Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis
Open accessAbstract
Automatic raingauge data often serve as an important input to hydrological and weather warning operations. They are not only fundamental in quantitative rainfall analysis, but also act as the ground truth in warning operation and forecast validation. Quality control is required before the data can be used quantitatively due to systematic and random errors. Extremely large random errors and unreasonably small or false zero values can hamper effective monitoring of heavy rain. Yet both are difficult to detect in real-time by objective means. In an attempt to address these problems, a rainfall data quality-control scheme based on radar-raingauge co-kriging analysis was developed. The important threshold values required in the data quality control of 60-min raingauge rainfall were determined from a detailed analysis of the distributions of rainfall residuals defined as the arithmetic difference and the logarithm of the ratio between a raingauge measurement and its co-kriging estimate. The scheme has been developed and is in real-time use in Hong Kong, a coastal city of about 1100 km2 area with more than 150 raingauges installed. Geographically, it is located in the subtropics and dominated by heavy convective rainfall in the summer. As a basis of the quality-control scheme, the co-kriging rainfall analysis was shown through a verification exercise to be superior to those obtained by the Barnes analysis and ordinary kriging of raingauge data. The performance of the quality-control algorithm was assessed using selected cases and controlled tests, and was found to be satisfactory, with a high error detection rate for the two targeted types of error. Limitations and operational issues identified during a real-time trial of the quality-control scheme are also discussed. Citation Yeung, H.Y., Man, C., Chan, S.T., and Seed, A., 2014. Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis. Hydrological Sciences Journal, 59 (7), 1285–1299. http://dx.doi.org/10.1080/02626667.2013.839873
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Machine learning-based QC algorithms enable the establishment of non-linear relationships between multi-source data and QC outcomes, facilitating identification of potentially effective features.
“The machine learning-based QC algorithm enables the establishment of non-linear relationships between multi-source data and QC outcomes, facilitating the identification of potentially effective features [24,32,33].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [24]
The Introduction describes a QC scheme that combines raingauge and radar information to estimate reference rainfall and assess gauge reports. The copy does not describe it as a machine-learning algorithm or say that it learns non-linear relationships or identifies effective features; the Probability Distributions of Rainfall Residuals section instead describes residual-based thresholds. The supplied passages have no page numbers. (LLM, conf=0.83)
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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
- Yeung, H. Y., Man, C., Chan, S. T., & Seed, A.
- Affiliations
- Hong Kong Observatory (CN)
- Chinese University of Hong Kong (HK)
- Bureau of Meteorology (AU)
- Corresponding author
- Yeung, H.Y.
- ORCID
- Seed, A.
- Venue
- Hydrological Sciences Journal
- Publisher
- Informa UK Limited
- Published
- 2014-01-09
- Volume
- 59
- Issue
- 7
- Pages
- 1293-1307
- ISSN
- 0262-6667 (ISSN-L)
- 2150-3435
- OpenAlex
- W2124182435
- Cited by
- 19
- References
- 13
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 6 · 2021: 3 · 2020: 1 · 2018: 2 · 2017: 1 · 2016: 2 · 2014: 1
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Hydrological Sciences Journal (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Keywords
- random error, real-time monitoring, Hong Kong, ordinary kriging
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