Cloud Particles Research Articles

Color Measurements of Dust Clouds Transiting the White Dwarf J0328-1219

The transiting dust clouds that orbit the white dwarf (WD) J0328-1219 produce dips with nearly identical depths at g′ and R bands. This resembles a similar finding for WD 1145+017 at near-IR wavelengths, which has been interpreted as requiring an absence of small particles in the dust clouds. Whereas the lack of depth difference at optical wavelengths for J0328-1219 can be used to rule-out the presence of small particles if the dust clouds are optically thin, a constraint on the particle size distribution is not possible if the dust clouds are optically thick.

Classification of Ice Particle Shapes Using Machine Learning on Forward Light Scattering Images

Abstract Machine learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), and the Small Ice Detector, version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here, results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-yr project in Fairbanks, Alaska. About 2.15 million light-scattering pattern images were collected during 3 years of measurements with the PPD-2K. Visual Geometry Group (VGG) convolutional neural network (CNN) was trained to categorize light-scattering patterns into eight categories. Initial training images (120 each category) were selected by human visual examination of data, and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identifies characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating, and the remaining 23.6% did not fit into any of those categories (irregular or saturated). Significance Statement The shapes and sizes of cloud particles can be extremely important for understanding the conditions that exist in the cloud. In this study, we show that more information about cloud particle characteristics can be identified by using machine learning than by traditional means. To demonstrate this, data are analyzed from a 3-yr study of ice fog and diamond dust events in Fairbanks, Alaska. The Particle Phase Discriminator instrument collected 2.15 million light-scattering pattern images of cloud particles during ground-based measurements. Neither traditional techniques nor machine learning were able to identify all categories, but a combination of both techniques led to a more complete view of ice particle shapes.

The Influence of Space Traffic on AIM/CIPS PMC Frequencies at 80°N

AbstractWe explore the effects of lower thermospheric water vapor deposited by launch vehicle plumes on polar mesospheric cloud (PMC) frequencies at 80°N. We use July‐averaged PMC frequencies from 2007 to 2022 from the Cloud Imaging and Particle Size (CIPS) instrument on NASA's Aeronomy of Ice in the Mesosphere (AIM) satellite. Launch sites worldwide are typically located near northern mid‐latitudes. Using the orbital launch record for the same time period, we find that the number of launches correlates with PMC frequencies with a coefficient of r = 0.60, which increases to r = 0.75 when only selecting launches from 2.5 to 21.5 local time (LT), indicating a weak LT dependence on global‐scale transport to 80°N. To support our findings, we use meridional winds from the Michelson Interferometer for Global High‐resolution Imaging experiment on NASA's Ionospheric Connection Explorer satellite and winds from the Horizontal Wind Model climatology to interpret the northward motion of air parcels at 105 km. We find the launch LT window that maximizes the correlation coefficient to be consistent with the expected maximum northward motion from the diurnal variation of mid‐latitude meridional winds. Comparisons with Microwave Limb Sounder satellite observations of upper mesospheric temperature and water vapor reveal a strong dependence of cloud frequency on water vapor (r = 0.86) but not on temperature (r = −0.26), indicating that water vapor is the primary source of PMC variability for the bright PMCs at 80°N. We therefore find that launch vehicle plumes originating primarily from northern mid‐latitudes modulate PMC frequency at 80°N in July.

On the temperature and emissivity of torrefied biomass and coal in group particle combustion

This laboratory study reports results on the group particle combustion of pulverized bituminous coal and various types of torrefied biomass. Combustion of particle streams in a drop tube furnace in air was concurrently monitored by a spectrometer and an electronic camera to obtain spectral emissivities and temperatures. As particle number density (PND) increased, biomass particles became more prone than coal to group combustion. Spectral emissivities increased with increasing PND from 0.2 to 0.4 for coal and from 0.1 to 0.3 for biomass, in the wavelength domain of λ = 600–1000 nm. Emissivities changed little with wavelength, giving credence to the gray body assumption. Particle cloud temperatures were in the range of 1650–1900 K, depending on PND, type of fuel, and location in the cloud; temperatures decreased with increasing PND. The radiative heat of the particle laden flames was predominantly attributed to burning chars in the flames and it increased with increasing PND.

