Ice Particle Size Research Articles

Micro-Scale Ice Shoveling Effect Induced by Magnetic-Responsive Microfins.

Icing is ubiquitous in nature and engineering applications, and imposes threats to road and air transportations, wind energy infrastructures, etc. However, current active de-icing solutions, especially the most popular one, i.e., heating, suffer from high energy consumption whilst passive methods are often ineffective at high-speed, long-term, or large-particle conditions. Herein, a promising strategy adopting magnetic-responsive microfins (MRS)featuring reversible deformations is developed for de-icing. A novel micro-scale ice shoveling effect induced by the localized destruction of the ice adhesion interface owing to the inhomogeneous deformation is demonstrated, and its dependence on the ice particle size and temperature is investigated. An analytical model is proposed to describe the mechanism of this effect, showing a linear relation between the position of the magnet and the induced force agreeing well with experiments, leading to a system straightforward to predict and control. Specifically, the de-icing capacity of the surface becomes prominent when small-scale ice particles merge to large ones, providing a promising solution for applications on aircraft, wind turbines, etc., as the first of its kind to remove large particles under high-speed conditions effectively.

Study on effects of anti-freeze protein and polyvinyl alcohol on the production and flow behavior of ice slurry

Ice slurry is a promising functional fluid with high thermal energy density and high heat transfer rate. However, the agglomeration of the ice particles in flowing ice slurry can cause blocking of tubes. In this study, anti-freeze protein (AFP) and polyvinyl alcohol (PVA) were selected as additives to prevent the agglomeration of ice particles in ice slurry. Ice slurry was generated using the release of the supercooled state of 3 mass% NaCl solution containing the additives. The size of the ice particles in the ice slurry was observed, and the average area of the ice particles was evaluated to reveal the effects of the additives on the ice particle size. Furthermore, the flow behavior of the ice slurry in a tube was observed, and the effects of the additives on the flow behavior and prevention of the agglomeration of the ice particles were evaluated experimentally. It was found that the average area decreased with increasing concentration of the additives, and the effect of AFP on the average area was particularly significant. In the case of without the additives, the ice particles flowed in the tube while the ice particles agglomerated. On the other hand, the ice particles in the ice slurry with the additives flowed without agglomeration in the tube, especially in the case of AFP addition. It was found that AFP prevented the agglomeration of the ice particles in the ice slurry flow. Moreover, the ice slurries with AFP show the significant tendency for pseudo-plastic fluid.

Instrument Design and In-Flight Performance of an Airborne Terahertz Ice Cloud Imager

The Airborne Terahertz Ice Cloud Imager (ATICI) is an airborne demonstration prototype of an ice cloud imager (ICI), which will be launched on the next generation of Fengyun satellites and plays an important role in heavy precipitation detection, typhoon, and medium-to-short-term meteorological/ocean forecasting. At present, it has 13 frequency channels covering 183–664 GHz, which are sensitive to scattering by cloud ice. This paper provides an overview of ATICI and proposes a receiving front-end design scheme using a planar mirror and a quasi-optical feed network which improves the main beam efficiency of each frequency band, with measured values better than 95.5%. It can detect factors such as ice particle size, ice water path, and ice water content in clouds by rotating the circular scanning of the antenna feed system. A high-sensitivity receiver system has been developed and tested for verification. The flight verification results show that the quasi-optical feed network subsystem works well and performs stably under vibration and temperature changes. The system sensitivity is better than 1.5 K, and the domestically produced high-frequency receiver has stable performance, which can meet the conditions of satellite applications. The ATICI performs well and meets expectations, verifying the feasibility of the Fengyun-5 ICI payload.

