scholarly journals Technical note: Equilibrium droplet size distributions in a turbulent cloud chamber with uniform supersaturation

2020 ◽  
Vol 20 (13) ◽  
pp. 7895-7909
Author(s):  
Steven K. Krueger
Keyword(s):  
Droplet Size ◽  
Cloud Condensation Nuclei ◽  
Lower Boundary ◽  
Droplet Size Distribution ◽  
Terminal Velocity ◽  
Technical Note ◽  
Cloud Chamber ◽  
Droplet Radius ◽  
Droplet Growth ◽  
The Mean

Abstract. In a laboratory cloud chamber that is undergoing Rayleigh–Bénard convection, supersaturation is produced by isobaric mixing. When aerosols (cloud condensation nuclei) are injected into the chamber at a constant rate, and the rate of droplet activation is balanced by the rate of droplet loss, an equilibrium droplet size distribution (DSD) can be achieved. We derived analytic equilibrium DSDs and probability density functions (PDFs) of droplet radius and squared radius for conditions that could occur in such a turbulent cloud chamber when there is uniform supersaturation. We neglected the effects of droplet curvature and solute on the droplet growth rate. The loss rate due to fallout that we used assumes that (1) the droplets are well-mixed by turbulence, (2) when a droplet becomes sufficiently close to the lower boundary, the droplet's terminal velocity determines its probability of fallout per unit time, and (3) a droplet's terminal velocity follows Stokes' law (so it is proportional to its radius squared). Given the chamber height, the analytic PDF is determined by the mean supersaturation alone. From the expression for the PDF of the radius, we obtained analytic expressions for the first five moments of the radius, including moments for truncated DSDs. We used statistics from a set of measured DSDs to check for consistency with the analytic PDF. We found consistency between the theoretical and measured moments, but only when the truncation radius of the measured DSDs was taken into account. This consistency allows us to infer the mean supersaturations that would produce the measured PDFs in the absence of supersaturation fluctuations. We found that accounting for the truncation radius of the measured DSDs is particularly important when comparing the theoretical and measured relative dispersions of the droplet radius. We also included some additional quantities derived from the analytic DSD: droplet sedimentation flux, precipitation flux, and condensation rate.

scholarly journals Technical Note: Equilibrium droplet size distributions in a turbulent cloud chamber with uniform supersaturation

2019 ◽  
Author(s):  
Steven K. Krueger
Keyword(s):  
Droplet Size ◽  
Cloud Condensation Nuclei ◽  
Lower Boundary ◽  
Droplet Size Distribution ◽  
Terminal Velocity ◽  
Technical Note ◽  
Cloud Chamber ◽  
Droplet Radius ◽  
Analytic Expressions ◽  
The Mean

Abstract. In a laboratory cloud chamber that is undergoing Rayleigh-Bénard convection, supersaturation is produced by isobaric mixing. When aerosols (cloud condensation nuclei) are injected into the chamber at a constant rate, and the rate of droplet activation is balanced by the rate of droplet loss, an equilibrium droplet size distribution (DSD) can be achieved. We derived analytic equilibrium DSDs and PDFs of droplet radius and squared radius for conditions that could occur in such a turbulent cloud chamber when there is uniform supersaturation. The loss rate due to fall out that we used assumes that (1) the droplets are well-mixed by turbulence, (2) when a droplet becomes sufficiently close to the lower boundary, the droplet’s terminal velocity determines its probability of fall out per unit time, and (3) a droplet’s terminal velocity follows Stokes’ Law (so it is proportional to its radius squared). Given the chamber height, the analytic PDF is determined by the mean supersaturation alone. From the expression for the PDF of the radius, we obtained analytic expressions for the first five moments of the radius, including moments for truncated DSDs. We used statistics from a set of measured DSDs to check for consistency with the analytic PDF. We found consistency between the theoretical and measured moments, but only when the truncation radius of the measured DSDs was taken into account. This consistency allows us to infer the mean supersaturations that would produce the measured PDFs in the absence of supersaturation fluctuations. We found that accounting for the truncation radius of the measured DSDs is particularly important when comparing the theoretical and measured relative dispersions of the droplet radius. We also included some additional quantities derived from the analytic DSD: droplet sedimentation flux, precipitation flux, and condensation rate.

scholarly journals Broadening of Cloud Droplet Spectra through Eddy Hopping: Turbulent Entraining Parcel Simulations

2018 ◽  
Vol 75 (10) ◽  
pp. 3365-3379 ◽  
Author(s):  
Gustavo C. Abade ◽  
Wojciech W. Grabowski ◽  
Hanna Pawlowska
Keyword(s):  
Droplet Size ◽  
Turbulent Mixing ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Cloud Microphysics ◽  
Droplet Growth ◽  
Size Spectrum ◽  
Entrainment Rate ◽  
The Mean ◽  
The Impact

