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Coincident radar data with Doppler radar measurements at X, Ku, Ka, and W bands on the NASA ER-2 aircraft overflying the NASA P-3 aircraft acquiring in situ microphysical measurements are used to characterize the relationship between radar measurements and ice microphysical properties. The data were obtained from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS). Direct measurements of the condensed water content and coincident Doppler radar measurements were acquired, facilitating improved estimates of ice particle mass, a variable that is an underlying factor for calculating and therefore retrieving the radar reflectivity Ze, median mass diameter Dm, particle terminal velocity, and snowfall rate S. The relationship between the measured ice water content (IWC) and that calculated from the particle size distributions (PSDs) using relationships developed in earlier studies, and between the calculated and measured radar reflectivity at the four radar wavelengths, are quantified. Relationships are derived between the measured IWC and properties of the PSD, Dm, Ze at the four radar wavelengths, and the dual-wavelength ratio. Because IWC and Ze are measured directly, the coefficients in the mass–dimensional relationship that best match both the IWC and Ze are derived. The relationships developed here, and the mass–dimensional relationship that uses both the measured IWC and Ze to find a best match for both variables, can be used in studies that characterize the properties of wintertime snow clouds. Significance Statement The goal of this study is to provide reliable microphysical measurements and algorithms to facilitate improvements in cloud model microphysical parameterizations and in retrieval of snow precipitation properties from spaceborne active remote sensors and to characterize ice and snow precipitation development within clouds. This work draws upon a unique set of in situ measurements of the ice and total water content coupled with overflying aircraft radar measurements at four radar wavelengths. Better estimates of the contributions of the ice phase to the total global precipitation using spaceborne radar data pave the way for assessing and advancing global climate modeling, thereby strengthening predictions of global climate change.

Clouds are a fundamental aspect of the Earth’s atmosphere. One of the major challenges in cloud-resolving models (CRM) is the formation and generation of new cloud ice particles from pre-existed ice and liquid. Based on the basic broad cloud types, it is helpful to distinguish between their fundamental microphysical properties. The four basic cloud types are defined as: (1) warm-based convective and stratiform clouds; and (2) cold-based convective and stratiform clouds. Recent studies of ice initiation in clouds have shown that most ice particles in the mixed-phase region of clouds are from secondary ice production (SIP) mechanisms but have generally concentrated on only one specific cloud system.In this study, Aerosol-Cloud model (AC) is used. AC includes the four mechanisms of secondary ice production as follows: ice-ice collisional breakup, raindrop freezing fragmentation, Hallett-Mossop (HM) process and sublimational breakup. The intent is to generalize the contribution of each SIP mechanism among basic cloud types. The numerical simulations are performed using our AC for each cloud type and validated against in-situ cloud observations. The observational data is collected during four different cloud observational campaigns, each representing a contrasting cloud type than others.Here, we study the contributions from each process of SIP (HM process, ice-ice collisional breakup, raindrop-freezing fragmentation and sublimational breakup) by performing control simulations of each basic cloud type. For the warm cloud convective clouds, the HM process prevails near freezing level and contributes significantly from 0 to -15oC. In cold-based convective clouds, the ice-ice collisional breakup is the most dominating SIP mechanism in each cloud type. In warm-based stratiform clouds, the HM process dominates the contribution of ice in the -5 to -15oC temperature range for updrafts up to 8 m/s. In the slightly warm-based convective clouds, the breakup due to ice-ice collision is the most dominating mechanism for the convective updrafts between -5oC and cloud top temperatures.

Ice fragments are generated by sublimation of ice particles in subsaturated conditions in natural clouds. Conceivably, such sublimational breakup would be expected to cause ice multiplication in natural clouds. Any fragment that survives will grow to become ice precipitation that may sublimate and fragment further. As a first step toward assessing this overlooked process, a formulation is proposed for the number of ice fragments from sublimation of ice particles for an atmospheric model. This is done by amalgamating laboratory observations from previously published studies. The concept of a “sublimated mass activity spectrum” for the breakup is applied to the dataset. The number of ice fragments is determined by the relative humidity over ice and the initial size of the parent ice particles. The new formulation applies to dendritic crystals and heavily rimed particles only. Finally, a thought experiment is performed for an idealized scenario of subsaturation with in-cloud descent. Scaling analysis yields an estimate of an ice enhancement ratio of about 5 (10) within a weak deep convective downdraft of about 2 m s−1, for an initial monodisperse population of dendritic snow (graupel) particles of 3 L−1 and 2 mm. During descent, there is a dynamic equilibrium between continual emission of fragments and their depletion by sublimation. A simplified bin microphysics parcel model exhibits this dynamical quasi equilibrium, consistent with the thought experiment. The fragments have average lifetimes of around 90 and 70 s for dendrites and graupel, respectively. Sublimational breakup is predicted to cause significant secondary ice production.

