PartMC: Particle-resolved Monte Carlo code for atmospheric aerosol simulation

Version 2.8.0
Released 2024-02-23

Repository: https://github.com/compdyn/partmc

Documentation::

Cite as: M. West, N. Riemer, J. Curtis, M. Michelotti, and J. Tian (2024) PartMC, ,

Copyright (C) 2005-2024 Nicole Riemer and Matthew West
Portions copyright (C) Andreas Bott, Richard Easter, Jeffrey Curtis, Matthew Michelotti, and Jian Tian
Licensed under the GNU General Public License version 2 or (at your option) any later version.
For details see the file COPYING or http://www.gnu.org/licenses/old-licenses/gpl-2.0.html.

Selected references:

N. Riemer, M. West, R. A. Zaveri, and R. C. Easter (2009) Simulating the evolution of soot mixing state with a particle-resolved aerosol model, J. Geophys. Res. 114(D09202), http://dx.doi.org/10.1029/2008JD011073.
N. Riemer, M. West, R. A. Zaveri, and R. C. Easter (2010) Estimating black carbon aging time-scales with a particle-resolved aerosol model, J. Aerosol Sci. 41(1), 143-158, http://dx.doi.org/10.1016/j.jaerosci.2009.08.009.
R. A. Zaveri, J. C. Barnard, R. C. Easter, N. Riemer, and M. West (2010) Particle-resolved simulation of aerosol size, composition, mixing state, and the associated optical and cloud condensation nuclei activation properties in an evolving urban plume, J. Geophys. Res. 115(D17210), http://dx.doi.org/10.1029/2009JD013616.
R. E. L. DeVille, N. Riemer, and M. West (2011) Weighted Flow Algorithms (WFA) for stochastic particle coagulation, J. Comp. Phys. 230(23), 8427-8451, http://dx.doi.org/10.1016/j.jcp.2011.07.027
J. Ching, N. Riemer, and M. West (2012) Impacts of black carbon mixing state on black carbon nucleation scavenging: Insights from a particle-resolved model, J. Geophys. Res. 117(D23209), http://dx.doi.org/10.1029/2012JD018269
M. D. Michelotti, M. T. Heath, and M. West (2013) Binning for efficient stochastic multiscale particle simulations, Multiscale Model. Simul. 11(4), 1071-1096, http://dx.doi.org/10.1137/130908038
N. Riemer and M. West (2013) Quantifying aerosol mixing state with entropy and diversity measures, Atmos. Chem. Phys. 13, 11423-11439, http://dx.doi.org/10.5194/acp-13-11423-2013
J. Tian, N. Riemer, M. West, L. Pfaffenberger, H. Schlager, and A. Petzold (2014) Modeling the evolution of aerosol particles in a ship plume using PartMC-MOSAIC, Atmos. Chem. Phys. 14, 5327-5347, http://dx.doi.org/10.5194/acp-14-5327-2014
R. M. Healy, N. Riemer, J. C. Wenger, M. Murphy, M. West, L. Poulain, A. Wiedensohler, I. P. O'Connor, E. McGillicuddy, J. R. Sodeau, and G. J. Evans (2014) Single particle diversity and mixing state measurements, Atmos. Chem. and Phys. 14, 6289-6299, http://dx.doi.org/10.5194/acp-14-6289-2014
J. H. Curtis, M. D. Michelotti, N. Riemer, M. Heath, and M. West (2016) Accelerated simulation of stochastic particle removal processes in particle-resolved aerosol models, J. Comp. Phys. 322, 21-32, http://dx.doi.org/10.1016/j.jcp.2016.06.029
J. Ching, N. Riemer, and M. West (2016) Black carbon mixing state impacts on cloud microphysical properties: Effects of aerosol plume and environmental conditions, J. Geophys. Res. 121(10), 5990-6013, http://dx.doi.org/10.1002/2016JD024851
J. Ching, J. Fast, M. West, and N. Riemer (2017) Metrics to quantify the importance of mixing state for CCN activity, Atmos. Chem. and Phys. 17, 7445-7458, http://dx.doi.org/10.5194/acp-17-7445-2017
J. Tian, B. T. Brem, M. West, T. C. Bond, M. J. Rood, and N. Riemer (2017) Simulating aerosol chamber experiments with the particle-resolved aerosol model PartMC, Aerosol Sci. Technol. 51(7), 856-867, http://dx.doi.org/10.1080/02786826.2017.1311988
J. H. Curtis, N. Riemer, and M. West (2017) A single-column particle-resolved model for simulating the vertical distribution of aerosol mixing state: WRF-PartMC-MOSAIC-SCM v1.0, Geosci. Model Dev. 10, 4057-4079, http://dx.doi.org/10.5194/gmd-10-4057-2017
J. Ching, M. West, and N. Riemer (2018) Quantifying impacts of aerosol mixing state on nucleation-scavenging of black carbon aerosol particles, Atmosphere 9(1), 17, http://dx.doi.org/10.3390/atmos9010017
M. Hughes, J. K. Kodros, J. R. Pierce, M. West, and N. Riemer (2018) Machine learning to predict the global distribution of aerosol mixing state metrics, Atmosphere 9(1), 15, http://dx.doi.org/10.3390/atmos9010015
R. E. L. DeVille, N. Riemer, and M. West (2019) Convergence of a generalized Weighted Flow Algorithm for stochastic particle coagulation, Journal of Computational Dynamics 6(1), 69-94, http://dx.doi.org/10.3934/jcd.2019003
N. Riemer, A. P. Ault, M. West, R. L. Craig, and J. H. Curtis (2019) Aerosol mixing state: Measurements, modeling, and impacts, Reviews of Geophysics 57(2), 187-249, http://dx.doi.org/10.1029/2018RG000615
C. Shou, N. Riemer, T. B. Onasch, A. J. Sedlacek, A. T. Lambe, E. R. Lewis, P. Davidovits, and M. West (2019) Mixing state evolution of agglomerating particles in an aerosol chamber: Comparison of measurements and particle-resolved simulations, Aerosol Science and Technology 53(11), 1229-1243, http://dx.doi.org/10.1080/02786826.2019.1661959
J. T. Gasparik, Q. Ye, J. H. Curtis, A. A. Presto, N. M. Donahue, R. C. Sullivan, M. West, and N. Riemer (2020) Quantifying errors in the aerosol mixing-state index based on limited particle sample size, Aerosol Science and Technology 54(12), 1527-1541, http://dx.doi.org/10.1080/02786826.2020.1804523
Z. Zheng, J. H. Curtis, Y. Yao, J. T. Gasparik, V. G. Anantharaj, L. Zhao, M. West, and N. Riemer (2021) Estimating submicron aerosol mixing state at the global scale with machine learning and earth system modeling, Earth and Space Science 8(2), e2020EA001500, http://dx.doi.org/10.1029/2020EA001500

