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Title: A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]
  1. Atmospheric Sciences and Global ChangePacific Northwest National Laboratory Richland WA USA
  2. Department of MeteorologyUniversity of Reading Reading UK
  3. Australian Bureau of Meteorology Melbourne Victoria Australia
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1605642
Alternate Identifier(s):
OSTI ID: 1605643
Grant/Contract Number:  
DE‐AC05‐76RLO1830; DE‐SC0014063
Resource Type:
Published Article
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Name: Journal of Advances in Modeling Earth Systems Journal Volume: 12 Journal Issue: 3; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English

Citation Formats

Hagos, Samson, Feng, Zhe, Plant, Robert S., and Protat, Alain. A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds. United States: N. p., 2020. Web. doi:10.1029/2019MS001798.
Hagos, Samson, Feng, Zhe, Plant, Robert S., & Protat, Alain. A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds. United States. doi:10.1029/2019MS001798.
Hagos, Samson, Feng, Zhe, Plant, Robert S., and Protat, Alain. Sun . "A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds". United States. doi:10.1029/2019MS001798.
@article{osti_1605642,
title = {A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds},
author = {Hagos, Samson and Feng, Zhe and Plant, Robert S. and Protat, Alain},
abstractNote = {},
doi = {10.1029/2019MS001798},
journal = {Journal of Advances in Modeling Earth Systems},
number = 3,
volume = 12,
place = {United States},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1029/2019MS001798

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