Articles | Volume 13, issue 7
https://doi.org/10.5194/essd-13-3337-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-3337-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
13 Jul 2021
Data description article |
| 13 Jul 2021
| 13 Jul 2021
EMDNA: an Ensemble Meteorological Dataset for North America
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta,
Canada
Martyn P. Clark
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta,
Canada
Department of Geography and Planning, University of Saskatchewan,
Saskatchewan, Canada
Simon Michael Papalexiou
Centre for Hydrology, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada
Department of Civil, Geological and Environmental Engineering,
University of Saskatchewan, Saskatchewan, Canada
Andrew J. Newman
National Center for Atmospheric Research, Boulder, Colorado, USA
Andrew W. Wood
National Center for Atmospheric Research, Boulder, Colorado, USA
Dominique Brunet
Meteorological Research Division, Environment and Climate Change
Canada, Toronto, Ontario, Canada
Paul H. Whitfield
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta,
Canada
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- Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation H. Abdelmoaty et al. https://doi.org/10.1029/2021EF002196
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- Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran H. Salehi et al. https://doi.org/10.3390/w16071028
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- An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates J. Hu et al. https://doi.org/10.5194/essd-17-3987-2025
- Modeling lane-level travel time reliability with uncertainty quantification A. Moeinaddini et al. https://doi.org/10.1080/19427867.2026.2632963
- Value of various elements of the hydrological forecasting chain: Is there a successful pathway for improving the overall performance? J. Davidson-Chaput et al. https://doi.org/10.1016/j.jhydrol.2025.133186
- Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets K. Mankin et al. https://doi.org/10.5194/hess-29-85-2025
- Evaluating Operational Risk in Environmental Modeling: Assessment of Reliability and Sharpness for Ensemble Selection S. Pokorny et al. https://doi.org/10.1061/JHYEFF.HEENG-5833
- Learning From Limited Temporal Data: Dynamically Sparse Historical Functional Linear Models With Applications to Earth Science J. Janssen et al. https://doi.org/10.1002/env.70018
- HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool E. Moges et al. https://doi.org/10.3389/feart.2022.884766
- Intercomparison of global ERA reanalysis products for streamflow simulations at the high-resolution continental scale R. Bain et al. https://doi.org/10.1016/j.jhydrol.2022.128624
- Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging X. Lu et al. https://doi.org/10.1007/s11629-021-6901-5
- Representativeness of the Precipitation Observing Network for Monitoring Precipitation Change and Variability in Canada H. Wan et al. https://doi.org/10.1080/07055900.2022.2144111
- High-resolution ensemble precipitation and temperature datasets for CONUS based on a probabilistic geospatial estimation approach G. Tang et al. https://doi.org/10.1016/j.jhydrol.2025.134761
- QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests Y. Zhang et al. https://doi.org/10.1029/2022WR032117
- The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. https://doi.org/10.1029/2020WR029001
- The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. https://doi.org/10.3389/fenvs.2023.1207508
- GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications G. Tang et al. https://doi.org/10.5194/gmd-17-1153-2024
- Evaluation of IMERG and ERA5 Precipitation-Phase Partitioning on the Global Scale W. Xiong et al. https://doi.org/10.3390/w14071122
- Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient U. Sepúlveda et al. https://doi.org/10.5194/hess-26-3419-2022
- Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al. https://doi.org/10.1002/joc.7926
- On the importance of the reference data: Uncertainty partitioning of bias-adjusted climate simulations over eastern Canada J. Lavoie et al. https://doi.org/10.1016/j.cliser.2025.100619
- Hydro-climatological changes and mine water management challenges in Canada under climate change W. Palash et al. https://doi.org/10.1088/1748-9326/adf762
- A station-based 0.1-degree daily gridded ensemble precipitation dataset for India A. Peringiyil et al. https://doi.org/10.1038/s41597-025-04474-2
- The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins G. Tang et al. https://doi.org/10.1029/2022WR033767
- Detailed investigation of discrepancies in Köppen-Geiger climate classification using seven global gridded products S. Hobbi et al. https://doi.org/10.1016/j.jhydrol.2022.128121
- Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. https://doi.org/10.1111/jfr3.12895
Saved (final revised paper)
Latest update: 17 Jun 2026
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.
Probabilistic estimates are useful to quantify the uncertainties in meteorological datasets....
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