Advancing Radar Echo Extrapolation With Hypergraph-Enhanced Latent Diffusion Model

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 (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z_DR</i>) and Differential Propagation Phase Shift (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_DP</i>) 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.