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  Interpretable multiscale machine learning-based parameterizations of convection for ICON

Heuer, H., Schwabe, M., Gentine, P., Giorgetta, M. A., & Eyring, V. (2024). Interpretable multiscale machine learning-based parameterizations of convection for ICON. Journal of Advances in Modeling Earth Systems, 16: e2024MS004398. doi:10.1029/2024MS004398.

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J Adv Model Earth Syst - 2024 - Heuer - Interpretable Multiscale Machine Learning‐Based Parameterizations of Convection for.pdf (Publisher version), 3MB
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J Adv Model Earth Syst - 2024 - Heuer - Interpretable Multiscale Machine Learning‐Based Parameterizations of Convection for.pdf
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application/pdf / [MD5]
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Copyright Date:
2024
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© The Authors

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 Creators:
Heuer, Helge, Author
Schwabe, Mierk, Author
Gentine, Pierre, Author
Giorgetta, Marco A.1, Author                 
Eyring, Veronika, Author
Affiliations:
1Wave Driven Circulations, Department Climate Physics, MPI for Meteorology, Max Planck Society, ou_3001854              

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Language(s): eng - English
 Dates: 2024-04-122024-08-012024-08-22
 Publication Status: Published online
 Pages: 26
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: BibTex Citekey: HeuerSchwabeEtAl2024
DOI: 10.1029/2024MS004398
 Degree: -

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Project name : European Research Council (ERC) Synergy Grant "Understanding and Modeling the Earth System with Machine Learning (USMILE)"
Grant ID : 855187
Funding program : Horizon 2020
Funding organization : European Union
Project name : -
Grant ID : -
Funding program : (bd1179)
Funding organization : Deutsches Klimarechenzentrum (DKRZ)

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Title: Journal of Advances in Modeling Earth Systems
  Other : JAMES
Source Genre: Journal
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Publ. Info: Washington, D.C. : American Geophysical Union
Pages: - Volume / Issue: 16 Sequence Number: e2024MS004398 Start / End Page: - Identifier: ISSN: 1942-2466
CoNE: https://pure.mpg.de/cone/journals/resource/19422466