Study on Multi-Scale Cloud Growth Characteristics of Frustoconical Dispersal Devices

This study aims to understand cloud growth behavior and enhance cloud safety and reliability by investigating the design of cloud dispersal devices. Based on the experimental results and simulation results, this study analyzes the dispersion characteristics of cloud materials within a frustoconical device with a semi-cone angle ranging from 0° to 10° across multiple scales. The collision aggregation model for cloud particles and the multi-scale coupling mechanism for cloud growth are established. The research shows that the semi-cone angle of the device extends the effective cloud growth duration and enlarges the cloud macroscopic size. At the mesoscopic scale, vortex phenomena are observed, causing particles to converge within the cloud, resulting in collisions and aggregation. The vortices enhance the continuity of the cloud concentration. The magnitude of these vortices demonstrates a positive correlation with the magnitude of the semi-cone angle of the dispersal device. For a macroscopically stable cloud, the high-concentration area within the cloud moves outward radially with an increase in the semi-cone angle. This study provides a theoretical foundation for cloud morphology control technology, contributing to enhancing the safety and reliability of cloud systems.

Two-dimensional Models of Microphysical Clouds on Hot Jupiters. I. Cloud Properties

We present a new two-dimensional, bin-scheme microphysical model of cloud formation in the atmospheres of hot Jupiters that includes the effects of longitudinal gas and cloud transport. We predict cloud particle size distributions as a function of planetary longitude and atmospheric height for a grid of hot Jupiters with equilibrium temperatures ranging from 1000 to 2100 K. The predicted 2D cloud distributions vary significantly from models that do not consider horizontal cloud transport and we discuss the microphysical and transport timescales that give rise to the differences in 2D versus 1D models. We find that the horizontal advection of cloud particles increases the cloud formation efficiency for nearly all cloud species and homogenizes cloud distributions across the planets in our model grid. In 2D models, certain cloud species are able to be transported and survive on the daysides of hot Jupiters in cases where 1D models would not predict the existence of clouds. We demonstrate that the depletion of condensible gas species varies as a function of longitude and atmospheric height across the planet, which impacts the resultant gas-phase chemistry. Finally, we discuss various model sensitivities including the sensitivity of cloud properties to microphysical parameters, which we find to be substantially less than the sensitivity to the atmospheric thermal structure and horizontal and vertical transport of condensible material.

Aerosol‐Cloud Interactions Near Cloud Base Deteriorating the Haze Pollution in East China

AbstractAtmospheric aerosols not only cause severe haze pollution, but also affect climate through changes in cloud properties. However, during the haze pollution, aerosol‐cloud interactions are not well understood due to a lack of in situ observations. In this study, we conducted simultaneous observations of cloud droplet and particle number size distribution, together with supporting atmospheric parameters, from ground to cloud base in East China using a high‐payload tethered airship. We found that high concentrations of aerosols and cloud condensation nuclei were constrained below cloud, leading to the pronounced “Twomey effect” near the cloud base. The cloud inhibited the pollutants dispersion by reducing surface heat flux and thus deteriorated the near‐surface haze pollution. Satellite retrievals matched well with the in situ observations for low stratus clouds, while were insufficient to quantify aerosol‐cloud interactions for other cases. Our results highlight the importance to combine in situ vertical and satellite observations to quantify the aerosol‐cloud interactions.

