Articles | Volume 18, issue 13
https://doi.org/10.5194/amt-18-3229-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-3229-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Adaptation of RainGaugeQC algorithms for quality control of rain gauge data from professional and non-professional measurement networks
Katarzyna Ośródka
Centre of the Weather Forecasting Service, Institute of Meteorology and Water Management – National Research Institute, ul. Podleśna 61, 01-673 Warsaw, Poland
Centre of the Weather Forecasting Service, Institute of Meteorology and Water Management – National Research Institute, ul. Podleśna 61, 01-673 Warsaw, Poland
Anna Jurczyk
Centre of the Weather Forecasting Service, Institute of Meteorology and Water Management – National Research Institute, ul. Podleśna 61, 01-673 Warsaw, Poland
Agnieszka Kurcz
Centre of the Weather Forecasting Service, Institute of Meteorology and Water Management – National Research Institute, ul. Podleśna 61, 01-673 Warsaw, Poland
Related authors
Jan Szturc, Anna Jurczyk, Katarzyna Ośródka, Agnieszka Kurcz, Magdalena Szaton, Mariusz Figurski, and Robert Pyrc
EGUsphere, https://doi.org/10.5194/egusphere-2025-1863, https://doi.org/10.5194/egusphere-2025-1863, 2025
Short summary
Short summary
During the flooding of the Oder River in Sep 2024, daily rainfall exceeded 200 mm. The reliability of high-resolution rainfall estimates available in real time was assessed: from rain gauges, radars, satellites, unconventional & multi-source, also reanalyses. Rain gauges, adjusted radar & multi-source estimates showed the highest accuracy, unconventional methods slightly lower. Numerical weather prediction models still offered reasonable reliability, but satellite estimates were less effective.
Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023, https://doi.org/10.5194/amt-16-4067-2023, 2023
Short summary
Short summary
A data-processing algorithm, RainGRS Clim, has been developed to work on precipitation accumulations such as daily or monthly totals. The algorithm makes the most of additional opportunities: access to high-quality data that are not operationally available and greater efficiency of the algorithms for data quality control and merging for longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.
Katarzyna Ośródka, Irena Otop, and Jan Szturc
Atmos. Meas. Tech., 15, 5581–5597, https://doi.org/10.5194/amt-15-5581-2022, https://doi.org/10.5194/amt-15-5581-2022, 2022
Short summary
Short summary
The quality control of sub-hourly rain gauge data is a challenging task due to the high variability and low spatial consistency of the data. We developed an innovative approach to the quality control of telemetric rain gauge data focused on assessing the reliability of individual observations. Our scheme employs weather radar data to detect erroneous rain gauge measurements and to assess the data reliability. The scheme is used operationally by the Polish meteorological and hydrological service.
Katarzyna Ośródka and Jan Szturc
Atmos. Meas. Tech., 15, 261–277, https://doi.org/10.5194/amt-15-261-2022, https://doi.org/10.5194/amt-15-261-2022, 2022
Short summary
Short summary
Weather radar data are used in weather monitoring and forecasting, but they are affected by numerous errors and require advanced corrections. Different systems are designed and implemented to suit specific local conditions, like the RADVOL-QC system. The radar errors are divided into several groups: disturbance by non-meteorological echoes (from the mountains, RLAN signals, wind turbines, etc.), beam blockage, attenuation, etc. Each of them has different properties and is corrected differently.
Jan Szturc, Anna Jurczyk, Katarzyna Ośródka, Agnieszka Kurcz, Magdalena Szaton, Mariusz Figurski, and Robert Pyrc
EGUsphere, https://doi.org/10.5194/egusphere-2025-1863, https://doi.org/10.5194/egusphere-2025-1863, 2025
Short summary
Short summary
During the flooding of the Oder River in Sep 2024, daily rainfall exceeded 200 mm. The reliability of high-resolution rainfall estimates available in real time was assessed: from rain gauges, radars, satellites, unconventional & multi-source, also reanalyses. Rain gauges, adjusted radar & multi-source estimates showed the highest accuracy, unconventional methods slightly lower. Numerical weather prediction models still offered reasonable reliability, but satellite estimates were less effective.
Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023, https://doi.org/10.5194/amt-16-4067-2023, 2023
Short summary
Short summary
A data-processing algorithm, RainGRS Clim, has been developed to work on precipitation accumulations such as daily or monthly totals. The algorithm makes the most of additional opportunities: access to high-quality data that are not operationally available and greater efficiency of the algorithms for data quality control and merging for longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.
Katarzyna Ośródka, Irena Otop, and Jan Szturc
Atmos. Meas. Tech., 15, 5581–5597, https://doi.org/10.5194/amt-15-5581-2022, https://doi.org/10.5194/amt-15-5581-2022, 2022
Short summary
Short summary
The quality control of sub-hourly rain gauge data is a challenging task due to the high variability and low spatial consistency of the data. We developed an innovative approach to the quality control of telemetric rain gauge data focused on assessing the reliability of individual observations. Our scheme employs weather radar data to detect erroneous rain gauge measurements and to assess the data reliability. The scheme is used operationally by the Polish meteorological and hydrological service.
Katarzyna Ośródka and Jan Szturc
Atmos. Meas. Tech., 15, 261–277, https://doi.org/10.5194/amt-15-261-2022, https://doi.org/10.5194/amt-15-261-2022, 2022
Short summary
Short summary
Weather radar data are used in weather monitoring and forecasting, but they are affected by numerous errors and require advanced corrections. Different systems are designed and implemented to suit specific local conditions, like the RADVOL-QC system. The radar errors are divided into several groups: disturbance by non-meteorological echoes (from the mountains, RLAN signals, wind turbines, etc.), beam blockage, attenuation, etc. Each of them has different properties and is corrected differently.
Cited articles
Alerskans, E., Lussana, C., Nipen, T. N., and Seierstad, I. A.: Optimizing Spatial Quality Control for a Dense Network of Meteorological Stations, J. Atmos. Ocean. Tech., 39, 973–984, https://doi.org/10.1175/JTECH-D-21-0184.1, 2022.
Bárdossy, A., Seidel, J., and El Hachem, A.: The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrol. Earth Syst. Sci., 25, 583–601, https://doi.org/10.5194/hess-25-583-2021, 2021.
Båserud, L., Lussana, C., Nipen, T. N., Seierstad, I. A., Oram, L., and Aspelien, T.: TITAN automatic spatial quality control of meteorological in-situ observations, Adv. Sci. Res., 17, 153–163, https://doi.org/10.5194/asr-17-153-2020, 2020.
Bell, S., Cornford, D., and Bastin, L.: How good are citizen weather stations? Addressing a biased opinion, Weather, 70, 75–84, https://doi.org/10.1002/wea.2316, 2015.
Berndt, C. and Haberlandt, U.: Spatial interpolation of climate variables in Northern Germany –Influence of temporal resolution and network density, J. Hydrol., 15, 184–202, https://doi.org/10.1016/j.ejrh.2018.02.002, 2018.
Chwala, C. and Kunstmann, H.: Commercial microwave link networks for rainfall observation: assessment of the current status and future challenges, Wiley Interdisciplinary Reviews: Water, 6, e1337, https://doi.org/10.1002/wat2.1337, 2019.
Colli, M., Lanza, L. G., La Barbera, P., and Chan, P. W.: Measurement accuracy of weighing and tipping-bucket rainfall intensity gauges under dynamic laboratory testing, Atmos. Res., 144, 186–194, https://doi.org/10.1016/j.atmosres.2013.08.007, 2014.
de Vos, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam, Hydrol. Earth Syst. Sci., 21, 765–777, https://doi.org/10.5194/hess-21-765-2017, 2017.
de Vos, L. W., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Quality control for crowdsourced personal weather stations to enable operational rainfall monitoring, Geophys. Res. Lett., 46, 8820–8829, https://doi.org/10.1029/2019GL083731, 2019.
