Search

Cloud microphysics fulfills a fundamental role in the formation and evolution of marine fog, but it is not fully understood. Numerous studies have addressed this by means of direct observations and modeling efforts. However, collision–coalescence of aerosols and fog droplets is a process often neglected. In this study we perform an analysis of the role of particle collections on the formation, development, and microphysical structure of marine fog. It was found that collisions open a path for aerosol activation by means of collisional activation. In addition, collisions contribute to the diffusional activation of fog particles by adding water mass to the growing aerosols, making them reach the required critical radius faster. Furthermore, collisions have a homogenizing effect on hygroscopicity, facilitating the activation of accumulation‐mode aerosols by increasing their diffusional growth. [ABSTRACT FROM AUTHOR]

Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations. Plain Language Summary: Clouds are made from many ice particles and liquid droplets, with a variety of sizes, shapes, and chemical compositions. Traditional approaches for representing small‐scale (microphysical) cloud processes start by defining some set of variables, which is expected to contain key information about cloud properties. These variables are "moments" corresponding to important statistics of the population of cloud particles, such as total mass of cloud water, or total number of particles. One can use physical arguments to develop a set of equations that describe how these bulk properties evolve in time. Traditional moment‐based models require much less computational power than more sophisticated microphysical models, but have limited accuracy. In this study, we explore an alternative approach, where a model "learns" how to represent the state of a cloud using a small number of variables. By investigating the geometry of this compressed representation, we find that we can represent the merging of cloud droplets (coalescence) using only three variables, so this approach may lead to more accurate models without requiring excessive computational resources. We can use this approach to design new models with favorable features, such as representing droplet coalescence using linear functions. Key Points: We use deep learning to find reduced‐order representations of the droplet size distribution from spectral bin microphysics simulationsDroplet coalescence has an intrinsic dimension of three, providing a lower limit on the number of degrees of freedom needed for bulk modelsWe obtain reduced‐order representations of the droplet size distribution where droplet coalescence can be represented as a linear process [ABSTRACT FROM AUTHOR]

Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

Collisional 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. Plain Language Summary: A convection‐cloud chamber is useful in understanding how turbulence affects the interaction between aerosols and cloud droplets. The current convection‐cloud chamber (the Pi Chamber) is likely too small to explore how turbulence affects the collision‐coalescence among cloud droplets. To see whether collisional growth may be observable in a larger cloud chamber, we use numerical simulations to model the cloud droplet size distributions under several different configurations of the cloud chamber. The results suggest that the likelihood of detectable collisional growth increases significantly in a tall chamber with two warm and two cold saturated side walls and rough wall surfaces. Key Points: Collision‐coalescence effects on a steady‐state droplet size distribution are stronger in a taller chamberWet side walls are essential for maintaining cloud liquid water in a chamber with a low width‐to‐height aspect ratioRougher surfaces increase surface heat and moisture fluxes, leading to larger liquid water content that promotes collision‐coalescence [ABSTRACT FROM AUTHOR]

Warm rain collision-coalescence has been persistently difficult to parameterize in bulk microphysics schemes. We use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that an important limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution, the simulation of cloud-to-rain conversion is substantially improved. We also find large sensitivity to the threshold size to distinguish cloud and rain in traditional schemes; substantial improvement is found by decreasing the threshold from 40 to 25 μm. Neither the use of an assumed functional form for the size distribution nor the choice of predicted distribution moments has a large impact on the ability of AMP to simulate rain production. When predicting four total moments of the liquid drop size distribution, either with a traditional two-category, two-moment scheme with a reduced size threshold, or a four-moment single-category scheme, errors in the evolution of mass and the cloud size distribution are similar, but the single-category scheme has a substantially better representation of the rain size distribution. Optimal moment combinations for the single-category approach are investigated and appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision-coalescence rates.

Abstract Collisional 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.

Aerosol‐cloud interaction remains one of the least understood processes in climate science arena, despite its profound impacts in radiation and water budget perturbations. The aerosol effects on clouds largely depend on aerosol characteristics. Here, we implemented 17‐year (2003–2020) data set of aerosol, cloud, and meteorological factors collected over East Asia—a highly polluted region with recent decreasing trend of air pollution due to control measures—to elucidate atmospheric regime‐dependent aerosol effects on water cloud properties by simultaneously accessing the response of air pollution control measures in cloud field. The study found a very close relationship between aerosol loading and cloud properties modifications in the continental region of East Asia with a significant response of air pollution control measures in the cloud field. The study further revealed that aerosols of the polluted continental atmosphere affected cloud micro‐ and macro‐physics differently than aerosols of the clean maritime atmosphere: in the former, increased aerosol loading increased the stability under cloud base and then enhanced cloud droplet collision‐coalescence process, resulting to increase cloud droplet size by decreasing cloud top height; whereas in the latter, increased aerosol loading decreased cloud droplet size without notable influence in the atmosphere thermodynamics and cloud top height. This study further showed a complex aerosol‐cloud interaction process in the polluted maritime atmosphere due to the mixed effect of polluted continental and clean maritime atmospheres. In all atmospheric regimes, cloud fraction was found to increase with the increase of aerosol loading. Plain Language Summary: Aerosols can act as cloud condensation nuclei to modify water cloud properties and then climate system and water cycle. At the same time, these aerosols scatter and absorb solar radiation, which can perturb atmospheric heating. Such change in the atmospheric heating can also play an important role in cloud properties modification. Because of such different routes, aerosol‐cloud interaction (ACI) remains one of the least understood topics in climate science. As ACI is strongly driven by aerosol property, it is important to investigate ACI process for different atmospheric backgrounds. This study analyzed a long‐term data of aerosol, cloud, and meteorological factors collected over East Asia, which is one of the major air pollution regions in the world, although air pollution is decreasing gradually in recent years. By analyzing data corresponding to different atmospheric regimes, we showed that aerosols of continental atmosphere can modify cloud micro‐ and macro‐physics by modifying atmospheric thermodynamics; whereas aerosols of maritime atmosphere are less capable to modify atmospheric thermodynamics, but they can decrease cloud droplet size. On the other hand, we found a complex ACI in polluted maritime atmosphere due to mixed effect of polluted continental and maritime atmospheric regimes. Key Points: Continental aerosols of East Asia were noted to strongly modulate cloud properties in time series analysesIncreased aerosols of polluted continental atmosphere increased cloud droplet size by modulating atmospheric thermodynamicsIncreased aerosols of clean maritime atmosphere decreased cloud droplet size without notable influence in atmospheric thermodynamics [ABSTRACT FROM AUTHOR]

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable ZDR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events. [ABSTRACT FROM AUTHOR]

Relative dispersion (ɛ) is a key expression used to parameterize various cloud processes in global circulation models (GCMs) and meteorological mesoscale models. Aerosols, updraft velocity (w), and different growth stages of warm clouds are known to affect relative dispersion. A two-dimensional detailed bin microphysical cloud model is used to investigate the combined impacts of aerosol number concentration (Na) and updraft velocity on relative dispersion in the collision–coalescence stage. In addition, the causes potentially controlling the changes in ɛ with updraft velocity are explored. There are three main influence regimes: the updraft velocity main influence regime, the aerosol main influence regime, and the joint influence regime. The cause of the variations in ɛ with updraft velocity is found to be different in the three main influence regimes. In the updraft velocity main influence regime, vigorous collision–coalescence due to stronger w results in a shift in the cloud droplet number concentration spectrum toward larger droplets, and the average cloud droplet radius increases, but the spectral width is less variable, so ε decreases. In the joint influence regime, stronger cloud droplet evaporation due to the stronger dragging effect of large cloud droplets widens the spectrum, mainly by reducing the cloud droplet number concentration (Nc) of 4–30 μm, and ε increases with the reduction in w. In the aerosol main influence regime, the strongest dragging effect reduces Nc at all radii with decreasing w, and the cloud droplet number concentration spectrum (CDNCS) narrows, which becomes the formation mechanism of the positive correlation between ε and w. Evaporation mainly causes a negative correlation between ε and Nc, but weak evaporation causes the correlation to become positive under the background of high aerosol concentration. At low aerosol concentrations, a strong collision–coalescence effect leads to a negative correlation between Nc and ε, but at high aerosol concentrations, the correlation is the opposite due to a weak collision–coalescence effect. [ABSTRACT FROM AUTHOR]

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable ZDR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events.

The SFT (swirling flow tundish) is a kind of tundish with SC (swirl chamber) placed in the flow injection zone. The gravity potential energy of the molten steel is converted into rotation kinetic energy as entering into the tundish from the bottom of the SC along the tangential direction. The inclusions tend to collide and aggregate as following the rotating molten steel in the SC. In this work, the collision-coalescence behavior of inclusion was investigated by the mathematical simulation with PBM (population balance model) model, and the removal rate of the inclusion was investigated by the DPM model. The results show that, under the same operating conditions, the average diameter of inclusion at the outlet of the tundish increases from 4.25 μm to 4.35 μm with the introduction of the SC, which means that the SC promotes the collision and aggregation of inclusion. The inclusion removal rate of SFT is 33.09% without considering the collision-coalescence, and it increases into 43.20% as considering the collision-coalescence. The results of considering the collision-coalescence of inclusion is more consistent with the actual movement of inclusion in the tundish. [ABSTRACT FROM AUTHOR]

Microphysics methods for climate models and numerical weather prediction typically track one, two, or three moments of a droplet size distribution for various categories of liquid, ice, and aerosol. Such methods rely on conversion parameters between these categories, which introduces uncertainty into predictions. While higher‐fidelity options such as bin and Lagrangian schemes exist, they require too many degrees of freedom for climate modeling applications and introduce numerical challenges. Here we introduce a flexible spectral microphysics method based on collocation of basis functions. This method generalizes to a linear bulk scheme when using few basis functions and to a smoothed bin scheme with more degrees of freedom. Tested in an idealized box setting, the method improves spectral accuracy for droplet collision‐coalescence and may eliminate the need for precipitation autoconversion rates required by bulk methods; furthermore, it generalizes well to multimodal distributions with less complexity than a bin method. The potential to extend this collocation representation to multiple hydrometeor classes suggests a path forward to unify liquid, ice, and aerosol microphysics in a single, flexible, computational framework for climate modeling. Plain Language Summary: Clouds and aerosols affect global warming by reflecting and absorbing radiation and by storing and transporting water. Climate models need a way to efficiently track the size and number of cloud droplets, ice, and aerosols in order to accurately predict the impact that these "microphysical" particles have on climate. Existing methods of microphysics rely on many uncertain parameters and are either too complicated or too simple to take advantage of today's computational resources. We propose a new way to represent cloud droplets that can both reduce uncertainties and make use of increased computing power. Key Points: A new microphysics method using collocation of basis functions is presentedThe method improves spectral accuracy over bulk methods at a lower cost than bin methodsThe method applies to a flexible range of computational complexity, providing a way to unify microphysics models [ABSTRACT FROM AUTHOR]

Inclusions can lead to various defects in the final steel products, and RH vacuum refining is an effective and vital technology to remove inclusions from molten steel. By the computational fluid dynamics (CFD) method, the inclusion mass/population conservation model is developed to have a deep insight into the inclusion transfer behavior in the argon molten steel flow of RH. Particularly, the argon slip velocity is introduced into the inclusion convection velocity in the mathematical model, and the inclusions attached by argon bubbles can return to the molten steel when bubbles escape from the free surface of the vacuum chamber. The numerical results of the fluid flow and the inclusion transfer behavior are validated by the measured data in the actual experiment. The argon bubbles significantly enhance the inclusion transport and collision-coalescence process. Besides, the variations of the inclusion volume concentration and the inclusion number density in the up snorkel are about 2 times those in the down snorkel. The number of inclusions with diameters less than 10 μm decreases with the vacuum treatment time, while the number of inclusions with diameters greater than 10 μm increases first and then decreases gradually. Near the free surface of the ladle, there are the greatest inclusion volume concentration, the greatest inclusion number density and the lowest inclusion characteristic radius.

Abstract Microphysics methods for climate models and numerical weather prediction typically track one, two, or three moments of a droplet size distribution for various categories of liquid, ice, and aerosol. Such methods rely on conversion parameters between these categories, which introduces uncertainty into predictions. While higher‐fidelity options such as bin and Lagrangian schemes exist, they require too many degrees of freedom for climate modeling applications and introduce numerical challenges. Here we introduce a flexible spectral microphysics method based on collocation of basis functions. This method generalizes to a linear bulk scheme when using few basis functions and to a smoothed bin scheme with more degrees of freedom. Tested in an idealized box setting, the method improves spectral accuracy for droplet collision‐coalescence and may eliminate the need for precipitation autoconversion rates required by bulk methods; furthermore, it generalizes well to multimodal distributions with less complexity than a bin method. The potential to extend this collocation representation to multiple hydrometeor classes suggests a path forward to unify liquid, ice, and aerosol microphysics in a single, flexible, computational framework for climate modeling.

