An improved forecast of precipitation type using correlation-based feature selection and multinomial logistic regression

Abstract

Accurate prediction of precipitation type is an important part of weather forecasting. But using meteorological insight to make such predictions from a small set of weather variables achieves only limited success. We use correlation-based feature selection to assemble an effective subset of the large number of weather variables available in short-range weather forecasts, and from these we obtain the coefficients for multinomial regression, which can then be used to predict precipitation type. We applied this approach to data for significant locations in South Korea, obtained from the European Centre for Medium-Range Weather Forecasts and from the Regional Data Assimilation and Prediction System, and achieved predictions which are respectively 15% and 13% more accurate than those contained in the original forecasts. • We improved the forecast of winter precipitation types in South Korea. • Feature selection was used to assemble an effective subset of weather variables. • A comparative analysis was conducted on various forecasting schemes. • Logistic regression with feature selection works well on precipitation forecast.