A new empirical parameterization (EP) for multiple groups of primary biological aerosol particles (PBAPs) is implemented in the aerosol–cloud model (AC) to investigate their roles as ice nucleating particles (INPs). The EP describes the heterogeneous ice nucleation by (1) fungal spores, (2) bacteria, (3) pollen, (4) detritus of plants, animals, and viruses, and (5) algae. Each group includes fragments from the originally emitted particles. A high-resolution simulation of a midlatitude mesoscale squall line by AC is validated against airborne and ground observations. Sensitivity tests are carried out by varying the initial vertical profiles of the loadings of individual PBAP groups. The resulting changes in warm and ice cloud microphysical parameters are investigated. The changes in warm microphysical parameters, including liquid water content and cloud droplet number concentration, are minimal (<10 %). Overall, PBAPs have little effect on the ice number concentration (<6 %) in the convective region. In the stratiform region, increasing the initial PBAP loadings by a factor of 1000 resulted in less than 40 % change in ice number concentrations. The total ice concentration is mostly controlled by various mechanisms of secondary ice production (SIP). However, when SIP is intentionally shut down in sensitivity tests, increasing the PBAP loading by a factor of 100 has an effect of less than 3 % on the ice phase. Further sensitivity tests revealed that PBAPs have little effect on surface precipitation and on the shortwave and longwave flux (<4 %) for a 100-fold perturbation in PBAPs.

Holographic cloud probes provide unprecedented information on cloud particle density, size and position. Each laser shot captures particles within a large volume, where images can be computationally refocused to determine particle size and shape. However, processing these holograms, either with standard methods or with machine learning (ML) models, requires considerable computational resources, time and occasional human intervention. ML models are trained on simulated holograms obtained from the physical model of the probe since real holograms have no absolute truth labels. Using another processing method to produce labels would be subject to errors that the ML model would subsequently inherit. Models perform well on real holograms only when image corruption is performed on the simulated images during training, thereby mimicking non-ideal conditions in the actual probe (Schreck et. al, 2022). Optimizing image corruption requires a cumbersome manual labeling effort. Here we demonstrate the application of the neural style translation approach (Gatys et. al, 2016) to the simulated holograms. With a pre-trained convolutional neural network (VGG-19), the simulated holograms are ``stylized'' to resemble the real ones obtained from the probe, while at the same time preserving the simulated image ``content'' (e.g. the particle locations and sizes). Two image similarity metrics concur that the stylized images are more like real holograms than the synthetic ones. With an ML model trained to predict particle locations and shapes on the stylized data sets, we observed comparable performance on both simulated and real holograms, obviating the need to perform manual labeling. The described approach is not specific to hologram images and could be applied in other domains for capturing noise and imperfections in observational instruments to make simulated data more like real world observations., Comment: 23 pages, 9 figures

Precipitation is fundamental to the hydrological cycle. There are two possible mechanisms for its formation in clouds generally: the "warm-rain process" and the "ice crystal process". This study uses a microphysically advanced aerosol-cloud (AC) model to understand the contributions from the warm (from the warm-rain process) and cold (from the ice crystal process) components of the surface precipitation in a pair of contrasting convective storms that are cold-based (STEPS) and slightly warm-based (MC3E). Tagging-tracer techniques enabled analysis of microphysical pathways leading to the simulated precipitation. The cold components of graupel and rain mass are higher than the corresponding warm components in both simulations of the STEPS and MC3E storms. About ~80% of accumulated surface precipitation is predicted to originate from ice-crystal process in both cases. In sensitivity tests with lowering of cloud base to warmer levels near the ground, the origin of most of the surface precipitation is switched to the warm-rain process in both cases, even though precipitation is mostly in the ice-phase aloft. This is reinforced by inclusion of maritime solute aerosol conditions additionally. In conclusion, this study explains the distinct behavior of precipitation processes in the cold-base and warm-base convective clouds.

