Snow Particles Research Articles

Mechanism of ion migration from the substrate material into snow cover at the end of the cold period

Earlier, it was established that maximum mineralization of the contact layer of snow occurs in spring at the interface with substrates (soil or ice). This study analyzes the temperature and moisture conditions during this period at the interface of the contact layer of snow with substrates by examining frozen sand blocks saturated with a solution containing complex gold ions, or blocks filled with polystyrene containing ions of molybdenum, copper, etc. It is assumed that the migration of ions from the underlying substrate into the contact layer of snow cover in spring occurs along quasi-liquid films on the surface of snow crystals, the thickness of which exceeds the equilibrium one. Migration becomes noticeable when the temperature at the snow–substrate contact reaches −13 °С and above. The appearance of quasi-liquid films on the surface of snow particles under variable temperature and moisture conditions is possible due to the condensation of water vapor, which during the day, with general heating of the system, can enter the contact layer of snow both from above and below. With an increase in snow density in the spring, the mineralization of the near-contact layer of snow cover increases. At the same time, linear relationships were revealed between the content of substrate components migrating into the near-contact layer of snow and the gradient of water vapor density in it. The reliability of the approximation of these dependencies for the gold thiosulfate complex is 0.98; for copper ions – 0.52; for hydrogen ions – 0.88; for sodium ions – 0.69, for chloride anions – 0.89. The results of the study substantiate the increased efficiency of geochemical prospecting for mineral deposits using snow cover in the spring.

Estimating ice water content for winter storms from millimeter-wavelength radar measurements using a synthesis of polarimetric and dual-frequency radar observations

Abstract The potential of millimeter-wavelength radar-based Ice Water Content (IWC) estimation is demonstrated using a Ka-band Scanning Polarimetric Radar (KASPR) for the U.S northeast coast winter storms. Two IWC relations for Ka-band polarimetric radar measurements are proposed: one that uses a combination of the radar reflectivity Z and the estimated total number concentration of snow particles Nt and the other based on the joint use of Z, specific differential phase KDP, and the degree of riming frim. A key element of the algorithms is to obtain the “Rayleigh-equivalent” value of Z measured at Ka band, i.e., the corresponding Z at longer radar wavelength for which Rayleigh scattering takes place. This is achieved via polarimetric retrieval of the mean volume diameter Dm and incorporating the relationship between the dual wavelength ratio DWRS/Ka and Dm. Those techniques allow for retrievals from single millimeter-wavelength radar measurements and do not necessarily require the DWR measurements, if the DWR-Dm relation and Rayleigh assumption for Ka-band KDP are valid. Comparison between the quasi-vertical profile product obtained from KASPR and the columnar vertical profile product generated from the nearby WSR-88D S-band radar measurements demonstrates that the DWRS/Ka can be estimated from the two close radars without the need for collocated radar beams and synchronized antenna scanning and can be used for determining the “Rayleigh-equivalent” value of Z. The performance of the suggested techniques is evaluated for seven winter storms using surface disdrometer and snow accumulation measurements.

Elucidation of spatiotemporal structures from high-resolution blowing-snow observations

Abstract. Systematic observations were conducted to investigate the spatiotemporal structures of blowing snow. Along a line perpendicular to the dominant wind direction on the lee side of a flat field, 15 snow particle counters (SPCs) and ultrasonic anemometers (USAs) were placed 1.5 m apart. Data were recorded at high frequencies of 100 kHz for SPCs and 1 kHz for USAs. The horizontal mass flux distributions, representing the spatiotemporal variability of blowing snow, exhibited non-uniformity in both time and space and manifested periodic changes akin to snow waves. Additionally, the presence of “snow snakes”, meandering near the snow surface, was observed. Quadrant analysis revealed predominant snow fluxes in quadrants Q1 (u′>0, w′>0) and Q4 (u′>0, w′<0). However, a more detailed parametric curve analysis indicated the existence of ejection events in Q2 (u′<0, w′>0) before snow waves and in front of snow snakes, shifting to Q1 and Q4 afterward, implying the consideration of both top-down and bottom-up mechanisms for burst sweep events.