Scale and conformal invariance in rotating interacting few-fermion systems

We show that rotating two-dimensional Fermi gases possess a nonrelativistic scale and conformal invariance at weak but nonzero interactions, where the scale invariance of universal short-range interactions is not yet broken by quantum effects. We demonstrate the symmetry in the excitation spectrum of few-fermion ensembles in a harmonic trap obtained by exact diagonalization. The excitation spectrum is shown to split in a set of primary states and derived excited states that consist of breathing modes as well as two different center-of-mass excitations, which describe cyclotron and guiding-center excitations of the total particle cloud. Furthermore, the conformal symmetry is manifest in the many-body wave function, where it dictates the form of the hyperradial component, which we demonstrate using Monte Carlo sampling of few-body wave functions. Published by the American Physical Society 2024

Enhancing signal-to-noise ratio in active laser imaging under cloud and fog conditions through combined matched filtering and neural network

Active laser imaging utilizes time-of-flight and echo intensity measurements to generate distance and intensity images of targets. However, scattering caused by cloud and fog particles, leads to imaging quality deterioration. In this study, we introduce a novel approach for improving imaging clarity in these environments. We employed a matched filtering method that leverages the distinction between signal and noise in the time domain to preliminarily extract the signal from one-dimensional photon-counting echo data. We further denoised the data by utilizing the Long Short-Term Memory (LSTM) neural network in extracting features from extended time-series data. The proposed method displayed notable improvement in the signal-to-noise ratio (SNR), from 7.227 dB to 31.35 dB, following an analysis of experimental data collected under cloud and fog conditions. Furthermore, processing positively affected the quality of the distance image with an increase in the structural similarity (SSIM) index from 0.7883 to 0.9070. Additionally, the point-cloud images were successfully restored. These findings suggest that the integration of matched filtering and the LSTM algorithm effectively enhances beam imaging quality in the presence of cloud and fog scattering. This method has potential application in various fields, including navigation, remote sensing, and other areas susceptible to complex environmental conditions.

Persistent mixed‐phase states in adiabatic cloud parcels under idealised conditions

AbstractBy assuming that liquid droplets and ice crystals within a computational grid box grow under the same conditions, mean‐field representations of mixed‐phase clouds in numerical models favour a quick cloud glaciation driven by the Wegener–Bergeron–Findeisen process. Consequently, maintenance of mixed‐phase states under the mean‐field approximation is conditioned to external dynamical forcing, such as sufficiently strong updraughts. In this work we go beyond the mean‐field representation and investigate the maintenance of mixed‐phase states in adiabatic (non‐entraining) cloud volumes by accounting for local variability in a particle's growth conditions in the turbulent cloud environment. This is done by using a Lagrangian microphysical scheme, where temperature and vapour mixing ratio are stochastic attributes attached to each cloud particle. Different dynamic scenarios show that microphysical variability and parametrised turbulence effects may significantly reduce cloud glaciation rates, resulting in much more resilient mixed‐phase states in idealised parcels containing non‐sedimenting and non‐aggregating cloud particles. We have stated a more refined criterion for the Wegener–Bergeron–Findeisen process activity in the bulk of a mixed‐phase cloud parcel (or computational grid box). This criterion is stated in terms of the conditional average supersaturations that are experienced by specific cloud particle types (liquid or ice), and not in terms of unconditional averages corresponding to parcel‐ or grid‐mean values of supersaturations. Formulation of a relation between conditional and unconditional average supersaturations poses an interesting closure problem in mixed‐phase cloud microphysics. Our stochastic microphysical model provides a Lagrangian closure to this problem and gives insights towards the development of a prognostic stochastic subgrid‐scale scheme for condensation/deposition in numerical models of mixed‐phase clouds.

Classifying Microwave Radiometer Observations over the Netherlands into Dry, Shallow, and Nonshallow Precipitation Using a Random Forest Model

Abstract Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using spaceborne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands—a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on five years of data (2016–20) and tested with two independent years (2015 and 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or Dual-Frequency Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb values as nonshallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb values, likely resulting from the presence of ice particles in nonprecipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles. Significance Statement Published research concerning rainfall retrieval algorithms from microwave radiometers is often focused on the accuracy of these algorithms. While shallow precipitation over land is often characterized as problematic in these studies, little progress has been made with these systems. In particular, precipitation formed by shallow clouds, where shallow refers to the clouds being close to Earth’s surface, is often missed. This study is focused on detecting shallow precipitation and its physical characteristics to further improve its detection from spaceborne sensors. As such, it contributes to understanding which shallow precipitation scenes are challenging to detect from microwave radiometers, suggesting possible ways for algorithm improvement.