Insights into the cooling rate and preservative effect of slurry ice on large yellow croaker (Pseudosciaena crocea) in terms of the fish/ice ratio, salinity, and the ice mass fraction

AbstractThe cooling effect of slurry ice as cooling medium on large yellow croaker (Pseudosciaena crocea) was studied by considering different fish/ice ratio, salinity and initial ice mass fraction (40%, 60%, and 80%). Results obtained with optical microscope clarified that the ice particle size and cooling time of slurry ice were decreased at increasing salinity. Best precooling efficiency was obtained with specific values of fish/ice ratio 1:2, salinity 5.0%, and initial ice mass fraction 60%. Then, the effects of precooling time (0, 3, 12, and 24 h) in slurry ice on the quality of large yellow croaker stored at 4°C were investigated. The results showed that slurry ice precooling could significantly (p < 0.05) inhibit the microbial reproduction and retard the quality deterioration of large yellow croaker. Therefore, slurry ice can be a promising precooling method for the quality maintenance of marine fish.Practical applicationsLarge yellow croaker is extremely perishable due to its nutrient abundance. The spoilage process of fish is vitally influenced by the changes of internal environment and external environment after harvest. Timely precooling can prominently improve the shelf‐life of perishable foods. Slurry ice, a two‐phase system consisting of seawater (or artificial seawater) and spherical ice microcrystals, is regarded as an alternative precooling medium. Compared with conventional ice, slurry ice has faster cooling rate, little physical damage to soft tissue such as aquatic products and has better fluidity allows it to comprehensively cover the fish surface, thereby protecting fish from oxidation.

Evaluating thermal conductivity of soil-rock mixtures in Qinghai-Tibet plateau based on theory models and machine learning methods

The thermal property of the scum layer (soil-rock mixtures) has dominant influence on the heat exchange efficiency between the lower rock layer and the upper environment in the open-pit mines of the cold regions. This paper presents a series of thermal conductivity tests (560 samples) on the scum particle to investigate the coupling effects of ice (moisture) content, temperature, and particle size distribution on the thermal properties. Previously reported models (47 empirical or theoretical models) were adopted to predicate the thermal conductivity of soil-rock mixtures in order to validate the evaluation ability of these models under the wide testing ranges. The comparison results indicate that the theoretical models, normalized model and linear/non-linear models all can not fully predict experimental results under the wide testing conditions. Three machine learning algorithms were used in the assessment presentation for the thermal properties of soil-rock mixtures. The performance of three machine learning algorithms were contrastively examined by using three important indexes (the coefficient of determination (R2), the root mean square error (RMSE) and the relative error (RE)). Based on the evaluation results, the performance ranking of three machine learning algorithms can be listed (GA-BP > SVR > RFR). This investigation is a beneficial attempt for the large data analysis to introduce the machine learning method into the determination of the thermal conductivity of soil-rock mixture under complex conditions.

Simulations of the impact of cloud condensation nuclei and ice-nucleating particles perturbations on the microphysics and radar reflectivity factor of stratiform mixed-phase clouds

Abstract. In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations on the morphology and abundance of ice particles in mixed-phase clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INPs affect radar reflectivities. Our experimental framework encompassed CCN concentrations ranging from 10 to 5000 cm−3 and INP concentrations from 0.001 to 10 L−1. Central to our findings is the observation that higher INP concentrations yield smaller ice particles, while an increase in CCN concentrations leads to a subtle growth in their dimensions. Consistent with existing literature, our results spotlight oblate-like crystals as dominant between temperatures of −20 and −16 °C. Notably, high-INP scenarios unveiled a significant prevalence of irregular polycrystals. The aspect ratio (AR) of ice particles exhibited a decline with the rise in both CCN and INP concentrations, highlighting the nuanced interrelation between CCN levels and ice particle shape, especially its ramifications on the riming mechanism. The forward-simulated radar reflectivities, spanning from −11.83 dBZ (low INP, 0.001 L−1) to 4.65 dBZ (high INP, 10 L−1), elucidate the complex dynamics between CCN and INPs in determining mixed-phase cloud characteristics. Comparable differences in radar reflectivity were also reported from observational studies of stratiform mixed-phase clouds in contrasting aerosol environments. Our meticulous analysis of KiD-AMPS simulation outputs, coupled with insights into aerosol-driven microphysical changes, thus underscores the significance of this study in refining our ability to understand and interpret observations and climate projections.

