Field
Value
Language
dc.contributor.author
Tang, Guoqiang
datacite.creator.affiliationIdentifier
https://ror.org/010x8gc63
en_US
datacite.creator.affiliation
University of Saskatchewan
en_US
datacite.creator.nameIdentifier
en_US
dc.contributor.author
Clark, Martyn
datacite.creator.affiliationIdentifier
https://ror.org/010x8gc63
en_US
datacite.creator.affiliation
University of Saskatchewan
en_US
datacite.creator.nameIdentifier
en_US
dc.contributor.author
Papalexiou, Simon Michael
datacite.creator.affiliationIdentifier
https://ror.org/03yjb2x39
en_US
datacite.creator.affiliation
University of Calgary
en_US
datacite.creator.nameIdentifier
en_US
dc.coverage.temporal
1950-01-01/2019-12-31
dc.date.accessioned
2022-02-03T21:12:49Z
dc.date.available
2022-02-03T21:12:49Z
dc.date.issued
2022-02-03
dc.identifier.uri
https://www.frdr-dfdr.ca/repo/dataset/8d30ab02-f2bd-4d05-ae43-11f4a387e5ad
dc.identifier.uri
https://doi.org/10.20383/102.0547
dc.description
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.
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.
Reference:
Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou. EM-Earth: The Ensemble Meteorological Dataset for Planet Earth. Bulletin of the American Meteorological Society. 2022
en_US
dc.publisher
Federated Research Data Repository / dépôt fédéré de données de recherche
dc.rights
Creative Commons Attribution 4.0 International (CC BY 4.0)
en_US
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
en_US
dc.subject
Precipitation
en_US
dc.subject
Temperature
en_US
dc.subject
Dew Point Temperature
en_US
dc.subject
Ensemble data
en_US
dc.subject
Probabilistic
en_US
dc.subject
Global
en_US
dc.title
EM-Earth: The Ensemble Meteorological Dataset for Planet Earth
en_US
globus.shared_endpoint.name
515c70c4-2eb8-4f2a-b406-7959b5edc28d
globus.shared_endpoint.path
/6/published/publication_542/
datacite.publicationYear
2022
datacite.date.Collected
1950-01-01/2019-12-31
datacite.resourceType
Dataset
en_US
datacite.geolocation.geolocationPlace
Global land;-;-;-
datacite.fundingReference.funderName
Global Water Futures (GWF)
en_US
datacite.fundingReference.awardNumber
en_US
datacite.fundingReference.awardTitle
en_US
frdr.crdc.code
RDF1050701
en_US
frdr.crdc.group_en
Earth and related environmental sciences
en_US
frdr.crdc.class_en
Hydrology
en_US
frdr.crdc.field_en
Hydrometeorology
en_US
frdr.crdc.group_fr
Sciences de la Terre et sciences de l'environnement connexes
fr_CA
frdr.crdc.class_fr
Hydrologie
fr_CA
frdr.crdc.field_fr
Hydrométéorologie
fr_CA
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