SMG-Net: A SimVP-Based Collaborative Model for Radar Echo Extrapolation in Precipitation Nowcasting

Radar echo extrapolation under severe convective conditions remains challenging because efficient prediction models still tend to suffer from strong-echo attenuation, boundary blurring, and performance degradation at longer lead times. To address these issues, this study proposes SMG-Net, a SimVP-based radar echo extrapolation model with a collaborative multistage design. The proposed framework integrates multiscale spatial enhancement, trend–disturbance differentiated temporal modeling, and gated hierarchical feature fusion to improve structural preservation and temporal stability. Experiments on a regional radar dataset show that SMG-Net achieves the lowest MSE (0.032) and the highest SSIM (0.830) among the compared models. At the 30 dBZ threshold, CSI, POD, and FAR reach 0.042, 0.045, and 0.250, respectively, indicating improved strong-echo detectability and reduced false alarms. The results further show that SMG-Net is particularly effective in preserving the morphology, boundary structure, and intensity distribution of medium- and strong-echo regions at longer lead times, while introducing only limited additional computational cost over the baseline SimVP. These findings indicate that SMG-Net improves the preservation of medium- and strong-echo structures in efficient radar echo extrapolation and has practical value for short-term precipitation nowcasting in severe convective scenarios.