Strategies for Evaluating Quality Assurance Procedures
Abstract
The evaluation strategies outlined in this paper constitute a set of tools beneficial to the development and documentation of robust automated quality assurance (QA) procedures. Traditionally, thresholds for the QA of climate data have been based on target flag rates or statistical confidence limits. However, these approaches do not necessarily quantify a procedure’s effectiveness at detecting true errors in the data. Rather, as illustrated by way of an “extremes check” for daily precipitation totals, information on the performance of a QA test is best obtained through a systematic manual inspection of samples of flagged values combined with a careful analysis of geographical and seasonal patterns of flagged observations. Such an evaluation process not only helps to document the effectiveness of each individual test, but, when applied repeatedly throughout the development process, it also aids in choosing the optimal combination of QA procedures and associated thresholds. In addition, the approach described here constitutes a mechanism for reassessing system performance whenever revisions are made following initial development.
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The quality-control process was designed so that each check has a low false-positive rate, that is, a low probability that valid observations are flagged as errors according to the evaluation approach outlined in Durre et al. (2008).
“As described in Lawrimore et al. (2011), the quality-control process was designed so that each check has a low false-positive rate, that is, a low probability that valid observations are flagged as errors according to the evaluation approach outlined in Durre et al. (2008).”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Durre et al. 2008
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- Authors
- Durre, I., Menne, M. J., & Vose, R. S.
- Affiliations
- National Oceanic and Atmospheric Administration (US)
- Corresponding author
- Durre, Imke
- Venue
- Journal of Applied Meteorology and Climatology
- Publisher
- American Meteorological Society
- Published
- 2008-06-01
- Volume
- 47
- Issue
- 6
- Pages
- 1785-1791
- ISSN
- 1558-8424 (ISSN-L)
- 1558-8432
- OpenAlex
- W2153859231
- Cited by
- 111
- References
- 15
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 13 · 2023: 11 · 2022: 8 · 2021: 15 · 2020: 7 · 2019: 4 · 2018: 4 · 2017: 3 · 2016: 1 · 2015: 7 · 2014: 5 · 2013: 4 · 2012: 8
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Applied Meteorology and Climatology (publishedVersion)
- 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
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- automated quality assurance, climate data quality
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