In this study, we examined the potential of the Korea Rapid-Development Thunderstorm (K-RDT) product obtained from a geostationary meteorological satellite to improve the short-term prediction of heavy rainfall caused by a mesoscale convective system over South Korea. Specifically, we utilized a simple nudging technique to integrate K-RDT data into the Simplified Arakawa Schubert (SAS) deep convection scheme of the Global/Regional Integrated Model System (GRIMs) Regional Model program (RMP). Our analysis focuses on selected cases of heavy rainfall. The nudging experiments outperformed the control experiments in terms of precipitation forecasts. Notably, the experiment that used longer nudging times produced the best results. Our results also demonstrate that the K-RDT, with its resolution of 1 km, can detect small-scale convective cells that have clear impacts on large-scale atmospheric fields. This suggests that incorporating such small-scale information into numerical weather prediction (NWP) models can significantly improve forecasting skill, especially when the model cannot represent subgrid-scale convection.

在这项研究中，我们研究了从地球静止气象卫星获得的韩国快速发展雷暴（K-RDT）产品的潜力，以改善对韩国上空中尺度对流系统引起的强降雨的短期预测。具体来说，我们利用简单的助推技术将 K-RDT 数据集成到全球/区域综合模型系统 (GRIM) 区域模型程序 (RMP) 的简化荒川舒伯特 (SAS) 深对流方案中。我们的分析重点是选定的强降雨案例。就降水预测而言，轻推实验优于对照实验。值得注意的是，使用较长推动时间的实验产生了最好的结果。我们的结果还表明，K-RDT 的分辨率为 1 km，可以检测对大尺度大气场有明显影响的小尺度对流单元。这表明，将此类小尺度信息纳入数值天气预报（NWP）模型可以显着提高预报技能，特别是当模型无法代表亚网格尺度对流时。