The growing trend toward urbanisation and the increasingly frequent occurrence of extreme weather events emphasise the need for further monitoring and understanding of weather in cities. In order to gain information on these intra-urban weather patterns, dense high-quality atmospheric measurements are needed. Crowdsourced weather stations (CWSs) could be a promising solution to realise such monitoring networks in a cost-efficient way. However, due to their nontraditional measuring equipment and installation settings, the quality of datasets from these networks remains an issue. This paper presents crowdsourced data from the “Leuven.cool” network, a citizen science network of around 100 low-cost weather stations (Fine Offset WH2600) distributed across Leuven, Belgium ($\mathrm{50}{}^{\circ }{\mathrm{52}}^{\prime }$ N, $\mathrm{4}{}^{\circ }{\mathrm{42}}^{\prime }$ E). The dataset is accompanied by a newly developed station-specific temperature quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for inter-station (between-station) and intra-station (station-specific) temperature biases by means of a random forest approach. The QC method is evaluated using data from four WH2600 stations installed next to official weather stations belonging to the Royal Meteorological Institute of Belgium (RMI). A positive temperature bias with a strong relation to the incoming solar radiation was found between the CWS data and the official data. The QC method is able to reduce this bias from 0.15 ± 0.56 to 0.00 ± 0.28 K. After evaluation, the QC method is applied to the data of the Leuven.cool network, making it a very suitable dataset to study local weather phenomena, such as the urban heat island (UHI) effect, in detail. (https://doi.org/10.48804/SSRN3F, Beele et al., 2022).

中文翻译：

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比利时鲁汶公民科学气象站网络气温数据的质量控制和校正方法

城市化趋势的日益增长和极端天气事件的日益频繁发生强调了进一步监测和了解城市天气的必要性。为了获得有关这些城市内天气模式的信息，需要进行密集的高质量大气测量。众包气象站 (CWS) 可能是一种以经济高效的方式实现此类监测网络的有前景的解决方案。然而，由于其非传统的测量设备和安装设置，来自这些网络的数据集的质量仍然是一个问题。本文介绍了来自“Leuven.cool”网络的众包数据，该网络是一个由分布在比利时鲁汶的大约 100 个低成本气象站（Fine Offset WH2600）组成的公民科学网络（$\mathrm{50}{}^{\circ }{\mathrm{52}}^{\text{'}}$ N, $\mathrm{4}{}^{\circ }{\mathrm{42}}^{\text{'}}$ E)。该数据集附有新开发的特定站点温度质量控制 (QC) 和校正程序。该程序由三个级别组成，这些级别消除了不合理的测量结果，同时还通过随机森林方法校正了站间（站间）和站内（特定于站）的温度偏差。QC 方法使用来自比利时皇家气象研究所 (RMI) 官方气象站旁边安装的四个 WH2600 站的数据进行评估。在 CWS 数据和官方数据之间发现了与入射太阳辐射密切相关的正温度偏差。QC 方法能够将此偏差从 0.15  ±  0.56 降低到 0.00  ± 0.28 K。经过评估，将QC方法应用于Leuven.cool网络的数据，使其成为一个非常适合详细研究局部天气现象的数据集，例如城市热岛（UHI）效应。（https://doi.org/10.48804/SSRN3F，Beele 等人，2022）。