The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.
An Overview of the Global Historical Climatology Network-Daily Database
Open accessAbstract
A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic bias). Daily updates are provided for many of the station records in GHCN-Daily. The dataset is also regularly reconstructed, usually once per week, from its 20+ data source components, ensuring that the dataset is broadly synchronized with its growing list of constituent sources. The daily updates and weekly reprocessed versions of GHCN-Daily are assigned a unique version number, and the most recent dataset version is provided on the GHCN-Daily website for free public access. Each version of the dataset is also archived at the NOAA/National Climatic Data Center in perpetuity for future retrieval.
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“GHCNd is itself made up of 30 unique sources (Menne et al. 2012) so the ISTI, stage 3, version 1.1.0, actually contains data from 87 unique sources.”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Menne et al. 2012
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“The ISTI monthly data were produced by merging monthly temperatures calculated from the GHCNd dataset (Menne et al. 2012) with over 50 additional smaller source archives to create an integrated set of monthly land station temperature records using GHCNd as the foundational source.”
From The Global Historical Climatology Network Monthly Temperature Dataset, Version 4 (2018) · cites this paper as Menne et al. 2012
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“Moreover, station observations failing quality control tests such as outlier and homogeneity checks may not be reliable (Menne et al., 2012), and many stations are only maintained over a relatively short period of time or portions of the year resulting in data gaps that could affect the analysis of climate variability or long-term trends (Rubin, 1976; Stooksbury et al., 1999).”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Menne et al., 2012
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“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 Menne et al., 2012
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Citations
Cites
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Lawrimore, J. H., Menne, M. J., Gleason, B. E., Williams, C. N., Wuertz, D. B., Vose, R. S., & Rennie, J. (2011) · Journal of Geophysical Research · doi:10.1029/2011jd016187 · Cited by 598
Self-citation
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Present and future Köppen-Geiger climate classification maps at 1-km resolution
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018) · Scientific Data · doi:10.1038/sdata.2018.214 · Cited by 7,482
ERA5-Land: a state-of-the-art global reanalysis dataset for land applications
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., et al. (2021) · Earth System Science Data · doi:10.5194/essd-13-4349-2021 · Cited by 5,540
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018) · Scientific Data · doi:10.1038/sdata.2017.191 · Cited by 3,529
MSWEP 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
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., & Rudolf, B. (2014) · Theoretical and Applied Climatology · doi:10.1007/s00704-013-0860-x · Cited by 1,385
Metadata
- Authors
- Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., & Houston, T. G.
- Affiliations
- NOAA National Centers for Environmental Information (US)
- Corresponding author
- Menne, Matthew J.
- Venue
- Journal of Atmospheric and Oceanic Technology
- Publisher
- American Meteorological Society
- Published
- 2012-07-01
- Volume
- 29
- Issue
- 7
- Pages
- 897-910
- ISSN
- 0739-0572 (ISSN-L)
- 1520-0426
- OpenAlex
- W2029604816
- Cited by
- 1,993
- References
- 34
- Citations by year
- 2026: 135 · 2025: 157 · 2024: 169 · 2023: 185 · 2022: 229 · 2021: 209 · 2020: 196 · 2019: 159 · 2018: 131 · 2017: 127 · 2016: 109 · 2015: 85 · 2014: 63 · 2013: 28 · 2012: 9
- FWCI
- 49.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Atmospheric and Oceanic Technology (publishedVersion)
- Funders
- U.S. Department of Energy
- Agência Nacional de Energia Elétrica
- Biological and Environmental Research
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Monitoring and Data Management
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Keywords
- GHCN-Daily, Global Historical Climatology Network, daily climate data, land surface climate, temperature, precipitation, snowfall, snow depth, data quality assurance, data homogenization, data reconstruction, climate monitoring
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