Void closure in a pulsed complex plasma in microgravity

A new experimental method for creating void-free complex (dusty) plasmas under microgravity conditions is presented. The method is based on a pulsed operation mode of a four-channel radio frequency generator for plasma sustainment. A dust cloud of micrometer-sized particles can be immersed in the bulk of a low temperature plasma under microgravity conditions. It typically contains a central volume depleted of particles—the void—that prevents the generation of large, continuous clouds. Experiments performed at different neutral gas pressures and discharge volumes during the microgravity phase of a parabolic flight show that the central void is closed completely once the pulsed operation mode is applied. The particle cloud shape and the density distribution within the cloud are practically independent of the pulse period within the investigated parameter range and mainly depend on the overall discharge parameters neutral gas pressure and discharge volume. This indicates that the pulsed operation of the plasma source does not introduce new physical effects on the particles aside from the void closure. The proposed method has great potential for future application in experimental facilities dedicated to fundamental studies of large three-dimensional, homogeneous complex plasma systems in microgravity.

Microphysical Characterization of Boundary Layer Ice Particles: Results from a 3-Year Measurement Campaign in Interior Alaska

Abstract A three-winter study has been conducted to better understand the relationship between atmospheric conditions and ice fog or diamond dust microphysics. Measurements were conducted east of downtown Fairbanks in interior Alaska during nonprecipitating conditions. Atmospheric conditions were measured with several weather stations around the Fairbanks region and two meteorological temperature profiler instruments (ATTEX MTP-5HE and MTP-5PE). Near-surface ice particle microphysical observations were conducted with the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), instrument, which measures particles from 8 to 112 μm (sphere equivalent). Panoramic camera images were captured and saved every 10 min throughout the campaign for visual assessment of atmospheric conditions. Machine learning was used to classify both cloud particle microphysical characteristics from the PPD-2K data and to categorize boundary layer conditions using the panoramic camera images. For panoramic camera images, data were categorized as cloudy, clear, fog, snowing, and a nearby power plant plume. For the PPD-2K machine learning study, the scattering pattern images were used to identify rough surface, pristine, sublimating, and spherical particles. Three additional categories were used to identify indeterminant or saturated images. These categories and categories derived from weather station data (e.g., temperature ranges) are used to quantify ice microphysical properties under different conditions. For the complete microphysical dataset, pristine plates or columns accounted for 15.5%, 16.3% appeared to be sublimating particles, and 43.4% were complex particles with either rough surfaces or multiple branches. Although the temperature was as warm as −20°C during measurements, only 1.3% of the particles were classified as liquid. Significance Statement Boundary layer ice particles are frequently present in the near-surface atmosphere when surface temperatures drop below −20°C. Substantial human impacts can occur due to visibility degradation and deposition of particles on surfaces. Understanding particle shape, size, and phase (liquid or solid) is important for understanding those impacts. This study presents the results of a 3-yr measurement campaign in Fairbanks, Alaska, in which we relate ice particle characteristics to lower atmospheric conditions. Results should improve weather forecasting and hazard prediction.

