Rainfall estimation using moving cars as rain gauges (RainCars)

提出的研究目的是研究一种全新的降雨估计方法，使用汽车作为移动雨量计，挡风玻璃雨刷作为传感器来检测降水。如果给汽车配备GPS和一个小的存储芯片来记录坐标、车速和雨刷频率，这个想法在技术上是很容易实现的。这项初步研究将从理论上探讨这种方法的好处。为此，假设雨刷速度和降雨率之间的关系（w -r关系）是有效的，并从实验室和现场实验中推导出来。开发了不同的交通模型，在流域的道路上产生汽车。雷达数据作为参考真值雨场。这些地区的降雨是从传统雨量计和动态汽车网络中采样的。利用地统计插值方法从这些网络计算不同尺度的面雨量，并与真值雷达数据进行比较。汽车传感器可以看作是一个地理传感器网络。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具有优势，这在必须使用大量测量单元覆盖大区域时至关重要。

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.