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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.

Model code and software

Algorithmic Differentiation for Cloud Schemes using CoDiPack (v1.8.1) Manuel Baumgartner https://doi.org/10.5281/zenodo.3461483

SciCompKL/CoDiPack: Version 1.8.1 Max Sagebaum, Tim Albring, Denis Demidov, Matthias Möller, Edwin van der Weide, Mike Lam https://doi.org/10.5281/zenodo.3460682

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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