Recent climate change has intensified wintertime convective snowbands over the Yellow Sea, causing severe localized heavy snowfall and socio-economic damage in coastal Korea. Current weather radar has observational limitations (radius, elevation angle) for these events, especially for systems originating from distant seas, making proactive hazardous weather monitoring challenging. Existing proxy radar research predominantly focuses on summertime precipitation. To address this, we developed a wintertime proxy radar model using Geo-Kompsat-2A (GK2A) satellite data and time-series deep learning. The optimal model was selected through qualitative and quantitative evaluations, using 30-minute time-series input data consisting of three 10-minute-interval images, without the Multi-Task Learning (MTL) application. This model achieved an accuracy of Probability Of Detection (POD) 0.599, False Alarm Rate (FAR) 0.390, Critical Success Index (CSI) 0.435, Pearson Correlation Coefficient (CC) 0.605, bias -0.532 dBZ, and Root Mean Squared Error (RMSE) 3.334 dBZ, partially mitigating the typical surface cooling/snow cover issue in winter satellite data. Furthermore, a sliding-window approach was utilized to generate data for a larger area, with a mean and 3/4 patch overlap yielding the most continuous results. This developed proxy radar was able to generate early-stage snowfall information from distant seas that was previously undetectable by conventional radar. This research demonstrates the promising role of wintertime proxy radar data as an auxiliary forecasting tool for proactive detection of hazardous weather and damage mitigation in radar-deficient regions.



Keywords : Satellite, Proxy radar, Precipitation, Deep learning, Radar limitation, Wintertime