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Machine learning emulation of gravity wave drag in numerical weather forecasting

Abstract:

We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1029/2021ms002477

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Role:
Author
ORCID:
0000-0002-1132-0961
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Name:
European Commission
Grant:
741112
Publisher:
American Geophysical Union
Journal:
Journal of Advances in Modeling Earth Systems More from this journal
Volume:
13
Issue:
7
Article number:
e2021MS002477
Publication date:
2021-07-08
Acceptance date:
2021-06-14
DOI:
EISSN:
1942-2466
Language:
English
Keywords:
Pubs id:
1185438
Local pid:
pubs:1185438
Deposit date:
2021-07-08

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