Ice Particle Size Distribution Research Articles

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.

Effect of source strength on the simulation of jet dispersion and hazard distances during accidental release of liquid CO2

Accidental release of liquid CO2 can pose a certain risk to people, animals and equipment near release points. In addition to asphyxiation, cryogenic burns and dry-ice inhalation are potential risks that must be considered. In this study, a source model was established considering the formation of dry ice, which can quickly provide the inlet boundary condition for the computational fluid dynamics (CFD) model to simulate the jet dispersion of liquid CO2. The size distribution of the dry ice particles was described by the Rosin-Rammler expression. The effects of size distribution, initial pressure, liquid mass fraction and orifice size on the temperature, velocity, CO2 concentration, mass flow rate (nonsublimated dry ice) and survival distance were analysed. A quantitively risk assessment (QRA) method was used to evaluated the joint impact of source strength, dose-effect and impingement distance. The hazard distances associated with asphyxiation, cryogenic burn and dry ice inhalation were assessed. The results showed that the prediction errors of the mass flow rate using the source model were lower than 20 %. The temperature, velocity and CO2 concentration predicted by the CFD model agree well with the experimental results. The hazard distance was very dependent on and sensitive to the source strength and dose-effect.

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.

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%.

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.

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.

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.

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.

Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm

Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Modeling Performance of SCALE‐AMPS: Simulations of Arctic Mixed‐Phase Clouds Observed During SHEBA

AbstractThe Advanced Microphysics Prediction System (AMPS), which adopts a two‐moment hybrid‐bin habit‐predicting scheme, was previously developed to study cloud microphysical processes that depend on ice habit; however, only one particular atmospheric model, the University of Wisconsin‐Nonhydrostatic Modeling System, has been used to test the AMPS. In this study, AMPS is implemented into the Scalable Computing for Advanced Library and Environment (SCALE) large‐eddy simulation model. The AMPS Eulerian advection scheme for non‐mass characteristic variables of ice particles, such as axis lengths, is refined to minimize numerical artifacts. The resulting SCALE‐AMPS model successfully reproduces features of mixed‐phase clouds observed during the Surface Heat Budget of the Arctic campaign, including liquid water path (LWP), ice particle size distributions, and ice habits, when ice particle number concentrations (Nice) are reproduced. Sensitivity studies show that increases in Nice result in reductions of LWP that are generally consistent with previous results. Interestingly, LWP reductions lead to changes in ice habits through increases in cloud temperature due to weaker cloud top radiative cooling. Furthermore, aspect ratios of precipitating particles also change following LWP reductions, because in Bigg's immersion freezing scheme, adopted in this study, the aspect ratios depend on the initial size of the ice particles and freezing rates depend on both temperature and droplet size. Because habits of ice particles affect their growth rates, fall speeds, and collision rates, the results obtained in this study reveal possible feedback processes of Arctic mixed‐phase clouds operating through ice habits.

High Ice Water Content Conditions Associated with Wintertime Elevated Convection in the Midwest

Abstract Aircraft flying through areas of high ice water content (HIWC) can experience engine damage and/or failure. HIWC is typically associated with convection and the microphysical properties of tropical oceanic and coastal convection are well documented as a result of several field campaigns in the past decade. HIWC appears to be less common in extratropical convection, but instances of HIWC-related aircraft issues have been recorded in extratropical weather, even during winter. The present study documents the microphysical properties of HIWC between −25° and 0°C and the meteorological and thermodynamic conditions around that HIWC from five flights from the In-Cloud Icing and Large-Drop Experiment (ICICLE) in the midwestern United States in February 2019. All five cases contained elevated convection above a strong low-level temperature inversion. Values for top-of-inversion mixing ratios and total column precipitable water were about 5 g kg−1 and 20 mm, respectively, according to soundings near each case. A maximum ice water content of 2.1 g m−3 was observed over a length scale of about 500 m, and ice particle size distributions had mean volume equivalent diameters around 1000 μm. Supercooled drizzle droplets were also observed in the vicinity of the HIWC, raising questions about the possible role of secondary ice production via the freezing and shattering of supercooled large droplets in HIWC formation. The generalizability of these results is limited by the small number of cases, but they provide some of the first in situ observations of extratropical winter HIWC and highlight the need for continued research on these conditions. Significance Statement High ice water content (HIWC) conditions can cause engine damage, stall, and failure in aircraft and can cause air data probes to report erroneous values. Most research on HIWC has focused on tropical convection. This paper serves to draw more attention to the aviation hazard posed by extratropical winter HIWC and the need for additional research into these environments. The cases examined also contained supercooled precipitation-sized liquid droplets. Combined with other recent research, these observations may help to motivate laboratory experiments investigating the role of secondary ice production via droplet freezing and shattering in the formation of HIWC.

