This site requires Cookies enabled in your browser for login.
Updating ...
WaterNet Home
WaterNet
for
pour le
Canada
Menu
WaterNet
Home
GWFO
Home
Catalogue
Master Index
Data
Centre
Collections
X
Defaults
Select All
Websites
X
Global Water Futures Observatories (GWFO) Global Water Futures (GWF) Global Institute for Water Security (GIWS) International Network of Alpine Research Catchment Hydrology
Legacy Research Programs
X
Changing Cold Regions Network (CCRN) Drought Research Initiative (DRI) International Network of Alpine Research Catchment Hydrology (Legacy Site) Improving Processes & Parameterization for Prediction in Cold Regions Hydrology (IP3) The Mackenzie Global Energy and Water Cycle Experiment (GEWEX) Study (MAGS)
Legacy sites
Map
Utilities
X
Account Settings Create a New Record Record List Alias List Editor
Edit Data Centre
Data Types
. . .
X
Clear
Select All
Advanced Search
Go to Top⇡
Related items loading ...
Fetching Chart ...
Overview Status and Provenance Access and Downloads
Program Affiliations
GWF: Global Water Futures
Related Research Project(s)
Related ProjectPart
GWF-CORE: Core Modelling and Forecasting
Dataset Title
EM-Earth: The Ensemble Meteorological Dataset for Planet Earth
Creators and Contributors
NameRoleEmailInstitution
Tang, Guoqiang
guoqiang.tang@usask.ca
University of Saskatchewan
Clark, Martyn
University of Saskatchewan
Papalexiou, Simon
University of Calgary
Abstract
Gridded meteorological estimates are essential for many applications. Most existing meteorological datasets are deterministic and have limitations in representing the inherent uncertainties from both the data and methodology used to create gridded products. We develop the Ensemble Meteorological Dataset for Planet Earth (EM-Earth) for precipitation, mean daily temperature, daily temperature range, and dew-point temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications. The deterministic estimates can be used like most meteorological datasets such as ERA5, MERRA2, and GPM IMERG. The probabilistic estimates can enable ensemble hydrological simulation and support easy uncertainty analysis.
Keywords
Keyword
Precipitation
Temperature
Dew Point Temperature
Ensemble data
Probabilistic
Global
Citations
Tang, G. , Clark, M. , Papalexiou, S. (2022) EM-Earth: The Ensemble Meteorological Dataset for Planet Earth. Federated Research Data Repository. https://doi.org/10.20383/102.0547
Dataset Creation Date
2022-02-03
Status of data collection/production
○ Planned
○ In Progress
○ Abandoned
◉ Complete
Data Update Frequency
○ Continually
○ Daily
○ Weekly
○ Biweekly
○ Monthly
○ Anually
○ As needed
○ Irregular
◉ None planned
○ Unknown
Terms of Use
Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0
Does the data have access restrictions?
▣ No restriction (data is currently open to public)
◻ Limited (data is currently under embargo until publication)
◻ Limited (data involves intellectual property issues related to local or traditional knowledge)
◻ Limited (release of data may cause harm to the environment or to the public)
◻ Limited (pre-existing data has been used and is subject to access restrictions)
◻ Limited (data involves human subjects)
◻ Limited (data is supported by industry partnerships)
◻ Limited (data is supported by government partnerships)
Download Links and Instructions
https://www.frdr-dfdr.ca/repo/dataset/8d30ab02-f2bd-4d05-ae43-11f4a387e5ad https://doi.org/10.20383/102.0547 Please read the README.txt before downloading. The document introduces the dataset structure, including the meaning of different folders and their total sizes, which can help you decide the best downloading option. You can also contact the authors (guoqiang.tang@usask.ca) if you have problems downloading the dataset.
Total Size of all Dataset Files (GB)
22 TB
© 2026 - WaterNet Version 2026-07-08
Global Water Futures Observatories
Powered by
G W F Net
T-2023-04-15-E17kE27cYUHE1aP5VxaGS4MAQ Dataset 1.2