Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 12
Articles | Volume 12, issue 12
https://doi.org/10.5194/gmd-12-5197-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-5197-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 12
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, Max Sagebaum, Nicolas R. Gauger, Peter Spichtinger, and André Brinkmann

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Latest update: 22 Nov 2024
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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.
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