Statistical prediction of heavy rain in South Korea

This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km× 10 km area-mean amount of rainfall at 6 stations (Seoul, Daejeon, Gangreung, Gwangju, Busan, and Jeju) in South Korea are used. And the corresponding 45 synoptic factors generated by the numerical model are used as potential predictors. Four statistical forecast models (linear regression model, logistic regression model, neural network model and decision tree model) for the occurrence of heavy rain are based on the model output statistics (MOS) method. They are separately estimated by the same training data. The thresholds are considered to forecast the occurrence of heavy rain because the distribution of estimated values that are generated by each model is too skewed.The results of four models are compared via Heidke skill scores. As a result, the logistic regression model is recommended.