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

Tang, G., Clark, M. P., Papalexiou, S. M., Newman, A. J., Wood, A. W., Brunet, D., & Whitfield, P. H. (2020). EMDNA: Ensemble Meteorological Dataset for North America. Earth System Science Data Discussions, 1-41. https://doi.org/10.5194/essd-2020-303

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

Paradigm Shift

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