This study is the first to reach a global view of the precipitation process partitioning, using a combination of satellite and global climate modeling data. The pathways investigated are 1) precipitating ice (ice/snow/graupel) that forms above the freezing level and melts to produce rain (S) followed by additional condensation and collection as the melted precipitating ice falls to the surface (R); 2) growth completely through condensation and collection (coalescence), warm rain (W); and 3) precipitating ice (primarily snow) that falls to the surface (SS). To quantify the amounts, data from satellite-based radar measurements—CloudSat, GPM, and TRMM—are used, as well as climate model simulations from the Community Atmosphere Model (CAM) and the Met Office Unified Model (UM). Total precipitation amounts and the fraction of the total precipitation amount for each of the pathways is examined latitudinally, regionally, and globally. Carefully examining the contributions from the satellite-based products leads to the conclusion that about 57% of Earth’s precipitation follows pathway S, 15% R, 23% W, and 5% SS, each with an uncertainty of ±5%. The percentages differ significantly from the global climate model results, with the UM indicating smaller fractional S, more R, and more SS; and CAM showing appreciably greater S, negative R (indicating net evaporation below the melting layer), a much larger percentage of W and much less SS. Possible reasons for the wide differences between the satellite- and model-based results are discussed.

This article illustrates how multifrequency radar observations can refine the mass–size parameterization of frozen hydrometeors in scattering models and improve the correlation between the radar observations and in situ measurements of microphysical properties of ice and snow. The data presented in this article were collected during the GPM Cold Season Precipitation Experiment (GCPEx) (2012) and Olympic Mountain Experiment (OLYMPEx) (2015) field campaigns, where the true mass–size relationship was not measured. Starting from size and shape distributions of ice particles measured in situ, scattering models are used to simulate an ensemble of reflectivity factors for various assumed mass–size parameterizations (MSP) of the power-law type. This ensemble is then collocated to airborne and ground-based radar observations, and the MSPs are refined by retaining only those that reproduce the radar observations to a prescribed level of accuracy. A versatile “retrieval dashboard” is built to jointly analyze the optimal MSPs and associated retrievals. The analysis shows that the optimality of an MSP depends on the physical assumptions made in the scattering simulators. This work confirms also the existence of a relationship between parameters of the optimal MSPs. Through the MSP optimization, the retrievals of ice water contentMand mean diameterDmseem robust to the change in meteorological regime (between GCPEx and OLYMPEx); whereas the retrieval of the diameter spreadSmseems more campaign dependent.

Microphysics parameterizations in large-scale models often account for subgrid variability in the calculation of process rates by integrating over assumed subgrid distributions of the input variables. The variances and covariances that define distribution width may be specified or diagnosed. The correlation ρ of cloud and rain mass mixing ratio/liquid water content (LWC) is a key input for accurate prediction of the accretion rate and a constant value is typically assumed. In this study, high-frequency aircraft measurements with a spatial resolution of ≈22 cm are used to evaluate the scaling behavior of cloud and rain LWC (qc and qr, respectively) and to demonstrate how and why covariability varies with length scale ℓ. It is shown that power spectral densities of both qc and qr exhibit scale invariance across a wide range of scales (2.04–142 m for qc; 33–1.45 × 104 m for qr). Because the cloud–rain cospectrum is also scale invariant, ρ is therefore expected to vary with ℓ. Direct calculation of ρ shows that it generally increases with ℓ, but there is significant variability in the ρ–ℓ relationship that primarily depends on cloud drop number concentration N and cloud cellular organization, suggesting that ρ may also vary with cloud regime. A parameterization of ρ as a function of ℓ and N is developed from aircraft observations and implications for diagnosis of ρ from limited-area model output are also discussed.

This study quantifies how far snow can fall into the melting layer (ML) before all snow has melted by examining a combination of in-situ observations from aircraft measurements in Lagrangian spiral descents from above through the ML and descents and ascents into the ML, as well as an extensive database of NOAA surface observer reports during the past 50 years. The airborne data contain information on the particle phase (solid, mixed, or liquid), population size distributions and shapes, along with temperature, relative humidity, and vertical velocity. A wide range of temperatures and ambient relative humidities are used for both the airborne and ground-based data. It is shown that an ice bulb temperature of 0°C, together with the air temperature and pressure (altitude), are good first order predictors of the highest temperature snowflakes can survive in the melting layer before completely melting. Particle size is also important, as is whether the particles are graupel or hail. If the relative humidity is too low, the particles will sublimate completely as they fall into the melting layer. Snow as warm as +7°C is observed from aircraft measurements and surface observations. Snow pellets survive to even warmer temperatures. Relationships are developed to represent the primary findings.