Uncertainties in Evaluating Global Electric Circuit Interactions With Atmospheric Clouds and Aerosols, and Consequences for Radiation and Dynamics

AbstractObservations and theory relevant to evaluating the importance of the effect of Ionosphere‐Earth current density, JZ, in the global electric circuit on cloud microphysics and cloud opacity at high latitudes, with presumed consequences for surface pressure, are reviewed and updated. The day‐to‐day input variations are less than 10% of their mean values, as are the reported cloud and pressure effects, but could be indicative of more important longer‐term effects, and of neglected processes in aerosol‐cloud interactions. There is consistent evidence for responses to both externally (solar‐wind) and internally (thunderstorm and shower‐cloud) generated changes in JZ and cloud opacity. The pressure correlations show non‐stationary behavior, with changes in phase relative to that of the solar wind input and JZ variations on interdecadal timescales. These might be due to the non‐stationary nature of the solar wind, and non‐stationary aerosol populations. For modeling the cloud effects there are considerable uncertainties in the size and charge distributions of aerosol particles, droplets, and ice particles. The cloud radiative coupling with periodic solar wind inputs may nudge internal tropospheric waves into phase coherence at times when the amplitude of the solar input and the duration of the input trains exceed minimum values.

Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples

Abstract. The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty of observing them directly. On the path to closing these gaps, we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection-permitting regional weather model setup is established using five different microphysics schemes (double-moment, spectral bin (“Fast Spectral Bin Microphysics”, FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a data set of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude diagrams of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, high reflectivities in the ice phase are simulated too frequently. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing too large or too dense graupel particles. Comparison of polarimetric radar signatures reveals issues of all schemes except the FSBM to correctly represent rain particle size distributions.

Multi-Case Analysis of Ice Particle Properties of Stratiform Clouds Using In Situ Aircraft Observations in Hebei, China

This study investigates the size distribution, the mean diameter, and the concentration of ice particles within stratiform clouds by using in situ observations from 29 flights in Hebei, China. Furthermore, it examines the empirical fitting of ice particle size distributions at different temperatures using Gamma and exponential functions. Without considering the first three bins of ice particles, the mean diameter of ice particles (size range 100–1550 µm) is found to increase with temperature from −15 to −9 °C but decrease with temperature from −9 to 0 °C. By considering the first three bins of ice particles using the empirical Gamma fitting relationship found in this study, the mean diameter of ice particles (size range 25–1550 µm) shows a similar variation trend with temperature, while the turning point changes from −9 to −10 °C. The ice particle number concentration increases from 13.37 to 50.23 L−1 with an average of 31.27 L−1 when temperature decreases from 0 to −9 °C. Differently, the ice concentration decreases from 50.23 to about 22.4 L−1 when temperature decreases from −9 to −12 °C. The largest mean diameter of ice particles at temperatures around −9 and −10 °C is most likely associated with the maximum difference of ice and water supersaturation at that temperature, making the ice particles grow the fastest. These findings provide valuable information for future physical parameterization development of ice crystals within stratiform clouds.

Physical characteristics of frozen hydrometeors inferred with parameter estimation

