@article{Tang-2021-EMDNA:,
title = "EMDNA: an Ensemble Meteorological Dataset for North America",
author = "Tang, Guoqiang and
Clark, Martyn and
Papalexiou, Simon Michael and
Newman, Andrew J. and
Wood, Andrew W. and
Brunet, Dominique and
Whitfield, Paul H. and
Tang, Guoqiang and
Clark, Martyn and
Papalexiou, Simon Michael and
Newman, Andrew J. and
Wood, Andrew W. and
Brunet, Dominique and
Whitfield, Paul H.",
journal = "Earth System Science Data, Volume 13, Issue 7",
volume = "13",
number = "7",
year = "2021",
publisher = "Copernicus GmbH",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G21-71001",
doi = "10.5194/essd-13-3337-2021",
pages = "3337--3362",
abstract = "Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in 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 the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) 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 Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. 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 improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire 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).",
}
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<abstract>Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in 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 the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) 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 Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. 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 improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire 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).</abstract>
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%0 Journal Article
%T EMDNA: an Ensemble Meteorological Dataset for North America
%A Tang, Guoqiang
%A Clark, Martyn
%A Papalexiou, Simon Michael
%A Newman, Andrew J.
%A Wood, Andrew W.
%A Brunet, Dominique
%A Whitfield, Paul H.
%J Earth System Science Data, Volume 13, Issue 7
%D 2021
%V 13
%N 7
%I Copernicus GmbH
%F Tang-2021-EMDNA:
%X Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in 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 the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) 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 Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. 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 improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire 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).
%R 10.5194/essd-13-3337-2021
%U https://gwf-uwaterloo.github.io/gwf-publications/G21-71001
%U https://doi.org/10.5194/essd-13-3337-2021
%P 3337-3362
Markdown (Informal)
[EMDNA: an Ensemble Meteorological Dataset for North America](https://gwf-uwaterloo.github.io/gwf-publications/G21-71001) (Tang et al., GWF 2021)
ACL
- Guoqiang Tang, Martyn Clark, Simon Michael Papalexiou, Andrew J. Newman, Andrew W. Wood, Dominique Brunet, Paul H. Whitfield, Guoqiang Tang, Martyn Clark, Simon Michael Papalexiou, Andrew J. Newman, Andrew W. Wood, Dominique Brunet, and Paul H. Whitfield. 2021. EMDNA: an Ensemble Meteorological Dataset for North America. Earth System Science Data, Volume 13, Issue 7, 13(7):3337–3362.
