- 1University of Stuttgart, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart, Germany (jochen.seidel@iws.uni-stuttgart.de)
- 2Swedish Meteorological and Hydrological Institute, Hydrological Research Unit, Norrköping, Sweden
- 3Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
The use of so-called opportunistic rainfall sensors like personal weather stations (PWS) and commercial microwave links has gained much attention over the recent year, as they clearly outnumber professional rain gauges which are operated by national weather services and other. However, the data quality of such sensors is typically low and thus their information cannot be used without thorough quality control. Various quality control algorithms for PWS rainfall data have been developed and published within the EU COST Action CA 20136 "Opportunistic Precipitation Sensing Network" (OPENSENSE) in the past years and are available on OPENSENSE's GitHub (El Hachem et al. 2024).
Some of the new functions for these QC filters include (1) an improved indicator correlation filter which was originally developed by Bárdossy et al. (2019) which now provides a skill score for the accepted PWS to assess quality of the indicator correlation with neighbouring references, (2) an algorithm to correct rainfall peaks in PWS data which may be caused by connection interruptions between the rain gauge and the base station and (3) a Python implementation of the QC algorithms for identifying faulty zeroes, high influxes and station outliers originally developed in R by de Vos et al. (2019).
These new features will subsequently be implemented in the new ‘pypwsqc’ Python package (https://zenodo.org/records/14177798) which is currently under development in the OPENSENSE COST Action. In this poster we present the new features and guidelines for usage.
References:
Bárdossy, A., Seidel, J., and El Hachem, A. (2021), The use of personal weather station observations to improve precipitation estimation and interpolation. Hydrol. Earth Syst. Sci., 25, 583–601.
El Hachem, A., Seidel, J., O'Hara, T., Villalobos Herrera, R., Overeem, A., Uijlenhoet, R., Bárdossy, A., and de Vos, L.W (2024), Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations, Hydrol. Earth Syst. Sci., 28, 4715–4731.
de Vos, L.W., Leijnse, H.,Overeem, A., and Uijlenhoet, R. (2019), Quality control for crowdsourced personal weather stations to enable operational rainfall monitoring. Geophysical Research Letters, 46, 8820–8829.
How to cite: Seidel, J., Petersson Wårdh, L., Illich, N., and Chwala, C.: Recent developments in the quality control of personal weather stations data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10988, https://doi.org/10.5194/egusphere-egu25-10988, 2025.
