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Cited by 1,993Climate variability and modelsEnvironmental Monitoring and Data ManagementPrecipitation Measurement and Analysis

An Overview of the Global Historical Climatology Network-Daily Database

Open access
Menne, Matthew J., Durre, Imke, Vose, Russell S., Gleason, Byron E., Houston, Tamara G.·2012·Journal of Atmospheric and Oceanic Technology
DOI 10.1175/jtech-d-11-00103.1
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Abstract

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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Crossref · 10.1175/jtech-d-11-00103.1

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

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Claims other papers make about this one

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  • GHCNd is made up of 30 unique sources.

    “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

    The Abstract gives a different count: our copy renders it as “201 data source components,” apparently 20 followed by a footnote marker. Section 2 describes multiple source collections, but does not give a count of 30 unique sources. (LLM, conf=0.78)

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    Experimental — accuracy not yet measured

  • ISTI monthly data were produced by merging monthly temperatures calculated from GHCNd with over 50 additional smaller source archives to create an integrated set of monthly land station temperature records using GHCNd as the foundational source.

    “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

  • Station observations failing quality control tests such as outlier and homogeneity checks may not be reliable.

    “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

  • 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 Menne et al., 2012

Self-check

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Related

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.
ORCID
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
DOI
10.1175/jtech-d-11-00103.1
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
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 › Earth and Planetary Sciences › Geology › 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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