Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 20
Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4715-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4715-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 20
Technical note
 | 
29 Oct 2024
Technical note |  | 29 Oct 2024

Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations

Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos

Data sets

Rainfall observations datasets from Personal Weather Stations L. W. de Vos https://doi.org/10.4121/uuid:6e6a9788-49fc-4635-a43d-a2fa164d37ec

Model code and software

OpenSenseAction/OPENSENSE_sandbox: v0.1.0 (v0.1.0) C. Chwala et al. https://doi.org/10.5281/zenodo.13929196

PWSQC code L. W. de Vos https://doi.org/10.5281/zenodo.10629489

AbbasElHachem/pws-pyqc: OpenSense Integration A. El Hachem https://doi.org/10.5281/zenodo.7310212

RVH-CR/intense-qc: v0.2.0 (v0.2.0) F. McClean and D. Pritchard https://doi.org/10.5281/zenodo.13920320

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Short summary
This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWSs). The methodology and usability along technical and operational guidelines for using every QC algorithm are presented. All three QC algorithms are available for users to explore in the OpenSense sandbox. They were applied in a case study using PWS data from the Amsterdam region in the Netherlands.  The results highlight the necessity for data quality control.
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