Speaker
Description
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, auxiliary data from so-called opportunistic sensors can be deployed. Crowdsourced personal weather stations (PWS) are an example of such opportunistic sensors. In recent years PWS have outnumbered official stations from national met services and therefore present a valuable data source, providing precipitation data of high temporal resolution in a dense network. As these gauges are not professionally placed and maintained, a thorough quality control (QC) prior to the application of PWS data is essential.
In recent years several QC frameworks have been developed. 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. To evaluate the effectiveness of the existing methods, a systematic benchmarking framework will be developed.
The approach uses rainfall data from both a PWS network and reference gauges and applies individual and sequential quality control (QC) processes to the PWS data. The QC´ed PWS data will then be evaluated against data from a nearby reference station. Evaluation metrics comprise bias, correlation, coefficient of variation, weekly or monthly precipitation sums as well as computing time and ease of application. Besides identifying the most appropriate workflow based for the proposed dataset, the tool could further help deriving a generalized pipeline of application and provide guidance for the choice of parameters.
References:
de 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:10.1029/2019GL083731
Bardossy, 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-25-583-2021
Chwala, 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