This paper discusses the effects of cloud turbulence, turbulent entrainment, and entrained cloud condensation nuclei (CCN) activation on the evolution of the cloud droplet size spectrum. We simulate an ensemble of idealized turbulent cloud parcels that are subject to entrainment events modeled as a random process. Entrainment events, subsequent turbulent mixing inside the parcel, supersaturation fluctuations, and the resulting stochastic droplet activation and growth by condensation are simulated using a Monte Carlo scheme. Quantities characterizing the turbulence intensity, entrainment rate, CCN concentration, and the mean fraction of environmental air entrained in an event are all specified as independent external parameters. Cloud microphysics is described by applying Lagrangian particles, the so-called superdroplets. These are either unactivated CCN or cloud droplets that grow from activated CCN. The model accounts for the addition of environmental CCN into the cloud by entraining eddies at the cloud edge. Turbulent mixing of the entrained dry air with cloudy air is described using the classical linear relaxation to the mean model. We show that turbulence plays an important role in aiding entrained CCN to activate, and thus broadening the droplet size distribution. These findings are consistent with previous large-eddy simulations (LESs) that consider the impact of variable droplet growth histories on the droplet size spectra in small cumuli. The scheme developed in this work is ready to be used as a stochastic subgrid-scale scheme in LESs of natural clouds.

scholarly journals Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions

2016 ◽  
Vol 113 (50) ◽  
pp. 14243-14248 ◽  
Author(s):  
Kamal Kant Chandrakar ◽  
Will Cantrell ◽  
Kelken Chang ◽  
David Ciochetto ◽  
Dennis Niedermeier ◽  
...  
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Aerosol Concentration ◽  
Droplet Radius ◽  
Droplet Growth ◽  
Moist Convection ◽  
Concentration Changes ◽  
Precipitation Formation

The influence of aerosol concentration on the cloud-droplet size distribution is investigated in a laboratory chamber that enables turbulent cloud formation through moist convection. The experiments allow steady-state microphysics to be achieved, with aerosol input balanced by cloud-droplet growth and fallout. As aerosol concentration is increased, the cloud-droplet mean diameter decreases, as expected, but the width of the size distribution also decreases sharply. The aerosol input allows for cloud generation in the limiting regimes of fast microphysics (τc<τt) for high aerosol concentration, and slow microphysics (τc>τt) for low aerosol concentration; here, τc is the phase-relaxation time and τt is the turbulence-correlation time. The increase in the width of the droplet size distribution for the low aerosol limit is consistent with larger variability of supersaturation due to the slow microphysical response. A stochastic differential equation for supersaturation predicts that the standard deviation of the squared droplet radius should increase linearly with a system time scale defined as τs−1=τc−1+τt−1, and the measurements are in excellent agreement with this finding. The result underscores the importance of droplet size dispersion for aerosol indirect effects: increasing aerosol concentration changes the albedo and suppresses precipitation formation not only through reduction of the mean droplet diameter but also by narrowing of the droplet size distribution due to reduced supersaturation fluctuations. Supersaturation fluctuations in the low aerosol/slow microphysics limit are likely of leading importance for precipitation formation.

scholarly journals Broadening of Cloud Droplet Size Spectra by Stochastic Condensation: Effects of Mean Updraft Velocity and CCN Activation

2018 ◽  
Vol 75 (2) ◽  
pp. 451-467 ◽  
Author(s):  
Gaetano Sardina ◽  
Stéphane Poulain ◽  
Luca Brandt ◽  
Rodrigo Caballero
Keyword(s):  
Chemical Composition ◽  
Droplet Size ◽  
Isotropic Turbulence ◽  
Cloud Condensation Nuclei ◽  
Collision Probability ◽  
Droplet Radius ◽  
Droplet Growth ◽  
Size Spectrum ◽  
Similar Reduction ◽  
Size Spectra

Abstract The authors study the condensational growth of cloud droplets in homogeneous isotropic turbulence by means of a large-eddy simulation (LES) approach. The authors investigate the role of a mean updraft velocity and of the chemical composition of the cloud condensation nuclei (CCN) on droplet growth. The results show that a mean constant updraft velocity superimposed onto a turbulent field reduces the broadening of the droplet size spectra induced by the turbulent fluctuations alone. Extending the authors’ previous results regarding stochastic condensation, the authors introduce a new theoretical estimation of the droplet size spectrum broadening that accounts for this updraft velocity effect. A similar reduction of the spectra broadening is observed when the droplets reach their critical size, which depends on the chemical composition of CCN. The analysis of the square of the droplet radius distribution, proportional to the droplet surface, shows that for large particles the distribution is purely Gaussian, while it becomes strongly non-Gaussian for smaller particles, with the left tail characterized by a peak around the haze activation radius. This kind of distribution can significantly affect the later stages of the droplet growth involving turbulent collisions, since the collision probability kernel depends on the droplet size, implying the need for new specific closure models to capture this effect.

