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State-space adjustment of radar rainfall and stochastic flow forecasting for use in real-time control of urban drainage systems

机译:雷达降雨的状态空间调整和随机流量预测用于城市排水系统的实时控制

摘要

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic greybox models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared to using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.
机译:将雷达降雨数据与雨量计测量值合并是解决从雷达测量值得出降雨强度时遇到的问题的常用方法。我们使用状态空间模型扩展了用于调整C波段雷达数据的现有方法,并将所得的降雨强度用作预测哥本哈根地区两个集水区出水量的输入。应用随机灰箱模型创建径流预报,不仅为我们提供点预报,而且还为预报不确定性提供量化。对结果进行评估,我们可以证明,与使用原始雷达数据相比,使用调整后的雷达数据可以改善径流预报,并且雨量计的测量结果也优于预报的输入。将数据合并方法与短期降雨预测算法相结合,可以进一步改善径流预报,从而可用于实时控制。

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