Study of surface layer characteristics in the presence of suspended snow particles using observational data and Large-Eddy Simulation

The snowdrift is a two-phase flow consisting of air and suspended particles. In the presence of snow particles in the air, additional stability appears in the surface layer due to the density gradient. The density gradient reduces turbulence and affects the properties of the surface layer. Therefore, to describe the properties of the flow with included snow particles, additional clarifications are required. A description of the surface layer parameterization with the presence of suspended snow particles is presented in this paper. The formulation of the effect of snow particles consists in reformulation of the Obukhov turbulent length scale. The novel surface layer parameterization allows to take into account the effect of snow particles on turbulent flow and may improve the estimates of friction velocity and boundary-layer height.The parameterization was successfully tested on the observational data. Description of snow particles influence was included in the Large-Eddy Simulation (LES) model. The numerical experiments confirmed an increase in the stability of the surface layer. Mechanism of suspended particles influence on the surface layer is analogous to a thermal stabilization of the turbulent flow, in which negative buoyancy acts to reduce the turbulent kinetic energy.

Relationship between Newly Fallen Snow Density and Polarimetric Parameters Obtained from X-Band Radar Observations along the Sea of Japan Coast in January 2021

Abstract The density of newly fallen snow ρN is an important parameter for assessing accumulated snowfall depth. We examined the relationships between polarimetric parameters of X-band radar and the ρN in dry snow cases with ground temperatures less than 0°C. Our study was based on observations at Niigata Prefecture, Japan, along the coastal region of the Sea of Japan. This region is subjected primarily to sea-effect snow during the winter monsoon season, and convective clouds and rimed snow are common. We assumed that snow particles that accumulated on the ground originated from altitudes above an altitude with a temperature of −15°C, and we focused on the ratio of the differential phase KDP to radar reflectivity Zh, which is influenced by both aspect ratio and inverse particle size. We found that KDP/Zh at an altitude with a temperature of −15°C exhibited a greater magnitude for lower ρN values. Its correlation coefficient was the best among the polarimetric parameters that we examined. The difference in ice crystal flatness is highlighted rather than the difference in size because aggregation growth has not progressed at this altitude. On the basis of this result, we propose an empirical relationship between KDP/Zh at an altitude with a temperature of −15°C and ρN on the ground, thereby facilitating the estimation of snowfall depth by combining the estimated ρN with the liquid equivalent snowfall rate from, for example, Zh or KDP. Significance Statement This study aims to estimate the density of newly fallen (just-accumulated) snow from polarimetric radar observations. Understanding the newly fallen snow density will help to determine the exact snowfall depth. Focusing on polarimetric parameters at an altitude with a temperature of −15°C, we conducted radar and ground-based observations of snow particles and found that the newly fallen snow density of dry snow can be estimated. We were able to highlight the difference in ice crystal flatness before aggregation growth progressed by focusing on higher altitudes.

Validation of numerical method of unsteady Reynolds-averaged Navier–Stokes coupled with discrete phase model and optimization for the snow-resistance performance of bogies of a high-speed train

This paper presents an investigation aimed at enhancing the snow resistance of bogies in a three-car grouped alpine high-speed train (HST) that operates in a snowy region. The study employs a combination of the unsteady Reynolds-averaged Navier–Stokes model, coupled with the discrete phase model. This modeling approach has been validated through wind tunnel tests, including flow field tests, two-phase flow tests, and snow-ice tests. The study comprehensively compares the characteristics of the wind-snow flow, spatial snow concentration, and the distribution of snow on the train's underbody between two scenarios: the original train's underbody structure and the multifactor collaborative optimization scheme (MCOS). The results indicate that the MCOS case facilitates the escape of snow particles from the bogie cavities by weakening the upward and reverse flows. Furthermore, the MCOS case significantly reduces the concentration of snow particles around the heat-producing elements and decreases the accumulation of snow on both the lower and upper surfaces of the bogies. As a result, it reduces snow accumulation on the bogies by 50.6%, 56.4%, 60.8%, and 62.4% at train speeds of 200, 250, 300, and 350 km/h, respectively. In summary, this research provides valuable insights for improving the snow resistance of HSTs operating in snowy regions, with potential applications in enhancing railway transportation safety and efficiency.

LiDAR-Based Snowfall Level Classification for Safe Autonomous Driving in Terrestrial, Maritime, and Aerial Environments.