Friction behaviors and flow resistances of rock-ice avalanches

Rock-ice avalanches have high mobility and enormous destructive potential. However, the friction behaviors and flow resistance during the transition of flow regimes of the rock-ice avalanche remains an unsolved problem. Based on a series of measurements of normal, shear stress and pore water pressure at the rotating drum in a temperature-controllable laboratory, we quantify the influence of the ice content, particle size, drum velocity, and meltwater on the friction behaviors. The findings reveal that ice acts as a low-friction particle, leading to a significant reduction in frictional resistance of both particle-particle and particle-base. Furthermore, meltwater lubricates the particles and provides buoyancy, which also increases the mobility of the rock-ice avalanche. In addition, the augmentation of flow resistance is attributed to the increased particle size and drum velocity, thereby intensifying shear motion. In order to propose an unify theoretical model to calculate the flow resistance of rock-ice avalanche in various flow regimes. A heuristic model that relies on the extended dynamics theory is modified and can be well described the flow resistance of the tests involved in this paper. This study in the friction behaviors and flow resistance depending on the internal/external factors may be considered in hazard assessments of rock-ice avalanches in cold high-mountain regions.

Responses of Simulated Arctic Mixed-Phase Clouds to Parameterized Ice Particle Shape

Abstract In simulations of Arctic mixed-phase clouds, cloud persistence and the liquid water path (LWP) are sensitive to ice particle number concentrations. Here, we explore sensitivities of cloud microphysical properties to the dominant ice particle shape (dendrites, plates, columns, or spheres) using the SCALE-AMPS large-eddy simulation model. AMPS is a bin microphysics scheme that predicts particle shapes based on the inherent growth ratio (IGR) of spheroids, which determines vapor depositional growth rates along the a and c axes, and the rimed and aggregate mass fractions. We examine the impacts of various IGR values on simulations of clouds observed during the M-PACE and SHEBA experiments. Under M-PACE (SHEBA) conditions, LWP varies between 49 (1.1) and 230 (6.7) g m−2, and the ice water path (IWP) varies between 3 (0.03) and 40 (0.12) g m−2, depending on the ice shape. The lowest LWP and the highest IWP are obtained when columnar particles dominate because their low terminal velocities and large capacitance and collisional area result in large vapor deposition and riming rates, whereas the highest LWP and lowest IWP are obtained when spherical particles dominate because their vapor deposition and riming rates are low. Because ice particle shape significantly influences simulated Arctic mixed-phase clouds, reliable simulations require accurately estimated IGR values under various atmospheric conditions. Finally, comparisons between the simulation results and observations show that the size distribution larger than 2000 μm is better reproduced when the increase in rimed mass that causes ice particles to become spherical is suppressed. Significance Statement Atmospheric models have difficulties in reproducing Arctic mixed-phase clouds because of uncertainties in the parameterization of microphysical processes. This is the first study to use a large-eddy simulation model implemented with a habit-predicting bin microphysics scheme to demonstrate the important role of ice particle shape on the microphysical properties of both heavy-riming and no-riming mixed-phase clouds. We found the vapor deposition and riming rates to be greatly influenced by ice particle shape. By comparing the ice particle size distribution, mass–diameter relationship, and area ratio between simulation results and observations, we show that a hexagonal ice shape model and a riming model that simply converts ice crystals to graupel may not accurately reproduce actual heavy-riming clouds.

