Statistical Prediction of Heavy Rain in South Korea

1. Introduction In recent decades, extreme weather events seem to be growing in frequency and risk due to water-related disasters. According to the World Meteorological Or- ganization report (ISDR and WMO, 2004) on World Water Day, 22 March 2004, the economic losses caused by water-related disasters, including floods, droughts and tropical cyclones, are on an increasing trend as follows: the yearly mean in the 1970s was about 131 billion US dollars, 204 billion dollars in the 1980s, and …

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.