Studies on autonomous driving have started to focus on snowy environments, and studies to acquire data and remove noise and pixels caused by snowfall in such environments are in progress. However, research to determine the necessary weather information for the control of unmanned platforms by sensing the degree of snowfall in real time has not yet been conducted. Therefore, in this study, we attempted to determine snowfall information for autonomous driving control in snowy weather conditions. To this end, snowfall data were acquired by LiDAR sensors in various snowy areas in South Korea, Sweden, and Denmark. Snow, which was extracted using a snow removal filter (the LIOR filter that we previously developed), was newly classified and defined based on the extracted number of snow particles, the actual snowfall total, and the weather forecast at the time. Finally, we developed an algorithm that extracts only snow in real time and then provides snowfall information to an autonomous driving system. This algorithm is expected to have a similar effect to that of actual controllers in promoting driving safety in real-time weather conditions.

Development of snow-blowing structures for protection roads from snow drifts

Introduction. The article discusses the use of the snow blowing method and the possibility of installing snowblowing structures on highways to protect against snow drifts. The problem of snow tolerance is identified due to the location of many highways in remote, hard-to-reach areas and difficult climatic conditions. Snow drifts significantly increase the cost of winter maintenance of roads, are a partial consequence of destruction and maintenance in the summer, and also pose a serious threat to safety, causing loss of control over the vehicle.Research methodology. The article discusses the application of the snow blowing method and the possibility of installing snow-blowing structures on roads to protect against snow drifts. The process of movement of snow particles in a snowstorm and the influence of these processes on snow blowing are considered.Results. The paper highlights the issues related to the analysis and assessment of the speed and composition of the air flow, weather conditions affecting the operation of structures. The main purpose and working conditions of snow-blowing structures on highways have been determined. The optimum speed at which the snow blowing effect is created is determined.Conclusion. The snow blowing process on the developed snow blowing structure in different conditions is modelled. Based on the considered approach, a comparative analysis of the operation of the snow blowing structure is performed. Based on the data obtained, the possibilities for the introduction of snow-blowing structures and refinement in real working conditions have been studied.

Aerodynamic simulation on roof-mounted intelligent awareness system of intelligent vehicle

With the rapid development of intelligent driving technology, it is urgent to conduct research on the distribution and motion characteristics of snow particles in the area of the intelligent vehicle awareness system under harsh snow and wind conditions. In this paper, the Unsteady Reynolds-Averaged Navier-Stokes (URANS) was first used to conduct 1:12 scaled aerodynamic simulation of the intelligent vehicle. It is found that the error of aerodynamic drag coefficient between simulation and test is 5.6%, indicating that the current numerical simulation model can predict the aerodynamic characteristics of intelligent vehicle accurately. Based on this, URANS was used coupled with Lagrangian Multiphase Model (LMP) to investigate the snow accumulation near the roof-mounted intelligent awareness system under different vehicle speeds and snowfall intensities. The study found that with the increase of snowfall intensity from moderate to heavy and vehicle speed at 40 km/h, the incidence of snow particles on various components significantly increases, and the number of adhered particles also increased accordingly. The particle adhesion rate of the awareness system has decreased by 1.24%, while the number of adhered snow particles increased significantly at the back of the roof-mounted intelligent awareness system and the wake window. Under moderate snow condition, as the vehicle speed increased from 40 to 60 km/h, the number of incident particles and adhered particles in the awareness system have significantly decreased by 24,592 and 317,019, respectively, but the particle adhesion rate has increased by a significant 2.88%. Compared to the same snowfall intensity but low speed, the range of particle distribution is significantly reduced and more scattered.

Buoyancy and Propulsion Mechanisms for Stable Movement in Snow Field

For small-sized mobile machines, moving on snow without sinking is challenging. Snowmobiles are often used to move on snow. However, in smaller-sized machines, the skis attached to the machine become small, so small snow bumps or soft powdered snow quickly cause the entire machine to be buried under the snow. Therefore, it is necessary for small robots moving on snow to give appropriate driving mechanisms for producing propulsive force and snow-floating force. To this end, we propose new driving mechanisms, a “passive wing wheel” and a “spiral screw,” for small mobility services that move on snow without sinking or getting stuck. The passive wing wheel has transformable wing-like paddles, which extend to behave like animal feet and shrink to form a wheel if necessary. The spiral screw is designed to produce propulsive force by pushing snow particles with the screw. Additionally, by combining two sets of screws having opposite threads, they gather snow underneath the body attached to them, which causes floating force. This report also proposes ways to prepare pseudo-snow, which resembles natural snow in hardness, viscosity, and property transformation due to deformation. We conducted experiments to measure the performance of the proposed design in both the pseudo-snow and natural snow. From experiments, we confirmed that the mechanism performs well in terms of propulsion and floating on snow.

Resolving abrupt frontal gradients in zooplankton community composition and marine snow fields with an autonomous Zooglider

AbstractAn autonomous Zooglider navigated across the California Current Front into low salinity, minty waters characteristic of the California Current proper in both summers of 2019 and 2021. Diving to 400 m depth, Zooglider transited another near‐surface frontal gradient somewhat inshore. These frontal gradients were generally associated with changes in intensity, size composition, and Diel Vertical Migration responses of acoustic backscatterers. They were also associated with pronounced changes in zooplankton community composition, as assessed by a shadowgraph imaging Zoocam. Zoocam detected a decline in concentrations of copepods, appendicularians, and marine snow in the offshore direction, and an overall shift in community structure to a higher proportion of carnivorous taxa (and, in 2019, of planktonic rhizaria). No taxon was consistently elevated at all the peak frontal gradients, but appendicularians, copepods, and rhizarians sometimes showed front‐related increases in concentration. Such frontal gradient regions represent relatively abrupt transitions to different communities of planktonic organisms and suspended marine snow particles, with consequences for predator–prey relationships and the dominant vectors of particle export into subsurface waters.

Pathways of ice multiplication in nimbostratus clouds during the Indian summer monsoon

The present study illustrates the microphysical parameters of nimbostratus clouds during the Indian summer monsoon using airborne and radar observations conducted as part of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX), and simulations with the Weather Research and Forecasting (WRF) model. The study also analyzes the impact of secondary ice production (emphasizing the microphysical pathways of Hallett-Mossop (HM) as represented in the models) on cloud processes using model sensitivity simulations. The effect of possible pathways of riming during the HM process is the focus of the sensitivity simulations. Aircraft observations showed higher ice particle concentrations in the Hallett–Mossop zone (−3 °C to - 8 °C) along with the existence of smaller and larger cloud droplets, rimed needles, columns, and snow particles. Observations strongly suggested the active presence of the HM process in this cloud. The observed mean values of microphysical parameters including liquid water content, ice number concentrations, and maximum vertical velocity, agreed well with model simulations. The number concentration and water content of ice crystals and snow decreased in the HM zone during the HM inactive case. A reduction in rainfall is also observed in the absence of HM. HM increases convection as it causes a sudden increase in the number concentration of small ice particles, which causes rainwater to freeze quickly and water vapour to be consumed by diffusional growth. Latent heating is produced by both effects, which energizes the convection.

Restoring vision in rain-by-snow weather with simple attention-based sampling cross-hierarchy Transformer

As an unnoticed specialized task in image restoration, rain-by-snow weather removal aims to eliminate the complicated coexisting rain streaks and snow particles. In this work, we propose a simple attention-based sampling cross-hierarchy Transformer (SASCFormer). Initially, we explore the proximity of convolution network and Transformer in hierarchical architectures and experimentally find they perform approximately for intra-stage feature representation. On this basis, we utilize a Transformer-like convolution block (TCB) to replace the computation-heavy self-attention while preserving the attention characteristics for adapting to the input content. Meanwhile, we demonstrate that cross-stage sampling progression is critical for the performance improvement in rain-by-snow weather removal, and propose a global–local self-attention sampling mechanism (GLSASM) that samples the features while preserving both the global and local dependencies. Finally, we synthesize two novel rain-by-snow weather-degraded benchmarks, RSCityscapes and RS100K datasets. Extensive experiments verify that our proposed SASCFormer achieves the best trade-off between the performance and inference time. In particular, our approach advances existing methods by 1.14dB∼4.89dB in peak signal-to-noise ratio. Related resources are available at https://github.com/chdwyb/Rain-by-snow.