Droździoł, R. and Absalon, D.: Evaluation of selected amateur rain gauges with Hellmann rain gauge measurements, Climate, 11, 107, https://doi.org/10.3390/cli11050107, 2023.
Garcia-Marti, I., Overeem, A., Noteboom, J. W., de Vos, L., de Haij, M., and Whan, K.: From proof-of-concept to proof-of-value: Approaching third-party data to operational workflows of national meteorological services, Int. J. Climatol., 43, 275–292, https://doi.org/10.1002/joc.7757, 2023.
Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., and Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales, J. Hydrol., 37, 100883, https://doi.org/10.1016/j.ejrh.2021.100883, 2021.
Hahn, C., Garcia-Marti, I., Sugier, J., Emsley, F., Beaulant, A.-L., Oram, L., Strandberg, E., Lindgren, E., Sunter, M., and Ziska, F.: Observations from Personal Weather Stations – EUMETNET Interests and Experience, Climate, 10, 192, https://doi.org/10.3390/cli10120192, 2022.
Haselow, L., Meissner, R., Rupp, H., and Miegel, K.: Evaluation of precipitation measurements methods under field conditions during a summer season: A comparison of the standard rain gauge with a weighable lysimeter and a piezoelectric precipitation sensor, J. Hydrol., 575, 537–543, https://doi.org/10.1016/j.jhydrol.2019.05.065, 2019.
Hoffmann, M., Schwartengräber, R., Wessolek, W., and Peters, A.: Comparison of simple rain gauge measurements with precision lysimeter data, Atmos. Res., 174–175, 120–123, https://doi.org/10.1016/j.atmosres.2016.01.016, 2016.
Hohmann, C., Kirchengast, G., O, S., Rieger, W., and Foelsche, U.: Small catchment runoff sensitivity to station density and spatial interpolation: hydrological modeling of heavy rainfall using a dense rain gauge network, Water, 13, 1381, https://doi.org/10.3390/w13101381, 2021.
Horita, F. E. A., Vilela, R. B., Martins, R. G., Bressiani, D. A., Palma, G., and Porto de Albuquerque, J.: Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil, in: ISCRAM 2018, Rochester, NY, USA, 20–23 May 2018, Proceedings of the 15th ISCRAM Conference – Rochester, NY, USA May 2018, 1040–1050, ISBN 9780692127605, 2018.
IMGW-PIB: Dane publiczne, https://danepubliczne.imgw.pl/pl/datastore?product=Meteo (last access: 7 July 2025), 2024a.
IMGW-PIB: Dane publiczne, https://danepubliczne.imgw.pl/pl/datastore?product=COMPO_PAC.comp.pac (last access: 7 July 2025), 2024b.
Jurczyk, A., Szturc, J., and Ośródka, K.: Quality-based compositing of weather radar QPE estimates, Meteorol. Appl., 27, e1812, https://doi.org/10.1002/met.1812, 2020a.
Jurczyk, A., Szturc, J., Otop, I., Ośródka, K., and Struzik, P.: Quality-based combination of multi-source precipitation data, Remote Sens., 12, 1709, https://doi.org/10.3390/rs12111709, 2020b.
Jurczyk, A., Ośródka, K., Szturc, J., Pasierb, M., and Kurcz, A.: Long-term multi-source precipitation estimation with high resolution (RainGRS Clim), Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023, 2023.
Krennert, T., Pistotnik, G., Kaltenberger, R., and Csekits, C.: Crowdsourcing of weather observations at national meteorological and hydrological services in Europe, Adv. Sci. Res., 15, 71–76, https://doi.org/10.5194/asr-15-71-2018, 2018.
Lanza, L. G. and Vuerich, E.: The WMO Field Intercomparison of Rain Intensity Gauges, Atmos. Res., 94, 534–543, https://doi.org/10.1016/j.atmosres.2009.06.012, 2009.
Loritz, R., Hrachowitz, M., Neuper, M., and Zehe, E.: The role and value of distributed precipitation data in hydrological models, Hydrol. Earth Syst. Sci., 25, 147–167, https://doi.org/10.5194/hess-25-147-2021, 2021.