In this study, a nonparametric method to estimate precipitating ice from multiple-frequency radar observations is investigated. The method does not require any assumptions regarding the distribution of ice particle sizes and relies on an efficient search procedure to incorporate information from observed particle size distributions (PSDs) in the estimation process. Similar to other approaches rooted in optimal-estimation theory, the nonparametric method is robust in the presence of noise in observations and uncertainties in the forward models. Over 200 000 PSDs derived from in situ observations collected during the Olympic Mountains Experiment (OLYMPEX) and Integrated Precipitation and Hydrology Experiment (IPHEX) field campaigns are used in the development and evaluation of the nonparametric estimation method. These PSDs are used to create a database of ice-related variables and associated computed radar reflectivity factors at the Ku, Ka, and W bands. The computed reflectivity factors are used to derive precipitating ice estimates and investigate the associated errors and uncertainties. The method is applied to triple-frequency radar observations collected during OLYMPEX and IPHEX. Direct comparisons of estimated ice variables with estimates from in situ instruments show results consistent with the error analysis. Global application of the method requires an extension of the supporting PSD database, which can be achieved through the processing of information from additional past and future field campaigns.

A numerical formulation is provided for secondary ice production during fragmentation of freezing raindrops or drizzle. This is obtained by pooling laboratory observations from published studies and considering the physics of collisions. There are two modes of the scheme: fragmentation during spherical drop freezing (mode 1) and during collisions of supercooled raindrops with more massive ice (mode 2). The empirical scheme is for atmospheric models. Microphysical simulations with a parcel model of fast ascent (8 m s−1) between −10° and −20°C are validated against aircraft observations of tropical maritime deep convection. Ice enhancement by an order of magnitude is predicted from inclusion of raindrop-freezing fragmentation, as observed. The Hallett–Mossop (HM) process was active too. Both secondary ice mechanisms (HM and raindrop freezing) are accelerated by a positive feedback involving collisional raindrop freezing. An energy-based theory is proposed explaining the laboratory observations of mode 1, both of approximate proportionality between drop size and fragment numbers and of their thermal peak. To illustrate the behavior of the scheme in both modes, the glaciation of idealized monodisperse populations of drops is elucidated with an analytical zero-dimensional (0D) theory treating the freezing in drop–ice collisions by a positive feedback of fragmentation. When drops are too few or too small (≪1 mm), especially at temperatures far from −15°C (mode 1), there is little raindrop-freezing fragmentation on realistic time scales of natural clouds, but otherwise, high ice enhancement (IE) ratios of up to 100–1000 are possible. Theoretical formulas for the glaciation time of such drop populations, and their maximum and initial growth rates of IE ratio, are proposed.

Two methods for deriving relationships between the equivalent radar reflectivity factor Ze and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Ze–S relationships. In the second method, measurements of Ze at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Ze–S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.

The squall-line event on 20 May 2011, during the Midlatitude Continental Convective Clouds (MC3E) field campaign has been simulated by three bin (spectral) microphysics schemes coupled into the Weather Research and Forecasting (WRF) Model. Semi-idealized three-dimensional simulations driven by temperature and moisture profiles acquired by a radiosonde released in the preconvection environment at 1200 UTC in Morris, Oklahoma, show that each scheme produced a squall line with features broadly consistent with the observed storm characteristics. However, substantial differences in the details of the simulated dynamic and thermodynamic structure are evident. These differences are attributed to different algorithms and numerical representations of microphysical processes, assumptions of the hydrometeor processes and properties, especially ice particle mass, density, and terminal velocity relationships with size, and the resulting interactions between the microphysics, cold pool, and dynamics. This study shows that different bin microphysics schemes, designed to be conceptually more realistic and thus arguably more accurate than bulk microphysics schemes, still simulate a wide spread of microphysical, thermodynamic, and dynamic characteristics of a squall line, qualitatively similar to the spread of squall-line characteristics using various bulk schemes. Future work may focus on improving the representation of ice particle properties in bin schemes to reduce this uncertainty and using the similar assumptions for all schemes to isolate the impact of physics from numerics.

In Part I of this two-part paper, a formulation was developed to treat fragmentation in ice–ice collisions. In the present Part II, the formulation is implemented in two microphysically advanced cloud models simulating a convective line observed over the U.S. high plains. One model is 2D with a spectral bin microphysics scheme. The other has a hybrid bin–two-moment bulk microphysics scheme in 3D. The case consists of cumulonimbus cells with cold cloud bases (near 0°C) in a dry troposphere. Only with breakup included in the simulation are aircraft observations of particles with maximum dimensions >0.2 mm in the storm adequately predicted by both models. In fact, breakup in ice–ice collisions is by far the most prolific process of ice initiation in the simulated clouds (95%–98% of all nonhomogeneous ice), apart from homogeneous freezing of droplets. Inclusion of breakup in the cloud-resolving model (CRM) simulations increased, by between about one and two orders of magnitude, the average concentration of ice between about 0° and −30°C. Most of the breakup is due to collisions of snow with graupel/hail. It is broadly consistent with the theoretical result in Part I about an explosive tendency for ice multiplication. Breakup in collisions of snow (crystals >~1 mm and aggregates) with denser graupel/hail was the main pathway for collisional breakup and initiated about 60%–90% of all ice particles not from homogeneous freezing, in the simulations by both models. Breakup is predicted to reduce accumulated surface precipitation in the simulated storm by about 20%–40%.

