Continuous growth of cloud droplets in cumulus cloud

Gotoh, Toshiyuki; Suehiro, Tamotsu; Saito, Izumi

doi:10.1088/1367-2630/18/4/043042

Cited by 25 publications

(13 citation statements)

References 66 publications

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“…We use the cloud microphysics simulator, which is a DNS model for cloud turbulence developed in previous studies (Gotoh et al 2016;. The supercomputers used in the present study are mostly the K-computer at the Research Organization for Information Science and Technology (RIST) in Kobe and the Fujitsu FX100 installed at Nagoya University.…”

Section: E Dnsmentioning

confidence: 99%

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Broadening of Cloud Droplet Size Distributions by Condensation in Turbulence

Saito

Gotoh

Watanabe

2019

Journal of the Meteorological Society of Japan

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To consider the growth of cloud droplets by condensation in turbulence, the Fokker-Planck equation is derived for the droplet size distribution (droplet spectrum). This is an extension of the statistical theory proposed by Chandrakar and coauthors in 2016 for explaining the broadening of the droplet spectrum obtained from the "Π-chamber", a laboratory cloud chamber. In this Fokker-Planck equation, the diffusion term represents the broadening effect of the supersaturation fluctuation on the droplet spectrum. The aerosol (curvature and solute) effects are introduced into the Fokker-Planck equation as the zero flux boundary condition at R 2 = 0, where R is the droplet radius, which is mathematically equivalent to the case of Brownian motion in the presence of a wall. The analytical expression for the droplet spectrum in the steady state is obtained and shown to be proportional to R exp (-cR 2), where c is a constant. We conduct direct numerical simulations of cloud droplets in turbulence and show that the results agree closely with the theoretical predictions and, when the computational domain is large enough to be comparable to the Π-chamber, agree with the results from the Π-chamber as well. We also show that the diffusion coefficient in the Fokker-Planck equation should be expressed in terms of the Lagrangian autocorrelation time of the supersaturation fluctuation in turbulent flow.

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Section: E Dnsmentioning

confidence: 99%

“…With the above motivation, the main purpose of the present study is to conduct DNSs using our DNS model the "cloud microphysics simulator" (Gotoh et al 2016), and compare the results with the statistical theory and the laboratory experiment by C16. First, we make several extensions to the statistical theory by C16.…”

Section: Introductionmentioning

confidence: 99%

Broadening of Cloud Droplet Size Distributions by Condensation in Turbulence

Saito

Gotoh

Watanabe

2019

Journal of the Meteorological Society of Japan

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“…We will begin by discussing a newly developed model, named as a cloud microphysics simulator, that can seamlessly compute the evolution of turbulence and cloud droplets up to raindrop formation within a cumulus cloud from microscopic viewpoints [25] (hereafter referred to as GSS). The method shares with [29,42] in the introduction of a small cubic box ascending within a cumulus cloud, but differs in the following points: (1) the updraft velocity of the box is self-consistently determined by the latent heat release, (2) the supersaturated state is sustained by the ascending motion through the self-consistently determined mean buoyancy, (3) the dynamics of the cloud droplets is directly integrated instead of using the bin method, and (4) the turbulent air flow, temperature and water vapor mixing ratio within the box are computed by DNS.…”

Section: Introductionmentioning

confidence: 99%

Turbulence and cloud droplets in cumulus clouds

Saito

Gotoh

2018

New J. Phys.

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In this paper, we report on the successful and seamless simulation of turbulence and the evolution of cloud droplets to raindrops over 10 minutes from microscopic viewpoints by using direct numerical simulation. Included processes are condensation-evaporation, collision-coalescence of droplets with hydrodynamic interaction, Reynolds number dependent drag, and turbulent flow within a parcel that is ascending within a self-consistently determined updraft inside a cumulus cloud. We found that the altitude and the updraft velocity of the parcel, the mean supersaturation, and the liquid water content are insensitive to the turbulence intensity, and that when the turbulence intensity increases, the droplet number density swiftly decreases while the spectral width of droplets rapidly increases. This study marks the first time the evolution of the mass density distribution function has been successfully calculated from microscopic computations. The turbulence accelerated to form a second peak in the mass density distribution function, leading to the raindrop formation, and the radius of the largest drop was over 300 μm at the end of the simulation. We also found that cloud droplets modify the turbulence in a way that is unlike the Kolmogorov-Obukhov-Corrsin theory. For example, the temperature and water vapor spectra at low wavenumbers become shallower thank 5 3 in the inertial-convective range, and decrease slower than exponentially in the diffusive range. This spectra modification is explained by nonlinear interactions between turbulent mixing and the evaporationcondensation process associated with large numbers of droplets.

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“…Several aspects of the droplet growth have been considered in Eulerian and Euler-Lagrangian numerical studies in the bulk of a cloud. Growth by condensation, allowing the humidity to fluctuate due to droplet inertia and turbulent mixing, was studied for example by Vaillancourt, Yau & Grabowski (2001), Andrejczuk et al (2006Andrejczuk et al ( , 2009, Lanotte, Seminara & Toschi (2009) and Gotoh, Suehiro & Saito (2016).…”

Section: Introductionmentioning

confidence: 99%

Droplet dynamics and fine-scale structure in a shearless turbulent mixing layer with phase changes

et al. 2017

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Three-dimensional direct numerical simulations of a shearless mixing layer in a small fraction of the cloud–clear air interface are performed to study the response of an ensemble of cloud water droplets to the turbulent entrainment of clear air into a cloud filament. The main goal of this work is to understand how mixing of cloudy and clear air evolves as turbulence and thermodynamics interact through phase changes, and how the cloud droplets respond. In the main simulation case, mixing proceeds between a higher level of turbulence in the cloudy filament and a lower level of turbulence in the clear air environment – the typical shearless mixing layer set-up. Fluid turbulence is driven solely by buoyancy, which incorporates feedbacks from the temperature, the vapour content and the liquid water content fields. Two different variations on the core set of shearless mixing layer simulations are discussed, a simulation in a larger domain and a simulation with the same turbulence level inside the filament and its environment. Overall, it is found that, as evaporation occurs for the droplets that enter subsaturated clear air regions, buoyancy comes to dominate the subsequent evolution of the mixing layer. The buoyancy feedback leads initially to downdraughts at the cloudy–clear air interface and to updraughts in the bulk regions. The strength of the turbulence after initial transients depends on the domain size, showing that the range of scales is an important parameter in the shearless mixing layer set-up. In contrast, the level of turbulence in the clear air is found to have little effect on the evolution of the mixing process. The distributions of cloud water droplet size, supersaturation at the droplet positions and vertical velocity are more sensitive to domain size than to the details of the turbulence profile, suggesting that the evolution of cloud microphysics is more sensitive to large-scale as opposed to small-scale properties of the flow.

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Continuous growth of cloud droplets in cumulus cloud

Cited by 25 publications

References 66 publications

Broadening of Cloud Droplet Size Distributions by Condensation in Turbulence

Broadening of Cloud Droplet Size Distributions by Condensation in Turbulence

Turbulence and cloud droplets in cumulus clouds

Droplet dynamics and fine-scale structure in a shearless turbulent mixing layer with phase changes

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