Articles | Volume 12, issue 12
Geosci. Model Dev., 12, 5197–5212, 2019
Geosci. Model Dev., 12, 5197–5212, 2019

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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Cited articles

Albring, T., Sagebaum, M., and Gauger, N. R.: Efficient Aerodynamic Design using the Discrete Adjoint Method in SU2, AIAA 2016-3518, 2016. a
Asai, T.: A Numerical Study of the Air-Mass Transformation over the Japan Sea in Winter, J. Meteorol. Soc. Jpn. Ser. II, 43, 1–15, 1965. a
Baumgartner, M.: Algorithmic Differentiation for Cloud Schemes using CoDiPack (v1.8.1), Zenodo,, 2019. a
Belikov, D. A., Maksyutov, S., Yaremchuk, A., Ganshin, A., Kaminski, T., Blessing, S., Sasakawa, M., Gomez-Pelaez, A. J., and Starchenko, A.: Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation, Geosci. Model Dev., 9, 749–764,, 2016. a
Bischof, C. H. and Eberhard, P.: Automatic differentiation of numerical integration algorithms, Math. Comp., 68, 717–731,, 1999. a, b, c
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.