Bias corrections of long‐term (1973–2004) daily precipitation data over the northern regions
Open accessAbstract
A consistent daily bias correction procedure was applied at 4802 stations over high latitude regions (North of 45°N) to quantify the precipitation gauge measurement biases of wind‐induced undercatch, wetting losses, and trace amount of precipitation for the last 30 years. These corrections have increased the gauge‐measured monthly precipitation significantly by up to 22 mm for winter months, and slightly by about 5 mm during summer season. Relatively, the correction factors (CF) are small in summer (10%), and very large in winter (80–120%) because of the increased effect of wind on gauge undercatch of snowfall. The CFs also vary over space particularly in snowfall season. Significant CF differences were found across the USA/Canada borders mainly due to differences in catch efficiency between the national gauges. Bias corrections generally enhance monthly precipitation trends by 5–20%. These results point to a need to review our current understanding of the Arctic fresh water budget and its change.
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Unmanned automatic weather stations without dedicated on-site maintenance or backup precipitation observation devices often suffer from operational instability and non-standard installation, making them prone to various data quality anomalies.
“Without dedicated on-site maintenance or backup precipitation observation devices, these unmanned stations often suffer from operational instability and non-standard installation, making them prone to various data quality anomalies [13][14][15].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [15]
The bias caused by undercatch can be substantial for stations located at high latitudes and in mountains.
“The bias caused by undercatch can be substantial for stations located at high latitudes and the mountains (Yang et al., 2005; Scaff et al., 2015).”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Yang et al., 2005
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SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., & Whitfield, P. H. (2020) · Earth System Science Data · doi:10.5194/essd-12-2381-2020 · Cited by 82 · 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
- Yang, D., Kane, D., Zhang, Z., Legates, D., & Goodison, B.
- Affiliations
- University of Alaska Fairbanks (US)
- University of Delaware (US)
- Environment and Climate Change Canada (CA)
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2005-10
- Volume
- 32
- Issue
- 19
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2027469749
- Cited by
- 293
- References
- 0
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 16 · 2023: 20 · 2022: 18 · 2021: 25 · 2020: 28 · 2019: 19 · 2018: 19 · 2017: 16 · 2016: 14 · 2015: 11 · 2014: 11 · 2013: 17 · 2012: 13
- FWCI
- 5.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Geophysical Research Letters (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Cryospheric studies and observations
- Keywords
- snowfall measurement, precipitation trends, seasonal variation
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