Optimal spatial assessment of short-time step precipitation for hydrological modelling is still an important research question considering the poor observation networks for high time resolution data. The main objective of this paper is to present a new approach for rainfall observation. The idea is to consider motorcars as moving rain gauges with windscreen wipers as sensors to detect precipitation. This idea is easily technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This study explores theoretically the benefits of such an approach. For that a valid relationship between wiper speed and rainfall rate considering uncertainty was assumed here. A simple traffic model is applied to generate motorcars on roads in a river basin. Radar data are used as reference rainfall fields. Rainfall from these fields is sampled with a conventional rain gauge network and with several dynamic networks consisting of moving motorcars, using different assumptions such as accuracy levels for measurements and sensor equipment rates for the car networks. Those observed point rainfall data from the different networks are then used to calculate areal rainfall for different scales. Ordinary kriging and indicator kriging are applied for interpolation of the point data with the latter considering uncertain rainfall observation by cars e.g. according to a discrete number of windscreen wiper operation classes. The results are compared with the values from the radar observations. The study is carried out for the 3300 kmsup2/sup Bode river basin located in the Harz Mountains in Northern Germany. The results show, that the idea is theoretically feasible and motivate practical experiments. Only a small portion of the cars needed to be equipped with sensors for sufficient areal rainfall estimation. Regarding the required sensitivity of the potential rain sensors in cars it could be shown, that often a few classes for rainfall observation are enough for satisfactory areal rainfall estimation. The findings of the study suggest also a revisiting of the rain gauge network optimisation problem.
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机译：考虑到对高分辨率数据的观测网络较差，对水文模型进行短时降水的最佳空间评估仍然是一个重要的研究问题。本文的主要目的是提出一种新的降雨观测方法。想法是将汽车视为带有雨刷的移动雨量计，作为检测降水的传感器。如果为汽车提供GPS和一个用于记录坐标，汽车速度和刮水器频率的小型存储芯片，则该想法在技术上很容易实现。这项研究从理论上探讨了这种方法的好处。为此，在此假设了考虑不确定性的雨刷速度与降雨率之间的有效关系。应用简单的交通模型在流域道路上生成汽车。雷达数据用作参考降雨场。使用常规的雨量计网络和几个由移动的汽车组成的动态网络，使用不同的假设（例如，测量的准确度级别和汽车网络的传感器设备费率）对来自这些领域的降雨进行采样。然后，将来自不同网络的那些观测点降雨数据用于计算不同规模的区域降雨。点数据的插值采用普通克里金法和指标克里金法，后者考虑到不确定的降雨观测，例如汽车。根据不连续的挡风玻璃刮水器操作类别。将结果与雷达观测值进行比较。这项研究是针对位于德国北部哈尔茨山脉的3300 km 2 博德河流域进行的。结果表明，该思想在理论上是可行的，可以激发实际实验。仅一小部分汽车需要配备传感器，以进行足够的区域降雨估算。关于汽车中潜在的雨水传感器的灵敏度要求，可以证明，通常有几类降雨观测足以满足令人满意的区域降雨估算。该研究的发现还暗示了对雨量计网络优化问题的重新审视。
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