Relative dispersion (ɛ) is a key expression used to parameterize various cloud processes in global circulation models (GCMs) and meteorological mesoscale models. Aerosols, updraft velocity (w), and different growth stages of warm clouds are known to affect relative dispersion. A two-dimensional detailed bin microphysical cloud model is used to investigate the combined impacts of aerosol number concentration (Na) and updraft velocity on relative dispersion in the collision–coalescence stage. In addition, the causes potentially controlling the changes in ɛ with updraft velocity are explored. There are three main influence regimes: the updraft velocity main influence regime, the aerosol main influence regime, and the joint influence regime. The cause of the variations in ɛ with updraft velocity is found to be different in the three main influence regimes. In the updraft velocity main influence regime, vigorous collision–coalescence due to stronger w results in a shift in the cloud droplet number concentration spectrum toward larger droplets, and the average cloud droplet radius increases, but the spectral width is less variable, so ε decreases. In the joint influence regime, stronger cloud droplet evaporation due to the stronger dragging effect of large cloud droplets widens the spectrum, mainly by reducing the cloud droplet number concentration (Nc) of 4–30 μm, and ε increases with the reduction in w. In the aerosol main influence regime, the strongest dragging effect reduces Nc at all radii with decreasing w, and the cloud droplet number concentration spectrum (CDNCS) narrows, which becomes the formation mechanism of the positive correlation between ε and w. Evaporation mainly causes a negative correlation between ε and Nc, but weak evaporation causes the correlation to become positive under the background of high aerosol concentration. At low aerosol concentrations, a strong collision–coalescence effect leads to a negative correlation between Nc and ε, but at high aerosol concentrations, the correlation is the opposite due to a weak collision–coalescence effect.

Abstract Warm rain collision‐coalescence has been persistently difficult to parameterize in bulk microphysics schemes. We use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that an important limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution, the simulation of cloud‐to‐rain conversion is substantially improved. We also find large sensitivity to the threshold size to distinguish cloud and rain in traditional schemes; substantial improvement is found by decreasing the threshold from 40 to 25 μm. Neither the use of an assumed functional form for the size distribution nor the choice of predicted distribution moments has a large impact on the ability of AMP to simulate rain production. When predicting four total moments of the liquid drop size distribution, either with a traditional two‐category, two‐moment scheme with a reduced size threshold, or a four‐moment single‐category scheme, errors in the evolution of mass and the cloud size distribution are similar, but the single‐category scheme has a substantially better representation of the rain size distribution. Optimal moment combinations for the single‐category approach are investigated and appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision‐coalescence rates.

Rainfall is triggered and mainly dominated by atmospheric thermo-dynamics and rich water vapor. Nonetheless, turbulence is also considered as an important factor influencing the evolution of rainfall microphysical parameters. To study such an influence, the present study utilized boundary layer wind profiler radar measurements. The separation point of the radar power spectral density data was carefully selected to classify rainfall and turbulence signals; the turbulent dissipation rate ε and rainfall microphysical parameters can be retrieved to analyze the relationship between ε and microphysical parameters. According to the retrievals of two rainfall periods in Beijing 2016, it was observed that (1) ε in the precipitation area ranged from 10-3.5 to 10-1 m2 s-3 and was positively correlated with the falling velocity spectrum width; (2) interactions between turbulence and raindrops showed that small raindrops got enlarge through collision and coalescence in weak turbulence, but large raindrops broke up into small drops under strong turbulence, and the separation value of ε being weak or strong varied with rainfall attributes; (3) the variation of rainfall microphysical parameters(characteristic diameters, number concentration, rainfall intensity, and water content) in the middle stage were stronger than those in the early and the later stages of rainfall event; (4) unlike the obvious impacts on raindrop size and number concentration, turbulence impacts on rain rate and LWC were not significant because turbulence did not cause too much water vapor and heat exchange. [ABSTRACT FROM AUTHOR]

