Abstract

Using the capability of extraction the main signal feature from meteorological fields with random noise, and eliminate random disturbance by principal component analysis with conducted on empirical orthogonal functions(EOF), and following the thinking clue of the ensemble prediction in numerical weather prediction (NWP), a novel nonlinear artificial intelligence ensemble prediction (NAIEP) model has been developed based on the multiple neural networks with identical expected output created by using the genetic algorithm (GA) of evolutionary computation. Basing on the sample of typhoon in July from 1980 to 2009 for 30 years in the South China Sea, setting up the genetic neural network (GNN) ensemble prediction (GNNEP) model which selecting the predictors by the method of Stepwise regression and EOF both in the predictors of climatology persistence and Numerical forecasting(NWP) products to predict the typhoon track. The mean error for 24 hours of this new model is 125.7km, and the results of prediction experiments showed that the NAIEP model is obviously more skillful than the climatology and persistence (CLIPER) model with the circumstance of identical predictors and sample cases.