Abstract Classical quality control (QC) methods of solar irradiance apply easy-to-implement physical or statistical limits that are incapable of detecting low-magnitude measuring errors due to the large width of the intervals. We previously presented the bias-based quality control (BQC), a novel method that flags samples in which the bias of several independent gridded datasets is larger for consecutive days than the historical value. The BQC was previously validated at 313 European and 732 Spanish stations finding multiple low-magnitude errors (e.g., shadows, soiling) not detected by classical QC methods. However, the need for gridded datasets, and ground measurements to characterize the bias, was hindering the BQC implementation. To solve this issue, we present a free web service, www.bqcmethod.com , that implements the BQC algorithm incorporating both the gridded datasets and the reference stations required to use the BQC across Europe from 1983 to 2018. Users only have to upload a CSV file with the global horizontal irradiance measurements to be analyzed. Compared to previous BQC versions, gridded products have been upgraded to SARAH-2, CLARA-A2, ERA5, and the spatial coverage has been extended to all of Europe. The web service provides a flexible environment that allows users to tune the BQC parameters and upload ancillary rain data that help in finding the causes of the errors. Besides, the outputs cover not only the visual and numerical QC flags but also daily and hourly estimations from the gridded datasets, facilitating the access to raster data.

中文翻译：

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BQC：用于质量控制整个欧洲太阳辐照度测量的免费网络服务

摘要 太阳辐照度的经典质量控制 (QC) 方法应用易于实施的物理或统计限制，由于间隔的宽度较大，这些限制无法检测低量级的测量误差。我们之前介绍了基于偏差的质量控制 (BQC)，这是一种标记样本的新方法，其中几个独立网格数据集的偏差连续几天大于历史值。BQC 之前在 313 个欧洲站和 732 个西班牙站进行了验证，发现经典 QC 方法未检测到的多个低幅度错误（例如，阴影、污染）。然而，需要网格数据集和地面测量来表征偏差，这阻碍了 BQC 的实施。为了解决这个问题，我们提供了一个免费的网络服务，www.bqcmethod.com，实现了 BQC 算法，结合了网格数据集和从 1983 年到 2018 年在整个欧洲使用 BQC 所需的参考站。用户只需上传带有要分析的全球水平辐照度测量值的 CSV 文件。与之前的BQC版本相比，网格化产品升级为SARAH-2、CLARA-A2、ERA5，空间覆盖范围扩大到全欧洲。Web 服务提供了一个灵活的环境，允许用户调整 BQC 参数并上传辅助降雨数据，以帮助找到错误的原因。此外，输出不仅涵盖视觉和数字 QC 标志，还涵盖来自网格数据集的每日和每小时估计，方便访问栅格数据。