Occurrence frequency and dynamical conditions of ice supersaturation (ISS, where relative humidity with respect to ice (RHi) greater than 100%) are examined in the upper troposphere around convective activity. Comparisons are conducted between in situ airborne observations and the Weather Research and Forecasting model simulations using four double-moment microphysical schemes at temperatures less than or or equal to -40degdegC. All four schemes capture both clear-sky and in-cloud ISS conditions. However, the clear-sky (in-cloud) ISS conditions are completely (significantly) limited to the RHi thresholds of the Cooper parameterization. In all of the simulations, ISS occurrence frequencies are higher by approximately 3-4 orders of magnitude at higher updraft speeds (greater than 1 m s(exp -1) than those at the lower updraft speeds when ice water content (IWC) greater than 0.01 gm(exp -3), while observations show smaller differences up to approximately 1-2 orders of magnitude. The simulated ISS also occurs less frequently at weaker updrafts and downdrafts than observed. These results indicate that the simulations have a greater dependence on stronger updrafts to maintain/generate ISS at higher IWC. At lower IWC (less than or equal or 0.01 gm(exp -3), simulations unexpectedly show lower ISS frequencies at stronger updrafts. Overall, the Thompson aerosol-aware scheme has the closest magnitudes and frequencies of ISS greater than 20% to the observations, and the modified Morrison has the closest correlations between ISS frequencies and vertical velocity at higher IWC and number density. The Cooper parameterization often generates excessive ice crystals and therefore suppresses the frequency and magnitude of ISS, indicating that it should be initiated at higher ISS (e.g.,lees than or equal to 25%).

NASA and the FAA (Federal Aviation Administration) conducted two flight campaigns to quantify onboard weather radar measurements with in-situ measurements of high concentrations of ice crystals found in deep convective storms. The ultimate goal of this research was to improve the understanding and develop onboard weather radar processing to detect regions of high ice water content ahead of an aircraft and enable tactical avoidance of the potentially hazardous conditions. Both High Ice Water Content (HIWC) RADAR campaigns utilized the NASA DC-8 Airborne Science Laboratory which was equipped with a Honeywell RDR-4000 weather radar and icing instruments to characterize the ice crystal clouds. The purpose of this paper is to summarize how these campaigns were conducted and highlight key results.

A reliable understanding of the microphysical properties of ice particles in atmospheric clouds is critical for assessing cloud radiative forcing effects in climate studies. Ice particle microphysical properties such as size, shape, and surface roughness all have substantial effects on the single-scattering characteristics of the particles. A recently developed ice particle probe, the Small Ice Detector-3 (SID-3), measures the two-dimensional near-forward light-scattering patterns of sampled ice particles. These scattering patterns provide a wealth of information for understanding the microphysical and radiative characteristics of ice particles. The SID-3 was operated successfully on 12 aircraft flights during the NASA Midlatitude Airborne Cirrus Properties Experiment (MACPEX) field campaign in April 2011. In this study, SID-3 measurements are used to investigate the frequency of occurrence of a number of ice particle properties observed during MACPEX. Individual scattering patterns (7.5°–23°) are used to infer properties of the observed particles as well as to calculate partial scattering functions (PSFs) for ensembles of particles in the measured size range (~5–100 μm). PSFs are compared to ray-tracing-based phase functions to infer additional properties of the particles. Two quantitative values—halo ratio and steepness ratio—are used to characterize PSFs. The MACPEX dataset suggests that most atmospheric ice particles have rough surfaces or are complex in nature. PSFs calculated for particles that were characterized as having smooth surfaces also appeared to more closely resemble rough crystal PSFs. PSFs measured with SID-3 compare well with those calculated for droxtals with rough surfaces.

Our study presents aircraft spiral ascent and descent observations intercepting a transition to riming processes during widespread stratiform precipitation. The sequence is documented using collocated scanning and profiling radar, including longer-wavelength dual polarization measurements and shorter-wavelength Doppler spectra. Riming regions are supported using aircraft measurements recording elevated liquid water concentrations, spherical particle shapes, and saturation with respect to water. Profiling cloud radar observations indicate riming regions during the event as having increasing particle fall speeds, rapid time-height changes, and bimodalities in Doppler spectra. These particular riming signatures are coupled to scanning dual polarization radar observations of higher differential reflectivity (ZDR) aloft. Moreover, reduced melting layer enhancements and delayed radar bright-band signatures in the column are also observed during riming periods, most notably with the profiling radar observations. The bimodal cloud radar Doppler spectra captured near riming zones indicate two time-height spectral ice peaks, one rimed particle peak, and one peak associated with pristine ice needle generation and/or growth between -4°C and -7°C also sampled by aircraft probes. We observe this pristine needle population near the rimed particle region which gives a partial explanation for the enhanced ZDR. The riming signatures aloft and radar measurements within the melting level are weaklymore » lag correlated (r~0.6) with smaller median drop sizes at the surface, as compared with later times when aggregation of larger particle sizes was believed dominant.« less

Polarimetric radar observations increasingly are used to understand cloud microphysical processes, which is critical for improving their representation in cloud and climate models. In particular, there has been recent focus on improving representations of ice collection processes (e.g., aggregation and riming), as these influence precipitation rate, heating profiles, and ultimately cloud life cycles. However, distinguishing these processes using conventional polarimetric radar observations is difficult, as they produce similar fingerprints. This necessitates improved analysis techniques and integration of complementary data sources. The Midlatitude Continental Convective Clouds Experiment (MC3E) provided such an opportunity. Quasi-vertical profiles of polarimetric radar variables in two MC3E stratiform precipitation events reveal episodic melting layer sagging. Integrated analyses using scanning and vertically pointing radar and aircraft measurements reveal that saggy bright band signatures are produced when denser, faster-falling, more isometric hydrometeors (relative to adjacent times) descend into the melting layer. In one case, strong circumstantial evidence for riming is found during bright band sagging times. A bin microphysical melting layer model successfully reproduces many aspects of the signature, supporting the observational analysis. If found to be a reliable indicator of riming, saggy bright bands could be a proxy for the presence of supercooled liquid water in stratiform precipitation, which may provide important information for mitigating aircraft icing risks and for constraining microphysical models.

In this study, two different particle models describing the structure and electromagnetic properties of snow are developed and evaluated for potential use in satellite combined radar–radiometer precipitation estimation algorithms. In the first model, snow particles are assumed to be homogeneous ice–air spheres with single-scattering properties derived from Mie theory. In the second model, snow particles are created by simulating the self-collection of pristine ice crystals into aggregate particles of different sizes, using different numbers and habits of the collected component crystals. Single-scattering properties of the resulting nonspherical snow particles are determined using the discrete dipole approximation. The size-distribution-integrated scattering properties of the spherical and nonspherical snow particles are incorporated into a dual-wavelength radar profiling algorithm that is applied to 14- and 34-GHz observations of stratiform precipitation from the ER-2 aircraftborne High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar. The retrieved ice precipitation profiles are then input to a forward radiative transfer calculation in an attempt to simulate coincident radiance observations from the Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR). Much greater consistency between the simulated and observed CoSMIR radiances is obtained using estimated profiles that are based upon the nonspherical crystal/aggregate snow particle model. Despite this greater consistency, there remain some discrepancies between the higher moments of the HIWRAP-retrieved precipitation size distributions and in situ distributions derived from microphysics probe observations obtained from Citation aircraft underflights of the ER-2. These discrepancies can only be eliminated if a subset of lower-density crystal/aggregate snow particles is assumed in the radar algorithm and in the interpretation of the in situ data.

A new parameterization of sticking efficiency for aggregation of ice crystals onto snow and graupel is presented. This parameter plays a crucial role for the formation of ice precipitation and for electrification processes. The parameterization is intended to be used in atmospheric models simulating the aggregation of ice particles in glaciated clouds. It should improve the ability to forecast snow. Based on experimental results and general considerations of collision processes, dependencies of the sticking efficiency on temperature, surface area, and collision kinetic energy of impacting particles are derived. The parameters have been estimated from some laboratory observations by simulating the experiments and minimizing the squares of the errors of the prediction of observed quantities. The predictions from the new scheme are compared with other available laboratory and field observations. The comparisons show that the parameterization is able to reproduce the thermal behavior of sticking efficiency, observed in published laboratory studies, with a peak around −15°C corresponding to dendritic vapor growth of ice. Finally, a new theory of sticking efficiency is proposed. It explains the empirically derived parameterization in terms of a probability distribution of the work that would be required to separate two contacting particles colliding in all possible ways among many otherwise identical collisions of the same pair with a given initial collision kinetic energy. For each collision, if this work done would exceed the initial collision kinetic energy, then there is no separation after impact. The probability of that occurring equals the sticking efficiency.

