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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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GMD | Articles | Volume 12, issue 12
Geosci. Model Dev., 12, 5197–5212, 2019
https://doi.org/10.5194/gmd-12-5197-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 12, 5197–5212, 2019
https://doi.org/10.5194/gmd-12-5197-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 11 Dec 2019

Methods for assessment of models | 11 Dec 2019

Algorithmic differentiation for cloud schemes (IFS Cy43r3) using CoDiPack (v1.8.1)

Manuel Baumgartner et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Manuel Baumgartner on behalf of the Authors (24 Oct 2019)  Author's response    Manuscript
ED: Publish as is (12 Nov 2019) by David Topping
Publications Copernicus
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Short summary
Numerical models in atmospheric sciences need to include physical processes through parameterizations, which are not explicitly resolved, e.g., the formation of clouds. As a consequence, the parameterizations contain uncertain parameters. We suggest using the technique of algorithmic differentiation (AD) to identify the most uncertain parameters within parameterizations. In this study, we illustrate AD by analyzing a scheme for liquid clouds incorporated into a parcel model framework.
Numerical models in atmospheric sciences need to include physical processes through...
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