Running PartMC with Docker

This is the fastest way to get running.

Step 1: Install Docker Community Edition.
- On Linux and MacOS this is straightforward. Download from here.
- On Windows the best version is Docker Community Edition for Windows, which requires Windows 10 Pro/Edu.

Step 2: (Optional) Run the PartMC test suite with:

  docker run -it --rm compdyn/partmc bash -c 'cd /build; make test'

Step 3: Run a scenario like the following. This example uses partmc/scenarios/4_chamber. This mounts the current directory ($PWD, replace with %cd% on Windows) into /run inside the container, changes into that directory, and then runs PartMC.
```
  cd partmc/scenarios/4_chamber
  docker run -it --rm -v $PWD:/run compdyn/partmc bash -c 'cd /run; /build/partmc chamber.spec'
```

In the above docker run command the arguments are:

-it: activates "interactive" mode so Ctrl-C works to kill the command
--rm: remove temporary docker container files after running
-v LOCAL:REMOTE: mount the LOCAL directory to the REMOTE directory inside the container
compdyn/partmc: the docker image to run
bash -c 'COMMAND': run COMMAND inside the docker container

The directory structure inside the docker container is:

/partmc           # a copy of the partmc git source code repository
/build            # the diretory in which partmc was compiled
/build/partmc     # the compiled partmc executable
/run              # the default diretory to run in

Dependencies

Required dependencies:

Fortran 2003 compiler - https://gcc.gnu.org/fortran/ or similar
CMake version 2.6.4 or higher - http://www.cmake.org/
NetCDF version 4.2 or higher - http://www.unidata.ucar.edu/software/netcdf/

Partmc

Install / Use

README

PartMC: Particle-resolved Monte Carlo code for atmospheric aerosol simulation

Running PartMC with Docker

Dependencies