the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Nov 2022
Mixed-phase Direct Numerical Simulation: Ice Growth in Cloud-Top Generating Cells
Abstract. A detailed microphysical model is developed using a Lagrangian-particle-based direct numerical simulation framework to simulate ice growth in a turbulent mixed-phase environment. The Lagrangian particle method is employed to track the interactions between ice, droplets, and turbulence at the native scales. The investigation reveals for the first time the mixed-phase processes at the sub-meter length scales using direct numerical simulation.
This paper examines the conditions that favor effective ice growth in the cloud top generating cells. Investigations over a range of environmental (macrophysical and turbulent) and microphysical conditions (ice number concentrations) that distinguish generating cells from their surrounding cloudy air were conducted. Results show that high liquid water content (LWC) or high relative humidity (RH) is critical to maintaining effective ice growth and mixed-phase. As a result, generating cells with high LWC and high RH provide favorable conditions for rapid ice growth. Sensitivity studies on ice number concentrations show that when the ice number concentration is below 1 cm-3, a typical range in the mixed-phase clouds, a high LWC is needed for efficient formation of big ice particles. The study also found that supersaturation fluctuations due to small-scale turbulent mixing have a negligible effect on the particle mean radius but substantially broaden the size spectra which can affect the subsequent collection process.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1142', Anonymous Referee #1, 25 Nov 2022
This paper investigates the growth of ice particles through the Bergeron-Findeisen process by using Direct Numerical Simulations (DNS). Â The paper is well written, and the results are interesting. I can recommend this paper for publication, but I would like the authors to answer next questions:
1) I would like to know more details about the simulations, such as grid spacing, time stepping and used numerics. It would be also interesting to know if the droplets/ice sediment, and with which velocity.Â
2) In the description of the measured environmental conditions (table 1) Â it would be useful to have the mixing ratios qc und qi and the number concentrations qnc and qni. It is also said that drizzle was measured, but I guess this has no influence on the experiments.
3) Â The DNS experiments have a dissipation rate of 10m2s-3. Is this not too high for such a cloud?
4) Please state the mean temperature in the experiments.
5) Why do you use a standard ice concentration in the DNS of 1000 l-1Â when only 1l-1Â was measured?
6) The experiment RH_noGC starts with unusually low radius of droplets (R=1.5 μm), which I do not think can be easily found in clouds. It is then not surprising that all liquid water quickly evaporates if RH<100%. Please motivate these experiments.Â
7) It is a common assumption in weather models/LES with a time step 1-20 seconds that liquid water is in thermodynamic equilibrium, but not the ice. The underlying assumption is that water condensation/evaporation is much faster than ice processes. Could the authors comment on how good this approximation is? These experiments could be very useful to verify the above-described approximation.
Citation: https://doi.org/10.5194/egusphere-2022-1142-RC1 - RC2: 'Comment on egusphere-2022-1142', Anonymous Referee #2, 13 Dec 2022
- AC1: 'Comment on egusphere-2022-1142', Sisi Chen, 23 Mar 2023
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1142', Anonymous Referee #1, 25 Nov 2022
This paper investigates the growth of ice particles through the Bergeron-Findeisen process by using Direct Numerical Simulations (DNS). Â The paper is well written, and the results are interesting. I can recommend this paper for publication, but I would like the authors to answer next questions:
1) I would like to know more details about the simulations, such as grid spacing, time stepping and used numerics. It would be also interesting to know if the droplets/ice sediment, and with which velocity.Â
2) In the description of the measured environmental conditions (table 1) Â it would be useful to have the mixing ratios qc und qi and the number concentrations qnc and qni. It is also said that drizzle was measured, but I guess this has no influence on the experiments.
3) Â The DNS experiments have a dissipation rate of 10m2s-3. Is this not too high for such a cloud?
4) Please state the mean temperature in the experiments.
5) Why do you use a standard ice concentration in the DNS of 1000 l-1Â when only 1l-1Â was measured?
6) The experiment RH_noGC starts with unusually low radius of droplets (R=1.5 μm), which I do not think can be easily found in clouds. It is then not surprising that all liquid water quickly evaporates if RH<100%. Please motivate these experiments.Â
7) It is a common assumption in weather models/LES with a time step 1-20 seconds that liquid water is in thermodynamic equilibrium, but not the ice. The underlying assumption is that water condensation/evaporation is much faster than ice processes. Could the authors comment on how good this approximation is? These experiments could be very useful to verify the above-described approximation.
Citation: https://doi.org/10.5194/egusphere-2022-1142-RC1 - RC2: 'Comment on egusphere-2022-1142', Anonymous Referee #2, 13 Dec 2022
- AC1: 'Comment on egusphere-2022-1142', Sisi Chen, 23 Mar 2023
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Replication Data for "Mixed-phase Direct Numerical Simulation: Ice Growth in Cloud-Top Generating Cells" Sisi Chen https://doi.org/10.7910/DVN/BKYGWW
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1834 KB) - Metadata XML