The possibility is suggested of estimating particle size distributions (PSD) solely based on the bulk quantities of the hydrometeors. The method, inspired by the maximum entropy principle, can be applied to any predefined general PSD form as long as the number of the free parameters is equal to or less than that of the bulk quantities available. As long as an adopted distribution is “physically based,” these bulk characterizations can recover a fairly accurate PSD estimate. This method is tested for ice particle measurements from the Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The total particle number, total mass, and mean size are taken as bulk quantities. The gamma distribution and two distributions obtained under the maximum entropy principle by taking the size and the particle mass, respectively, as a restriction variable are adopted for fit. The fitting error for the two maximum entropy–based distributions is comparable to that of a standard direct fitting method with the gamma distribution. The same procedure works almost equally well when the mean size is removed from the constraint, especially for an exponential distribution. The results suggest that the total particle number and the total mass of the hydrometeors are sufficient for determining the PSD to a reasonable accuracy when a “physically based” distribution is assumed. In addition to the in situ cloud measurements, remote sensing measurements such as those from radar as well as satellite can be adopted as physical constraints. Possibilities of exploiting different types of measurements should be further pursued.

The detailed microphysical processes and properties within the melting layer (ML)—the continued growth of the aggregates by the collection of the small particles, the breakup of these aggregates, the effects of relative humidity on particle melting—are largely unresolved. This study focuses on addressing these questions for in-cloud heights from just above to just below the ML. Observations from four field programs employing in situ measurements from above to below the ML are used to characterize the microphysics through this region. With increasing temperatures from about −4° to +1°C, and for saturated conditions, slope and intercept parameters of exponential fits to the particle size distributions (PSD) fitted to the data continue to decrease downward, the maximum particle size (largest particle sampled for each 5-s PSD) increases, and melting proceeds from the smallest to the largest particles. With increasing temperature from about −4° to +2°C for highly subsaturated conditions, the PSD slope and intercept continue to decrease downward, the maximum particle size increases, and there is relatively little melting, but all particles experience sublimation.

Ice cloud bulk scattering models are derived for 445 discrete wavelengths between 0.2 µm and 100 µm. The methodology for deriving these optical models is based on microphysical data from 11 field campaigns using a variety of in situ probes, and incorporates a correction to mitigate the impact of ice particles that shatter at the probe inlets. The models are also based on a new library of ice habit single scattering properties developed for plates, droxtals, hollow and solid columns, hollow and solid bullet rosettes, an aggregate of solid columns, and a small/large aggregate of plates. Three sets of models are developed that assume the use of solid columns only, the aggregate of solid columns only, and a general habit mixture that incorporates all the habits. The consistency of the resulting models is explored. While the general habit mixture provides consistency with in situ microphysical measurements and the closest agreement with polarized reflectivities observed by the POLDER instrument on the PARASOL satellite, the aggregate of severely roughened solid columns provides the closest agreement between solar and infrared optical thicknesses. Finally, spectral results are presented for the shortwave and longwave models.

Detailed measurements of ice crystals in cirrus clouds were used to compare with results from the Community Atmospheric Model Version 5 (CAM5) global climate model. The observations are from two different field campaigns with contrasting conditions: Atmospheric Radiation Measurements Spring Cloud Intensive Operational Period in 2000 (ARM-IOP), which was characterized primarily by midlatitude frontal clouds and cirrus, and Tropical Composition, Cloud and Climate Coupling (TC4), which was dominated by anvil cirrus. Results show that the model typically overestimates the slope parameter of the exponential size distributions of cloud ice and snow, while the variation with temperature (height) is comparable. The model also overestimates the ice/snow number concentration (0th moment of the size distribution) and underestimates higher moments (2nd through 5th), but compares well with observations for the 1st moment. Overall the model shows better agreement with observations for TC4 than for ARM-IOP in regards to the moments. The mass-weighted terminal fall speed is lower in the model compared to observations for both ARM-IOP and TC4, which is partly due to the overestimation of the size distribution slope parameter. Sensitivity tests with modification of the threshold size for cloud ice to snow autoconversion (Dcs) do not show noticeable improvement in modeled moments, slope parameter and mass weighed fall speed compared to observations. Further, there is considerable sensitivity of the cloud radiative forcing to Dcs, consistent with previous studies, but no value of Dcs improves modeled cloud radiative forcing compared to measurements. Since the autoconversion of cloud ice to snow using the threshold size Dcs has little physical basis, future improvement to combine cloud ice and snow into a single category, eliminating the need for autoconversion, is suggested.

