Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC)
Open accessPublished version of a preprint
Abstract
The RainGaugeQC scheme described in this paper is intended for real-time quality control of telemetric rain gauge data. It consists of several checks: detection of exceedance of the natural limit and climate-based threshold as well as checking of the conformity of rain gauge and radar observations, the consistency of time series from heated and unheated sensors, and the spatial consistency of adjacent gauges. The proposed approach is focused on assessing the reliability of individual rain gauge observations. A quantitative indicator of reliability, called the quality index (QI), describes the quality of each measurement as a number in the range from 0.0 (completely unreliable measurement) to 1.0 (perfect measurement). The QI of a measurement which fails any check is lowered, and only a measurement very likely to be erroneous is replaced with a “no data” value. The performance of this scheme has been evaluated by analysing the spatial distribution of the precipitation field and comparing it with precipitation observations and estimates provided by other techniques. The effectiveness of the RainGaugeQC scheme was also analysed in terms of the statistics of QI reduction. The quality information provided is very useful in further applications of rain gauge data. The scheme is used operationally by the Polish national meteorological and hydrological service (Institute of Meteorology and Water Management – National Research Institute).
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Compared with traditional approaches, multi-source collaborative QC methods integrate various observational data and effectively reduce the false alarm rate in anomaly detection.
“Compared with traditional approaches, these methods integrate various observational data, including Doppler weather radar, geostationary meteorological satellite, and weather detector, effectively reducing the false alarm rate in anomaly detection [27][28][29].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [28]
Multi-source data collaborative QC for precipitation has demonstrated significant advantages.
“With the advancement of ground-and satellite-based observation technologies and the explosive growth of meteorological data, multi-source data collaborative QC for precipitation has demonstrated significant advantages [27][28][29][30][31].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [28]
When an observed hourly precipitation has a lower CR, the record is highly likely to be attributed to manual operational errors, such as garden watering or cleaning the gauge without disconnecting communication.
“Consequently, when an observed hourly precipitation with a lower CR, the record is highly likely to be attributed to manual operational errors, such as garden watering, cleaning the gauge without disconnecting communication [23,28,29].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [28]
Cites
Quality control of a global hourly rainfall dataset
Lewis, E., Pritchard, D., Villalobos-Herrera, R., Blenkinsop, S., McClean, F., Guerreiro, S., Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., et al. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105169 · Cited by 67 · see citing works
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Automatic quality control of weather data for timely decisions in agriculture
Dandrifosse, S., Jago, A., Huart, J. P., Michaud, V., Planchon, V., & Rosillon, D. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100445 · Cited by 24 · see citing works
A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data
Sun, H., Zhou, Q., Shi, L., Li, C., Qin, S., Yao, D., Xu, M., Huang, Y., Hu, Q., & Guan, Y. (2025) · Remote Sensing · doi:10.3390/rs17243976 · Cited by 4 · see citing works
- Authors
- Ośródka, K., Otop, I., & Szturc, J.
- Affiliations
- Institute of Meteorology and Water Management (PL)
- Corresponding author
- Szturc, Jan
- Venue
- Atmospheric Measurement Techniques
- Publisher
- Copernicus GmbH
- Published
- 2022-10-04
- Volume
- 15
- Issue
- 19
- Pages
- 5581-5597
- ISSN
- 1867-1381 (ISSN-L)
- 1867-8548
- OpenAlex
- W4301395444
- Cited by
- 20
- References
- 1
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 4 · 2023: 5 · 2022: 1
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Atmospheric measurement techniques (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Instytut Meteorologii i Gospodarki Wodnej – Państwowy Instytut Badawczy
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Keywords
- quality index
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