Challenges in obtaining reliable measurements of point rainfall
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Extensive data recorded from storm systems passing over the well‐instrumented 21.4 km 2 Goodwin Creek watershed in northern Mississippi are used to highlight uncertainties associated with the measurement of surface rainfall, focusing on data quality control, gauge calibration, out‐of‐level gauge orifices, and wind effects on rain gauge catch. Assessment of the wind effect on gauge catch is central to the presented analyses, including an in‐depth evaluation of a recent technique for estimating the rainfall undercatch by means of wind and raindrop size information. Our findings emphasize that quantification of the wind effect on rain gauge catch is difficult because of uncertainties associated with measuring rainfall, drop size distribution, and the wind at gauge rim height. On the basis of our evaluation the sophisticated wind effect correction technique that makes use of raindrop size and wind information is much less effective than traditional methods based on rainfall rate and wind observations alone.
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Traditional precipitation-threshold-based QC methods exhibit high rates of missed detections and false alarms for isolated heavy rainfall caused by human operational errors or continuous large precipitation values from equipment malfunctions.
“However, these methods still exhibit high rates of missed detections and false alarms in cases such as isolated heavy rainfall caused by human operational errors or continuous large precipitation values resulting from equipment malfunctions [21][22][23][24][25].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [22]
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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
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
- Sieck, L. C., Burges, S. J., & Steiner, M.
- Affiliations
- University of Washington (US)
- NSF National Center for Atmospheric Research (US)
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 2007-01
- Volume
- 43
- Issue
- 1
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W1881425009
- Cited by
- 196
- References
- 0
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 13 · 2023: 15 · 2022: 20 · 2021: 15 · 2020: 11 · 2019: 10 · 2018: 9 · 2017: 12 · 2016: 11 · 2015: 15 · 2014: 6 · 2013: 6 · 2012: 9
- FWCI
- 6.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Water Resources Research (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 › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- gauge calibration, data quality control, raindrop size distribution, rainfall intensity
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