GA-ANN Forecast Model for Typhoon Gale in South China Sea Based on MDS

Abstract

Based on 33-year typhoon information of South China Sea (SCS) in 1980-2012 and NCEP/NCAR reanalysis data, taking Climatology and Persistence (CLIPER) and earlier physical quantities predictors selected by Stepwise Regression (SWR) and Multidimensional Scaling (MDS) methods as model inputs, the Genetic Algorithm-Artificial Neural Network (GA-ANN) forecast model was built for typhoon gale. The forecast verification results for independent samples in MDS-GA-ANN model show that mean absolute error of 24h forecast for wind velocities at 36 grid points around typhoon centers from July to September is 1.6m/s. Using the same samples, the prediction results of MDS-GA-ANN models for independent samples were compared with that of traditional SWR models. Taking July as example, prediction abilities for 29 MDS-GA-ANN models (81%) among 36 grid points around typhoon centers are superior to that of SWR models; only 2 grid points of MDS-GA-ANN models are worse than that of SWR models (6%). Therefore, prediction ability for most of 36 grid points using MDS-GA-ANN models is superior to that of SWR models and can meet business requirements of meteorological stations at present.