Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 21
Articles | Volume 16, issue 21
https://doi.org/10.5194/gmd-16-6211-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-6211-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 21
Development and technical paper
 | 
02 Nov 2023
Development and technical paper |  | 02 Nov 2023

Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method

Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima

Related authors

Exploring Cloud Microphysics Modeling Concepts the Pythonic Way
Sylwester Arabas, Agnieszka Żaba, Daria Klimaszewska, Emma C. Ware, Clare E. Singer, Manhal Alhilali, Jason Barr, Daniel G. Partridge, Shin-ichiro Shima, and Robert Wood
EGUsphere, https://doi.org/10.5194/egusphere-2026-3906,https://doi.org/10.5194/egusphere-2026-3906, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Cleo: the numerical methods of a new superdroplet model including a droplet breakup algorithm (v0.52.0)
Clara J. A. Bayley, Ann Kristin Naumann, Florian Poydenot, Raphaela Vogel, Bjorn Stevens, and Shin-Ichiro Shima
Geosci. Model Dev., 19, 6121–6142, https://doi.org/10.5194/gmd-19-6121-2026,https://doi.org/10.5194/gmd-19-6121-2026, 2026
Short summary
The Cold-Air Outbreaks in the Marine Boundary Layer Experiment model-observation intercomparison project (COMBLE-MIP), Part I: Model specification, observational constraints, and preliminary findings
Timothy W. Juliano, Florian Tornow, Ann M. Fridlind, Andrew S. Ackerman, Gregory S. Elsaesser, Bart Geerts, Christian P. Lackner, David Painemal, Israel Silber, Mikhail Ovchinnikov, Gunilla Svensson, Michael Tjernström, Peng Wu, Alejandro Baró Pérez, Peter Bogenschutz, Dmitry Chechin, Kamal Kant Chandrakar, Jan Chylik, Andrey Debolskiy, Rostislav Fadeev, Anu Gupta, Luisa Ickes, Michail Karalis, Martin Köhler, Branko Kosovic, Peter Kuma, Weiwei Li, Evgeny Mortikov, Hugh Morrison, Roel A. J. Neggers, Anna Possner, Tomi Raatikainen, Lea Raillard, Sami Romakkaniemi, Niklas Schnierstein, Shin-ichiro Shima, Nikita Silin, Mikhail Tolstykh, Étienne Vignon, Lulin Xue, Meng Zhang, and Xue Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2026-1237,https://doi.org/10.5194/egusphere-2026-1237, 2026
Short summary
The Cold-Air Outbreaks in the Marine Boundary Layer Experiment model-observation intercomparison project (COMBLE-MIP), Part I: Model specification, observational constraints, and preliminary findings
Timothy W. Juliano, Florian Tornow, Ann M. Fridlind, Andrew S. Ackerman, Gregory S. Elsaesser, Bart Geerts, Christian P. Lackner, David Painemal, Israel Silber, Mikhail Ovchinnikov, Gunilla Svensson, Michael Tjernström, Peng Wu, Alejandro Baró Pérez, Peter Bogenschutz, Dmitry Chechin, Kamal Kant Chandrakar, Jan Chylik, Andrey Debolskiy, Rostislav Fadeev, Anu Gupta, Luisa Ickes, Michail Karalis, Martin Köhler, Branko Kosović, Peter Kuma, Weiwei Li, Evgeny Mortikov, Hugh Morrison, Roel A. J. Neggers, Anna Possner, Tomi Raatikainen, Sami Romakkaniemi, Niklas Schnierstein, Shin-ichiro Shima, Nikita Silin, Mikhail Tolstykh, Lulin Xue, Meng Zhang, and Xue Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-6217,https://doi.org/10.5194/egusphere-2025-6217, 2026
Preprint archived
Short summary
A Lagrangian Particle Tracking Framework for the Super-Droplet Method: Development, Implementation, and Application of Backward and Forward Algorithms in SCALE-SDM 5.2.6-2.3.1
Chongzhi Yin, Shin-Ichiro Shima, and Chunsong Lu
EGUsphere, https://doi.org/10.5194/egusphere-2025-6221,https://doi.org/10.5194/egusphere-2025-6221, 2025
Short summary
More articles (2)

Cited articles

Abade, G. C., Grabowski, W. W., and Pawlowska, H.: Broadening of Cloud Droplet Spectra through Eddy Hopping: Turbulent Entraining Parcel Simulations, J. Atmos. Sci., 75, 3365–3379, https://doi.org/10.1175/JAS-D-18-0078.1, 2018. a, b
Akinlabi, E. O., Wacławczyk, M., Mellado, J. P., and Malinowski, S. P.: Estimating turbulence kinetic energy dissipation rates in the numerically simulated stratocumulus cloud-top mixing layer: Evaluation of different methods, J. Atmos. Sci., 76, 1471–1488, https://doi.org/10.1175/JAS-D-18-0146.1, 2019. a
Arabas, S. and Shima, S.-i.: Large-eddy simulations of trade wind cumuli using particle-based microphysics with Monte Carlo coalescence, J. Atmos. Sci., 70, 2768–2777, https://doi.org/10.1175/JAS-D-12-0295.1, 2013. a
Arabas, S. and Shima, S.: On the CCN (de)activation nonlinearities, Nonlin. Processes Geophys., 24, 535–542, https://doi.org/10.5194/npg-24-535-2017, 2017. a
Arabas, S., Pawlowska, H., and Grabowski, W.: Effective radius and droplet spectral width from in-situ aircraft observations in trade-wind cumuli during RICO, Geophys. Res. Lett., 36, L11803, https://doi.org/10.1029/2009GL038257, 2009. a
More articles (65)
Download
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Read more
Share
Special issue
Particle-based methods for simulating atmospheric aerosol...