Steady-State Supersaturation Distributions for Clouds under Turbulent Forcing

Abstract The supersaturation equation for a vertically moving adiabatic cloud parcel is analyzed. The effects of turbulent updrafts are incorporated in the shape of a stochastic Lagrangian model, with spatial and time correlations expressed in terms of turbulent kinetic energy. Using the Fokker–Planck equation, the steady-state probability distributions of supersaturation are analytically computed for a number of approximations involving the time-scale separation between updraft fluctuations and phase relaxation, and droplet or ice particle size fluctuations. While the analytical results are presented in general for single-phase clouds, the calculated distributions are used to compute mixed-phase cloud properties—mixed fraction and mean liquid water content in an initially icy cloud—and are argued to be useful for generalizing and constructing new parameterization schemes. Significance Statement Supersaturation is the fuel for the development of clouds in the atmosphere. In this paper, our goal is to better understand the supersaturation budget of clouds embedded in a turbulent environment by analyzing the basic equations of cloud microphysics. It is found that the turbulent characteristics of an air parcel substantially affect the cloud’s supersaturation budget and hence its life cycle. This is also shown in the context of mixed-phase clouds where, depending on the turbulent regime, different liquid-to-ice ratios are found. Consequently, the theoretical approach of this paper is crucial to develop tools to parameterize small-scale atmospheric features, like clouds, into global circulation models to improve climate projections for the future.

Contrail radiative dependence on ice particle number concentration

Recent studies on low aromatic fuels have shown that lower soot number emissions may reduce contrail ice particle number concentrations (N ice). Here we implemented, in a sophisticated radiative transfer model, two ice particle size distribution schemes in order to estimate the contrail radiative forcing’s (RFs) dependence on these prospective N ice reductions resulting from the introduction of sustainable aviation fuels. The results show that an 85% contrail N ice reduction produces a 35% smaller contrail RF, while neglecting all non-radiative effects. This estimate of an RF reduction only considers the effects of the N ice change assumed here, and neglects other potentially important microphysical mechanisms that may change the relationship between soot number emissions and N ice. A comparison of our results with previous published estimates from full climate model simulations, shows similar RF reductions to those which also take into account non-radiative mechanisms, evidencing the need for more studies in order to allocate the contribution from radiative and non-radiative changes, as this would guide possible mitigation implementations. Despite these modeled contrail RF reductions being largely independent of the assumed ice water content (IWC), it is only through simultaneous improvement of the IWC and N ice representation in models that contrail RF estimates can be better constrained. This is because our calculated RF varied by a factor of 3 when assuming a ±30% IWC range; and by a factor of 5 if a, still conservative, ±60% IWC range was prescribed, suggesting that the differences in the prescribed IWC and N ice values in different models may explain the large discrepancies amongst published RF estimates. Recent estimates of higher N ice values, and lower IWCs found in contrails even after several hours, compared to surrounding cirrus under similar atmospheric conditions, were assessed to conclude that it is mainly the differences in IWC that make young contrails have a smaller RF, and to reduce our previous estimate for linear contrail RF for year 2006 by 65%.

Application of efficient and sustainable freeze-dissolving technology in manufacturing of KHCO3 ultrafine particles

The development of ultrafine particles provided a new way to solve problems in the fields of energy, environment, and medicine, and had become one of the most promising technologies. Therefore, the application of ultrafine particles required the development of cleaner, greener, and more efficient preparation methods. The new freeze-dissolving technology has been applied in manufacturing of KHCO3 ultrafine particles, with an aqueous solution of 0.02–0.1 g KHCO3/g water. Frozen ice particles were formed after dripping the solution into liquid nitrogen. The antisolvent ethanol was used to dissolve the ice spherical template at a temperature below 273.15 K, and the pre-formed KHCO3 ultrafine particles inside the ice template remained in the ethanol aqueous solution. The ice particles were put into the freeze dryer to isolate the ultrafine KHCO3 particles. Compared with the particles produced with traditional freeze-drying technology, the ultrafine powder/particles produced by the freeze-dissolving technology were smaller with narrower size distribution. The freeze-dissolving technology has demonstrated a much more sustainable and efficient manufacturing process than the traditional freeze-drying process. In addition, the influence of the concentrations of KHCO3 and the sizes of ice particles were investigated with the discussions of mechanisms.

Estimation of the mass fraction and distribution of ice in a lunar regolith simulant from seismic velocity

The exploration of ice distribution on the Moon and in other extraterrestrial environments has recently accelerated because of the growing interest in the distribution of ice in, and its acquisition from, the Moon's polar regions. Understanding of the relationship between the mass fraction of ice and the velocities of P-wave and S-wave is expected to allow ice amounts to be estimated from seismic exploration results. To clarify this relationship, we numerically investigated the effect of ice on elastic wave velocities using a digital rock physics approach for regolith with heterogeneous pore geometries. We digitized a lunar regolith simulant and modeled ice distribution in its pore spaces. Numerical simulations employing a finite element method were then performed to calculate the P-wave and S-wave velocities of the regolith simulant while varying the ice particle size and the mass fraction of ice. The results demonstrate that the characteristics of the velocity changes with increases in the mass fraction of ice differ depending on the particle size distribution of the ice. For the same mass fraction of ice, P-wave and S-wave velocities are higher for smaller ice clusters (up to 1.75 times for P-wave velocity and 1.76 times for S-wave velocity). Using the numerical simulation results, we succeeded in developing an empirical equation for estimating the mass fraction of ice from P-wave and S-wave velocities. This equation should be useful for quantifying ice content from lunar seismic survey data.

Evaluation of the Ice Particle Simulation of Microphysics Schemes with Aircraft Measurements of a Stratiform Cloud in North China

Abstract Airborne microphysical measurements of a frontal precipitation event in North China were used to evaluate five microphysics schemes for predicting the bulk properties of ice particles. They are the Morrison and Thompson schemes, which use predetermined categories, the 1-ice- and 2-ice-category configurations of the Predicted Particle Properties (P3) scheme and the Ice-Spheroids Habit Model with Aspect-Ratio Evolution (ISHMAEL) scheme, which model the evolution of particle properties, and the spectral bin fast version (SBM_fast) microphysics scheme within the Weather Research and Forecasting (WRF) Model. WRF simulations with these schemes successfully reproduced the observed temperature and the liquid and total water content profiles at corresponding times and locations, allowing for a credible comparison of the predictions of particle properties with the aircraft measurements. The simulated results with the 1-ice-category P3 scheme are in good agreement with the observations for all the particle properties we examined. The 2-ice-category P3 scheme overestimates the spectrum width and underestimates the number concentration, which can be alleviated by reducing the ice collection efficiency. The simulation with the SBM_fast scheme deviates from the observed ice particle size distributions since the mass–diameter relationship of snow-sized particles adopted in this scheme may not be applicable to this stratiform cloud case.

Numerical study on critical flow velocity of ice slurry in the pipe of ice source heat pump system

The traditional evaporator of water source heat pump system is easy to freeze when the water source temperature is too low in winter. A novel ice source heat pump system is proposed, which can use low temperature surface water to supply heat to buildings. One of the key problems for safe transportation is velocity range of ice slurry in ice outlet pipeline. In order to study the critical flow velocity of ice slurry in the pipe, an Euler model of horizontal straight pipe and 90° elbow pipe were established by Fluent software. The influence of five factors on the critical flow velocity of two kinds of pipes, including initial ice packing fraction, ice particle size, pipe diameter, length of straight pipe and radius curvature of elbow was studied. It found that the initial ice content should be controlled from 10% to 15%. The inlet flow rate was 20% higher than the critical flow rate under the most unfavorable condition. Ice particle size was less than 0.2 mm. The total length of ice discharge pipeline was less than 50 m. Curvature radius of the bend should be met the minimum requirements of the specification of the pipe used.

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Combining Triple‐Moment Ice With Prognostic Liquid Fraction in the P3 Microphysics Scheme: Impacts on a Simulated Squall Line

AbstractThe Predicted Particle Properties (P3) bulk microphysics scheme has been recently modified to combine the two major innovations. The triple‐moment approach to represent ice, allowing for a freely evolving spectral dispersion of the size distribution, is combined with the predicted liquid fraction, which enables an explicit representation of mixed‐phase particles. The impacts of this combination are examined in the context of high‐resolution (1‐km horizontal grid spacing) simulations of an observed mid‐latitude squall line using the Global Environmental Multiscale atmospheric model. The simulation of mixed‐phase particles results in a faster squall line propagation speed and stronger cold pool due to greater cooling from the microphysical processes of sublimation, melting and evaporation. There is a reduction in the mass of ice reaching the surface resulting from a decrease in the mean size of melting ice particles aloft with the predicted liquid fraction. Compared to the original double‐moment configuration, triple‐moment P3 configuration results in larger mean ice sizes at the surface. The reflectivity structure is improved with the new version, now with a more pronounced bright band in the melting zone with the predicted liquid fraction.

Sensitivity of convectively driven tropical tropopause cirrus properties to ice habits in high-resolution simulations

Abstract. Cirrus clouds that form in the tropical tropopause layer (TTL) can play a key role in vertical transport through the upper troposphere and lower stratosphere, which can significantly impact the radiative energy budget and stratospheric chemistry. However, the lack of realistic representation of natural ice cloud habits in microphysical parameterizations can lead to uncertainties in cloud-related processes and cloud–climate feedbacks. The main goal of this study is to investigate the role of different cloud regimes and the associated ice habits in regulating the properties of the TTL. We compare aircraft measurements from the StratoClim field campaign to a set of numerical experiments at the scale of large-eddy simulations (LESs) for the same case study that employ different microphysics schemes. Aircraft measurements over the southern slopes of the Himalayas captured high ice water content (HIWC) up to 2400 ppmv and ice particle aggregates exceeding 700 µm in size with unusually long residence times. The observed ice particles were mainly of liquid origin, with a small amount formed in situ. The corresponding profile of ice water content (IWC) from the ERA5 reanalysis corroborates the presence of HIWC detrained from deep-convective plumes in the TTL but underestimates HIWC by an order of magnitude. In the TTL, only the scheme that predicts ice habits can reproduce the observed HIWC, ice number concentration, and bimodal ice particle size distribution. The lower range of particle sizes is mostly represented by planar and columnar habits, while the upper range is dominated by aggregates. Large aggregates with sizes between 600 and 800 µm have fall speeds of less than 20 cm s−1, which explains the long residence time of the aggregates in the TTL. Planar ice particles of liquid origin contribute substantially to HIWC. The columnar and aggregate habits are in the in situ range with lower IWC and number concentrations. For all habits, the ice number concentration increases with decreasing temperature. For the planar ice habit, relative humidity is inversely correlated with fall speed. This correlation is less evident for the other two ice habits. In the lower range of supersaturation with respect to ice, the columnar habit has the highest fall speed. The difference in ice number concentration across habits can be up to 4 orders of magnitude, with aggregates occurring in much smaller numbers. We demonstrate and quantify the linear relationship between the differential sedimentation of pristine ice crystals and the size of the aggregates that form when pristine crystals collide. The slope of this relationship depends on which pristine ice habit sediments faster. Each simulated ice habit is associated with distinct radiative and latent heating rates. This study suggests that a model configuration nested down to LES scales with a microphysical parameterization that predicts ice shape evolution is crucial to provide an accurate representation of the microphysical properties of TTL cirrus and thus the associated (de)hydration process.

Viscosity of ice-in-oil slurries

Ice slurries are phase change materials extensively used in refrigeration technology. This work describes an experimental study and empirical modeling that was carried out to characterize the rheological behavior of ice-in-oil slurries. Decane and crushed ice were mixed to prepare test samples with ice volume percentages ranging from 3.5 to 17.7%. The size of the particles was 0.27±0.13mm. The viscosity measurements are performed at −2.5, −5.0, and −10.0°C using a rotational viscometer with a three-bladed impeller. The maximum relative velocity was ∼3.1 for 17.7% vol. concentration. A Bingham viscoplastic model was used to predict the rheological behavior of ice-in-oil slurries. The fractal dimension, packing limit, size of ice particles, and inter-particle cohesive forces were all considered in rheological calculations to make the model extensively applicable. The model accuracy is then examined using third-party experiments and experimental findings from the current study. The model appears to be a viable tool for predicting the viscosity of ice-in-oil slurries.

Quantifying Uncertainty in Ice Particle Velocity–Dimension Relationships Using MC3E Observations

Abstract Ice particle terminal fall velocity (Vt) is fundamental for determining microphysical processes, yet remains extremely challenging to measure. Current theoretical best estimates of Vt are functions of Reynolds number. The Reynolds number is related to the Best number, which is a function of ice particle mass, area ratio (Ar), and maximum dimension (Dmax). These estimates are not conducive for use in most models since model parameterizations often take the form , where (α, β) depend on habit and Dmax. A previously developed framework is used to determine surfaces of equally plausible (α, β) coefficients whereby ice particle size/shape distributions are combined with Vt best estimates to determine mass- (VM) or reflectivity-weighted (VZ) velocities that closely match parameterized VM,SD or VZ,SD calculated using the (α, β) coefficients using two approaches. The first uses surfaces of equally plausible (a, b) coefficients describing mass (M)–dimension relationships (i.e., ) to calculate mass- or reflectivity-weighted velocity from size/shape distributions that are then used to determine (α, β) coefficients. The second investigates how uncertainties in Ar, Dmax, and size distribution N(D) affect VM or VZ. For seven of nine flight legs flown on 20 and 23 May 2011 during the Mesoscale Continental Convective Clouds Experiment (MC3E), uncertainty from natural parameter variability—namely, the variability in ice particle parameters in similar meteorological conditions—exceeds uncertainties arising from different Ar assumptions or Dmax estimates. The combined uncertainty between Ar, Dmax, and N(D) produced smaller variability in (α, β) compared to varying M(D), demonstrating M(D) must be accurately quantified for model fall velocities. Primary sources of uncertainty vary considerably depending on environmental conditions. Significance Statement Ice particle fall velocity is fundamental for numerous processes within clouds, and hence is a critical property that must be accurately represented in weather and climate models. Using aircraft observations of ice particle shapes and sizes obtained in clouds behind midlatitude thunderstorms, this work develops a new framework for estimating ice particle fall velocities and their uncertainty, including quantifying the importance of different uncertainty sources from cloud microphysics measurements. Natural parameter variability contributes the most uncertainty in ice particle fall velocity estimates, although other sources can also be important contributors to uncertainty in certain conditions. Additional work examining ice particle data is needed to further understand how dependent uncertainty in certain ice particle properties are to local environmental conditions.

Estimating Microphysics Properties in Ice-Dominated Clouds from Airborne Ka–W-band Dual-Wavelength Ratio Reflectivity Factor in Close Proximity to In Situ Probes

Abstract Properties of frozen hydrometeors in clouds remain difficult to sense remotely. Estimates of number concentration, distribution shape, ice particle density, and ice water content are essential for connecting cloud processes to surface precipitation. Progress has been made with dual-frequency radars, but validation has been difficult because of lack of particle imaging and sizing observations collocated with the radar measurements. Here, data are used from two airborne profiling (up and down) radars, the W-band Wyoming Cloud Radar and the Ka-band Profiling Radar, allowing for Ka–W-band dual-wavelength ratio (DWR) profiles. The aircraft (the University of Wyoming King Air) also carried a suite of in situ cloud and precipitation probes. This arrangement is optimal for relating the “flight-level” DWR (an average from radar gates below and above flight level) to ice particle size distributions measured by in situ optical array probes, as well as bulk properties such as minimum snow particle density and ice water content. This comparison reveals a strong relationship between DWR and the ice particle median-volume diameter. An optimal range of DWR values ensures the highest retrieval confidence, bounded by the radars’ relative calibration and DWR saturation, found here to be about 2.5–7.5 dB. The DWR-defined size distribution shape is used with a Mie scattering model and an experimental mass–diameter relationship to test retrievals of ice particle concentration and ice water content. Comparison with flight-level cloud-probe data indicate good performance, allowing microphysical interpretations for the rest of the vertical radar transects.