High resolution and accurate rainfall information is essential to modeling and predicting hydrological processes. Crowdsourced personal weather stations (PWSs) have become increasingly popular in recent years and can provide dense spatial and temporal resolution in rainfall estimates. However, their usefulness could be limited due to less trust in crowdsourced data compared to traditional data sources. Using crowdsourced PWSs data without a robust evaluation of its trustworthiness can result in inaccurate rainfall estimates as PWSs are installed and maintained by non-experts. In this study, we advance the Reputation System for Crowdsourced Rainfall Networks (RSCRN) to bridge this trust gap by assigning dynamic trust scores to PWSs. Based on rainfall data collected from 18 PWSs in two dense clusters in Houston, Texas, USA as a case study, we found that using RSCRN-derived trust scores can increase the accuracy of 15-min PWS rainfall estimates when compared to rainfall observations recorded at the city's high-fidelity rainfall stations. Overall, RSCRN rainfall estimates improved for 77% (48 out of 62) of the analyzed storm events, with a median root-mean-square error (RMSE) improvement of 27.3%. Compared to an existing PWS quality control method, results showed that RSCRN improved rainfall estimates for 71% of the storm events (44 out of 62), with a median RMSE improvement of 18.7%. Using RSCRN-derived trust scores can make the rapidly growing network of PWSs a more useful resource for hydrologic applications, greatly improving knowledge of rainfall patterns in areas with dense PWSs.

中文翻译：

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评估众包降雨网络的可信度：一种声誉系统方法

高分辨率和准确的降雨信息对于水文过程建模和预测至关重要。近年来，众包个人气象站 (PWS) 变得越来越流行，并且可以在降雨估计中提供密集的空间和时间分辨率。然而，与传统数据源相比，由于对众包数据的信任度较低，因此它们的实用性可能会受到限制。由于 PWS 是由非专家安装和维护的，因此在没有对其可信度进行可靠评估的情况下使用众包 PWS 数据可能会导致降雨量估计不准确。在这项研究中，我们推进了众包降雨网络的声誉系统 (RSCRN)，通过为 PWS 分配动态信任分数来弥合这种信任差距。基于从美国德克萨斯州休斯顿两个密集集群的 18 个 PWS 收集的降雨数据作为案例研究，我们发现，与城市高保真降雨站记录的降雨观测相比，使用 RSCRN 衍生的信任分数可以提高 15 分钟 PWS 降雨估计的准确性。总体而言，在分析的风暴事件中，77%（62 次中的 48 次）的 RSCRN 降雨量估计值有所提高，均方根误差 (RMSE) 中位数提高了 27.3%。与现有的 PWS 质量控制方法相比，结果表明，RSCRN 改进了 71% 的风暴事件（62 次中的 44 次）的降雨量估计值，RMSE 改进中位数为 18.7%。使用 RSCRN 衍生的信任分数可以使快速增长的 PWS 网络成为更有用的水文应用资源，极大地提高对 PWS 密集地区降雨模式的了解。