Journal article icon

Journal article

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz’96 Model

Abstract:

Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized m...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Authors


More by this author
Role:
Author
ORCID:
0000-0002-0469-2740
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Subgroup:
Atmos Ocean & Planet Physics
Role:
Author
ORCID:
0000-0001-8244-0218
More by this author
Role:
Author
ORCID:
0000-0001-7805-0102
Publisher:
American Geophysical Union Publisher's website
Journal:
Journal of Advances in Modeling Earth Systems Journal website
Volume:
12
Issue:
3
Article number:
e2019MS001896
Publication date:
2020-03-04
Acceptance date:
2020-02-14
DOI:
EISSN:
1942-2466
Pubs id:
1089302
Local pid:
pubs:1089302

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP