Impact of cloud base turbulence on CCN activation: Single size CCN

2021 ◽  
Keyword(s):  
Droplet Size ◽  
Initial Conditions ◽  
Cloud Condensation Nuclei ◽  
Droplet Size Distribution ◽  
Spectral Width ◽  
Cloud Base ◽  
Computational Domain ◽  
Critical Supersaturation ◽  
The Impact ◽  
Single Size

Abstract This paper examines the impact of cloud-base turbulence on activation of cloud condensation nuclei (CCN). Following our previous studies, we contrast activation within a non-turbulent adiabatic parcel and an adiabatic parcel filled with turbulence. The latter is simulated by applying a forced implicit large eddy simulation within a triply periodic computational domain of 643 m3. We consider two monodisperse CCN. Small CCN have a dry radius of 0.01 micron and a corresponding activation (critical) radius and critical supersaturation of 0.6 micron and 1.3%, respectively. Large CCN have a dry radius of 0.2 micron and feature activation radius of 5.4 micron and critical supersaturation 0.15 %. CCN are assumed in 200 cm−3 concentration in all cases. Mean cloud base updraft velocities of 0.33, 1, and 3 m s−1 are considered. In the non-turbulent parcel, all CCN are activated and lead to a monodisperse droplet size distribution above the cloud base, with practically the same droplet size in all simulations. In contrast, turbulence can lead to activation of only a fraction of all CCN with a non-zero spectral width above the cloud base, of the order of 1 micron, especially in the case of small CCN and weak mean cloud base ascent. We compare our results to studies of the turbulent single-size CCN activation in the Pi chamber. Sensitivity simulations that apply a smaller turbulence intensity, smaller computational domain, and modified initial conditions document the impact of specific modeling assumptions. The simulations call for a more realistic high-resolution modeling of turbulent cloud base activation.

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 A New Mechanism of Droplet Size Distribution Broadening during Diffusional Growth

2013 ◽  
Vol 70 (7) ◽  
pp. 2051-2071 ◽  
Author(s):  
Alexei Korolev ◽  
Mark Pinsky ◽  
Alex Khain
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Initial Conditions ◽  
Droplet Size Distribution ◽  
Cloud Base ◽  
Diffusional Growth ◽  
Cloud Droplets ◽  
Droplet Concentration ◽  
Droplet Sizes ◽  
Precipitation Formation

Abstract A new mechanism has been developed for size distribution broadening toward large droplet sizes. This mechanism may explain the rapid formation of large cloud droplets, which may subsequently trigger precipitation formation through the collision–coalescence process. The essence of the new mechanism consists of a sequence of mixing events between ascending and descending parcels. When adiabatically ascending and descending parcels having the same initial conditions at the cloud base arrive at the same level, they will have different droplet sizes and temperatures, as well as different supersaturations. Isobaric mixing between such parcels followed by further ascents and descents enables the enhanced growth of large droplets. The numerical simulation of this process suggests that the formation of large 30–40-μm droplets may occur within 20–30 min inside a shallow adiabatic stratiform layer. The dependencies of the rate of the droplet size distribution broadening on the intensity of the vertical fluctuations, their spatial amplitude, rate of mixing, droplet concentration, and other parameters are considered here. The effectiveness of this mechanism in different types of clouds is discussed.

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 An Observational Study on Cloud Spectral Width in North China

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 109 ◽  
Author(s):  
Yuan Wang ◽  
Shengjie Niu ◽  
Chunsong Lu ◽  
Yangang Liu ◽  
Jingyi Chen ◽  
...  
Keyword(s):  
Size Range ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Spectral Width ◽  
Relative Dispersion ◽  
Cloud Base ◽  
Cloud Droplets ◽  
Factors Affecting ◽  
Cloud Droplet Size Distribution ◽  
Stratiform Clouds

Cloud droplet size distribution (CDSD) is a critical characteristic for a number of processes related to clouds, considering that cloud droplets are formed in different sizes above the cloud-base. This paper analyzes the in-situ aircraft measurements of CDSDs and aerosol concentration ( N a ) performed in stratiform clouds in Hebei, China, in 2015 to reveal the characteristics of cloud spectral width, commonly known as relative dispersion ( ε , ratio of standard deviation (σ) to mean radius (r) of the CDSD). A new algorithm is developed to calculate the contributions of droplets of different sizes to ε . It is found that small droplets with the size range of 1 to 5.5 μm and medium droplets with the size range of 5.5 to 10 μm are the major contributors to ε, and the medium droplets generally dominate the change of ε. The variation of ε with N a can be well explained by comparing the normalized changes of σ and r ( k σ / σ and k r / r ), rather than k σ and k r only ( k σ is Δσ/Δ N a and k r is Δr/Δ N a ). From the perspective of external factors affecting ε change, the effects of N a and condensation are examined. It is found that ε increases initially and decreases afterward as N a increases, and “condensational broadening” occurs up to 1 km above cloud-base, potentially providing observational evidence for recent numerical simulations in the literature.

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.

Fluid Flow Phenomena and Droplet Size Distribution Along the Feed Spreader of Large Oil/Water Tank Separators

2009 ◽  
Author(s):  
Jose G. Severino ◽  
Luis E. Gomez ◽  
Steve J. Leibrandt ◽  
Ram S. Mohan ◽  
Ovadia Shoham
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Separation Efficiency ◽  
Droplet Size Distribution ◽  
Separation Performance ◽  
Water Tank ◽  
Water Dispersion ◽  
Flow Phenomena ◽  
Oil Water ◽  
The Impact

Large gravity separation tanks play an essential role in crude oil production in many fields worldwide. These tanks are used to separate water from an oil-rich stream before safely returning it to the environment. The oil/water dispersion enters the tanks through a feed spreader consisting of an array of pipes with small effluent nozzles. A major challenge is being able to predict oil/water dispersion distribution along the spreader as well as, the maximum water droplet size exiting through the effluent nozzles, under a given set of conditions. The capacity of the studied tank is 80,000 barrels (12,719 m3). Current feed stream is about 60,000 bpd (9,540 m3/day) of wet crude containing about 20% water by volume. A significant increase in flow rates and water volume fraction is anticipated [7], as more wells are added and existing ones mature. This work is aimed at investigating the separation performance of these tanks under current and future flow conditions; focusing primarily on the flow phenomena and droplet size distribution inside the spreader. The main objective is then to identify the impact of the spreader’s geometry and piping configuration on flow behavior and tank’s separation efficiency. The final product provides key information needed for mechanistic modeling the tank separation performance and optimizing tank components’ design. The feed spreader is simulated using Computational Fluid Dynamics (CFD) to assess oil/water flow distribution inside the network. Droplet size distribution along branch-pipes effluent nozzles in, including droplet breakup and coalescence has been studied using the Gomez mechanistic model [2] with input from CFD results. An experimental investigation of the spreader using a scaled prototype was also conducted to better understand flow phenomena and verify the CFD models. Results confirm the occurrence of significant maldistribution of the water and oil phases along the spreader that could impair separation efficiency.

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

<p>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.</p>

Cloud water composition in relation to the droplet size distribution and cloud base altitude

2000 ◽  
Vol 31 ◽  
pp. 301-302
Author(s):  
W. Wieprecht ◽  
D. Moeller ◽  
K. Acker ◽  
R. Auel ◽  
D. Kalass
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Droplet Size Distribution ◽  
Cloud Water ◽  
Cloud Base ◽  
Water Composition

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.

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