Advancing Radar Echo Extrapolation With Hypergraph-Enhanced Latent Diffusion Model
Xiaoni Sun, Yong Zhang, Xin Di, Xinglin Piao, Fei Wang, Guodong Jing, Dawei Lin, Baocai Yin · IEEE Transactions on Geoscience and Remote Sensing · 2025
Radar Echo Extrapolation (REE) can facilitate accurate and expeditious nowcasting of precipitation, reducing the reliance on complex Numerical Weather Prediction (NWP) models. Spatial-temporal forecasting methods dominate this task because they can fully exploit the spatio-temporal dependencies and complex dynamic patterns inherent in radar echo data. However, they struggle with handling uncertainty and incorporating domain-specific knowledge, often resulting in blurry or unrealistic predictions. We propose a Hypergraph-enhanced Latent Diffusion Model (HyDiff) to address these limitations. The EchoDiff has been utilized to aid in accurate extrapolation. To accurately describe precipitation microphysics while adhering to the hydro-microphysical and multiscale coupling principles, the method integrates additional semantic information into the model. Specifically, the Differential Reflectivity Factor (ZDR) and Differential Propagation Phase Shift (KDP) are incorporated into the model as additional semantic information. Furthermore, we introduce a Hypergraph Neural Network (HGNN) into the extrapolation method to capture correlation information across regions. Experiments show that HyDiff effectively handles uncertainty, incorporates domain-specific prior knowledge, and generates forecasts with high operational utility.