Simultaneous measurements of cloud electric field and raindrop size at the Earth's surface have been analyzed to investigate an anticipated relation between the two cloud parameters. A significant positive correlation is observed between the magnitude of the surface electric field and raindrop size supported by a theoretical framework advanced here. It is shown that in the presence of an electric field, raindrops increase their size through electrically induced collision and coalescence processes near the Earth's surface. This study also demonstrates that the presence of an electric field inside the cloud can enhance the rain intensity by influencing the growth rate of raindrops. This new insight may be useful for a proper understanding of the rain microphysics and for developing better numerical weather models for simulation and forecasting of heavy rainfall events associated with stronger in‐cloud electrical environments. Plain Language Summary: The rainfall observed at the Earth's surface is a manifestation of different cloud microphysical processes. The condensation of water vapor on cloud condensation nuclei (CCN) and subsequent collision/coalescence between cloud droplets are the two dominant processes of rain formation in the warm phase of cloud. Many laboratory investigations suggest that both processes are quite sensitive to the electrification of the cloud. But direct evidence of the influence of cloud electric field on the rain formation in the real atmospheric condition is lacking till now. With simultaneous observations of the cloud electric field and the raindrop size distribution below a few strongly electrified clouds, for the first time, it has been shown that larger cloud electric field remains associated with larger raindrops at the Earth's surface. A mathematical framework has also been developed to support the observations. It has also been shown that the cloud electric field can modify the rain rate at the Earth's surface by enhancing the growth of raindrops. Key Points: Simultaneous measurements of surface electric field, raindrop size and rain rate have been analyzedThe results indicate that the electric field below strongly electrified cloud can enhance the size of raindrops and hence the rain rateThese results may be useful for better understanding and prediction of heavy rainfall events associated with stronger electrification [ABSTRACT FROM AUTHOR]

A parameterization for the collision-coalescence process is presented, based on the methodology of basis functions. The whole drop spectra is depicted as a linear combination of two lognormal distribution functions, in which all distribution parameters are formulated by means of six distribution moments included in a system of equations, thus eliminating the need of fixing any parameters. This basis functions parameterization avoids the classification of drops in artificial categories such as cloud water (cloud droplets) or rain water (raindrops). The total moment tendencies are calculated using a machine learning approach, in which one deep neural network was trained for each of the total moment orders involved. The neural networks were trained using randomly generated data following a uniform distribution, over a wide range of parameters employed by the parameterization. An analysis of the predicted total moment errors was performed, aimed to stablish the accuracy of the parameterization at reproducing the integrated distribution moments representative of physical variables. The applied machine learning approach shows a good accuracy level when compared to the output of an explicit collision-coalescence model. [ABSTRACT FROM AUTHOR]

Rain enhancement is the main activity field of weather modification in the countries that suffer from water stress. The charged aerosol is a new man-made condensation nucleus to enhance the water vapor condensation. In this article, the negative-polarity air plasma jet array with plasma volume larger than 2 cm3 was used to generate the charged aerosols. Aerosols are mainly charged in the unipolar atmosphere. The averaged charge number of charged aerosols was measured to be 5818e. The attractive electric force of charged aerosols was more than ten times higher than the sum of thermophoretic forces and diffusophoretic forces. These charged aerosols accelerated the collision–coalescence process inside the cloud chamber, generated large droplets in the range of 40–70 μm, and increased the settling amount of moisture by 58.8% [ABSTRACT FROM AUTHOR]

