Growth Of Cloud Droplets Research Articles

Alternate Chemical Compounds as a Condensation Nucleus in Cloud Seeding

Cloud seeding involves boosting precipitation by releasing substances into the air that act as cloud condensation or ice nuclei. These substances encourage the development of clouds and precipitation. It’s like giving Mother Nature a gentle push to assist with rainfall in specific areas. The current work aimed to suggest Al2O3 as an alternate compound in cloud seeding rather than silver iodide. In this research, a unique approach is used to identify condensation nuclei, which play a crucial role in cloud formation and droplet growth. Various samples and four sources were included in the current study; refrigerated helfa powder, Himalayan salt, generator powder, and pollen, were analyzed using different physicochemical instruments. The proportions of chemical compounds in the samples show that there is 1.392% of Al2O3 in Refrigerated helfa which is the highest than in the other 3 sources, while the proportions of elements in the samples indicate that refrigerated helfa contains the lowest toxic compound, and although Al2O3 is insoluble in water, it is hygroscopic and can absorb 6.4% of humidity within 24 hours. As for the surface tension, refrigerated helfa shows lower density and surface tension than the other three sources with values of 0.9480 and 47.89 respectively. Al2O3 shows high humid absorptivity and refrigerated helfa can be used as a main source for Al2O3 which has a low effect on biota and is recommended for use in cloud seeding. However further work is recommended to be carried out in using Al2O3 as an alternative compound to silver iodide in cloud seeding.

Development of the full Lagrangian approach for modeling dilute dispersed media flows (a review)

Continuum models of media with zero pressure are widely used in various branches of physics and mechanics, including studies of a dilute dispersed phase in multiphase flows. In zero-pressure media, the particle trajectories may intersect, “folds” and “puckers” of the phase volume may arise, and “caustics” (the envelopes of particle trajectories) may appear, near which the density of the medium sharply increases. In recent decades, the phenomena of clustering and aerodynamic focusing of inertial admixture in gas and liquid flows have attracted increasing attention of researchers. This is due to the importance of taking into account the inhomogeneities in the impurity concentration when describing the transport of aerosol pollutants in the environment, the mechanisms of droplet growth in rain clouds, scattering of radiation by dispersed inclusions, initiation of detonation in two-phase mixtures, as well as when solving problems of two-phase aerodynamics, interpretation of measurements obtained by LDV or PIV methods, and in many other applications. These problems gave an impetus to a significant increase in the number of publications devoted to the processes of accumulation and clustering of inertial particles in gas and liquid flows. Within the framework of classical two-fluid models and standard Eulerian approaches assuming single-valuedness of continuum parameters of the media, it turns out impossible to describe zones of multi-valued velocity fields and density singularities in flows with crossing particle trajectories. One of the alternatives is the full Lagrangian approach proposed by the author earlier. In recent years, this approach has been further developed in combination with averaged Eulerian and Lagrangian (vortex-blob method) methods for describing the dynamics of the carrier phase. Such combined approaches made it possible to study the structure of local zones of accumulation of inertial particles in vortex, transient, and turbulent flows. This article describes the basic ideas of the full Lagrangian approach, provides examples of the most significant results which illustrate the unique capabilities of the method, and gives an overview of the main directions of further development of the method as applied to transient, vortex, and turbulent flows of “gas-particle” media. Some of the ideas discussed and the results presented below are of a more general interest, since they are also applicable to other models of zero-pressure media.

CCN Activation and Droplet Growth in Pi Chamber Simulations with Lagrangian Particle–Based Microphysics

Abstract Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius. Significance Statement Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment.

Relationship between Synoptic Weather Patterns and Topography on Snowfall in the Idaho Mountainous Regions during the SNOWIE Project

Abstract Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the Intermountain West of the United States. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) Model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multiscale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder–feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding. Significance Statement Understanding snowfall amounts and distribution in the mountains and how it is linked to topography, synoptic flow, and microphysical processes will help in the development of effective strategies for cloud seeding operations, managing runoff, reservoir, and mitigating flood risks, garnering substantial interest from stakeholders and the government agencies.

