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Applications of deep learning to ocean data inference and subgrid parameterization

Abstract:

Oceanographic observations are limited by sampling rates, while ocean models are limited by finite resolution and high viscosity and diffusion coefficients. Therefore, both data from observations and ocean models lack information at small and fast scales. Methods are needed to either extract information, extrapolate, or upscale existing oceanographic data sets, to account for or represent unresolved physical processes. Here we use machine learning to leverage observations and model data by pr...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1029/2018ms001472

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Role:
Author
ORCID:
0000-0002-6029-419X
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Physics
Sub department:
Atmospheric Oceanic and Planetary Physics
Oxford college:
Wadham College
Role:
Author
ORCID:
0000-0002-8472-4828
Publisher:
American Geophysical Union Publisher's website
Journal:
Journal of Advances in Modeling Earth Systems Journal website
Volume:
11
Issue:
1
Pages:
376-399
Publication date:
2019-01-04
Acceptance date:
2018-12-27
DOI:
EISSN:
1942-2466
ISSN:
1942-2466
Source identifiers:
969924
Language:
English
Keywords:
Pubs id:
pubs:969924
UUID:
uuid:5a8dbd48-453d-4c90-8239-3c5203235d30
Local pid:
pubs:969924
Deposit date:
2019-02-13

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