A new snow depth forecast data using cumulative distribution function matching in South Korea

Abstract

Over the past decade, heavy snow has caused the third-largest amount of disaster damage in South Korea, following typhoons and heavy rain. To prevent damage from heavy snow effectively, it is necessary to forecast weather conditions. The Korea Meteorological Administration uses the Local Data Assimilation and Prediction System (LDAPS) to forecast hydrometeorological factors. However, the performance of LDAPS snow depth data is inferior to that of other models and requires correction. In this study, a cumulative distribution function (CDF) matching was used to correct LDAPS snow depth data. The CDF matching was carried out by utilizing ERA5-Land snow depth data to generate snow depth forecasting data for 12, 24, and 36-hour intervals. The forecasting data for snow depth is expected to generate snow disaster risk prediction data that can help reduce disaster losses on the Korean Peninsula.