Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF 5-year value: 5.768
IF 5-year
CiteScore value: 8.9
SNIP value: 1.713
IPP value: 5.53
SJR value: 3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
h5-index value: 51
GMD | Articles | Volume 12, issue 12
Geosci. Model Dev., 12, 5197–5212, 2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 12, 5197–5212, 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.

Related authors

In-Situ observation of New Particle Formation in the upper troposphere/lower stratosphere of the Asian Monsoon Anticyclone
Ralf Weigel, Christoph Mahnke, Manuel Baumgartner, Antonis Dragoneas, Bärbel Vogel, Felix Ploeger, Silvia Viciani, Francesco D'Amato, Silvia Bucci, Bernard Legras, Beiping Luo, and Stephan Borrmann
Atmos. Chem. Phys. Discuss.,,, 2020
Preprint under review for ACP
Short summary
Reappraising the appropriate calculation of a common meteorological quantity: Potential Temperature
Manuel Baumgartner, Ralf Weigel, Ulrich Achatz, Allan H. Harvey, and Peter Spichtinger
Atmos. Chem. Phys. Discuss.,,, 2020
Revised manuscript accepted for ACP
Short summary
Towards a bulk approach to local interactions of hydrometeors
Manuel Baumgartner and Peter Spichtinger
Atmos. Chem. Phys., 18, 2525–2546,,, 2018
Short summary

Related subject area

Atmospheric Sciences
TITAM (v1.0): the Time-Independent Tracking Algorithm for Medicanes
Enrique Pravia-Sarabia, Juan José Gómez-Navarro, Pedro Jiménez-Guerrero, and Juan Pedro Montávez
Geosci. Model Dev., 13, 6051–6075,,, 2020
Short summary
Effects of horizontal resolution and air–sea coupling on simulated moisture source for East Asian precipitation in MetUM GA6/GC2
Liang Guo, Ruud J. van der Ent, Nicholas P. Klingaman, Marie-Estelle Demory, Pier Luigi Vidale, Andrew G. Turner, Claudia C. Stephan, and Amulya Chevuturi
Geosci. Model Dev., 13, 6011–6028,,, 2020
Short summary
On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART
Ondřej Tichý, Lukáš Ulrych, Václav Šmídl, Nikolaos Evangeliou, and Andreas Stohl
Geosci. Model Dev., 13, 5917–5934,,, 2020
Short summary
Sensitivity of aerosol optical properties to the aerosol size distribution over central Europe and the Mediterranean Basin using the WRF-Chem v. coupled model
Laura Palacios-Peña, Jerome D. Fast, Enrique Pravia-Sarabia, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 13, 5897–5915,,, 2020
Short summary
PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data
Yilong Wang, Grégoire Broquet, François-Marie Bréon, Franck Lespinas, Michael Buchwitz, Maximilian Reuter, Yasjka Meijer, Armin Loescher, Greet Janssens-Maenhout, Bo Zheng, and Philippe Ciais
Geosci. Model Dev., 13, 5813–5831,,, 2020

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
Publications Copernicus
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...