Preprints
https://doi.org/10.5194/essd-2020-303
https://doi.org/10.5194/essd-2020-303

  17 Dec 2020

17 Dec 2020

Review status: this preprint is currently under review for the journal ESSD.

EMDNA: Ensemble Meteorological Dataset for North America

Guoqiang Tang1,2, Martyn P. Clark1,2, Simon Michael Papalexiou2,3, Andrew J. Newman4, Andrew W. Wood4, Dominique Brunet5, 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
  • 3Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatchewan, Canada
  • 4National Center for Atmospheric Research, Boulder, Colorado
  • 5Meteorological Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada

Abstract. Probabilistic methods are very useful to estimate the spatial variability in meteorological conditions (e.g., spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, equally plausible ensemble members are used to approximate the probability distribution, hence uncertainty, of a spatially distributed meteorological variable conditioned on the available information. The ensemble can be used to evaluate the impact of the uncertainties in a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using the Bayesian Model Averaging. Optimal Interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated based on OI estimates and spatiotemporally correlated random fields. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvement for precipitation occurring in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We also discuss the limitations of the current framework and highlight that persistent efforts are needed to further develop probabilistic methods and ensemble datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The whole dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

Guoqiang Tang et al.

 
Status: open (until 03 Mar 2021)
Status: open (until 03 Mar 2021)
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Guoqiang Tang et al.

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EMDNA: Ensemble Meteorological Dataset for North America Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou, Andrew J. Newman, Andrew W. Wood, Dominique Brunet, and Paul H. Whitfield https://doi.org/10.20383/101.0275

Guoqiang Tang et al.

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Short summary
Probabilistic estimates are useful to quantify the uncertainties in meteorological datasets. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018. It is expected to be useful for hydrological and meteorological applications in North America.