Enhanced Short-Term Precipitation Forecasting with Radar Data Using Swin Transformer Network

Numerical Weather Prediction (NWP) systems integrate observational data from automatic weather station, upper air observation station, radar, and satellites through data assimilation to produce weather forecasts. However, the extensive computational demands of NWP models pose challenges for short-term predictions within a two-hour window. Among various observational data, radar data is directly correlated with atmospheric moisture particles and provides critical information for precipitation forecasting. In South Korea, radar data from 10 observation sites is combined to generate composite fields with a 5-minute interval and 500-meter spatial resolution. In this study, we developed a precipitation prediction model using a Swin Transformer-based network that directly utilizes radar data, bypassing the computationally intensive NWP models. Our research aimed to develop a model capable of predicting the next 18 consecutive precipitation fields based on the previous 4 consecutive fields. We compared our model's performance with the optical flow-based extrapolation method (pysteps) and the persistence model, using Root Mean Square Error (RMSE) as the evaluation metric. Results demonstrate that the Swin Transformer-based network outperforms both the optical flow and persistence models across all forecast time frames, indicating its robustness and accuracy in short-term precipitation prediction.