搞气象AI的可以看这篇，用孟买雷达数据训练的U-Net模型预测90分钟降水，CSI比传统方法高不少。
这篇论文提出基于物理引导的深度时空超本地雷达临近预报框架，使用多变量U-Net结合多仰角反射率、多普勒径向速度和径向速度梯度代理特征。模型利用2023年5月至8月孟买多普勒雷达观测数据训练，预测未来90分钟内每7.5分钟间隔的12个复合反射率场。在90分钟提前时间，对于≥10、≥20和≥30 dBZ阈值，临界成功指数分别为0.437、0.332和0.193。与持续性法相比，该模型在更长提前时间上具有更低的RMSE和更高的空间相关性。训练后可在标准计算机上实时运行，几秒内生成预报。
Physics-Based Deep Spatiotemporal Hyperlocal Radar Nowcasting with a Multi-Variable U-Net for High-Resolution Precipitation Forecasting
Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions. Conventional short-range forecasting with high-resolution numerical weather prediction requires frequent data assimilation, model initialization, and spin-up, introducing computational latency. Machine learning provides an alternative by learning storm evolution directly from high-frequency observations and producing forecasts quickly after training. This is particularly relevant for Mumbai, India, where monsoon convection, land-sea interactions, and localized intense rainfall make short-term prediction difficult. Here, we develop a compact radar-only nowcasting framework that combines multi-elevation reflectivity, Doppler radial velocity, and radial-velocity-gradient proxy features within an encoder-decoder U-Net. Using the most recent radar volume scan, the model predicts 12 future composite reflectivity fields at 7.5-min intervals up to 90 min lead time. The derived velocity magnitude, divergence-like, directional-shear, and vorticity-like channels represent kinematic signatures associated with convergence and boundary interactions without requiring full wind-field retrieval. A high-reflectivity attention module improves sensitivity to convective cores, and physics-guided attribution examines whether the learned sensitivities are meteorologically meaningful. The model is trained using Mumbai Doppler radar observations from May to August 2023 and evaluated on temporally independent events. At 90 min lead time, Critical Success Index values are 0.437, 0.332, and 0.193 for $\geq$10, $\geq$20, and $\geq$30 dBZ thresholds, respectively. Compared with persistence, the model gives lower RMSE and higher spatial correlation at longer lead times. Once trained, it runs on a standard computer, generating nowcasts within seconds for real-time use.