Accurate Quantitative Precipitation Forecasts (QPF) at high spatial and temporal resolution are a perquisite for urban flood prediction. Commonly Lagrangian extrapolation of the rainfall patterns recognized by radar data, forms the basis of such forecasts for the near future (up to 2 h lead time) – referred here as nowcasting. Nevertheless, due to the intermittent nature of the rainfall at such fine scales, the predictability of storms is limited to about 20 min. The predictability loss is caused mainly by the inability of the radar to capture the true rainfall field and because the Lagrangian Persistence is unable to model the temporal evolution of the rainfall field. In this study we focus on the first problem, on how to extend the predictability limit of rainfall at such scales by improving the rainfall field fed into the nowcast model. To overcome the errors associated with the radar intensities, merging techniques between radar and gauge measurements are advised. Among different employed techniques (mean field bias, kriging with external drift and quantile mapping based correction) the conditional merging between the radar and rainfall-gauge measurements seems to capture at best the spatial and temporal patterns of the rainfall at the desired fine scales (1 km² and 5 min). Moreover, when fed to two nowcast models, the conditional merging doesn’t only increase the predictability of storms from 20 min to longer than 1 h, but as well it improves the agreement of radar based QPF with the gauge measurements. The results are drawn from 110 events observed in the period 2000–2018 by the Hannover Radar (Germany) in an area with a radius of 115 km, where 100 recording gauges were available. As the urban hydrological models are commonly validated on gauge measurements, nowcasting with conditionally merged data, provides a useful tool for urban flood prediction for lead times up to 1 h.

在高时空分辨率下的精确定量降水预报（QPF）对城市洪水预报是必要的。通常由雷达数据识别的降雨模式的拉格朗日外推法，构成了对不久的将来（长达2小时的前置时间）的这种预报的基础-这里称为临近预报。但是，由于降雨的间歇性质如此细小，因此风暴的可预测性仅限于约20分钟。可预测性的损失主要是由于雷达无法捕获真实的降雨场，以及拉格朗日持续性无法模拟降雨场的时间演变造成的。在这项研究中，我们关注第一个问题，即如何通过改善输入到临近预报模型中的降雨场来扩展这种规模的降雨的可预测性极限。为了克服与雷达强度有关的误差，建议在雷达和仪表测量之间合并技术。在不同的采用技术中（平均场偏，具有外部漂移的克里金法和基于分位数映射的校正），雷达和雨量计测量之间的条件合并似乎可以最好地捕获所需细尺度下的雨量的时空模式（1公里²和5分钟）。此外，当应用于两个临近预报模型时，条件合并不仅将暴风雨的可预测性从20分钟提高到超过1小时，而且还提高了基于雷达的QPF与量规测量的一致性。结果是从2000年至2018年期间汉诺威雷达（德国）在半径115 km的区域内观测到的110个事件得出的，那里有100个记录仪。由于城市水文模型通常在量具测量中得到验证，因此有条件合并数据的临近预报将为预测长达1小时的提前期提供城市洪水预报的有用工具。