High‐intensity precipitation represents a threat for several regions of the world because of the related risk of natural disasters (e.g. floods and landslides). This work focuses on low‐level precipitation enhancement that occurs in the cloud warm layer and has been observed in relation to collision‐coalescence (CC) leading to flash floods and extreme rainfall events in tropical and temperate latitudes. Specifically, signatures of precipitation enhancement (referred to as CC‐dominant precipitation) are investigated in the observations from the Global Precipitation Measurement (GPM) core mission Dual‐frequency Precipitation Radar (DPR) over the central/eastern Contiguous United States (CONUS) during June 2014–May 2018. A classification scheme for CC‐dominant precipitation, developed for dual‐polarization S‐band radar measurements and applied in a previous work to X‐band radar observations in complex terrain, is used as a benchmark. The scheme is here applied to the GPM ground validation dataset that matches ground‐based radar observations across CONUS to space‐borne DPR retrievals. The occurrence of CC‐dominant precipitation is documented and the corresponding signatures of CC‐dominant precipitation at Ku‐ and Ka‐band are studied. CC‐dominant profiles show distinguishing features when compared to profiles not dominated by CC, e.g. characteristic vertical slopes of reflectivity at Ku‐ and Ka‐band in the liquid layer, lower freezing‐level height, and shallower ice layer, which are linked to environmental conditions driving the peculiar CC microphysics. This work aims at improving satellite quantitative precipitation estimation, particularly GPM retrievals, by targeting CC development in precipitation columns. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (RSD) characteristics in summer season rainfall of two observational sites (Taiwan (24°58′N, 121°10′E) and Palau (7°20′N, 134°28′E)) in western Pacific are studied by using five years of impact type disdrometer data. In addition to disdrometer data, Tropical Rainfall Measuring Mission, Moderate Resolution Imaging Spectroradiometer, and ERA-Interim data sets are used to illustrate the dynamical and microphysical characteristics associated with summer season rainfall of Taiwan and Palau. Taiwan and Palau's raindrop spectra showed a significant difference, with a higher concentration of middle and large drops in Taiwan than Palau rainfall. RSD stratified on the basis of rain rate showed a higher mass-weighted mean diameter ( Dm) and a lower normalized intercept parameter (log10 Nw) in Taiwan than Palau rainfall. Precipitation classification into stratiform and convective regimes showed higher Dm values in Taiwan than Palau. Furthermore, for both the locations, the convective precipitation has a higher Dm value than stratiform precipitation. The radar reflectivity-rain rate relations ( Z = A* R b) of Taiwan and Palau showed a clear variation in the coefficient and a less variation in exponent values . Terrain-influenced clouds extended to higher altitudes over Taiwan resulted with higher Dm and lower log10 Nw values as compared to Palau. [ABSTRACT FROM AUTHOR]

Precipitation forms in warm clouds via collision-coalescence. This process is difficult to observe directly in situ and its implementation in numerical models is uncertain. We use aircraft observations of the drop-size distribution (DSD) near marine stratocumulus tops to estimate collision-coalescence rates. Marine stratocumulus is a useful system to study collisional growth because it is initiated near the cloud top and the clouds evolve slowly enough to obtain statistically useful data from aircraft. We compare rate constants estimated from observations with reference rate constants derived from a collision-coalescence box model, the result of which is termed the enhancement factor (EF). We evaluate two hydrodynamic collision-coalescence kernels, one quiescent and one including the effects of small-scale turbulence. Due to sampling volume limitations, DSDs must be averaged over length-scales much greater than those relevant to the underlying physics, such that we also examine the role of averaging length-scale with respect to process representation. Averaging length-scales of 1.5 and 30 km are used, corresponding roughly to the horizontal grid lengths of cloud-resolving models and high-resolution climate models, respectively. EF values range from 0.1 to 40, with the greatest EFs associated with small mode diameter cases and a generally decreasing trend with drop size. For any given drop size or averaging length-scale, there is about an order of magnitude variability in EFs. These results suggest that spatial variability on length-scales smaller than 1.5 km prevents accurate retrieval of rate constants from large-scale average DSDs. Large-scale models must therefore account for small-scale variability to represent cloud microphysical processes accurately. The turbulent kernel reduces EFs for all drop sizes, but can only account for at most half of the calculated EFs in marine stratocumulus. [ABSTRACT FROM AUTHOR]

The variability in the microphysical properties of raindrop size distribution (DSD) over the rain shadow region of Indian peninsula is investigated in this study. Simultaneous in-situ measurements of precipitating cloud systems by an impact disdrometer, Micro Rain Radar (24 GHz), and wind profiler (1.28 GHz) are used in this study. The normalized Gamma distribution parameters for five DSD clusters of precipitating systems, corresponding to the non-convective, deep convective, shallow convective, mixed-type, and stratiform rain types were obtained using the k-means clustering algorithm. Four DSDs are nearly bounded by shape parameters, −3 < μ < 25, except for stratiform DSDs (−3 < μ < 45) indicating a wide variation in DSD shape. Bimodal (mono-modal) distributions in terms of frequency of occurrence of N w are observed in the non-convective, deep convective and shallow convective (stratiform and mixed) clouds. The bimodality is attributed to the large raindrops formed by the collision and coalescence (CC) process, and the small raindrops due to breakup that survive evaporation and reach the ground. The mono-modality of stratiform rain is attributed to the in-efficient CC and CC-breakup process. These rain microphysical processes are further characterized using the radar reflectivity factor (Z)-Rainfall Intensity (R) relationships of the form Z = aR b . Finally, we found that the convective DSDs over the region appear to be a composite of both maritime and continental characteristics. • Microphysical properties of non-convective, shallow, convective, mixed, and stratiform rains are described. • Parameterization of normalized Gamma DSD is done for DSD clusters. DSDs are maritime-continental origin • Collision-coalescence process dominates in non-convective, shallow, deep convective, and mixed rain. [ABSTRACT FROM AUTHOR]

