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The role played by fluctuations of supersaturation in the growth of cloud droplets is examined in this study. The stochastic condensation framework and the three regimes of activation of cloud droplets— namely, mean dominant, fluctuation influenced, and fluctuation dominant—are used for analyzing the data from high-resolution large-eddy simulations of the Pi convection-cloud chamber. Based on a detailed budget analysis the significance of all the terms in the evolution of the droplet size distribution equation is evaluated in all three regimes. The analysis indicates that the mean-growth rate is a dominant process in shaping the droplet size distribution in all three regimes. Turbulence introduces two sources of stochasticity, turbulent transport and particle lifetime, and supersaturation fluctuations. The transport of cloud droplets plays an important role in all three regimes, whereas the direct effect of supersaturation fluctuations is primarily related to the activation and growth of the small droplets in the fluctuation-influenced and fluctuation-dominant regimes. We compare our results against the previous studies (experimental and theory) of the Pi chamber, and discuss the limitations of the existing models based on the stochastic condensation framework. Furthermore, we extend the discussion of our results to atmospheric clouds, and in particular focus on recent adiabatic turbulent cloud parcel simulations based on the stochastic condensation framework, and emphasize the importance of entrainment/mixing and turbulent transport in shaping the droplet size distribution.

The convection–cloud chamber enables measurement of aerosol and cloud microphysics, as well as their interactions, within a turbulent environment under steady‐state conditions. Increasing the size of a convection–cloud chamber, while holding the imposed temperature difference constant, leads to increased Rayleigh, Reynolds and Nusselt numbers. Large–eddy simulation coupled with a bin microphysics model allows the influence of increased velocity, time, and spatial scales on cloud microphysical properties to be explored. Simulations of a convection–cloud chamber, with fixed aspect ratio and increasing heights of H = 1, 2, 4, and (for dry conditions only) 8 m are performed. The key findings are: Velocity fluctuations scale as H1/3, consistent with the Deardorff expression for convective velocity, and implying that the turbulence correlation time scales as H2/3. Temperature and other scalar fluctuations scale as H−3/7. Droplet size distributions from chambers of different sizes can be matched by adjusting the total aerosol injection rate as the horizontal cross‐sectional area (i.e., as H2 for constant aspect ratio). Injection of aerosols at a point versus distributed throughout the volume makes no difference for polluted conditions, but can lead to cloud droplet size distribution broadening in clean conditions. Cloud droplet growth by collision and coalescence leads to a broader right tail of the distribution compared to condensation growth alone, and this tail increases in magnitude and extent monotonically as the increase of chamber height. These results also have implications for scaling within turbulent, cloudy mixed‐layers in the atmosphere, such as fog layers. Plain Language Summary In a convection–cloud chamber, turbulent convection is generated by heating the bottom surface and cooling the top surface. Water‐supersaturated conditions are achieved by maintaining the bottom and top surfaces wet. When aerosols are injected into the resulting turbulent, supersaturated flow, cloud droplets are formed and grow by vapor condensation, and possibly by collision and coalescence. The relative roles of condensation due to mean properties and fluctuating properties, as well as the role of collisional growth, depend on the time and velocity scales of the turbulence, all of which depend on the height of the chamber. In this work, turbulence and cloud properties in a convection–cloud chamber are simulated for several chamber heights. It is found that time and velocity scales increase with chamber height; cloud droplet size distributions can be approximately matched by appropriately increasing the aerosol injection rate; and the relevance of collisional growth increases with chamber height. These findings will help guide future computational and laboratory implementations of cloud formation in thermal, moist convection. Key Points Increasing the height of a convection‐cloud chamber leads to an increase in characteristic velocity and time scales Cloud droplet size distributions can be approximately matched by increasing the total aerosol injection rate as the square of the height Concentration of large cloud droplets due to collision and coalescence increases monotonically with increasing height

Water vapor supersaturation in the atmosphere is produced in a variety of ways, including the lifting of a parcel or via isobaric mixing of parcels. However, irrespective of the mechanism of production, the water vapor supersaturation in the atmosphere has typically been modeled as a Gaussian distribution. In the current theoretical and numerical study, the nature of supersaturation produced by mixing processes is explored. The results from large eddy simulation and a Gaussian mixing model reveal the distribution of supersaturations produced by mixing to be negatively skewed. Further, the causes of skewness are explored using large eddy simulations (LES) and the Gaussian mixing model (GMM). The correlation in forcing of temperature and water vapor fields is recognized as playing a key role.

Bin microphysics schemes are useful tools for cloud simulations and are often considered to provide a benchmark for model intercomparison. However, they may experience issues with numerical diffusion, which are not well quantified, and the transport of hydrometeors depends on the choice of advection scheme, which can also change cloud simulation results. Here, an atmospheric large‐eddy simulation model is adapted to simulate a statistically steady‐state cloud in a convection cloud chamber under well‐constrained conditions. Two bin microphysics schemes, a spectral bin method and the method of moments, as well as several advection methods for the transport of the microphysical variables are employed for model intercomparison. Results show that different combinations of microphysics and advection schemes can lead to considerable differences in simulated cloud properties, such as cloud droplet number concentration. We find that simulations using the advection scheme that suffers more from numerical diffusion tends to have a smaller droplet number concentration and liquid water content, while simulation with the microphysics scheme that suffers more from numerical diffusion tends to have a broader size distribution and thus larger mean droplet sizes. Sensitivities of simulations to bin resolution, spatial resolution, and temporal resolution are also tested. We find that refining the microphysical bin resolution leads to a broader cloud droplet size distribution due to the advection of hydrometeors. Our results provide insight for using different advection and microphysics schemes in cloud chamber simulations, which might also help understand the uncertainties of the schemes used in atmospheric cloud simulations. Plain Language Summary We investigate the dependence of high‐resolution cloud simulations on the algorithms used to represent the cloud microphysics and advective transport. The model setup is constrained and guided by observed steady‐state clouds in a convection cloud chamber. Two bin microphysics algorithms along with several advection methods are used to simulate the clouds with the same initial and boundary conditions. All simulations reach a steady state, and considerable intermodel variations in simulated cloud properties are found when using different advection and microphysics schemes. The case‐by‐case variations show some correlations with the estimated degree of numerical diffusion suffered by different schemes. Results from model intercomparisons help better understand uncertainties in simulations of clouds in the chamber and in the real atmosphere. Key Points Large‐eddy simulations of a convection cloud chamber are conducted using several microphysics and advection schemes Changes in advection schemes can produce similar variability in simulated cloud properties as do changes in the microphysics schemes Refining microphysical bin widths leads to a broader droplet size distribution due to the advection of hydrometeors

The Pi Cloud Chamber offers a unique opportunity to study aerosol‐cloud microphysics interactions in a steady‐state, turbulent environment. In this work, an atmospheric large‐eddy simulation (LES) model with spectral bin microphysics is scaled down to simulate these interactions, allowing comparison with experimental results. A simple scalar flux budget model is developed and used to explore the effect of sidewalls on the bulk mixing temperature, water vapor mixing ratio, and supersaturation. The scaled simulation and the simple scalar flux budget model produce comparable bulk mixing scalar values. The LES dynamics results are compared with particle image velocimetry measurements of turbulent kinetic energy, energy dissipation rates, and large‐scale oscillation frequencies from the cloud chamber. These simulated results match quantitatively to experimental results. Finally, with the bin microphysics included the LES is able to simulate steady‐state cloud conditions and broadening of the cloud droplet size distributions with decreasing droplet number concentration, as observed in the experiments. The results further suggest that collision‐coalescence does not contribute significantly to this broadening. This opens a path for further detailed intercomparison of laboratory and simulation results for model validation and exploration of specific physical processes. Key Points A large‐eddy simulation with spectral bin cloud microphysics is scaled to simulate a laboratory convection chamber The simulated mixing state and turbulence properties reasonably compare with a simple flux model and with measurements The simulation replicates published observations from the Pi Chamber, including steady‐state clouds and size distribution broadening.

We report an interesting incidental liver finding during ECG-gated cardiac computed tomography (CT) in a newborn with infracardiac total anomalous pulmonary venous connection to the portal vein. This case shows a unique abnormality in hepatic perfusion that was initially mistaken for hepatic vein thrombosis. We review the altered hepatic blood flow distribution in this pathologic anatomy to help explain the observed hepatic perfusion abnormality on CT. This understanding will enable an imager to anticipate hepatic perfusion patterns in similar patients, potentially avoiding misdiagnosis and unnecessary further testing.