McKee, J. L. and Binns, A. D.: A review of gauge–radar merging methods for quantitative precipitation estimation in hydrology, Can. Water Resour. J., 41, 186–203, https://doi.org/10.1080/07011784.2015.1064786, 2016.
Muller, C. L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody G., Overeem, A., and Lei, R. R.: Crowdsourcing for climate and atmospheric sciences: current status and future potential, Int. J. Climatol., 35, 3185–3203, https://doi.org/10.1002/joc.4210, 2015.
Nipen, T. N., Seierstad, I. A., Lussana, C., Kristiansen, J., and Hov, Ø.: Adopting citizen observations in operational weather prediction, B. Am. Meteor. Soc., 101, E43–E47, https://doi.org/10.1175/BAMS-D-18-0237.1, 2020.
Ośródka, K. and Szturc, J.: Improvement in algorithms for quality control of weather radar data (RADVOL-QC system), Atmos. Meas. Tech., 15, 261–277, https://doi.org/10.5194/amt-15-261-2022, 2022.
Ośródka, K., Szturc J., and Jurczyk A.: Chain of data quality algorithms for 3-D single-polarization radar reflectivity (RADVOL-QC system), Meteorol. Appl., 21, 256–270, https://doi.org/10.1002/met.1323, 2014.
Ośródka, K., Otop, I., and Szturc, J.: Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC), Atmos. Meas. Tech., 15, 5581–5597, https://doi.org/10.5194/amt-15-5581-2022, 2022.
Overeem, A., Leijnse, H., van der Schrier, G., van den Besselaar, E., Garcia-Marti, I., and de Vos, L. W.: Merging with crowdsourced rain gauge data improves pan-European radar precipitation estimates, Hydrol. Earth Syst. Sci., 28, 649–668, https://doi.org/10.5194/hess-28-649-2024, 2024.
Pasierb, M., Bałdysz, Z., Szturc, J., Nykiel, G., Jurczyk, A., Osródka, K., Figurski, M., Wojtczak, M., and Wojtkowski, C.: Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation, Meteorol. Appl., 31, e2218, https://doi.org/10.1002/met.2218, 2024.
Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020.
Schnepper, T., Groh, J., Gerke, H. H., Reichert, B., and Pütz, T.: Evaluation of precipitation measurement methods using data from a precision lysimeter network, Hydrol. Earth Syst. Sci., 27, 3265–3292, https://doi.org/10.5194/hess-27-3265-2023, 2023.
Segovia-Cardozo, D. A., Rodríguez-Sinobas, L., Díez-Herrero, A., Zubelzu, S., and Canales-Ide, F.: Understanding the Mechanical Biases of Tipping-Bucket Rain Gauges: A Semi-Analytical Calibration Approach, Water, 13, 2285, https://doi.org/10.3390/w13162285, 2021.
Sha, Y., Gagne II, D. J., West, G., and Stull, R.: Deep-Learning-Based Precipitation Observation Quality Control, J. Atmos. Ocean. Tech., 38, 1075–1091, https://doi.org/10.1175/JTECH-D-20-0081.1, 2021.
Sinclair, S. and Pegram, G.: Combining radar and rain gauge rainfall estimates using conditional merging, Atmos. Sci. Lett., 6, 19–22, https://doi.org/10.1002/asl.85, 2005.
Sokol, Z., Szturc, J., Orellana-Alvear, J., Popová, J., Jurczyk, A., and Célleri, R.: The role of weather radar in rainfall estimation and its application in meteorological and hydrological modelling – A review, Remote Sens., 13, 351, https://doi.org/10.3390/rs13030351, 2021.
Szturc, J., Jurczyk, A., Ośródka, K., Wyszogrodzki, A., and Giszterowicz, M.: Precipitation estimation and nowcasting at IMGW (SEiNO system), Meteorology Hydrology and Water Management, 6, 3–12, https://doi.org/10.26491/mhwm/76120, 2018.
