A Nonlinear Artificial Intelligence Ensemble Prediction Model Based on EOF For Typhoon Track
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 persistance 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.
EOF ensemble prediction genetic algorithm neutral network typhoon track
Xiao-yan Huang Long Jin Xv-ming Shi
Guangxi Meteorological Observatory Nanning, 530022,China Guangxi Climate Center Guangxi Meteorological Bureau Nanning, 530022,China Guangxi Research Institute of Meteorological Disasters Mitigation Nanning, 530022,China
国际会议
昆明、丽江
英文
1329-1333
2011-04-15（万方平台首次上网日期，不代表论文的发表时间）