Quality Control Algorithms for Precipitation Data - An Intercomparison using Personal Weather Stations

Official rain gauge networks are usually too sparse to capture the spatio-temporal variability of precipitation. To increase network density and thus improve quantitative precipitation estimates, data from crowdsourced personal weather stations (PWS) can be deployed. As these gauges are not professionally placed and maintained, a thorough quality control (QC) prior to the application of PWS data is essential. Although there are currently no standards and guidelines on the QC of rainfall data, two open-source QC frameworks have been developed in recent years. Those are: first, the pypwsqc package (Chwala et al., 2026), which was developed in particular as QC for PWS networks and includes algorithms developed by de Vos et al. (2019) and Bárdossy et al. (2021); and second, RainfallQC, which covers the GSDR-QC framework developed by Lewis et al. (2021). Those QC frameworks are published as Python packages and include several modular methods, filters or checks that can be applied either individually or as a whole framework. In this case study, we will explore whether a merged QC approach that combines checks from both frameworks yields better results than the single application of any framework. For this intercomparison, we exploit high-temporal resolution data from a dense network of 12 reliable rain gauges, and around 300 PWS from Reutlingen, Germany. The PWS output of the best QC approach will then be benchmarked against data from nearby professional gauges using precipitation sums and maxima for single events as well as the whole investigation period.Our results suggest best practices for carrying out QC on rainfall data from PWS, and for different types of rainfall events. We suggest that developing, maintaining and continuously improving open-source QC algorithms supports the use of PWS data in hydrological research. Referencesde Vos, L. W., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Quality control for crowdsourced personal weather stations to enable operational rainfall monitoring, Geophysical Research Letters, 46, 8820–8829, 2019. DOI:10.1029/2019GL083731Bardossy, A., Seidel, J., El Hachem, A.:The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrology and Earth System Sciences, 25, 583-601, 2021. https://doi.org/10.5194/hess-25-583-2021Chwala, C. et al.: Open-source tools for processing opportunistic rainfall sensor data: An overview of existing tools and the new opensense software packages poligrain, pypwsqc and mergeplg. Submitted to Hydrology and Earth System Sciences, 2026. Lewis, E., Pritchard, D., Villalobos-Herrera, R., Blenkinsop, S., McClean, F., Guerreiro, S., Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rustemeier, E., Fowler, H. J.: Quality control of a global hourly rainfall dataset, Environmental Modelling & Software, 144, 2021. https://doi.org/10.1016/j.envsoft.2021.105169