A Drifting and Blowing Snow Scheme in the Weather Research and Forecasting Model

AbstractWind‐driven redistribution of surface snow is one of the key factors leading to heterogeneous accumulation of snow at small scales. Understanding the processes that lead to this heterogeneous accumulation is, therefore, of great importance to many glaciological and hydrological questions. High‐quality information on the wind field is necessary to realistically represent drifting snow. Here, we introduce a novel, intermediate‐complexity drifting and blowing snow module for the Weather Research and Forecasting (WRF) model that integrates seamlessly into the standard WRF infrastructure. The module also accounts for snow particle sublimation and considers the thermodynamic feedback on the atmospheric fields. In an idealized model environment the module was tested for physical consistency. Sensitivity experiments showed that drifting snow sublimation has on the one hand local effects on the erosion and deposition patterns and on the other hand non‐local effects on the larger‐scale atmosphere. The simple and computationally efficient implementation allows this module to be used for small‐scale and large‐scale simulations in polar and glaciated regions.

Absorption properties by irregularly shaped particles with inclusions at visible and near-infrared wavelength regions

The absorption properties of snow and ice particles containing impurities such as black carbon (BC) and mineral dust (DS) were investigated using the geometrical optics approximation method (GOM). This study focuses on analyzing the single scattering co-albedo (SSCA) characteristics of non-spherical particles, aiming to enhance the interpretation of radiation observations in snow and ice regions. Particle sizes were varied (rva=50,200 and 500μm) and two types of inclusions (1 ppmw of BC and 100 ppmw DS) were considered. Significant differences in SSCA, ranging from one to two orders of magnitude were observed at λ=0.5μm between the “homogeneous model” with effective medium approximation method (EMA) and the “inhomogeneous model” with 50 BC inclusions. Similarly, differences of a factor of 2 to 4 were found for DS with 100 ppmw at λ=0.5μm and BC with 1 ppmw at λ=0.875μm. To bridge intermediate SSCA values between these models, we propose an “inhomogeneous model with EMA”, introducing two parameters to account for impurity concentration and inclusion aggregation. Our findings highlight the importance of selecting appropriate single scattering calculation methods in the analysis of observation data, particularly when dealing with BC impurity with significant absorption characteristics. This research contributes to a deeper understanding of solar radiation absorption in snow and ice regions, with potential implications for the optical remote sensing of snow and ice and climate modeling studies.

Effects of the turbulent incoming flow condition on the wind–snow flow characteristics and accretion distribution in the cutting area of the high-speed railway

In addressing snowdrift disasters in high-speed railway cutting areas, this study utilized the improved delayed detached eddy simulation turbulence model, synthetic eddy method, and Eulerian two-phase flow model (Euler–Euler). Coupled with grid dynamic deformation techniques, the investigation facilitated extracting temporal variations in snow particle accumulation profiles within the cutting areas. The predicting accuracy of the numerical method was validated through the wind tunnel and wind–snow experimental data. Building upon this foundation, the study investigated how uniform and turbulent flow conditions impact snow distribution in the high-speed railway cutting areas. The analysis examined the turbulent flow structures, friction velocity distribution, and snow accretion contours within the cutting area. Findings indicate that the snow mass on the windward slope in the cutting area remains consistent under both flow conditions. However, there are significant differences in the rate of snow mass growth and the characteristics of snow accretion distribution. The disappearance of vortex structures at the base of the leeward slope is crucial for indicating the transition in the accumulation states of the surrounding snow particles. Under turbulent incoming flow conditions, the transition from large-scale vortex systems to vortex pairs on the leeward slope of the cutting is delayed, resulting in a postponed stable growth period for snow accretion. Additionally, strong erosion between the track bed and the leeward slope results in reduced snow accretion mass in these areas. In particular, at the simulation time of t = 150 min, under turbulent incoming flow conditions, the overall snow accumulation in the cutting decreased by 20.8% compared to the uniform incoming flow conditions. The snow mass in the leeward slope area decreased by 38.3%, and the track bed area experienced a 23.3% reduction in snow accumulation. In contrast, the snow quantity on the windward slope remained relatively consistent.

