EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Aerosol Thermodynamics using Machine Learning

Holger Tost1, Peter Spichtinger1, and the BINARY Team*
Holger Tost and Peter Spichtinger and the BINARY Team
  • 1Johannes Gutenberg University Mainz, Institute for Atmospheric Physics, Mainz, Germany (
  • *A full list of authors appears at the end of the abstract

Aerosol chemical composition is an important driver for hygroscopic growth and hence cloud activation. A major fraction of the aerosol consists of inorganic components, for which thermodynamic equilibrium models are commonly used to describe the chemical composition, including total water uptake.
However, these thermodynamics are relatively computationally expensive calculations, minimising the Gibbs free energy of the total system. Consequently, faster computations are desirable, which can be facilitated using machine learning techniques.

In this study, we apply neuronal networks, being trained on the output of an equilibrium thermodynamics model (ISORROPIA 2), to represent both the chemical composition and associated aerosol water uptake. We investigate the quality of the trained network against independent data from the equilibrium model and find a good agreement of the trained network model against the original data. Furthermore, we also test the applicability of the trained model in a parameter space outside of the trained data set to analyse whether the trained network is able to properly represent the physico-chemical system, and hence a suitable replacement of the equilibrium model by a neuronal network is appropriate.


André Brinkmann, Andreas Hildebrandt, Peter Hoor, Daniel Kunkel, Annette Miltenberger, Philipp Reutter, Bertil Schmidt, Elmar Schömer, Michael Wand, Sebastian Brodehl, Sarah Brüning, Thorsten Kaluza, Yuliya Kazachkova, Stefan Niebler, Frederic Schimmelpfennig, Ann-Christin Wörl

How to cite: Tost, H. and Spichtinger, P. and the BINARY Team: Aerosol Thermodynamics using Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5208,, 2022.