Modeling the Subgrid-Scale Scalar Variance: A Priori Tests and Application to Supersaturation in Cloud Turbulence

Abstract The subgrid-scale (SGS) scalar variance represents the “unmixedness” of the unresolved small scales in large-eddy simulations (LES) of turbulent flows. Supersaturation variance can play an important role in the activation, growth, and evaporation of cloud droplets in a turbulent environment, and therefore efforts are being made to include SGS supersaturation fluctuations in microphysics models. We present results from a priori tests of SGS scalar variance models using data collected in turbulent Rayleigh–Bénard convection in the Michigan Tech Pi chamber for Rayleigh numbers Ra ∼ 108–109. Data from an array of 10 thermistors were spatially filtered and used to calculate the true SGS scalar variance, a scale-similarity model, and a gradient model for dimensionless filter widths of h/Δ = 25, 14.3, and 10 (where h is the height of the chamber and Δ is the spatial filter width). The gradient model was found to have fairly low correlations (ρ ∼ 0.2), with the most probable values departing significantly from the one-to-one line in joint probability density functions (JPDFs). However, the scale-similarity model was found to have good behavior in JPDFs and was highly correlated (ρ ∼ 0.8) with the true SGS variance. Results of the a priori tests were robust across the parameter space considered, with little dependence on Ra and h/Δ. The similarity model, which only requires an additional test filtering operation, is therefore a promising approach for modeling the SGS scalar variance in LES of cloud turbulence and other related flows.

Droplet Growth or Evaporation Does Not Buffer the Variability in Supersaturation in Clean Clouds

Abstract Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection–Cloud Chamber generates a turbulent cloud with a microphysical steady state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and collocated Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to , i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth. Significance Statement The saturation ratio (humidity) in clouds controls the growth rate and formation of cloud droplets. When air in a turbulent cloud mixes, the humidity varies in space and time throughout the cloud. This is important because it means cloud droplets experience different growth histories, thereby resulting in broader size distributions. It is often assumed that growth and evaporation of cloud droplets buffers out some of the humidity variations. Measuring these variations has been difficult, especially in the field. The purpose of this study is to measure the saturation ratio distribution in clouds with a range of conditions. We measure the in-cloud saturation ratio using a convection cloud chamber with clean to polluted cloud properties. We found in clouds with low concentrations of droplets that the variations in the saturation ratio are not suppressed.

Designing a Convection‐Cloud Chamber for Collision‐Coalescence Using Large‐Eddy Simulation With Bin Microphysics

AbstractCollisional growth of cloud droplets is an essential yet uncertain process for drizzle and precipitation formation. To improve the quantitative understanding of this key component of cloud‐aerosol‐turbulence interactions, observational studies of collision‐coalescence in a controlled laboratory environment are needed. In an existing convection‐cloud chamber (the Pi Chamber), collisional growth is limited by low liquid water content and short droplet residence times. In this work, we use numerical simulations to explore various configurations of a convection‐cloud chamber that may intensify collision‐coalescence. We employ a large‐eddy simulation (LES) model with a size‐resolved (bin) cloud microphysics scheme to explore how cloud properties and the intensity of collision‐coalescence are affected by the chamber size and aspect ratio, surface roughness, side‐wall wetness, side‐wall temperature arrangement, and aerosol injection rate. Simulations without condensation and evaporation within the domain are first performed to explore the turbulence dynamics and wall fluxes. The LES wall fluxes are used to modify the Scalar Flux‐budget Model, which is then applied to demonstrate the need for non‐uniform side‐wall temperature (two side walls as warm as the bottom and the two others as cold as the top) to maintain high supersaturation in a tall chamber. The results of LES with full cloud microphysics reveal that collision‐coalescence is greatly enhanced by employing a taller chamber with saturated side walls, non‐uniform side‐wall temperature, and rough surfaces. For the conditions explored, although lowering the aerosol injection rate broadens the droplet size distribution, favoring collision‐coalescence, the reduced droplet number concentration decreases the frequency of collisions.

Secondary droplet activation during condensational growth in convective clouds and its detection from satellites

By acting as cloud condensation nuclei (CCN), aerosol particles play a key role in the climate system. The CCN can be activated into cloud droplets at the cloud base (i.e., primary activation), or above it (i.e., secondary activation). This study shows the conditions required for secondary activation during the condensational growth phase in convective clouds. It also proposes a methodology for detecting this secondary activation from satellites. Using a spectral bin adiabatic parcel model, we simulate the vertical profile of cloud microphysical properties and demonstrate how different aerosol size distributions and updraft velocities greatly affect the secondary activation initiation and the cloud properties. The secondary activation slows down the cloud drop effective radius (re) growth rate with increasing height, and decreasing temperature (T), due to the relatively larger population of smaller droplets in the cloud parcel. Therefore, the vertical profile of re growth with height is slower than the adiabatic rate when the secondary activation occurs.The proposed physical principle was verified by satellite-retrieved T-re profiles and adiabatic cloud drop number concentrations (Nd) over the Amazon region. The results obtained from this study can be utilized to identify the secondary droplet activation during the condensational growth phase, which can lead to an overestimation of the retrieved Nd as well as suppression of warm rain. This improves our knowledge and observational capabilities of the role of aerosol particles in the microphysics, dynamics, and precipitation behavior of convective clouds. Plain text summarySmall particles serve as sites for cloud droplets' condensation at the cloud base (primary droplet activation) or above it (secondary activation). Once a droplet is activated at the cloud base, the surrounding water vapor condenses on it and the droplet grows - this is called condensational growth. The cloud droplet number concentrations (Nd) can be obtained by the satellite-retrieved vertical growth rate of droplet size in the condensational growth phase. However, new small droplets formed during this phase (i.e., secondary activation), might cause an overestimate in the retrieved Nd. This study presents the physical principle of secondary activation during condensational growth. Using a model, we simulate the condensational growth of aerosol particles, which act as cloud condensation nuclei (CCN), for different particle size distributions and thermodynamic conditions (i.e., vertical velocities). Our simulations show that secondary activation slows the cloud droplet growth rate with height due to the larger competition for the available water vapor promoted by the new drops. Comparisons between satellite-retrieved Nd of convective clouds and in-situ measurements show that the satellite retrievals overestimate Nd when the secondary activation of droplets is neglected. Our findings improve the understanding of aerosols' role in the condensational growth of droplets in convective clouds.

The Effects of Aerosol on Small‐Scale Cloud‐Environment Mixing: Implications for Simulating and Observing Inhomogeneous Mixing

AbstractEntrainment‐mixing modulates the number and size of cloud droplets, and thus affects the optical properties of clouds and their role in the climate system. Using a statistical turbulence model coupled with Lagrangian cloud microphysics, we analyze the role of aerosol in the entrainment‐mixing process, which, for instance, prevent the full evaporation of cloud droplets, leaving behind haze particles. To test a commonly applied indicator for inhomogeneous mixing, a set of different mixing scenarios is simulated. We conclude that if a hard separation radius between cloud droplets and haze particles is chosen, the typical classification into homogeneous and inhomogeneous mixing can be wrong because larger haze particles might be misidentified as cloud droplets, making the mixing appear more homogeneous. Furthermore, we show that the growth of cloud droplets on the expense of other hydrometeors due to differences in aerosol loading and particle curvature (Ostwald ripening) can produce cloud microphysical signatures indistinguishable from inhomogeneous mixing. Finally, we investigate how the consideration of haze particles can mitigate the previously reported negative impacts of inhomogeneous mixing on the cloud albedo. These findings should be considered when interpreting observations and simulations of small‐scale entrainment‐mixing.

Interaction of microphysics and dynamics in a warm conveyor belt simulated with the ICOsahedral Nonhydrostatic (ICON) model

Abstract. Warm conveyor belts (WCBs) produce a major fraction of precipitation in extratropical cyclones and modulate the large-scale extratropical circulation. Diabatic processes, in particular associated with cloud formation, influence the cross-isentropic ascent of WCBs into the upper troposphere and additionally modify the potential vorticity (PV) distribution, which influences the larger-scale flow. In this study we investigate heating and PV rates from all diabatic processes, including microphysics, turbulence, convection, and radiation, in a case study that occurred during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) campaign using the Icosahedral Nonhydrostatic (ICON) modeling framework. In particular, we consider all individual microphysical process rates that are implemented in ICON's two-moment microphysics scheme, which sheds light on (i) which microphysical processes dominate the diabatic heating and PV structure in the WCB and (ii) which microphysical processes are the most active during the ascent and influence cloud formation and characteristics, providing a basis for detailed sensitivity experiments. For this purpose, diabatic heating and PV rates are integrated for the first time along online trajectories across nested grids with different horizontal resolutions. The convection-permitting simulation setup also takes the reduced aerosol concentrations over the North Atlantic into account. Our results confirm that microphysical processes are the dominant diabatic heating source during ascent. Near the cloud top longwave radiation cools WCB air parcels. Radiative heating and corresponding PV modification in the upper troposphere are non-negligible due to the longevity of the WCB cloud band. In the WCB ascent region, the process rates from turbulent heating and microphysics partially counteract each other. From all microphysical processes condensational growth of cloud droplets and vapor deposition on frozen hydrometeors most strongly influence diabatic heating and PV, while below-cloud evaporation strongly cools WCB air parcels prior to their ascent and increases their PV value. PV production is the strongest near the surface with substantial contributions from condensation, melting, evaporation, and vapor deposition. In the upper troposphere, PV is reduced by diabatic heating from vapor deposition, condensation, and radiation. Activation of cloud droplets as well as homogeneous and heterogeneous freezing processes have a negligible diabatic heating contribution, but their detailed representation is important for, e.g., hydrometeor size distributions. Generally, faster-ascending WCB trajectories are heated markedly more than more slowly ascending WCB trajectories, which is linked to larger initial specific humidity content providing a thermodynamic constraint on total microphysical heating. Yet, the total diabatic heating contribution of convectively ascending trajectories is relatively small due to their small fraction in this case study. Our detailed case study documents the effect of different microphysical processes implemented in ICON's two-moment scheme for heating and PV rates in a WCB from a joint Eulerian and Lagrangian perspective. It emphasizes the predominant role of microphysical processes and provides a framework for future experiments on cloud microphysical sensitivities in WCBs.

Adiabatic Evolution of Cloud Droplet Spectral Width: A New Look at an Old Problem

AbstractSpectral width of the cloud droplet spectrum is important for radiative properties and drizzle/rain development in warm ice‐free clouds. We use an adiabatic rising parcel model to study activation and diffusional growth of cloud droplets, focusing on the spectral width evolution, and contrasting clean and polluted environments. A comprehensive droplet growth equation is used that includes kinetic, solute, and surface tension effects. We show that those effects have an appreciable impact on the spectral width evolution above the cloud base. Without those effects, the droplet area standard deviation should not change once activation is completed. In contrast, simulation results show that the area standard deviation does increase with height, especially for weak updrafts and polluted environments. Implications of those results for cloud modeling, especially applying conventional bin microphysics, are discussed.

Scaling of Turbulence and Microphysics in a Convection–Cloud Chamber of Varying Height

AbstractThe 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.

Regional Dependency of the Cloud Droplet Growth Process in Combined Analysis of Aqua MODIS and CloudSat CPR

This paper shows the CFODD of the regional dependence of cloud growth processes in low-level clouds obtained by the combined use of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite and the Cloud Profiling Radar (CPR) on the CloudSat satellite. This enabled the evaluation of the cloud growth process based on the cloud optical thickness (τ) and the effective radius of cloud particle (Re), similar to previous studies that performed statical analysis on low-level clouds over the globe. Our targets were regions in East Asian, Californian, and Peruvian. In all analysis areas, our results showed that the internal structure of clouds changed as Re increased, indicating cloud growth. In the East Asian region, the maximum τ remained relatively constant even when cloud droplet size grew. In contrast, in the regions of Californian and Peruvian, the maximum τ increased with Re during the condensation growth process and then decreased as drizzle particles transformed into rain. It was also found that Re was smaller in the East Asian region unlike in Californian and Peruvian. This indicates that there are more aerosols in the East Asian region, which is consistent with its geographical characteristics.

Sources of Stochasticity in the Growth of Cloud Droplets: Supersaturation Fluctuations versus Turbulent Transport

Abstract 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.

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

Collision Fluctuations of Lucky Droplets with Superdroplets

Abstract It was previously shown that the superdroplet algorithm for modeling the collision–coalescence process can faithfully represent mean droplet growth in turbulent clouds. An open question is how accurately the superdroplet algorithm accounts for fluctuations in the collisional aggregation process. Such fluctuations are particularly important in dilute suspensions. Even in the absence of turbulence, Poisson fluctuations of collision times in dilute suspensions may result in substantial variations in the growth process, resulting in a broad distribution of growth times to reach a certain droplet size. We quantify the accuracy of the superdroplet algorithm in describing the fluctuating growth history of a larger droplet that settles under the effect of gravity in a quiescent fluid and collides with a dilute suspension of smaller droplets that were initially randomly distributed in space (“lucky droplet model”). We assess the effect of fluctuations upon the growth history of the lucky droplet and compute the distribution of cumulative collision times. The latter is shown to be sensitive enough to detect the subtle increase of fluctuations associated with collisions between multiple lucky droplets. The superdroplet algorithm incorporates fluctuations in two distinct ways: through the random spatial distribution of superdroplets and through the Monte Carlo collision algorithm involved. Using specifically designed numerical experiments, we show that both on their own give an accurate representation of fluctuations. We conclude that the superdroplet algorithm can faithfully represent fluctuations in the coagulation of droplets driven by gravity.

Satellite‐Based Detection of Secondary Droplet Activation in Convective Clouds

AbstractWe present a new approach of analyzing and interpreting vertical profiles of cloud microstructure obtained by satellite remote sensing. The method is based on a spectral bin microphysics adiabatic parcel model and aims to elucidate the effects of aerosols on the evolution of convective clouds and related microphysical processes, including the activation of cloud condensation nuclei (CCN), the growth of cloud droplets, and the formation of precipitation. Characteristic features in the vertical profiles of effective radius (re) and temperature (T) reveal different microphysical zones in convective clouds related to the change increase of re with decreasing T. The classification of the different microphysical zones includes the (a) condensational growth of droplets, (b) growth by coalescence, (c) rainout, (d) secondary droplet activation zone (SAZ), (e) mixed‐phase of ice particles and water droplets, and (f) glaciation of the cloud. The detection of the SAZ is introduced here for the first time. This method allows us to identify the activation of aerosol particles above cloud base and their role in the invigoration of deep convective clouds.

Evaluation of a quasi-steady-state approximation of the cloud droplet growth equation (QDGE) scheme for aerosol activation in global models using multiple aircraft data over both continental and marine environments

Abstract. This research introduces a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The scheme employs a quasi-steady-state approximation of the cloud droplet growth equation (QDGE) to efficiently simulate aerosol activation, the vertical profile of supersaturation, and the activated cloud droplet number concentration (CDNC) near the cloud base. The calculated maximum supersaturation values using the QDGE scheme were compared with multiple parcel model simulations under various aerosol and environmental conditions. The differences are all below 0.18 %, indicating good performance and accuracy of the QDGE scheme. We evaluated the QDGE scheme by specifying observed environmental thermodynamic variables and aerosol information from 31 cloud cases as input and comparing the simulated CDNC with cloud observations. The average of mean relative error (MRE‾) of the simulated CDNC for cloud cases in each campaign ranges from 17.30 % in Brazil to 25.90 % in China, indicating that the QDGE scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Additionally, we carried out an error analysis by calculating the maximum information coefficient (MIC) between the MRE and input variables for the individual campaigns and all cloud cases. MIC values were then sorted by aerosol properties, pollution level, environmental humidity, and dynamic condition according to their relative importance to MRE. Based on the error analysis, we found that the magnitude of MRE is more relevant to the specification of input aerosol pollution level in marine regions and aerosol hygroscopicity in continental regions than to other variables in the simulation.

Aerosol first indirect effect over narrow longitude regions of North Pacific and same-latitude lands

Aerosol first indirect effect (FIE), which causes variations of cloud droplet effective radius (re) and then cloud radiative effect (CRE), is one of the critical factors leading to uncertainties in climate model simulations. Different from most previous studies over continental regions, using 10-year observation data from CERES, this study investigates the statistical relationships between aerosol optical depth (AOD) and non-precipitating single-layer liquid phase cloud re and surface shortwave CRE (CRESW) over narrow longitude regions of North Pacific and its eastern and western lands at equal latitudes, along with the estimation of aerosol FIE. Both surface CRESW and cloud re are highly affected by aerosols. When AOD is less than 0.3–0.4 and liquid water path (LWP) is greater than 30 g/m2, positive AOD-CRESW and negative AOD-cloud re relationships are found over both ocean and land. With the increase of AOD, the sensitivity of CRESW and cloud re to aerosol is weakened, but both have greater fluctuations. The latitude dependence of the CRESW and cloud re variations with AOD are weak. The increases in liquid water path (LWP) when LWP is in a certain range (30–120 g/m2 over ocean and 30–90 g/m2 over land) can highly increase CRESW and promote the growth of cloud droplets. We also find that FIE values are positive under clean condition, while negative under polluted condition. Associated with the much less aerosol amount and more sufficient water supply, the FIE values over the ocean are distinctly larger than that over the land.

An experimental and modelling analysis of cloud droplet growth from vehicular emissions with non‐ideal microphysics over an Asian mega‐city

AbstractLarge cities in the developing world have unregulated traffic with dysfunctional diesel operated vehicles spewing out copious amounts of micron to sub‐micron sized soot and black carbon particles. These particles mix with other aerosol particles as they are lifted by buoyant eddies. It is shown that the particles are rendered partly soluble as they get coated with soluble sulphate to activate into cloud droplets. This multi‐component aerosol mixture thus comprises of fully soluble as well as partially soluble components so that mass accommodation and diffusional mass transfer considerations need to be carefully quantified over small non‐planar surfaces of aerosol particles sourced from such emissions. The emitted soot and black carbon particles have number concentrations comparable to the sulphate mode and are small enough that the assumption of an infinitely dilute solution droplet is untenable. An X‐ray fluorescence spectrometry indicates the presence of inorganic ions confirming the validity of non‐ideal solution effects warranting a closer look at the osmotic coefficients. Additionally, laboratory values of the binary diffusion coefficients of water vapour cannot be applied to processes operating over the cloud base located several kilometres above where the pressure and temperature are different. A full Lennard‐Jones diffusivity model is used with an accounting of the dipole moment of water vapour which most models ignore. This first study, combining these micro‐scale enhancements are included in a Weather Research and Forecasting (WRF) case study over an Asian city. Not only are significant differences obtained in the cloud morphology when compared to a baseline case, but modelled cloud optical depths agree better with observations.