BQC: A free web service to quality control solar irradiance measurements across Europe

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...

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Veröffentlicht in:Solar energy 2020-11, Vol.211, p.1-10
Hauptverfasser: Urraca, Ruben, Sanz-Garcia, Andres, Sanz-Garcia, Iñigo
Format: Artikel
Sprache:eng
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Zusammenfassung: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. •Automatic visualization of QC results to facilitate error detection.•Users only need to upload a CSV file with ground measurements.•Outputs include point estimations of SARAH-2, CLARA-A2, and ERA5.
ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2020.09.055