Accurate and high-resolution precipitation nowcasting from radar echo sequences is crucial for disaster mitigation and economic planning, yet it remains a significant challenge. Key difficulties include modeling complex multi-scale evolution, correcting inter-frame feature misalignment caused by displacement, and efficiently capturing long-range spatiotemporal context without sacrificing spatial fidelity. To address these issues, we present the Multi-scale Feature Communication Rectified Flow (RF) Network (MFC-RFNet), a generative framework that integrates multi-scale communication with guided feature fusion. To enhance multi-scale fusion while retaining fine detail, a Wavelet-Guided Skip Connection (WGSC) preserves high-frequency components, and a Feature Communication Module (FCM) promotes bidirectional cross-scale interaction. To correct inter-frame displacement, a Condition-Guided Spatial Transform Fusion (CGSTF) learns spatial transforms from conditioning echoes to align shallow features. The backbone adopts rectified flow training to learn near-linear probability-flow trajectories, enabling few-step sampling with stable fidelity. Additionally, lightweight Vision-RWKV (RWKV) blocks are placed at the encoder tail, the bottleneck, and the first decoder layer to capture long-range spatiotemporal dependencies at low spatial resolutions with moderate compute. Evaluations on four public datasets (SEVIR, MeteoNet, Shanghai, and CIKM) demonstrate consistent improvements over strong baselines, yielding clearer echo morphology at higher rain-rate thresholds and sustained skill at longer lead times. These results suggest that the proposed synergy of RF training with scale-aware communication, spatial alignment, and frequency-aware fusion presents an effective and robust approach for radar-based nowcasting.

翻译：基于雷达回波序列实现准确且高分辨率的临近降水预报对于灾害缓解和经济规划至关重要，但这仍然是一个重大挑战。关键难点包括：建模复杂的多尺度演变过程，校正由位移引起的帧间特征错位，以及在不牺牲空间保真度的前提下高效捕获长程时空上下文信息。为解决这些问题，我们提出了多尺度特征通信整流流网络，这是一个将多尺度通信与引导特征融合相结合的生成式框架。为在增强多尺度融合的同时保留精细细节，小波引导跳跃连接保留了高频分量，而特征通信模块促进了双向跨尺度交互。为校正帧间位移，条件引导空间变换融合从条件回波中学习空间变换以对齐浅层特征。网络主干采用整流流训练来学习近似线性的概率流轨迹，从而实现具有稳定保真度的少步采样。此外，在编码器尾部、瓶颈层和首个解码器层放置了轻量级视觉RWKV块，以中等计算成本在低空间分辨率下捕获长程时空依赖关系。在四个公共数据集上的评估表明，相对于强基线模型取得了持续的改进，在更高降雨率阈值下产生了更清晰的回波形态，并在更长的预见期保持了预报技巧。这些结果表明，所提出的将整流流训练与尺度感知通信、空间对齐及频率感知融合相结合的方法，为基于雷达的临近预报提供了一种有效且稳健的途径。