Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-13-4107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| Highlight paper
| 
08 Sep 2020
Development and technical paper | Highlight paper |
| 08 Sep 2020
| 08 Sep 2020
Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2
Shin-ichiro Shima
CORRESPONDING AUTHOR
Graduate School of Simulation Studies, University of Hyogo, Kobe, Japan
RIKEN Center for Computational Science, Kobe, Japan
Yousuke Sato
Faculty of Science, Hokkaido University, Sapporo, Japan
RIKEN Center for Computational Science, Kobe, Japan
Akihiro Hashimoto
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
Ryohei Misumi
National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan
Viewed
Total article views: 6,534 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Dec 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 5,220 | 1,250 | 64 | 6,534 | 91 | 68 |
- HTML: 5,220
- PDF: 1,250
- XML: 64
- Total: 6,534
- BibTeX: 91
- EndNote: 68
Total article views: 5,562 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Sep 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,690 | 837 | 35 | 5,562 | 51 | 37 |
- HTML: 4,690
- PDF: 837
- XML: 35
- Total: 5,562
- BibTeX: 51
- EndNote: 37
Total article views: 972 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Dec 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 530 | 413 | 29 | 972 | 40 | 31 |
- HTML: 530
- PDF: 413
- XML: 29
- Total: 972
- BibTeX: 40
- EndNote: 31
Viewed (geographical distribution)
Total article views: 6,534 (including HTML, PDF, and XML)
Thereof 5,740 with geography defined
and 794 with unknown origin.
Total article views: 5,562 (including HTML, PDF, and XML)
Thereof 4,976 with geography defined
and 586 with unknown origin.
Total article views: 972 (including HTML, PDF, and XML)
Thereof 764 with geography defined
and 208 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
22 citations as recorded by crossref.
- New developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-stepping E. de Jong et al. 10.21105/joss.04968
- Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method E. de Jong et al. 10.5194/gmd-16-4193-2023
- Supersaturation, buoyancy, and deep convection dynamics W. Grabowski & H. Morrison 10.5194/acp-21-13997-2021
- Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 P. Zmijewski et al. 10.5194/gmd-17-759-2024
- Between Broadening and Narrowing: How Mixing Affects the Width of the Droplet Size Distribution J. Lim & F. Hoffmann 10.1029/2022JD037900
- An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n‐Moment Bulk Microphysics Scheme M. Bieli et al. 10.1029/2022MS002994
- Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions Y. Liu et al. 10.1007/s00376-022-2077-3
- The Relationship of Aerosol Properties and Ice‐Nucleating Particle Concentrations in Beijing Y. Ren et al. 10.1029/2022JD037383
- Comparison of Lagrangian Superdroplet and Eulerian Double-Moment Spectral Microphysics Schemes in Large-Eddy Simulations of an Isolated Cumulus Congestus Cloud K. Chandrakar et al. 10.1175/JAS-D-21-0138.1
- A Lagrangian particle-based numerical model for surfactant-laden droplets at macroscales M. Denys et al. 10.1063/5.0101930
- Semianalytic Functions to Calculate the Deposition Coefficients for Ice Crystal Vapor Growth in Bin and Bulk Microphysical Models J. Harrington et al. 10.1175/JAS-D-20-0307.1
- Collision Fluctuations of Lucky Droplets with Superdroplets X. Li et al. 10.1175/JAS-D-20-0371.1
- Collisional growth in a particle-based cloud microphysical model: insights from column model simulations using LCM1D (v1.0) S. Unterstrasser et al. 10.5194/gmd-13-5119-2020
- Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud D. McGuffin et al. 10.1029/2022JD036599
- Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method T. Matsushima et al. 10.5194/gmd-16-6211-2023
- snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation D. Ori et al. 10.5194/gmd-14-1511-2021
- Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. 10.5194/acp-23-15413-2023
- Secondary Ice Formation in Idealised Deep Convection—Source of Primary Ice and Impact on Glaciation A. Miltenberger et al. 10.3390/atmos11050542
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics H. Morrison et al. 10.1029/2019MS001689
- Process-Tracking Scheme Based on Bulk Microphysics to Diagnose the Features of Snow Particles A. Hashimoto et al. 10.2151/sola.2020-009
- Evaluating an Ice Crystal Trajectory Growth (ICTG) Model on a Quasi‐Idealized Simulation of a Squall Line C. Laurencin et al. 10.1029/2021MS002764
- Ice Particle Properties Inferred From Aggregation Modelling M. Karrer et al. 10.1029/2020MS002066
17 citations as recorded by crossref.
- New developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-stepping E. de Jong et al. 10.21105/joss.04968
- Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method E. de Jong et al. 10.5194/gmd-16-4193-2023
- Supersaturation, buoyancy, and deep convection dynamics W. Grabowski & H. Morrison 10.5194/acp-21-13997-2021
- Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 P. Zmijewski et al. 10.5194/gmd-17-759-2024
- Between Broadening and Narrowing: How Mixing Affects the Width of the Droplet Size Distribution J. Lim & F. Hoffmann 10.1029/2022JD037900
- An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n‐Moment Bulk Microphysics Scheme M. Bieli et al. 10.1029/2022MS002994
- Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions Y. Liu et al. 10.1007/s00376-022-2077-3
- The Relationship of Aerosol Properties and Ice‐Nucleating Particle Concentrations in Beijing Y. Ren et al. 10.1029/2022JD037383
- Comparison of Lagrangian Superdroplet and Eulerian Double-Moment Spectral Microphysics Schemes in Large-Eddy Simulations of an Isolated Cumulus Congestus Cloud K. Chandrakar et al. 10.1175/JAS-D-21-0138.1
- A Lagrangian particle-based numerical model for surfactant-laden droplets at macroscales M. Denys et al. 10.1063/5.0101930
- Semianalytic Functions to Calculate the Deposition Coefficients for Ice Crystal Vapor Growth in Bin and Bulk Microphysical Models J. Harrington et al. 10.1175/JAS-D-20-0307.1
- Collision Fluctuations of Lucky Droplets with Superdroplets X. Li et al. 10.1175/JAS-D-20-0371.1
- Collisional growth in a particle-based cloud microphysical model: insights from column model simulations using LCM1D (v1.0) S. Unterstrasser et al. 10.5194/gmd-13-5119-2020
- Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud D. McGuffin et al. 10.1029/2022JD036599
- Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method T. Matsushima et al. 10.5194/gmd-16-6211-2023
- snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation D. Ori et al. 10.5194/gmd-14-1511-2021
- Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. 10.5194/acp-23-15413-2023
5 citations as recorded by crossref.
- Secondary Ice Formation in Idealised Deep Convection—Source of Primary Ice and Impact on Glaciation A. Miltenberger et al. 10.3390/atmos11050542
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics H. Morrison et al. 10.1029/2019MS001689
- Process-Tracking Scheme Based on Bulk Microphysics to Diagnose the Features of Snow Particles A. Hashimoto et al. 10.2151/sola.2020-009
- Evaluating an Ice Crystal Trajectory Growth (ICTG) Model on a Quasi‐Idealized Simulation of a Squall Line C. Laurencin et al. 10.1029/2021MS002764
- Ice Particle Properties Inferred From Aggregation Modelling M. Karrer et al. 10.1029/2020MS002066
Latest update: 24 Apr 2024
Short summary
Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds based on kinetic description and subsequently demonstrated that a large-eddy simulation of a cumulonimbus which predicts ice particle morphology without assuming ice categories or mass–dimension relationships is possible. Our results strongly support the particle-based modeling methodology’s efficacy for simulating mixed-phase clouds.
Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds...