Abstract. Frozen hydrometeors are found in a huge range of shapes and sizes, with variability on much smaller scales than those of typical model grid boxes or satellite fields of view. Neither models nor in situ measurements can fully describe this variability, so assumptions have to be made in applications including atmospheric modelling and radiative transfer. In this work, parameter estimation has been used to optimise six different assumptions relevant to frozen hydrometeors in passive microwave radiative transfer. This covers cloud overlap, convective water content and particle size distribution (PSD), the shapes of large-scale snow and convective snow, and an initial exploration of the ice cloud representation (particle shape and PSD combined). These parameters were simultaneously adjusted to find the best fit between simulations from the European Centre for Medium-range Weather Forecasts (ECMWF) assimilation system and near-global microwave observations covering the frequency range 19 to 190 GHz. The choices for the cloud overlap and the convective particle shape were particularly well constrained (or identifiable), and there was even constraint on the cloud ice PSD. The practical output is a set of improved assumptions to be used in version 13.0 of the Radiative Transfer for TOVS microwave scattering package (RTTOV-SCATT), taking into account newly available particle shapes such as aggregates and hail, as well as additional PSD options. The parameter estimation explored the full parameter space using an efficient assumption of linearly additive perturbations. This helped illustrate issues such as multiple minima in the cost function, and non-Gaussian errors, that would make it hard to implement the same approach in a standard data assimilation system for weather forecasting. Nevertheless, as modelling systems grow more complex, parameter estimation is likely to be a necessary part of the development process.

Investigating the Condensation of Benzene (C6H6) in Titan’s South Polar Cloud System with a Combination of Laboratory, Observational, and Modeling Tools

Abstract We have combined laboratory, modeling, and observational efforts to investigate the chemical and microphysical processes leading to the formation of the cloud system that formed at an unusually high altitude (>250 km) over Titan’s south pole after the northern spring equinox. We present here a study focused on the formation of C6H6 ice clouds at 87°S. As the first step of our synergistic approach, we have measured, for the first time, the equilibrium vapor pressure of pure crystalline C6H6 at low temperatures (134–158 K) representative of Titan’s atmosphere. Our laboratory data indicate that the experimental vapor pressure values are larger than those predicted by extrapolations found in the literature calculated from higher-temperature laboratory measurements. We have used our experimental results along with temperature profiles and C6H6 mixing ratios derived from observational data acquired by the Cassini Composite Infrared Spectrometer (CIRS) as input parameters in the coupled microphysics radiative transfer Community Aerosol and Radiation Model for Atmospheres (CARMA). CARMA simulations constrained by these input parameters were conducted to derive C6H6 ice particle size distribution, gas volume mixing ratios, gas relative humidity, and cloud altitudes. The impact of the vapor pressure on the CIRS data analysis and in the CARMA simulations was investigated and resulted in both cases in benzene condensation occurring at lower altitude in the stratosphere than previously thought. In addition, the stratospheric C6H6 gas abundances predicted with the new saturation relationship are ∼1000× higher than previous calculations between 150–200 km, which results in larger particle sizes.

Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study

Abstract. This study investigates the link between rain and ice microphysics across the melting layer in stratiform rain systems using measurements from vertically pointing multi-frequency Doppler radars. A novel methodology to examine the variability of the precipitation rate and the mass-weighted melted diameter (Dm) across the melting region is proposed and applied to a 6 h long case study, observed during the TRIPEx-pol field campaign at the Jülich Observatory for Cloud Evolution Core Facility and covering a gamut of ice microphysical processes. The methodology is based on an optimal estimation (OE) retrieval of particle size distributions (PSDs) and dynamics (turbulence and vertical motions) from observed multi-frequency radar Doppler spectra applied both above and below the melting layer. First, the retrieval is applied in the rain region; based on a one-to-one conversion of raindrops into snowflakes, the retrieved drop size distributions (DSDs) are propagated upward to provide the mass-flux-preserving PSDs of snow. These ice PSDs are used to simulate radar reflectivities above the melting layer for different snow models and they are evaluated for a consistency with the actual radar measurements. Second, the OE snow retrieval where Doppler spectra are simulated based on different snow models, which consistently compute fall speeds and electromagnetic properties, is performed. The results corresponding to the best-matching models are then used to estimate snow fluxes and Dm, which are directly compared to the corresponding rain quantities. For the case study, the total accumulation of rain (2.30 mm) and the melted equivalent accumulation of snow (1.93 mm) show a 19 % difference. The analysis suggests that the mass flux through the melting zone is well preserved except the periods of intense riming where the precipitation rates were higher in rain than in the ice above. This is potentially due to additional condensation within the melting zone in correspondence to high relative humidity and collision and coalescence with the cloud droplets whose occurrence is ubiquitous with riming. It is shown that the mean mass-weighted diameter of ice is strongly related to the characteristic size of the underlying rain except the period of extreme aggregation where breakup of melting snowflakes significantly reduces Dm. The proposed methodology can be applied to long-term observations to advance our knowledge of the processes occurring across the melting region; this can then be used to improve assumptions underpinning spaceborne radar precipitation retrievals.

Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

Abstract. Ground-based radar and radiometer data observed during the 2017–2018 winter season over the Pyeongchang area on the east coast of the Korean Peninsula were used to simultaneously estimate both the cloud liquid water path and snowfall rate for three types of snow clouds: near-surface, shallow, and deep. Surveying all the observed data, it is found that near-surface clouds are the most frequently observed cloud type with an area fraction of over 60 %, while deep clouds contribute the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with the vast majority hardly reaching 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and the liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager (GPM/GMI) channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as an a priori database. Under an idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution, and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.32, 0.41, and 0.62, respectively, for near-surface, shallow, and deep snow clouds over land surfaces; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increasing from 0.32 to 0.52, while a smaller improvement is found for shallow and deep clouds. This study provides a general picture of the microphysical characteristics of the different types of snow clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

Polarimetric Radar Variables in Snowfall at Ka- and W-Band Frequency Bands: A Comparative Analysis

AbstractDual-frequency millimeter-wavelength radar observations in snowfall are analyzed in order to evaluate differences in conventional polarimetric radar variables such as differential reflectivity (ZDR) specific differential phase shift (KDP) and linear depolarization ratio (LDR) at traditional cloud radar frequencies at Ka and W bands (~35 and ~94 GHz, correspondingly). Low radar beam elevation (~5°) measurements were performed at Oliktok Point, Alaska, with a scanning fully polarimetric radar operating in the horizontal–vertical polarization basis. This radar has the same gate spacing and very close beam widths at both frequencies, which largely alleviates uncertainties associated with spatial and temporal data matching. It is shown that observed Ka- and W-band ZDR differences are, on average, less than about 0.5 dB and do not have a pronounced trend as a function of snowfall reflectivity. The observed ZDR differences agree well with modeling results obtained using integration over nonspherical ice particle size distributions. For higher signal-to-noise ratios, KDP data derived from differential phase measurements are approximately scaled as reciprocals of corresponding radar frequencies indicating that the influence of non-Rayleigh scattering effects on this variable is rather limited. This result is also in satisfactory agreement with data obtained by modeling using realistic particle size distributions. Observed Ka- and W-band LDR differences are strongly affected by the radar hardware system polarization “leak” and are generally less than 4 dB. Smaller differences are observed for higher depolarizations, where the polarization “leak” is less pronounced. Realistic assumptions about particle canting and the system polarization isolation lead to modeling results that satisfactorily agree with observational dual-frequency LDR data.

Establishment and Preliminary Application of the Forward Modeling Method for Doppler Spectral Density of Ice Particles

Owing to the various shapes of ice particles, the relationships between fall velocity, backscattering cross-section, mass, and particle size are complicated. This affects the application of cloud radar Doppler spectral density data in the retrieval of the microphysical properties of ice crystals. In this study, under the assumption of six particle shape types, the relationships between particle mass, fall velocity, backscattering cross-section, and particle size were established based on existing research. Variations of Doppler spectral density with the same particle size distribution (PSD) of different ice particle types are discussed. The radar-retrieved liquid and ice PSDs, water content, and mean volume-weighted particle diameter were compared with airborne in situ observations in the Xingtai, Hebei Province, China, in 2018. The results showed the following. (1) For the particles with the same equivalent diameter (De), the fall velocity of the aggregates was the largest, followed by hexagonal columns, hexagonal plates, sector plates, and stellar crystals, with the ice spheres falling two to three times faster than ice crystals with the same De. Hexagonal columns had the largest backscattering cross-section, followed by stellar crystals and sector plates, and the backscattering cross-sections of hexagonal plates and the two types of aggregates were very close to those of ice spheres. (2) The width of the simulated radar Doppler spectral density generated by various ice crystal types with the same PSD was mainly affected by the particle’s falling velocity, which increased with the particle size. Turbulence had different degrees of influence on the Doppler spectrum of different ice crystals, and it also brought large errors to the PSD retrieval. (3) PSD comparisons showed that each ice crystal type retrieved from the cloud radar corresponded well to aircraft observations within a certain scale range, when assuming that only a certain type of ice crystals existed in the cloud, which could fully prove the feasibility of retrieving ice PSDs from the reflectivity spectral density.