Lagrangian stochastic microphysics at unresolved scales in turbulent cloud simulations

2020 ◽  
Author(s):  
Gustavo Abade ◽  
Marta Waclawczyk ◽  
Wojciech W. Grabowski ◽  
Hanna Pawlowska
Keyword(s):  
Droplet Size ◽  
Cloud Condensation Nuclei ◽  
Droplet Size Distribution ◽  
Cloud Microphysics ◽  
Cloud Droplets ◽  
Adiabatic Cooling ◽  
Subgrid Scales ◽  
The Mean ◽  
Microphysical Processes ◽  
Droplet Size Distributions

&lt;p&gt;Turbulent clouds are challenging to model and simulate due to uncertainties in microphysical processes occurring at unresolved subgrid scales (SGS). These processes include the transport of cloud particles, supersaturation fluctuations, turbulent mixing, and the resulting stochastic droplet activation and growth by condensation. In this work, we apply two different Lagrangian stochastic schemes to model SGS cloud microphysics. Collision and coalescence of droplets are not considered. Cloud droplets and unactivated cloud condensation nuclei (CCN) are described by Lagrangian particles (superdroplets). The first microphysical scheme directly models the supersaturation fluctuations experienced by each Lagrangian superdroplet as it moves with the air flow. Supersaturation fluctuations are driven by turbulent fluctuations of the droplet vertical velocity through the adiabatic cooling/warming effect. The second, more elaborate scheme uses both temperature and vapor mixing ratio as stochastic attributes attached to each superdroplet. It is based on the probability density function formalism that provides a consistent Eulerian-Lagrangian formulation of scalar transport in a turbulent flow. Both stochastic microphysical schemes are tested in a synthetic turbulent-like cloud flow that mimics a stratocumulus topped boundary layer. It is shown that SGS turbulence plays a key role in broadening the droplet-size distribution towards larger sizes. Also, the feedback on water vapor of stochastically activated droplets buffers the variations of the mean supersaturation driven the resolved transport. This extends the distance over which entrained CNN are activated inside the cloud layer and produces multimodal droplet-size distributions.&lt;/p&gt;

scholarly journals Progress on Predicting the Breadth of Droplet Size Distributions Observed in Small Cumuli

2011 ◽  
Vol 68 (12) ◽  
pp. 2921-2929 ◽  
Author(s):  
Jennifer L. Bewley ◽  
Sonia Lasher-Trapp
Keyword(s):  
Droplet Size ◽  
Cloud Condensation Nuclei ◽  
Droplet Diameter ◽  
Droplet Size Distribution ◽  
Trade Wind ◽  
Droplet Growth ◽  
Size Distributions ◽  
Model Resolution ◽  
Modeling Framework ◽  
Droplet Size Distributions

Abstract A modeling framework representing variations in droplet growth by condensation, resulting from different saturation histories experienced as a result of entrainment and mixing, is used to predict the breadth of droplet size distributions observed at different altitudes within trade wind cumuli observed on 10 December 2004 during the Rain in Cumulus over the Ocean (RICO) field campaign. The predicted droplet size distributions are as broad as those observed, contain similar numbers of droplets, and are generally in better agreement with the observations when some degree of inhomogeneous droplet evaporation is considered, allowing activation of newly entrained cloud condensation nuclei. The variability of the droplet growth histories, resulting primarily from entrainment, appears to explain the magnitude of the observed droplet size distribution widths, without representation of other broadening mechanisms. Additional work is needed, however, as the predicted mean droplet diameter is too large relative to the observations and likely results from the model resolution limiting dilution of the simulated cloud.