Study of Surface Layer Characteristics in the Presence of Suspended Snow Particles Using Observational Data and Large Eddy Simulation

Study of Surface Layer Characteristics in the Presence of Suspended Snow Particles Using Observational Data and Large Eddy Simulation

Numerical simulation of the cloud seeding operation of a convective rainfall event occurred in Beijing

Observation-validated cloud seeding simulation is valuable in assisting in evaluating seeding effect, but its sensitivity to microphysics schemes and cloud seeding parameterizations is rarely investigated. In this research three cloud seeding parameterizations (the Hsie, Demott and Xue parameterizations) are coupled with two microphysics schemes (the Thompson and Milbrandt schemes), to perform simulations of the rainfall suppression cloud seeding operation on a convective rainfall event occurred in North China on 1 July 2021, aiming to evaluate the seeding effect and investigate its sensitivity to microphysics schemes and seeding parameterizations. The differences of rainfall suppression effect between three seeding parameterizations are smaller than those between two microphysics schemes. The rainfall suppression ratios produced by the simulations configured with the Milbrandt scheme range from 16.3% to 24.7%, while those with the Thompson scheme range from 0.47% to 1.38%. The difference of the seeding effect between these two microphysics schemes arises from their different method in parameterizing the snow deposition growth process. In addition, in the seeding simulations with the Milbrandt scheme, the surface rainfall decrease region is followed by a rainfall increase region. This rainfall decrease-increase pattern is caused by the fact that the reduced graupel particles caused by cloud seeding falls to the ground earlier with its larger fall velocity, while the seeding-increased snow particle falls to the ground later due to its smaller fall velocity. This result suggests there is an ephemeral cloud seeding window for rainfall suppression operation.

Evaluating Ice Phase Microphysics in the Simulation of a Snowstorm Over Northern China

AbstractThe complexity of ice particles in the atmosphere makes it difficult to model microphysical growth processes accurately. In this study, we simulated a snowfall case over Northern China Plain using two different microphysics schemes, that is, Thompson and Morrison schemes, in the Advanced Research WRF (Weather Research and Forecasting) model. Both schemes are able to reproduce the event, albeit with a slightly weaker precipitation compared with the surface observation. However, the radar reflectivity factor in Morrison simulation is higher than the radar observation to ∼10 dBZ. Further analysis reveals that such stronger radar reflectivity in the Morrison simulation might be caused by larger collection efficiency, which would lead to more active self‐aggregation process in prediction of snow number concentration and then larger snow particle size. Sensitivity tests show that using an alternative formula of collection efficiency produces smaller radar reflectivity that is in better agreement with observations. This study highlights the accurate representation of self‐aggregation process and underscores the needs of further improvement of ice microphysics schemes for the better snowfall simulations.

Joint Retrieval of Multiple Species of Ice Hydrometeor Parameters from Millimeter and Submillimeter Wave Brightness Temperature Based on Convolutional Neural Networks

Submillimeter wave radiometers are promising remote sensing tools for sounding ice cloud parameters. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS−SG) is the first operational submillimeter wave radiometer used for ice cloud remote sensing. Ice clouds simultaneously contain three species of ice hydrometeors—ice, snow, and graupel—the physical distributions and submillimeter wave radiation characteristics of which differ. Therefore, jointly retrieving the mass parameters of the three ice hydrometeors from submillimeter brightness temperatures is very challenging. In this paper, we propose a multiple species of ice hydrometeor parameters retrieval algorithm based on convolutional neural networks (CNNs) that can jointly retrieve the total content and vertical profiles of ice, snow, and graupel particles from submillimeter brightness temperatures. The training dataset is generated by a numerical weather prediction (NWP) model and a submillimeter wave radiative transfer (RT) model. In this study, an end to end ICI simulation experiment involving forward modeling of the brightness temperature and retrieval of ice cloud parameters was conducted to verify the effectiveness of the proposed CNN retrieval algorithm. Compared with the classical Unet, the average relative errors of the improved RCNN–ResUnet are reduced by 11%, 25%, and 18% in GWP, IWP, and SWP retrieval, respectively. Compared with Bayesian Monte Carlo integration algorithm, the average relative error of the total content retrieved by RCNN–ResUnet is reduced by 71%. Compared with BP neural network algorithm, the average relative error of the vertical profiles retrieved by RCNN–ResUnet is reduced by 69%. In addition, this algorithm was applied to actual Advanced Technology Microwave Sounder (ATMS) 183 GHz observed brightness temperatures to retrieve graupel particle parameters with a relative error in the total content of less than 25% and a relative error in the profile of less than 35%. The results show that the proposed CNN algorithm can be applied to future space borne submillimeter wave radiometers to jointly retrieve mass parameters of ice, snow, and graupel.