Preprints
https://doi.org/10.5194/egusphere-2022-143
https://doi.org/10.5194/egusphere-2022-143
02 May 2022
 | 02 May 2022

MultilayerPy (v1.0): A Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films

Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang

Abstract. Kinetic multi-layer models of aerosols and films have become the state-of-the-art method of describing complex aerosol processes at particle and film level. We present MultilayerPy: an open-source framework for building, running and optimising kinetic multi-layer models – namely the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB), and the kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP). The modular nature of this package allows the user to iterate through various reaction schemes, diffusion regimes and experimental conditions in a systematic way. In this way, models can be customised and the raw model code itself, produced in a readable way by MultilayerPy, is fully customisable. Optimisation to experimental data using local or global optimisation algorithms is included in the package along with the option to carry out statistical sampling and Bayesian inference of model parameters with a Markov Chain Monte Carlo (MCMC) sampler (via the emcee Python package). MultilayerPy abstracts the model building process into separate building blocks, increasing the reproducibility of results and minimising human error. This paper describes the general functionality of MultilayerPy and demonstrates this with use cases based on the oleic acid-ozone heterogeneous reaction system. The tutorials in the source code (written as Jupyter notebooks) and the documentation aim to encourage users to take advantage of this tool, which is intended to be developed in conjunction with the user base.

Journal article(s) based on this preprint

22 Sep 2022
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151, https://doi.org/10.5194/gmd-15-7139-2022,https://doi.org/10.5194/gmd-15-7139-2022, 2022
Short summary

Adam Milsom et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-143', Anonymous Referee #1, 30 May 2022
  • RC2: 'Comment on egusphere-2022-143', Anonymous Referee #2, 07 Jun 2022
  • AC1: 'Comment on egusphere-2022-143', Christian Pfrang, 16 Jul 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-143', Anonymous Referee #1, 30 May 2022
  • RC2: 'Comment on egusphere-2022-143', Anonymous Referee #2, 07 Jun 2022
  • AC1: 'Comment on egusphere-2022-143', Christian Pfrang, 16 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Christian Pfrang on behalf of the Authors (16 Jul 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Aug 2022) by Sylwester Arabas
RR by Anonymous Referee #2 (18 Aug 2022)
ED: Publish subject to technical corrections (25 Aug 2022) by Sylwester Arabas
AR by Christian Pfrang on behalf of the Authors (01 Sep 2022)  Author's response   Manuscript 

Journal article(s) based on this preprint

22 Sep 2022
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151, https://doi.org/10.5194/gmd-15-7139-2022,https://doi.org/10.5194/gmd-15-7139-2022, 2022
Short summary

Adam Milsom et al.

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Short summary
MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human readable way.