Technical note: Overview and comparison of three quality control algorithms for rainfall data from personal weather stations

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, an examination and comparison of three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control of rainfall data is presented. The focus was on a series of rainfall events occurring in the Amsterdam area between May 2017–May 2018. Quality issues observed include faulty zeros i.e., the underreporting of rainfall, significant gaps in the dataset, and systematic bias often caused by incorrect setup and installation of the PWS. The analysis shows that all three algorithms improve PWS data quality when cross-referenced against rain radar. The considered algorithms have different strengths and weaknesses depending on PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The need for further objective quantitative benchmarking of QC algorithms requiring freely available test datasets representing a range of environments, gauge densities, and weather patterns is highlighted.