Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
doi:10.5194/acp-2017-45
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Technical note
23 Jan 2017
Review status
This discussion paper is under review for the journal Atmospheric Chemistry and Physics (ACP).
Technical Note: Monte-Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets
Thomas Berkemeier1,a, Markus Ammann2, Ulrich K. Krieger3, Thomas Peter3, Peter Spichtinger4, Ulrich Pöschl1, Manabu Shiraiwa1,5, and Andrew J. Huisman6 1Max Planck Institute for Chemistry, Multiphase Chemistry Department, 55128, Mainz, Germany
2Paul Scherrer Institute, Laboratory of Environmental Chemistry, 5232, Villigen, Switzerland
3ETH Zurich, Institute for Atmospheric and Climate Science, 8092, Zurich, Switzerland
4Johannes Gutenberg University, Institute for Atmospheric Physics, 55128, Mainz, Germany
5University of California Irvine, Department of Chemistry, 92697, Irvine, CA, USA
6Union College, Department of Chemistry, 12308, Schenectady, NY USA
anow at: Georgia Institute of Technology, School of Chemical and Biomolecular Engineering, 30320, Atlanta, GA, USA
Abstract. We present a Monte-Carlo Genetic Algorithm (MCGA) for efficient, automated and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find the solution of an optimization problem and to explore the space of solutions with similar model output. It addresses a problem inherent to complex models whose extensive input parameter sets might not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools. The MCGA algorithm has been used successfully to constrain parameters such as reaction rate coefficients, diffusion coefficients and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere-biosphere interface. It should be portable to any numerical model with similar computational expense and extent of the fitting parameter space.

Citation: Berkemeier, T., Ammann, M., Krieger, U. K., Peter, T., Spichtinger, P., Pöschl, U., Shiraiwa, M., and Huisman, A. J.: Technical Note: Monte-Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-45, in review, 2017.
Thomas Berkemeier et al.
Thomas Berkemeier et al.
Thomas Berkemeier et al.

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Short summary
Kinetic process models are efficient tools in multiphase atmospheric chemistry used to unravel the mechanisms governing chemical and physical transformation. However, determination of kinetic parameters such as reaction rate or diffusion coefficients from multiple data sets is often difficult or ambiguous. This study presents a novel optimization algorithm and framework to determine these parameters in an automated fashion and to gain information about parameter uncertainty and uniqueness.
Kinetic process models are efficient tools in multiphase atmospheric chemistry used to unravel...
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