Technical note
A guide to using three open-source quality control algorithms for rainfall data from personal weather stations
Abbas El Hachem (University of Stuttgart, Federal Waterways Engineering and Research Institute (BAW))
Jochen Seidel (University of Stuttgart)
Tess O'hara (Newcastle University)
Roberto Villalobos Herrera (University of Costa Rica)
Aart Overeem (Royal Netherlands Meteorological Institute (KNMI))
Remko Uijlenhoet (TU Delft - Water Resources)
András Bárdossy (University of Stuttgart)
Lotte De Vos (Royal Netherlands Meteorological Institute (KNMI))
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Abstract
The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.
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