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Other ORP type . 2018
License: CC BY
Data sources: Datacite
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Other ORP type . 2018
License: CC BY
Data sources: ZENODO
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ZENODO
Other ORP type . 2018
License: CC BY
Data sources: Datacite
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Trained Model For Predicting Lhf, Shf, Qt And Sl Throughout Ng-Aqua

appsOther research productkeyboard_double_arrow_right Other ORP type 05 Jul 2018Publisher:Zenodo
Authors: Brenowitz, Noah D;

doi: 10.5281/zenodo.1306260 , 10.5281/zenodo.1306259

Trained Model For Predicting Lhf, Shf, Qt And Sl Throughout Ng-Aqua

Abstract

Trained neural network parametrizations. These are trained over the entire domain of the NG-Aqua using the SAM-derived estimates of the tendencies. The inputs are: qt, sl, U, V, FQT, FSL, SST, SOLIN The outputs are: LHF, SHF, qt, sl

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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
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