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This content will become publicly available on March 25, 2019

Title: Deep learning to represent subgrid processes in climate models

Authors:
ORCiD logo ; ;
Publication Date:
Grant/Contract Number:
SC0012152; SC00-12548; SC0014203
Type:
Published Article
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 115 Journal Issue: 39; Journal ID: ISSN 0027-8424
Publisher:
Proceedings of the National Academy of Sciences
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
OSTI Identifier:
1468882

Rasp, Stephan, Pritchard, Michael S., and Gentine, Pierre. Deep learning to represent subgrid processes in climate models. United States: N. p., Web. doi:10.1073/pnas.1810286115.
Rasp, Stephan, Pritchard, Michael S., & Gentine, Pierre. Deep learning to represent subgrid processes in climate models. United States. doi:10.1073/pnas.1810286115.
Rasp, Stephan, Pritchard, Michael S., and Gentine, Pierre. 2018. "Deep learning to represent subgrid processes in climate models". United States. doi:10.1073/pnas.1810286115.
@article{osti_1468882,
title = {Deep learning to represent subgrid processes in climate models},
author = {Rasp, Stephan and Pritchard, Michael S. and Gentine, Pierre},
abstractNote = {},
doi = {10.1073/pnas.1810286115},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 39,
volume = 115,
place = {United States},
year = {2018},
month = {9}
}

Works referenced in this record:

Random Forests
journal, January 2001