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PyCHAM (v2.1.1): a Python box model for simulating aerosol chambers.

Authors :
O'Meara, Simon Patrick
Xu, Shuxuan
Topping, David
Alfarra, M. Rami
Capes, Gerard
Lowe, Douglas
Shao, Yunqi
McFiggans, Gordon
Source :
Geoscientific Model Development. Feb2021, Vol. 14 Issue 2, p675-702. 28p.
Publication Year :
2021

Abstract

In this paper the CHemistry with Aerosol Microphysics in Python (PyCHAM) box model software for aerosol chambers is described and assessed against benchmark simulations for accuracy. The model solves the coupled system of ordinary differential equations for gas-phase chemistry, gas–particle partitioning and gas–wall partitioning. Additionally, it can solve for coagulation, nucleation and particle loss to walls. PyCHAM is open-source, whilst the graphical user interface, modular structure, manual, example plotting scripts, and suite of tests for troubleshooting and tracking the effect of modifications to individual modules have been designed for optimal usability. In this paper, the modelled processes are individually assessed against benchmark simulations, and key parameters are described. Examples of output when processes are coupled are also provided. Sensitivity of individual processes to relevant parameters is illustrated along with convergence of model output with increasing temporal resolution and number of size bins. The latter sensitivity analysis informs our recommendations for model setup. Where appropriate, parameterisations for specific processes have been chosen for their general applicability, with their rationale detailed here. It is intended for PyCHAM to aid the design and analysis of aerosol chamber experiments, with comparison of simulations against observations allowing improvement of process understanding that can be transferred to ambient atmosphere simulations. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1991959X
Volume :
14
Issue :
2
Database :
Academic Search Index
Journal :
Geoscientific Model Development
Publication Type :
Academic Journal
Accession number :
149218802
Full Text :
https://doi.org/10.5194/gmd-14-675-2021