Szturc, J., Ośródka, K., Jurczyk, A., Otop, I., Linkowska, J., Bochenek, B., and Pasierb, M.: Quality control and verification of precipitation observations, estimates, and forecasts, in: Precipitation Science. Measurement, Remote Sensing, Microphysics and Modeling, edited by: Michaelides, S., Elsevier, 91–133, https://doi.org/10.1016/B978-0-12-822973-6.00002-0, 2022.
Urban, G. and Strug, K.: Evaluation of precipitation measurements obtained from different types of rain gauges, Meteorol. Z., 30, 445–463, https://doi.org/10.1127/metz/2021/1084, 2021.
Urban, G., Kowalewski, M. K., Sawicki, J., and Borowiecki, K.: Assessment of the quality of measurements from selected amateur rain gauges, Meteorol. Z., 33, 159–174, https://doi.org/10.1127/metz/2024/1199, 2024.
van Andel, N.: Quality control development for near real-time rain gauge networks for operational rainfall monitoring, Master Thesis, Faculty of Physics, Utrecht University, Utrecht, https://studenttheses.uu.nl/handle/20.500.12932/40939, 2021.
Villalobos-Herrera, R., Blenkinsop, S., Guerreiro, S. B., O'Hara, T., and Fowler, H. J.: Sub-hourly resolution quality control of rain gauge data significantly improves regional sub-daily return level estimates, Q. J. Roy. Meteor. Soc., 148, 3252–3271, https://doi.org/10.1002/qj.4357, 2022.
WMO-No. 488: Guide on the Global Observing System, World Meteorological Organization, updated in 2017, Geneva, 215 pp., ISBN 978-92-63-10488-5, 2015.
WMO-No. 1257: Guide to Operational Weather Radar Best Practices. Volume VI: Weather Radar Data Processing, World Meteorological Organization, Geneva, provisional edition, 166 pp., https://community.wmo.int/en/activity-areas/imop/new-provisional-2024-edition-guide-operational-weather-radar-best-practices (last access: 7 July 2025), 2024.
Wood, S. J., Jones, D. A., and Moore, R. J.: Accuracy of rainfall measurement for scales of hydrological interest, Hydrol. Earth Syst. Sci., 4, 531–543, https://doi.org/10.5194/hess-4-531-2000, 2000.
Yan, Q., Zhang, B., Jiang, Y., Liu, Y., Yang, B., and Wang, H.: Quality control of hourly rain gauge data based on radar and satellite multi-source data, J. Hydroinform., 26, 1042–1058, https://doi.org/10.2166/hydro.2024.272, 2024.
Yousefi, K. P., Yilmaz, M. T., Öztürk, K., Yucel, I., and Yilmaz, K. Y.: Time-independent bias correction methods compared with gauge adjustment methods in improving radar-based precipitation estimates, Hydrolog. Sci. J., 68, 1963–1983, https://doi.org/10.1080/02626667.2023.2248108, 2023.
Zhang, Y., Ren, Y., Ren, G., and Wang G.: Bias Correction of Gauge Data and its Effect on Precipitation Climatology over Mainland China, J. Appl. Meteorol. Clim., 58, 2177–2196, https://doi.org/10.1175/JAMC-D-19-0049.1, 2019.
Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D., Zhang, T., Chen, Q., Assumpção, T. A., Yang, P., Heidari, B., Rieckermann, J., Minsker, B., Bi, W., Cai, X., Solomatine, D., and Popescu, I.: Crowdsourcing methods for data collection in geophysics: State of the art, issues, and future directions, Rev. Geophys., 56, 698–740, https://doi.org/10.1029/2018RG000616, 2018.
Short summary
This paper describes a modified RainGaugeQC system for real-time quality control of rain gauge measurements made every 10 min. It has been significantly modified to control data from non-professional and private measurement networks of lower quality than professional data. The modifications went in the direction of performing more sophisticated data control, applying weather radar data, and taking into account various aspects such as consistency analysis of data time series and bias detection.
This paper describes a modified RainGaugeQC system for real-time quality control of rain gauge...