Classification of Ice Crystal Images from Airborne Cloud Particle Imager Probe (CPI) Using Convolutional Neural Networks (CNN)

Classification of Ice Crystal Images from Airborne Cloud Particle Imager Probe (CPI) Using Convolutional Neural Networks (CNN)

On the retrieval of cloud optical thickness from spectral radiances - A sensitivity study with high albedo surfaces

Measurements of spectral zenith radiance in the 320–950 nm wavelength range have been carried out since 2021 at the Thule High Arctic Atmospheric Observatory (THAAO, https://www.thuleatmos-it.it/, 76.5° N, 68.8° W, 225 m a.s.l) located in Pituffik, northern Greenland. This study evaluates whether such observations could provide, in principle, scientifically meaningful cloud optical depth (τ) estimates, what would be the limitations, which are the necessary auxiliary measurements, and which radiance wavelengths would be better suited for the goal. Although clouds play a critical role in the Arctic, a climatology of τ in high albedo conditions is particularly difficult to obtain. THAAO might have the instrument capabilities to provide such a long-term dataset. We use a radiative transfer package to simulate visible spectra with different cloud and surface conditions, assuming homogeneous overcast sky and liquid water clouds of fixed geometrical thickness. Simulations are run to reproduce typical conditions encountered at THAAO when measurements are carried out. We find that the assumption of a broadband albedo instead of a spectrally-resolved one is the source of the largest uncertainties. Tests on size and phase of cloud particles showed that a 50% uncertainty in reff leads to a ∼10% error in τ, and that a 10% contamination of ice crystals in a low-level liquid water cloud leads to an error in τ estimates of less than 5%. All the tests showed that the most critical τ range is between the thin and thick cloud regimes (τ ∼ 7–15), where the retrievals can be less reliable. Otherwise, tests suggest that in the environmental conditions that characterize late spring and summer at THAAO, and given the observatory measurements capabilities, estimates of τ for low-level clouds could be accurately retrieved both in high and low surface albedo conditions by means of zenith radiance measurements in the UV-Vis-NIR range.

Enhancing particle string detection in electrorheological plasmas using asymmetrical kernel convolutional networks

Under different plasma conditions and electric fields in a complex plasma the plasma particles organize themselves in a string-like or chain-like manner. A phase transition from string-like to an isotropic particle distribution is observed at different electrical conditions. The streaming of charged ions around plasma particles with the surrounding electric field gives the plasma its electrorheological properties. The visibility of individual particles in a complex plasma opens up the opportunity to examine properties and phase transitions of such electrorheological fluids in detail. Because of the limited one-dimensional symmetry, determining the configuration of a particle and recognizing strings in particle distributions is not always straightforward. Several approaches have already been used to analyse particle clouds while either considering each particle locally or considering the particle cloud as a whole without providing information about single particle configurations. This paper presents a new machine learning approach that takes advantage of particle distributions over the entire particle cloud and detects all string-like particles at once, using a convolutional neural network in form of an encoder-decoder network with asymmetric kernel convolutions. This not only enhances the result quality but also accelerates the evaluation process, possibly enabling real-time analyses on electrorheological phase transitions, while achieving an accuracy of over 95% on manually labelled data.

The unitary Fermi gas at large charge and large N

We study the unitary Fermi gas in a harmonic trapping potential starting from a microscopic theory in the limit of large charge and large number of fermion flavors N. In this regime, we present an algorithmic procedure for extracting data from perturbation theory, order-by-order, without the need for other assumptions. We perform a gradient expansion in the interior of the particle cloud, sufficiently far from the cloud edge where the particle density drops rapidly to zero. In this latter region we present the first microscopic computation characterizing the contribution of the edge terms. The microscopic theory reproduces the predictions of the superfluid eft, including the action, the form of the gap equation, and the energy of the system in a harmonic trap (which maps, via the non-relativistic state-operator correspondence, to the scaling dimension of the lowest operator of charge Q). We additionally give the Wilsonian coefficients at leading order in N up to nnlo in the large-charge expansion.

A lightweight holographic imager for cloud microphysical studies from an untethered balloon

Abstract. We describe the construction and testing of an in situ cloud particle imager based on digital holography. The instrument was designed to be low cost and lightweight for vertical profiling of clouds with an untethered weather balloon. This capability is intended to address the lack of in situ cloud microphysical observations that are required for improving the understanding of cloud processes, calibration of climate and weather models, and validation of remote sensing observation methods. From a balloon sounding through multiple bands of cloud, we show that we can retrieve shape information and size distributions of the cloud particles as a function of altitude. Microphysical retrievals from an imaging satellite are compared to these in situ observations, and significant differences are identified, consistent with those identified in prior evaluation campaigns.

