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

Version 2.5.0
Released 2018-11-17

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

Homepage: http://lagrange.mechse.illinois.edu/partmc/

Cite as: M. West, N. Riemer, J. Curtis, M. Michelotti, and J. Tian (2018) PartMC, ,

Copyright (C) 2005-2021 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.

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, Weighted Flow Algorithms (WFA) for stochastic particle coagulation, J. Comp. Phys. 230(23), 8427-8451, 2011, http://dx.doi.org/10.1016/j.jcp.2011.07.027
• J. Ching, N. Riemer, and M. West, Impacts of black carbon mixing state on black carbon nucleation scavenging: Insights from a particle-resolved model, J. Geophys. Res. 117(D23209), 2012, http://dx.doi.org/10.1029/2012JD018269
• M. D. Michelotti, M. T. Heath, and M. West, Binning for efficient stochastic multiscale particle simulations, Multiscale Model. Simul. 11(4), 1071-1096, 2013, http://dx.doi.org/10.1137/130908038
• N. Riemer and M. West, Quantifying aerosol mixing state with entropy and diversity measures, Atmos. Chem. Phys. 13, 11423-11439, 2013, http://dx.doi.org/10.5194/acp-13-11423-2013
• J. Tian, N. Riemer, M. West, L. Pfaffenberger, H. Schlager, and A. Petzold, Modeling the evolution of aerosol particles in a ship plume using PartMC-MOSAIC, Atmos. Chem. Phys. 14, 5327-5347, 2014, 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, Single particle diversity and mixing state measurements, Atmos. Chem. and Phys. 14, 6289-6299, 2014, http://dx.doi.org/10.5194/acp-14-6289-2014
• J. H. Curtis, M. D. Michelotti, N. Riemer, M. Heath, and M. West, Accelerated simulation of stochastic particle removal processes in particle-resolved aerosol models, J. Comp. Phys. 322, 21-32, 2016, http://dx.doi.org/10.1016/j.jcp.2016.06.029
• J. Ching, N. Riemer, and M. West, Black carbon mixing state impacts on cloud microphysical properties: Effects of aerosol plume and environmental conditions, J. Geophys. Res. 121(10), 5990-6013, 2016 http://dx.doi.org/10.1002/2016JD024851
• J. Ching, J. Fast, M. West, and N. Riemer, Metrics to quantify the importance of mixing state for CCN activity, Atmos. Chem. and Phys. 17, 7445-7458, 2017 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, Simulating aerosol chamber experiments with the particle-resolved aerosol model PartMC, Aerosol Sci. Technol. 51(7), 856-867, 2017 http://dx.doi.org/10.1080/02786826.2017.1311988
• J. H. Curtis, N. Riemer, and M. West, 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, 2017 http://dx.doi.org/10.5194/gmd-10-4057-2017
• J. Ching, M. West, and N. Riemer, Quantifying impacts of aerosol mixing state on nucleation-scavenging of black carbon aerosol particles, Atmosphere 9(1), 17, 2018 http://dx.doi.org/10.3390/atmos9010017
• M. Hughes, J. K. Kodros, J. R. Pierce, M. West, and N. Riemer, Machine learning to predict the global distribution of aerosol mixing state metrics, Atmosphere 9(1), 15, 2018 http://dx.doi.org/10.3390/atmos9010015

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

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/

Optional dependencies:

• MOSAIC chemistry code version 2012-01-25 - Available from Rahul Zaveri - Rahul.Zaveri@pnl.gov
• MPI parallel support - http://www.open-mpi.org/
• GSL for random number generators - http://www.gnu.org/software/gsl/
• SUNDIALS ODE solver version 2.6 for condensation support - http://www.llnl.gov/casc/sundials/
• gnuplot for testcase plotting - http://www.gnuplot.info/

1. Install cmake and NetCDF (see above). The NetCDF libraries are required to compile PartMC. The netcdf.mod Fortran 90 module file is required, and it must be produced by the same compiler being used to compile PartMC.

2. Unpack PartMC:

    tar xzvf partmc-2.5.0.tar.gz
   

3. Change into the main PartMC directory (where this README file is located):

    cd partmc-2.5.0
   

4. Make a directory called build and change into it:

    mkdir build
    cd build
   

5. If desired, set environment variables to indicate the install locations of supporting libraries. If running echo $SHELL indicates that you are running bash, then you can do something like:

    export NETCDF_HOME=/
    export MOSAIC_HOME=${HOME}/mosaic-2012-01-25
    export SUNDIALS_HOME=${HOME}/opt
    export GSL_HOME=${HOME}/opt
   

   Of course the exact directories will depend on where the libraries are installed. You only need to set variables for libraries installed in non-default locations, and only for those libraries you want to use. Everything except NetCDF is optional.

   If echo $SHELL instead is tcsh or similar, then the environment variables can be set like setenv NETCDF_HOME / and similarly.

6. Run cmake with the main PartMC directory as an argument (note the double-c):

    ccmake ..
   

7. Inside ccmake press c to configure, edit the values as needed, press c again, then g to generate. Optional libraries can be activated by setting the ENABLE variable to ON. For a parallel build, toggle advanced mode with t and set the CMAKE_Fortran_COMPILER to mpif90, then reconfigure.

8. Optionally, enable compiler warnings by pressing t inside ccmake to enable advanced options and then setting CMAKE_Fortran_FLAGS to:

    -O2 -g -fimplicit-none -W -Wall -Wconversion -Wunderflow -Wimplicit-interface -Wno-compare-reals -Wno-unused -Wno-unused-parameter -Wno-unused-dummy-argument -fbounds-check
   

9. Compile PartMC and test it as follows.

    make
    make test
   

10. To run just a single test do something like:

     ctest -R bidisperse   # argument is a regexp for test names
    

11. To see what make is doing run it like:

    VERBOSE=1 make
    

12. To run tests with visible output or to make some plots from the tests run them as follows. Note that tests often rely on earlier tests in the same directory, so always run test_1, then test_2, etc. Tests occasionally fail due to random sampling, so re-run the entire sequence after failures. For example:

    cd test_run/emission
    ./test_emission_1.sh
    ./test_emission_2.sh
    ./test_emission_3.sh            # similarly for other tests
    gnuplot -persist plot_species.gnuplot # etc...
    

13. To run full scenarios, do, for example:

    cd ../scenarios/1_urban_plume
    ./1_run.sh
    

The main partmc command reads .spec files and does the run specified therein. Either particle-resolved runs, sectional-code runs, or exact solutions can be generated. A run produces one NetCDF file per output timestep, containing per-particle data (from particle-resolved runs) or binned data (from sectional or exact runs). The extract_* programs can read these per-timestep NetCDF files and output ASCII data (the extract_sectional_* programs are used for sectional and exact model output).