A NEW METHOD FOR NON-LINEAR CLASSIFY AND NON-LINEAR REGRESSION Ⅱ :APPLICATION OF SUPPORT VECTOR MACHINE TO WEATHER FORECAST

A novel weather forecast method using the support vector machine (SVM) is introduced. Both of SVM model of area rainfall categorical forecast of 15 mm excess and SVM model of single-station temperature regression in Sichuan basin are built upon ECMWF analysis fields of 500 hPa height, 850 hPa temperature, and sea level pressure from April to September through 1990—2000. Extensive experiments are performed with performances evaluated by the Threat Scores (TS) or Correlation Coefficient. Empirical results demonstrate much improved performance compared with those given by standard statistic analysis and forecast methods.