An Overview of the Global Historical Climatology Network-Daily Database

By M. Menne, I. Durre, R. Vose, B. Gleason, Tamara G. Houston. 2012

1,768 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7fa10285fd3532aea2e64d0d6de06608266b4366

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Citations in the corpus

Young People's Burden: Requirement of Negative CO2 Emissions (2016, neutral): “…combines data from (1) meteorological station data of the Global Historical Climatology Network (GHCN) described by Peterson and Vose (1997) and Menne et al. (2012), (2) Antarctic research station data reported by the Scientiﬁc Committee on Antarctic Research (SCAR), (http://www.antarctica.ac.…”
How unprecedented was the February 2021 Texas cold snap? (2021, background): “(c) To complement blended gridded data products, we use station temperature data from the Global Historical Climatology Network (GHCN) dataset compiled by the National Ocean and Atmospheric Administration [12] and available at https://www1.ncdc.noaa.gov/pub/data/ghcn/ daily/.”
How climate extremes—not means—define a species' geographic range boundary via a demographic tipping point (2014, methodology): “…in latitude to a mechanistic model based on air temperature time series, we extracted daily maximum temperature time-series data from all Indiana weather stations for the 50-year period 1960– 2010 as supplied by the Global Historical Climatology Network (GHCN)-Daily database (Menne et al. 2012).”
Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS (2018, neutral): “Reporting times in the CONUS range from midnight −12 to +9 hours UTC for the stations in the comprehensive GHCN-D gauge database (Menne et al., 2012; Figure 2c in Beck et al., 2019), suggesting that for some stations, up to half of the daily P accumulations may be assigned to the wrong day.”
Automated threshold selection for extreme value analysis via ordered goodness-of-fit tests with adjustment for false discovery rate (2018, methodology): “A description of the data network can be found in Menne et al. (2012). After an initial screening to remove sites with less than 50 years of available data, there were 720 remaining sites across the three chosen coastal states.”
A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis (2018, methodology): “Daily SWE observations were obtained from sites in the Global Historical Climatology Network (Menne et al., 2012) that are located within 320 km of the Taylor Park dam.”
Random Forest Spatial Interpolation (2020, methodology): “Catalonia was chosen as a study area because it has a well-established network of meteorological stations and observations are freely available through the daily Global Historical Climatological Network (GHCN-daily) [40].”
Projecting global urban land expansion and heat island intensification through 2050 (2019, methodology): “In the second phase, we model the relationship between LST and the daily maximum and minimum air temperature (Kloog et al 2014), with observations from the Global Historical Climatology Network-Daily version-3 dataset (Menne et al 2012).”
On Rigorous Drought Assessment Using Daily Time Scale: Non-Stationary Frequency Analyses, Revisited Concepts, and a New Method to Yield Non-Parametric Indices (2017, methodology): “The author also acknowledges the Global Historical Climatology Network [23] from which the daily rainfall observed at nine locations were obtained via the link http://www.”
U.S. Daily Temperatures: The Meaning of Extremes in the Context of Nonnormality (2014, background): “These stations were selected on the basis of being high quality, well resolved, and well distributed (Menne et al. 2012).”
Explaining Extreme Events of 2015 from a Climate Perspective (2016, methodology): “We utilize 987 meteorological stations extracted from GHCN-D (Menne et al. 2012) having at least 100 years of nonmissing daily observations during 1901–2014, as well as mostly complete data in 2015.”
Daily precipitation concentration around the world according to several indices (2016, methodology): “A total of 66,409 daily time series are available for the period 1950-2014, taken from the Global Historical Climatology Network-Daily Database (GHCN-Daily) (Menne et al., 2012).”
Fast Calibrated Additive Quantile Regression (2017, methodology): “Müller (2013) proves that Ṽ achieves a lower asymptotic frequentist risk than V in a setting where the prior is increasingly dominated by the likelihood as n increases, so that the asymptotic variance of β̂ does not depend on the prior.”