IMPA-Net: Meteorology-Aware Multi-Scale Fusion and Dynamic Loss for Extreme Radar-Based Precipitation Nowcasting

Minimizing pixel-wise errors in precipitation nowcasting inherently biases models toward smooth predictions, causing failures in resolving extreme convective events. To address this, we propose IMPA-Net, a meteorology-aware framework centered on spectral consistency. The architecture integrates three innovations: a parameter-free Spatial Mixer to encode multi-variate physical interactions (e.g., terrain-wind coupling); an Integrated Multi-scale Predictive Attention (IMPA) module to capture dynamics from Meso-β to Meso-γ scales; and a Meteorology-Aware Dynamic Loss (MAD-Loss) that employs asymmetric penalties to counteract regression-to-the-mean. Experiments demonstrate a 37.3% relative improvement in HSS for severe convection (≥45 dBZ). Crucially, RAPSD analysis confirms that IMPA-Net maintains spectral energy consistency across high-frequency bands, enabling it to successfully simulate the complex "dissipation-initiation" lifecycle that existing baselines fail to capture. These findings validate that integrating domain knowledge advances the physical plausibility of data-driven forecasting.