Quantitative monthly precipitation forecasting using cyclostationary empirical orthogonal function and canonical correlation analysis

An empirical statistical system for quantitative forecasting of monthly precipitation in Korea has been developed using the cyclostationary empirical orthogonal function s(CSEOF) and the canonical correlation analysis (CCA) with sea surface temperature (SST) data as the predictor. Monthly Korean precipitation and SST data are comprehensively analyzed using the empirical orthogonal function (EOF) technique and the CSEOF technique, respectively, and the CSEOF technique can exhibit the spatial distribution and temporal evolution characteristics of variability along with recurrent seasons of precipitation in Korea. Through a multivariate regression method, the CCA technique is used to forecast precipitation with different lead times, and the forecasting results indicate that the CSEOF-CCA forecasting model agrees well with the observation data and is particularly useful in forecasting seasonal precipitation variations in Korea.