Broadening of the Cloud Droplet Size Distribution due to Thermal Radiative Cooling: Turbulent Parcel Simulations

2020 ◽  
Vol 77 (6) ◽  
pp. 1993-2010
Author(s):  
Mares Barekzai ◽  
Bernhard Mayer
Keyword(s):  
Thermal Radiation ◽  
Droplet Size ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Growth Efficiency ◽  
Droplet Growth ◽  
Radiative Cooling ◽  
Radiative Effect ◽  
Diffusional Growth ◽  
The Impact

Abstract Despite impressive advances in rain forecasts over the past decades, our understanding of rain formation on a microphysical scale is still poor. Droplet growth initially occurs through diffusion and, for sufficiently large radii, through the collision of droplets. However, there is no consensus on the mechanism to bridge the condensation coalescence bottleneck. We extend the analysis of prior methods by including radiatively enhanced diffusional growth (RAD) to a Markovian turbulence parameterization. This addition increases the diffusional growth efficiency by allowing for emission and absorption of thermal radiation. Specifically, we quantify an upper estimate for the radiative effect by focusing on droplets close to the cloud boundary. The strength of this simple model is that it determines growth-rate dependencies on a number of parameters, like updraft speed and the radiative effect, in a deterministic way. Realistic calculations with a cloud-resolving model are sensitive to parameter changes, which may cause completely different cloud realizations and thus it requires considerable computational power to obtain statistically significant results. The simulations suggest that the addition of radiative cooling can lead to a doubling of the droplet size standard deviation. However, the magnitude of the increase depends strongly on the broadening established by turbulence, due to an increase in the maximum droplet size, which accelerates the production of drizzle. Furthermore, the broadening caused by the combination of turbulence and thermal radiation is largest for small updrafts and the impact of radiation increases with time until it becomes dominant for slow synoptic updrafts.

scholarly journals Quantifying the aerosol effect on droplet size distribution at cloud top

2019 ◽  
Vol 19 (11) ◽  
pp. 7839-7857
Author(s):  
Lianet Hernández Pardo ◽  
Luiz Augusto Toledo Machado ◽  
Micael Amore Cecchini ◽  
Madeleine Sánchez Gácita
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Initial Conditions ◽  
Cloud Condensation Nuclei ◽  
Droplet Size Distribution ◽  
Number Concentration ◽  
Population Characteristics ◽  
Aerosol Size Distribution ◽  
Effective Diameter ◽  
Distribution Sensitivity

Abstract. This work uses the number concentration-effective diameter phase-space to test cloud sensitivity to variations in the aerosol population characteristics, such as the aerosol size distribution, number concentration and hygroscopicity. It is based on the information from the top of a cloud simulated by a bin-microphysics single-column model, for initial conditions typical of the Amazon, using different assumptions regarding the entrainment and the aerosol size distribution. It is shown that the cloud-top evolution can be very sensitive to aerosol properties, but the relative importance of each parameter is variable. The sensitivity to each aerosol characteristic varies as a function of the parameter tested and is conditioned by the base values of the other parameters, showing a specific dependence for each configuration of the model. When both the entrainment and the bin treatment of the aerosol are allowed, the largest influence on the droplet size distribution sensitivity was obtained for the median radius of the aerosols and not for the total number concentration of aerosols. Our results reinforce that the cloud condensation nuclei activity can not be predicted solely on the basis of the w∕Na supersaturation-based regimes.

scholarly journals Influence of Microphysical Variability on Stochastic Condensation in a Turbulent Laboratory Cloud

2018 ◽  
Vol 75 (1) ◽  
pp. 189-201 ◽  
Author(s):  
N. Desai ◽  
K. K. Chandrakar ◽  
K. Chang ◽  
W. Cantrell ◽  
R. A. Shaw
Keyword(s):  
Relaxation Time ◽  
Size Distribution ◽  
Droplet Size ◽  
Droplet Size Distribution ◽  
Number Concentration ◽  
Droplet Growth ◽  
Diffusional Growth ◽  
Phase Relaxation ◽  
Distribution Width ◽  
Phase Relaxation Time

Diffusional growth of droplets by stochastic condensation and a resulting broadening of the size distribution has been considered as a mechanism for bridging the cloud droplet growth gap between condensation and collision–coalescence. Recent studies have shown that supersaturation fluctuations can lead to a broadening of the droplet size distribution at the condensational stage of droplet growth. However, most studies using stochastic models assume the phase relaxation time of a cloud parcel to be constant. In this paper, two questions are asked: how variability in droplet number concentration and radius influence the phase relaxation time and what effect it has on the droplet size distributions. To answer these questions, steady-state cloud conditions are created in the laboratory and digital inline holography is used to directly observe the variations in local number concentration and droplet size distribution and, thereby, the integral radius. Stochastic equations are also extended to account for fluctuations in integral radius and obtain new terms that are compared with the laboratory observations. It is found that the variability in integral radius is primarily driven by variations in the droplet number concentration and not the droplet radius. This variability does not contribute significantly to the mean droplet growth rate but does contribute significantly to the rate of increase of the size distribution width.

Sign in / Sign up

Export Citation Format

Share Document