SHORT-TERM CLIMATE PREDICTION OF MEI-YU RAINFALL FOR TAIWAN USING CANONICAL CORRELATION-ANALYSIS

Canonical correlation analysis (CCA) is used for predicting Mei-Yu (Ma y-June) rainfall for eight major stations in Taiwan based on the antec edent November-December sea-surface temperatures (SSTs) over the Pacif ic Ocean (50 degrees N-40 degrees S, 120 degrees E-90 degrees W). To r educe the large dimensionality of the SST data set, an empirical ortho gonal function analysis is first performed and the leading nine eigenm odes of SST are retained as predictors. The root-mean-square error and the Pearson product-moment correlation coefficient are used to serve as a yardstick in overall forecast evaluation. Forecasts are made for the period 1986-1995, which is independent from the developmental data sets. A moderate skill is achieved for most stations. In particular, Mei-Yu rainfall is more predicable for the last 3 years (1993-1995), w hen the island experienced a long spell of deficient rainfall. A cross -validation technique is used to estimate the overall hindcast skill o f the CCA model for the period of 1956-1995 and results suggest that c ertain stations have more skill than others. Likewise, a CCA 'climatol ogical prediction' is conducted in a cross-validated mode. (C) 1998 Ro yal Meteorological Society.