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PreprintCited by 82Precipitation Measurement and AnalysisClimate variability and modelsEnvironmental Monitoring and Data Management

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SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

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Tang, Guoqiang, Clark, Martyn P., Newman, Andrew J., Wood, Andrew W., Papalexiou, Simon Michael, Vionnet, Vincent, Whitfield, Paul H.·2020·Earth system science data
DOI 10.5194/essd-2020-92

Preprint — published as 10.5194/essd-12-2381-2020 · Paper page

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Abstract

Abstract. Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially-complete station observations for North America, this study seeks to develop a SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin), and maximum temperature (Tmax) data for 27280 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records and were also used as an assessment benchmark. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming 30 % observations of stations were missing – this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean-value corrections were applied to the final estimates. The median Kling-Gupta efficiency (KGE) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin and Tmax, respectively. The SCDNA is closer to station observations than four benchmark gridded product, and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modelling. The dataset is available at https://doi.org/10.5281/zenodo.3735534 (Tang et al., 2020).

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Authors
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., & Whitfield, P. H.
Affiliations
  • University of Saskatchewan (CA)
  • NSF National Center for Atmospheric Research (US)
  • Environment and Climate Change Canada (CA)
Corresponding author
Tang, Guoqiang
ORCID
Venue
Earth system science data
Publisher
Copernicus GmbH
Published
2020-05-13
Volume
12
Issue
4
Pages
2381-2409
ISSN
1866-3508 (ISSN-L)
DOI
10.5194/essd-2020-92
OpenAlex
W3024736694
Cited by
82
References
0
Citations by year
2026: 6 · 2025: 11 · 2024: 6 · 2023: 9 · 2022: 26 · 2021: 23 · 2020: 1
FWCI
4.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Earth system science data (publishedVersion, cc-by)
  • HARVEST (University of Saskatchewan) (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Canada First Research Excellence Fund (Global Water Future)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
  • 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
Keywords
precipitation, daily minimum temperature, daily maximum temperature, weather station observations, data reconstruction, data infilling, quantile mapping, reanalysis data, North America, long-term climate records, quality control

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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