Deep learning for quality control of surface physiographic fields using satellite Earth observations
Deep learning for quality control of surface physiographic fields using satellite Earth observations
About this item
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Publisher
Katlenburg-Lindau: Copernicus GmbH
Journal title
Language
English
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Publication information
Publisher
Katlenburg-Lindau: Copernicus GmbH
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Scope and Contents
Contents
A purposely built deep learning algorithm for the Verification of Earth System ParametERization (VESPER) is used to assess recent upgrades to the global physiographic datasets underpinning the quality of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), which is used in both numerical weather...
Alternative Titles
Full title
Deep learning for quality control of surface physiographic fields using satellite Earth observations
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_0622d3ff660c49aabfa0f4a9c15e4169
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_0622d3ff660c49aabfa0f4a9c15e4169
Other Identifiers
ISSN
1607-7938,1027-5606
E-ISSN
1607-7938
DOI
10.5194/hess-27-4661-2023
