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Preprints
https://doi.org/10.5194/essd-2020-92
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-92
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 May 2020

13 May 2020

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A revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

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

Guoqiang Tang1,2, Martyn P. Clark1,2, Andrew J. Newman3, Andrew W. Wood3, Simon Michael Papalexiou2,4, Vincent Vionnet5, and Paul H. Whitfield1,2 Guoqiang Tang et al.
  • 1University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
  • 2Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  • 3National Center for Atmospheric Research, Boulder, Colorado
  • 4Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatchewan, Canada
  • 5Environmental Numerical Research Prediction, Environment and Climate Change Canada, Dorval, Quebec, Canada

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).

Guoqiang Tang et al.

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Guoqiang Tang et al.

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SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 G. Tang, M. P. Clark, A. J. Newman, A. W. Wood, S. M.Papalexiou, V. Vionnet, and P. H. Whitfield https://doi.org/10.5281/zenodo.3735534

Guoqiang Tang et al.

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Latest update: 30 Sep 2020
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Short summary
Station observations are critical for hydrological and meteorological studies, but often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27280 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates, which can be used in a variety of applications.
Station observations are critical for hydrological and meteorological studies, but often contain...
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