Rainfall estimation using moving cars as rain gauges (RainCars)

这项研究的目的是研究一种全新的雨量估算方法，该方法使用汽车作为移动雨量计，以挡风玻璃雨刷作为传感器来检测降雨量。如果汽车配备GPS和一个用于记录坐标、车速和雨刷频率的小型存储芯片，这个想法在技术上很容易实现。这项初步研究将从理论上探索这种方法的好处。为此，通过室内和田间试验，建立了雨刷速度与降雨量之间的有效关系(W-R关系)。开发了不同的交通模型来在河流流域的道路上产生汽车。雷达资料被用作参考真实雨量场。这些地区的降雨量是从传统雨量计和动态车载网络中采集的。使用地统计插值法从这些网络计算不同尺度的面雨量，并与真实雷达数据进行比较。车载传感器可以看作是一个地球传感器网络。它允许以分散的方式在本地测量和处理信息，因此在可伸缩性方面有好处，这在必须用大量测量单位覆盖大片区域时至关重要。

Objective of the proposed research is the investigation of a completely new approach for rainfall estimation using motorcars as moving rain gauges with windscreen wipers as sensors to detect precipitation. This idea would easily be technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This initial research will explore theoretically the benefits of such an approach. For that valid relationships between wiper speed and rainfall rate (W-R-relationship) are assumed and derived from laboratory and field experiments. Different traffic models are developed to generate motorcars on roads in a river basin. Radar data are used as reference truth rainfall fields. Rainfall from these fields is sampled from the conventional rain gauge and dynamic car networks. Areal rainfall is calculated from these networks for different scales using geostatistical interpolation methods and compared against truth radar data. The car sensors can be considered as a geosensor network. It allows to measure and process information locally in a decentralized way and thus has benefits with respect to scalability, which is crucial when large areas have to be covered with large amounts of measurement units.