The Cloud Indicator: A novel algorithm for automatic detection and classification of clouds using airborne in situ observations

Airborne in situ observations of aerosols and clouds provide relevant data sets crucial to improving the understanding of cloud microphysical processes and reducing uncertainties connected to future climate predictions. However, the selection and classification of cloud sequences in such data sets can be very time-consuming if done manually, and criteria used by the community are numerous and prone to misclassification, especially when coarse aerosol particles (> 1 μm diameter) are present.In this study, we present the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The Cloud Indicator was developed using data from three international airborne field campaigns, including ATom (Atmospheric Tomography; 2016–2018), A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics; 2017), and FIREX-AQ (Fire Influence on Regional to Global Environments Experiment and Air Quality; 2019). The algorithm utilizes size distribution measurements, combined with measurements of relative humidity and temperature, to automatically detect flight sequences in clouds and classify the cloud type. As an additional criterion for the Cloud Indicator, we established the cloud-aerosol volume factor fCA to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning thereby reducing misclassifications. The Cloud Indicator algorithm was developed with data from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) which was calibrated with a novel calibration procedure – introduced in this study - that results in a refractive index independent calibration and works also in the field. However, the Cloud Indicator algorithm is not restricted specifically to the CAPS and thus allows its application to a variety of in situ instruments that measure coarse aerosol and cloud particle size.Case studies from ATom and A-LIFE demonstrate the ability of the Cloud Indicator to precisely screen (airborne) in situ data sets for clouds. The algorithm separates the data set into cloud-free periods, Aerosol-Cloud Transition Regime (ACTR), liquid clouds, clouds in the Mixed-Phase Temperature Regime (MPTR), and cirrus clouds. The unique ability of the Cloud Indicator to successfully differentiate between layers of enhanced coarse-mode aerosol concentrations and clouds is demonstrated by measurements in a complex mixture of clouds embedded into a mineral dust layer during the A-LIFE. The empirically derived parameter thresholds of the Cloud Indicator are in good agreement with values in the literature.

The characteristics of cloud macro-parameters caused by the seeder–feeder process inside clouds measured by millimeter-wave cloud radar in Xi'an, China

Abstract. The seeding effect of upper clouds on lower clouds affects the evolution of clouds, especially the seeding from upper ice clouds on lower stratiform clouds or convective clouds, which can stimulate the precipitation of lower clouds and even produce extreme precipitation. When seeders of the seeding cloud enter the feeding cloud, the interaction between cloud particles results in the change in macro- and micro-parameters of the feeding cloud. Based on the observation data of a ground-based Ka-band millimeter-wave cloud radar (MMCR) and microwave radiometer (MWR) in spring and autumn from 2021 to 2022, the seeder–feeder phenomenon among double-layer clouds in Xi'an, China, is studied. The study on 11 cases of seeder–feeder processes shows that the processes can be divided into three types by defining the height difference (HD) between the seeding cloud base and the feeding cloud top and the effective seeding depth (ESD). Through analysis of the reflectivity factor (Z) and the radial velocity (Vr) of cloud particles detected by the MMCR and on the retrieved cloud dynamics parameters (vertical velocity of airflow, Va, and terminal velocity of cloud particles, Vf), it is shown that the reflectivity factor and particle terminal velocity in the cloud are significantly enhanced during the seeder–feeder period for the three types of processes. But the enhancement magnitudes of the three seeder–feeder processes are different. The results also show that the impact of seeding on the feeding cloud is limited. The lower the height and thinner the thickness of the HD, the lower the height and thicker the thickness of the ESD. On the contrary, the higher the height and the thicker the thickness of the HD, the higher the height and the thinner the thickness of the ESD.