发表机构
Microsoft Corporation; City University of Hong Kong; University of Cambridge（微软公司; 香港城市大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MW-Nowcast，一种六小时集合雷达临近预报模型，联合学习确定性预测器和生成器，在美欧中测试中优于领先方法，将极端降水预警时间翻倍至6小时。
AI 中文摘要
延长极端降水的可靠临近预报可为山洪等高影响事件期间的预警和应急响应提供关键的额外时间。基于雷达的生成式机器学习模型已能实现高技巧的超本地化降水临近预报，但对强降水的准确预测仍局限于最初几个小时。由于风暴尺度结构比单个单体可预测的时间更长，一个自然的策略是预测该结构，同时仅对不确定的局部增长、衰减、重组和风暴生成进行生成式建模。在此，我们介绍了微软天气临近预报（MW-Nowcast），一个六小时集合雷达临近预报模型，它联合学习一个确定性预测器以捕捉集合成员间共享的有组织的降水结构，以及一个生成器以在该共享预测周围产生多样的局部残差。在美国、欧洲和中国的独立测试数据上，MW-Nowcast在整个6小时时段内对强和极端降水实现了比领先方法更高的检测技巧。对于最强降雨，MW-Nowcast在三个地区均将可用预警时间翻倍，提供了6小时预报，其技巧此前仅领先生成基线在3小时内实现。成本损失决策分析表明，即使在4-6小时，MW-Nowcast对广泛的应用仍保留显著价值，而替代方法在此提供甚少益处。这些额外的时间可给预报员和应急管理人员在极端降雨来临前预警和行动的时间，有助于保护生命和财产。
英文摘要
Extending reliable nowcasting of extreme precipitation could provide critical additional time for warnings and emergency response during high-impact events such as flash floods. Radar-based generative machine-learning models have enabled skilful hyperlocal precipitation nowcasting, but accurate prediction of intense precipitation remains confined to the first few hours. Because storm-scale structure is predictable for longer than individual cells, a natural strategy is to predict that structure while generatively modelling only the uncertain local growth, decay, reorganisation and initiation of storms. Here we present Microsoft Weather Nowcast (MW-Nowcast), a six-hour ensemble radar nowcasting model that jointly learns a deterministic predictor to capture organised precipitation structure shared across ensemble members, and a generator to produce diverse local residuals around this shared prediction. Across independent test data from the United States, Europe and China, MW-Nowcast achieves higher detection skill than leading methods for heavy and extreme precipitation throughout the 6 h horizon. For the most intense rainfall, MW-Nowcast doubles the available warning time across all three regions, delivering 6 h forecasts with skill previously limited to 3 h for the leading generative baseline. A cost-loss decision analysis shows that MW-Nowcast retains substantial value for a broad range of applications even at 4-6 h, where alternative methods offer little benefit. These additional hours can give forecasters and emergency managers the time to warn and act before extreme rainfall strikes, helping to protect lives and property.
Comments62 pages, 31 figures, 4 tables; includes Extended Data Figures and Supplementary Information
