Articles | Volume 13, issue 11
Geosci. Model Dev., 13, 5119–5145, 2020
https://doi.org/10.5194/gmd-13-5119-2020

Special issue: Particle-based methods for simulating atmospheric aerosol...

Geosci. Model Dev., 13, 5119–5145, 2020
https://doi.org/10.5194/gmd-13-5119-2020
Development and technical paper
30 Oct 2020
Development and technical paper | 30 Oct 2020

Collisional growth in a particle-based cloud microphysical model: insights from column model simulations using LCM1D (v1.0)

Simon Unterstrasser et al.

Related authors

On numerical broadening of particle-size spectra: a condensational growth study using PyMPDATA 1.0
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
Geosci. Model Dev., 15, 3879–3899, https://doi.org/10.5194/gmd-15-3879-2022,https://doi.org/10.5194/gmd-15-3879-2022, 2022
Short summary
Box model trajectory studies of contrail formation using a particle-based cloud microphysics scheme
Andreas Bier, Simon Unterstrasser, and Xavier Vancassel
Atmos. Chem. Phys., 22, 823–845, https://doi.org/10.5194/acp-22-823-2022,https://doi.org/10.5194/acp-22-823-2022, 2022
Short summary
Contrails and their impact on shortwave radiation and photovoltaic power production – a regional model study
Simon Gruber, Simon Unterstrasser, Jan Bechtold, Heike Vogel, Martin Jung, Henry Pak, and Bernhard Vogel
Atmos. Chem. Phys., 18, 6393–6411, https://doi.org/10.5194/acp-18-6393-2018,https://doi.org/10.5194/acp-18-6393-2018, 2018
Short summary
Collection/aggregation algorithms in Lagrangian cloud microphysical models: rigorous evaluation in box model simulations
Simon Unterstrasser, Fabian Hoffmann, and Marion Lerch
Geosci. Model Dev., 10, 1521–1548, https://doi.org/10.5194/gmd-10-1521-2017,https://doi.org/10.5194/gmd-10-1521-2017, 2017
Short summary
Properties of young contrails – a parametrisation based on large-eddy simulations
Simon Unterstrasser
Atmos. Chem. Phys., 16, 2059–2082, https://doi.org/10.5194/acp-16-2059-2016,https://doi.org/10.5194/acp-16-2059-2016, 2016
Short summary

Related subject area

Atmospheric sciences
Downscaling atmospheric chemistry simulations with physically consistent deep learning
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022,https://doi.org/10.5194/gmd-15-6677-2022, 2022
Short summary
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022,https://doi.org/10.5194/gmd-15-6341-2022, 2022
Short summary
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022,https://doi.org/10.5194/gmd-15-6259-2022, 2022
Short summary
Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022,https://doi.org/10.5194/gmd-15-6197-2022, 2022
Short summary
OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022,https://doi.org/10.5194/gmd-15-6221-2022, 2022
Short summary

Cited articles

Alfonso, L. and Raga, G. B.: The impact of fluctuations and correlations in droplet growth by collision–coalescence revisited – Part 1: Numerical calculation of post-gel droplet size distribution, Atmos. Chem. Phys., 17, 6895–6905, https://doi.org/10.5194/acp-17-6895-2017, 2017. a, b
Alfonso, L., Raga, G. B., and Baumgardner, D.: The validity of the kinetic collection equation revisited, Atmos. Chem. Phys., 8, 969–982, https://doi.org/10.5194/acp-8-969-2008, 2008. a
Andrejczuk, M., Reisner, J. M., Henson, B., Dubey, M. K., and Jeffery, C. A.: The potential impacts of pollution on a nondrizzling stratus deck: Does aerosol number matter more than type?, J. Geophys. Res., 113, D19204, https://doi.org/10.1029/2007JD009445, 2008. a
Andrejczuk, M., Grabowski, W. W., Reisner, J., and Gadian, A.: Cloud-aerosol interactions for boundary layer stratocumulus in the Lagrangian cloud model, J. Geophys. Res., 115, D22214, https://doi.org/10.1029/2010JD014248, 2010. a
Arabas, S., Jaruga, A., Pawlowska, H., and Grabowski, W. W.: libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++, Geosci. Model Dev., 8, 1677–1707, https://doi.org/10.5194/gmd-8-1677-2015, 2015. a, b
Download
Short summary
Particle-based cloud models use simulation particles for the representation of cloud particles like droplets or ice crystals. The collision and merging of cloud particles (i.e. collisional growth a.k.a. collection in the case of cloud droplets and aggregation in the case of ice crystals) was found to be a numerically challenging process in such models. The study presents verification exercises in a 1D column model, where sedimentation and collisional growth are the only active processes.