EMDNA: an Ensemble Meteorological Dataset for North America
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Published:2021-07-13
Issue:7
Volume:13
Page:3337-3362
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Tang GuoqiangORCID, Clark Martyn P., Papalexiou Simon MichaelORCID, Newman Andrew J.ORCID, Wood Andrew W.ORCID, Brunet Dominique, Whitfield Paul H.ORCID
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).
Funder
Global Water Futures
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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