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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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Claim

On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models

Research datakeyboard_double_arrow_right Dataset 22 Apr 2022 English Publisher:Zenodo
Authors: Muñoz-Esparza, Domingo; Becker, Charlie; Sauer, Jeremy A.; Gagne II, David John; Schreck, John; Kosovic;

doi: 10.5281/zenodo.6478731 , 10.5281/zenodo.6478730

On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models

Abstract

Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."

Related Organizations
Keywords

machine learning, surface layer, LES, tensorflow

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visibility
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
1
Average
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11