Articles | Volume 17, issue 22
https://doi.org/10.5194/acp-17-13509-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/acp-17-13509-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Nov 2017
Research article |
| 14 Nov 2017
Stochastic coalescence in Lagrangian cloud microphysics
Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland
Hanna Pawlowska
Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland
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Cited
31 citations as recorded by crossref.
- Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions Y. Liu et al. 10.1007/s00376-022-2077-3
- Direct Lagrangian tracking simulation of droplet growth in vertically developing cloud Y. Kunishima & R. Onishi 10.5194/acp-18-16619-2018
- Fractal Reconstruction of Sub-Grid Scales for Large Eddy Simulation E. Akinlabi et al. 10.1007/s10494-019-00030-2
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics H. Morrison et al. 10.1029/2019MS001689
- A microphysical investigation of different convective cells during the precipitation event with sustained high-resolution observations Z. Huang et al. 10.1007/s11707-022-1076-0
- 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
- Empirical values and assumptions in the microphysics of numerical models F. Tapiador et al. 10.1016/j.atmosres.2018.09.010
- Modeling of Cloud Microphysics: Can We Do Better? W. Grabowski et al. 10.1175/BAMS-D-18-0005.1
- Cloud Microphysical Implications for Marine Cloud Brightening: The Importance of the Seeded Particle Size Distribution F. Hoffmann & G. Feingold 10.1175/JAS-D-21-0077.1
- Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1 C. Yin et al. 10.5194/gmd-17-5167-2024
- The impact of fluctuations and correlations in droplet growth by collision–coalescence revisited – Part 2: Observational evidence of gel formation in warm clouds L. Alfonso et al. 10.5194/acp-19-14917-2019
- Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration? M. Maahn et al. 10.5194/amt-12-3151-2019
- Impact of Giant Sea Salt Aerosol Particles on Precipitation in Marine Cumuli and Stratocumuli: Lagrangian Cloud Model Simulations P. Dziekan et al. 10.1175/JAS-D-21-0041.1
- 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 S. Shima et al. 10.5194/gmd-13-4107-2020
- Coalescense with arbitrary-parameter kernels and monodisperse initial conditions: A study within combinatorial framework M. Łepek et al. 10.1016/S0034-4877(21)00058-6
- Lagrangian condensation microphysics with Twomey CCN activation W. Grabowski et al. 10.5194/gmd-11-103-2018
- 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
- 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
- Effect of Turbulence on Collisional Growth of Cloud Droplets X. Li et al. 10.1175/JAS-D-18-0081.1
- Condensational and Collisional Growth of Cloud Droplets in a Turbulent Environment X. Li et al. 10.1175/JAS-D-19-0107.1
- Roles of Drop Size Distribution and Turbulence in Autoconversion Based on Lagrangian Cloud Model Simulations D. Oh & Y. Noh 10.1029/2022JD036495
- Efficient simulation of stochastic interactions among representative Monte Carlo particles M. Beutel et al. 10.1051/0004-6361/202347304
- University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern large-eddy simulation tool for warm cloud modeling with Lagrangian microphysics P. Dziekan et al. 10.5194/gmd-12-2587-2019
- libcloudph++ 2.0: aqueous-phase chemistry extension of the particle-based cloud microphysics scheme A. Jaruga & H. Pawlowska 10.5194/gmd-11-3623-2018
- Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud D. McGuffin et al. 10.1029/2022JD036599
- Collision Fluctuations of Lucky Droplets with Superdroplets X. Li et al. 10.1175/JAS-D-20-0371.1
- 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
- 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
- Improving collisional growth in Lagrangian cloud models: development and verification of a new splitting algorithm J. Schwenkel et al. 10.5194/gmd-11-3929-2018
- A physically based raindrop–cloud droplet accretion parametrization for use in bulk microphysics schemes T. Ahmed et al. 10.1002/qj.3850
- Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes A. Seifert & S. Rasp 10.1029/2020MS002301
31 citations as recorded by crossref.
- Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions Y. Liu et al. 10.1007/s00376-022-2077-3
- Direct Lagrangian tracking simulation of droplet growth in vertically developing cloud Y. Kunishima & R. Onishi 10.5194/acp-18-16619-2018
- Fractal Reconstruction of Sub-Grid Scales for Large Eddy Simulation E. Akinlabi et al. 10.1007/s10494-019-00030-2
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics H. Morrison et al. 10.1029/2019MS001689
- A microphysical investigation of different convective cells during the precipitation event with sustained high-resolution observations Z. Huang et al. 10.1007/s11707-022-1076-0
- 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
- Empirical values and assumptions in the microphysics of numerical models F. Tapiador et al. 10.1016/j.atmosres.2018.09.010
- Modeling of Cloud Microphysics: Can We Do Better? W. Grabowski et al. 10.1175/BAMS-D-18-0005.1
- Cloud Microphysical Implications for Marine Cloud Brightening: The Importance of the Seeded Particle Size Distribution F. Hoffmann & G. Feingold 10.1175/JAS-D-21-0077.1
- Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1 C. Yin et al. 10.5194/gmd-17-5167-2024
- The impact of fluctuations and correlations in droplet growth by collision–coalescence revisited – Part 2: Observational evidence of gel formation in warm clouds L. Alfonso et al. 10.5194/acp-19-14917-2019
- Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration? M. Maahn et al. 10.5194/amt-12-3151-2019
- Impact of Giant Sea Salt Aerosol Particles on Precipitation in Marine Cumuli and Stratocumuli: Lagrangian Cloud Model Simulations P. Dziekan et al. 10.1175/JAS-D-21-0041.1
- 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 S. Shima et al. 10.5194/gmd-13-4107-2020
- Coalescense with arbitrary-parameter kernels and monodisperse initial conditions: A study within combinatorial framework M. Łepek et al. 10.1016/S0034-4877(21)00058-6
- Lagrangian condensation microphysics with Twomey CCN activation W. Grabowski et al. 10.5194/gmd-11-103-2018
- 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
- 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
- Effect of Turbulence on Collisional Growth of Cloud Droplets X. Li et al. 10.1175/JAS-D-18-0081.1
- Condensational and Collisional Growth of Cloud Droplets in a Turbulent Environment X. Li et al. 10.1175/JAS-D-19-0107.1
- Roles of Drop Size Distribution and Turbulence in Autoconversion Based on Lagrangian Cloud Model Simulations D. Oh & Y. Noh 10.1029/2022JD036495
- Efficient simulation of stochastic interactions among representative Monte Carlo particles M. Beutel et al. 10.1051/0004-6361/202347304
- University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern large-eddy simulation tool for warm cloud modeling with Lagrangian microphysics P. Dziekan et al. 10.5194/gmd-12-2587-2019
- libcloudph++ 2.0: aqueous-phase chemistry extension of the particle-based cloud microphysics scheme A. Jaruga & H. Pawlowska 10.5194/gmd-11-3623-2018
- Super‐Droplet Method to Simulate Lagrangian Microphysics of Nuclear Fallout in a Homogeneous Cloud D. McGuffin et al. 10.1029/2022JD036599
- Collision Fluctuations of Lucky Droplets with Superdroplets X. Li et al. 10.1175/JAS-D-20-0371.1
- 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
- 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
- Improving collisional growth in Lagrangian cloud models: development and verification of a new splitting algorithm J. Schwenkel et al. 10.5194/gmd-11-3929-2018
- A physically based raindrop–cloud droplet accretion parametrization for use in bulk microphysics schemes T. Ahmed et al. 10.1002/qj.3850
- Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes A. Seifert & S. Rasp 10.1029/2020MS002301
Latest update: 17 Jul 2024
Short summary
Raindrops form when small cloud droplets collide with each other. In most computer models of clouds, this process is described using the Smoluchowski equation. We compare the Smoluchowski equation with computer simulations in which each droplet within a small part of the cloud is modeled. We show, depending on the simulation setup, that the Smoluchowski equation can give overly slow or fast rain formation. This implies that many cloud models used do not correctly represent rain formation.
Raindrops form when small cloud droplets collide with each other. In most computer models of...
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