RadarDiT: An advanced radar echo extrapolation model for three gorges reservoir area via diffusion transformer

Study region: The Three Gorges Reservoir Area (TGRA) Study focus: TGRA faces increasing vulnerability to extreme precipitation events driven by complex convective weather systems. Radar echo extrapolation—predicting future precipitation patterns from current radar data—is essential for early warning systems but faces significant challenges in this topographically complex region. While data-driven approaches have advanced the field, current convolutional neural network-based diffusion models struggle with the TGRA's dynamic meteorological conditions due to their reliance on translational invariance, which often fails to capture rapid weather transitions in complex terrain. New hydrogeological insights from the region: To address these limitations, we introduce RadarDiT, a Vision Transformer-based diffusion model specifically engineered for radar extrapolation in the TGRA. First, we develop a five-year radar dataset capturing diverse convective weather phenomena unique to this region. Then, leveraging this dataset, RadarDiT employs multi-layer Vision Transformers that effectively model global dependencies and complex spatial relationships, enabling accurate prediction of convective cell evolution. Our model demonstrates superior performance in maintaining strong echo and spatial coherence over longer forecast horizons. Quantitative evaluations across multiple metrics and thresholds confirm RadarDiT's enhanced skill in forecasting heavy precipitation events, with particular improvements in Critical Success Index at higher radar echo values. This work establishes a foundation for more reliable nowcasting systems in regions with complex terrain and dynamic weather patterns, directly supporting enhanced disaster preparedness and response strategies.