基于物理的深度时空超本地雷达临近预报：使用多变量U-Net进行高分辨率降水预报
Physics-Based Deep Spatiotemporal Hyperlocal Radar Nowcasting with a Multi-Variable U-Net for High-Resolution Precipitation Forecasting
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中文总结 AI 辅助
针对城市降水临近预报难题，开发结合多变量的雷达临近预报框架，利用U-Net模型及相关模块方法，通过雷达观测训练评估，在提前90分钟预报时相比传统方法有优势，能快速生成预报用于实时决策。
中文摘要 AI 辅助
10 - 90分钟内的降水临近预报对城市地区的洪水管理和实时决策很重要。传统高分辨率数值天气预报的短期预报需要频繁数据同化等，存在计算延迟。机器学习可直接从高频观测中学习风暴演变并快速预报。本文针对印度孟买的复杂气象情况，开发了一个仅基于雷达的临近预报框架，将多仰角反射率等特征结合到编码器 - 解码器U-Net中。该模型利用最新雷达体扫预测未来12个复合反射率场，间隔7.5分钟，最长提前90分钟。通过特定模块和方法提升性能，并经训练和评估。在90分钟提前期时，不同阈值下的关键成功指数有相应结果，与持续性预报相比有优势，且能在标准计算机上快速生成临近预报用于实时应用。
英文摘要
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.
发表机构
- Centre for Climate Studies, Indian Institute of Technology Bombay（气候研究中心，印度理工学院孟买分校）
- Regional Meteorological Centre (RMC)（区域气象中心）
机构由 AI 辅助整理，请以论文原文为准。
