Comprehensive Automated Quality Assurance of Daily Surface Observations
Open accessAbstract
This paper describes a comprehensive set of fully automated quality assurance (QA) procedures for observations of daily surface temperature, precipitation, snowfall, and snow depth. The QA procedures are being applied operationally to the Global Historical Climatology Network (GHCN)-Daily dataset. Since these data are used for analyzing and monitoring variations in extremes, the QA system is designed to detect as many errors as possible while maintaining a low probability of falsely identifying true meteorological events as erroneous. The system consists of 19 carefully evaluated tests that detect duplicate data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of the values flagged as errors is used to set the threshold for each procedure such that its false-positive rate, or fraction of valid values identified as errors, is minimized. In addition, the tests are arranged in a deliberate sequence in which the performance of the later checks is enhanced by the error detection capabilities of the earlier tests. Based on an assessment of each individual check and a final evaluation for each element, the system identifies 3.6 million (0.24%) of the more than 1.5 billion maximum/minimum temperature, precipitation, snowfall, and snow depth values in GHCN-Daily as errors, has a false-positive rate of 1%−2%, and is effective at detecting both the grossest errors as well as more subtle inconsistencies among elements.
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For precipitation, quality control procedures consist of three parts, adopting methods from Durre et al. (2010), Hamada et al. (2011), and Beck et al. (2019).
“For precipitation, quality control procedures consist of three parts.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Durre et al. (2010)
The Precipitation section describes three relationships used in precipitation consistency checks, so Durre et al. (2010) supports using its methods for precipitation quality control. It does not say that precipitation quality-control procedures as a whole consist of three parts; that appears to describe the citing paper's own procedure. No page number is available in our copy. (LLM, conf=0.81)
Experimental — accuracy not yet measuredTo ensure station observations undergo strict and comprehensive quality control, the study adopted the methods used to produce previous station-based datasets.
“To ensure station observations undergo strict and comprehensive quality control, we adopted the methods used to produce previous station-based datasets.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Durre et al. (2010)
Although Durre et al. (2010) and Hamada et al. (2011) share some common traits for precipitation, both of them are adopted to ensure quality control reliability.
“Note that although Durre et al. (2010) and Hamada et al. (2011) share some common traits for precipitation, both of them are adopted to ensure quality control reliability.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Durre et al. (2010)
For Tmin and Tmax, the study followed the method designed by Durre et al. (2010), which is adopted by GHCN-D.
“For T min and T max, we followed the method designed by Durre et al. (2010) which is adopted by GHCN-D (Menne et al., 2012).”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Durre et al. (2010)
For precipitation, quality control strategies in SCDNA are from three studies.
“For precipitation, quality control strategies are from three studies.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Durre et al. (2010)
Five types of checks from Durre et al. (2010) are adopted for the quality control of temperature in SCDNA.
“Five types of checks (Durre et al., 2010) are adopted for the quality control of temperature.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Durre et al., 2010
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Cites
Strategies for Evaluating Quality Assurance Procedures
Durre, I., Menne, M. J., & Vose, R. S. (2008) · Journal of Applied Meteorology and Climatology · doi:10.1175/2007jamc1706.1 · Cited by 111 · see citing works
Self-citation
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Research works printed but not linked: 4
- Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290. · DOI · Paper page
- Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily maximum and minimum temperatures: Crea- tion and analysis of a new gridded data set. J. Geophys. Res., 111, D05101, doi:10.1029/2005JD006280. · DOI
- Guttman, N. B., and R. G. Quayle, 1990: A review of cooperative temperature data validation. J. Atmos. Oceanic Technol., 7, 334-339.
- Trenberth, K. E., and Coauthors, 2007: Observations: Surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 235-336.
Cited by
Sorted by citations
An Overview of the Global Historical Climatology Network-Daily Database
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., & Houston, T. G. (2012) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-11-00103.1 · Cited by 1,993 · see citing works
Self-citationMSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., & Adler, R. F. (2019) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-17-0138.1 · Cited by 1,411 · see citing works
KJELLSTROM, T., LEMKE, B., & OTTO, M. (2013) · Industrial Health · doi:10.2486/indhealth.2012-0174 · Cited by 126 · see citing works
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
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
Bliefernicht, J., Salack, S., Waongo, M., Annor, T., Laux, P., & Kunstmann, H. (2022) · International Journal of Climatology · doi:10.1002/joc.7467 · Cited by 35 · see citing works
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
Advancing Near-Real-Time Quality Controls of Meteorological Observations
Toreti, A., Bratu, M., Müller, E., van der Grijn, G., Oliveira, I., Zampieri, M., & Ceglar, A. (2022) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-21-0171.1 · Cited by 3 · see citing works
- Authors
- Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., & Vose, R. S.
- Affiliations
- NOAA National Centers for Environmental Information (US)
- Venue
- Journal of Applied Meteorology and Climatology
- Publisher
- American Meteorological Society
- Published
- 2010-08-01
- Volume
- 49
- Issue
- 8
- Pages
- 1615-1633
- ISSN
- 1558-8424 (ISSN-L)
- 1558-8432
- OpenAlex
- W2139396291
- Cited by
- 354
- References
- 1
- Citations by year
- 2026: 23 · 2025: 15 · 2024: 24 · 2023: 24 · 2022: 41 · 2021: 42 · 2020: 33 · 2019: 21 · 2018: 18 · 2017: 21 · 2016: 16 · 2015: 22 · 2014: 23 · 2013: 11 · 2012: 13
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Applied Meteorology and Climatology (publishedVersion)
- Funders
- U.S. Department of Energy
- Biological and Environmental Research
- 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 › Global and Planetary Change › Climate variability and models
- Keywords
- automated quality assurance, GHCN-Daily, surface temperature, precipitation, snowfall, snow depth, data deduplication, false positive rate, data inconsistency, historical climate data
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