Microphysical processes are important for the development of clouds and thus Earth's climate. For example, turbulent fluctuations in the water vapor mixing ratio, r, and temperature, T, cause fluctuations in the saturation ratio, S. Because S is the driving factor in the condensational growth of droplets, fluctuations may broaden the cloud droplet size distribution due to individual droplets experiencing different growth rates. The small-scale turbulent fluctuations in the atmosphere that are relevant to cloud droplets are difficult to quantify through field measurements. We investigate these processes in the laboratory using Michigan Tech's Π Chamber. The Π Chamber utilizes Rayleigh–Bénard convection (RBC) to create the turbulent conditions inherent in clouds. In RBC it is common for a large-scale circulation (LSC) to form. As a consequence of the LSC, the temperature field of the chamber is not spatially uniform. In this paper, we characterize the LSC in the Π Chamber and show how it affects the shape of the distributions of r, T, and S. The LSC was found to follow a single roll with an updraft and downdraft along opposing walls of the chamber. Near the updraft (downdraft), the distributions of T and r were positively (negatively) skewed. At each measuring position, S consistently had a negatively skewed distribution, with the downdraft being the most negative.

This study utilizes microwave plasma-assisted atomic layer deposition (MPALD) in remote mode to deposit Al 2 O 3 thin films with increased growth per cycle (GPC). Optical emission spectroscopy (OES) was used to identify the plasma configuration in the ALD chamber. MPALD–Al 2 O 3 thin films were deposited at temperatures ranging from room temperature to 200 °C and the electrical parameters were investigated with Al/Al 2 O 3 /p–Si metal oxide semiconductor (MOS) structures. A GPC of 0.24 nm was observed for the films deposited at room temperature. The fixed oxide charge densities ( N fix ) in all films were of the order of 10 12  cm −2 . The interface state density ( D it ) exhibited a distinct minimum for the films deposited at 100 °C. The dependence of built-in voltage, N fix, and D it on Al 2 O 3 deposition temperature was investigated. This can be used as a measure of the electrical applicability of these thin films.

Technological advancements are integral in achieving the sustainability goals of a nation. In the financial sector, technology-integrated Fintech Adoption (FA) is considered a revolutionary change in financial services delivery. Substantial empirical evidences exist to establish how the Environmental Performance (EP) of the organizations is affected by Green Innovation (GI) and Green Finance (GF). In the finance sector, only limited researches were carried out to understand the relationship between Environmental Performance, Fintech Adoption, Green Innovation and Green Finance. The present study was undertaken among the financial institutions in the Bagmati province of Nepal. The empirical research findings endorse that the Adoption of Fintech significantly impact Green Innovation, Environmental Performance and Green Finance. The study also confirmed that a firm’s Environmental Performance is significantly affected by Green Finance and Green Innovation. The researchers established the mediating effect of Green Innovation in the association among the variables Adoption of Fintech, Environmental Performance and Green Finance. The study confirms that in enhancing the Environmental Performance of the financial institutions, Green Innovation, Fintech Adoption, and Green Finance play a crucial role.

Radiative transfer through clouds can be impacted by variations in particle number size distribution, but also in particle spatial distribution. Due to turbulent mixing and inertial effects, spatial correlations often exist, even on scales reaching the cloud droplet separation distance. The resulting clusters and voids within the droplet field can lead to deviations from exponential extinction. Prior work has numerically investigated these departures from exponential attenuation in absorptive and scattering media; this work takes a step towards determining the feasibility of detecting departures from exponential behavior due to spatial correlation in turbulent clouds generated in a laboratory setting. Large Eddy Simulation (LES) is used to mimic turbulent mixing clouds generated in a laboratory convection cloud chamber. Light propagation through the resulting polydisperse and spatially correlated particle fields is explored via Monte Carlo ray tracing simulations. The key finding is that both mean radiative flux and standard deviation about the mean differ when correlations exist, suggesting that an experiment using a laboratory convection cloud chamber could be designed to investigate non-exponential behavior. Total forward flux is largely unchanged (due to scattering being highly forward-dominant for the size parameters considered), allowing it to be used for conditional sampling based on optical thickness. Direct and diffuse forward flux means are modified by approximately one standard deviation. Standard deviations of diffuse forward and backward fluxes are strongly enhanced, suggesting that fluctuations in the scattered light are a more sensitive metric to consider. The results also suggest the possibility that measurements of radiative transfer could be used to infer the strength and scales of correlations in a turbulent cloud, indicating entrainment and mixing effects.