This paper presents a method to distinguish and link inhomogeneous mixing with subsequent ascent and collision-coalescence. Three stratocumulus clouds analyzed were collected over the U.S. Department of Energy's Atmospheric Radiation Measurement Southern Great Plains site during the March 2000 cloud Intensive Observation Period. The criteria are presented to distinguish the two processes. Inhomogeneous mixing with subsequent ascent is identified if cloud along an aircraft horizontal leg is non-drizzling and the relationship between cloud volume-mean radius and liquid water content is negative; in contrast, drizzling and positive relationship between the above two properties are the criteria for collision-coalescence. To link the two processes, threshold function, the possibility of occurrence of collision-coalescence, is employed; the big droplets generated during the inhomogeneous mixing with subsequent ascent increase the threshold function, initiates collision-coalescence and produces drizzle drops. To the authors' knowledge, this is the first study on distinguishing and linking inhomogeneous mixing with subsequent ascent and collision-coalescence based on observational data. [ABSTRACT FROM AUTHOR]

In recent years, Yttria-stabilized zirconia based thermal barrier coatings (TBCs) are deposited by newly-developed high-efficiency supersonic atmospheric plasma spraying (SAPS) technology. The final microstructure of the plasma-sprayed coatings is strongly dependent on the size distribution of spray particles. It has been corroborated through experiments that there is a special phenomenon of particle melting refining in SAPS, as compared with the conventional atmospheric plasma spraying (APS). This phenomenon greatly affects the final particle size and distribution, which has not been explained reasonably up to now. Therefore, it is necessary to investigate the melting refining behavior of in-flight particles to control the particle size and to analyze the coating properties. In this paper, the breakup of particle is presented to characterize the phenomenon of particle melting refining, and the peak of size distribution becomes bigger with increasing the spray distances, which is explained by collision-coalescence. Furthermore, based on the maximum entropy formalism, the particle-size distribution is calculated and the result is in good accordance with the plasma spraying experiment results, which verifies the mechanism analysis presented in this paper. This work could provide more efficient applications of the SAPS technology in high-performance TBCs. [ABSTRACT FROM AUTHOR]

Abstract: Point-particle based direct numerical simulation (PPDNS) has been a productive research tool for studying both single-particle and particle-pair statistics of inertial particles suspended in a turbulent carrier flow. Here we focus on its use in addressing particle-pair statistics relevant to the quantification of turbulent collision rate of inertial particles. PPDNS is particularly useful as the interaction of particles with small-scale (dissipative) turbulent motion of the carrier flow is mostly relevant. Furthermore, since the particle size may be much smaller than the Kolmogorov length of the background fluid turbulence, a large number of particles are needed to accumulate meaningful pair statistics. Starting from the relative simple Lagrangian tracking of so-called ghost particles, PPDNS has significantly advanced our theoretical understanding of the kinematic formulation of the turbulent geometric collision kernel by providing essential data on dynamic collision kernel, radial relative velocity, and radial distribution function. A recent extension of PPDNS is a hybrid direct numerical simulation (HDNS) approach in which the effect of local hydrodynamic interactions of particles is considered, allowing quantitative assessment of the enhancement of collision efficiency by fluid turbulence. Limitations and open issues in PPDNS and HDNS are discussed. Finally, on-going studies of turbulent collision of inertial particles using large-eddy simulations and particle-resolved simulations are briefly discussed. [Copyright &y& Elsevier]

Aerosol dynamics (AD) is the discipline that deals with changes in particle size distributions in space and time. AD is based on (1) certain fundamental principles embodied in a set of equations, (2) experimental methods and instrumentation and (3) numerical and computational methods. Over the last few decades, AD has emerged as an enabling discipline in the design of aerosol reactors employed in the gas phase synthesis of fine powders, the characterization of particle emissions from sources such as coal-fired power plants and the atmospheric aerosol. The development of basic AD concepts since early in the 20th century is traced to the present. Major gaps that remain in the field and likely advances over the next few years are discussed. Although accurate predictions of particle size from first principles are difficult to make in practical applications, AD principles can be used to explain trends in product properties for flame and laser ablation reactors that operate under very different temperatures and quench rates. [Copyright &y& Elsevier]