The Small Ice Detector, version 2 (SID-2), High-performance Instrumented Airborne Platform for Environmental Research (HIAPER; SID-2H) was used to detect small ice particles in the early stages of ice formation in the high liquid water environment of tropical maritime cumulus clouds sampled during the Ice in Clouds Experiment—Tropical (ICE-T) field campaign. Its performance in comparison to other probes and the development of new corrections applied to the data are presented. The SID-2H detected small ice crystals among larger particles. It correctly identified water drops, and discriminated between round and irregular particle shapes in water-dominated clouds with errors less than 5%. Remaining uncertainties in the sensing volume and the volume over which coincidence of particles occurred, result in the data being used here in a qualitative manner to identify the presence of ice, and its habits and sizes.

This study examines the occurrence and morphology of frozen drop aggregates in thunderstorm anvils from the US Midwest and describes the environmental conditions where they are found. In situ airborne data collected in anvils using several particle imaging and sizing probes and bulk total water instrumentation during the 2012 Deep Convective Clouds and Chemistry Experiment are examined for the presence of frozen drop aggregates. These types of particles, especially chains of frozen drops, have been only rarely reported before and are hypothesized to aggregate due to electrical forces in the clouds. They were identified in nine of the anvil cases examined to-date, suggesting that they are common features in Midwestern anvils. High concentrations of individual frozen droplets occurred on the tops and edges of one particular set of anvils, while regions closer to the center and bottom of the anvils exhibited fewer frozen drops and more frozen drop aggregates. Bulk ice water content measurements across these anvils could only be explained by contributions from both small particles (frozen droplets) and large particles (large aggregates of frozen droplets). Dual Doppler radar analysis confirmed the presence of deep and strong (> 15 m s−1) updrafts in the parent cloud of one of the anvils. These features contrast with previous anvil measurements in tropical/maritime anvils that evidently do not exhibit the same frequency of frozen drop aggregates.

This study explores relationships between lightning, cloud microphysics, and tropical cyclone (TC) storm structure in Hurricane Karl (16 September 2010) using data collected by the NASA DC-8 and Global Hawk (GH) aircraft during NASA’s Genesis and Rapid Intensification Processes (GRIP) experiment. The research capitalizes on the unique opportunity provided by GRIP to synthesize multiple datasets from two aircraft and analyze the microphysical and kinematic properties of an electrified TC. Five coordinated flight legs through Karl by the DC-8 and GH are investigated, focusing on the inner-core region (within 50 km of the storm center) where the lightning was concentrated and the aircraft were well coordinated. GRIP datasets are used to compare properties of electrified and nonelectrified inner-core regions that are related to the noninductive charging mechanism, which is widely accepted to explain the observed electric fields within thunderstorms. Three common characteristics of Karl’s electrified regions are identified: 1) strong updrafts of 10–20 m s−1, 2) deep mixed-phase layers indicated by reflectivities >30 dBZ extending several kilometers above the freezing level, and 3) microphysical environments consisting of graupel, very small ice particles, and the inferred presence of supercooled water. These characteristics describe an environment favorable for in situ noninductive charging and, hence, TC electrification. The electrified regions in Karl’s inner core are attributable to a microphysical environment that was conducive to electrification because of occasional, strong convective updrafts in the eyewall.

The primary goal of this study is to derive ice particle terminal velocities from micron to centimeter sizes and for atmospheric pressures covering the range 200–1000 hPa from data spanning a wide range of locations, temperatures, and altitudes and to parameterize the results for use in cloud through cloud models. The study uses data from 10 field programs spanning the temperature range −86° to 0°C and encompassing a total of about 800 000 km of cloud horizontal pathlengths and includes measurements of ice particle size distributions (PSDs) and direct measurements of the ice water content (IWC). The necessary ice particle variables are derived using variables that are interconnected rather than varying independently from observations reported in the literature. A secondary goal of the study is to quantify the properties of ice cloud particle ensembles over a wide range of temperatures to further the understanding of how ice particle ensembles and ice clouds develop. Functional forms for the PSDs and mass– and area–dimensional relationships are developed from the observations and summarized in a table. The PSDs are found to be nearly exponential at temperatures from about −40° to −10°C although deviations from exponentiality are noted outside of this range. It is demonstrated that previous pressure-dependent corrections to ice fall speeds lead to overestimated terminal velocities for particles smaller than 1 mm, particularly so for sizes below 100 μm, with consequent effects on modeled lifetimes of cold ice clouds.

Various improvements were made to a state-of-the-art aerosol–cloud model and comparison of the model results with observations from field campaigns was performed. The strength of this aerosol–cloud model is in its ability to explicitly resolve all the known modes of heterogeneous cloud droplet activation and ice crystal nucleation. The model links cloud particle activation with the aerosol loading and chemistry of seven different aerosol species. These improvements to the model resulted in more accurate prediction especially of droplet and ice crystal number concentrations in the upper troposphere and enabled the model to directly sift the aerosol indirect effects based on the chemistry and concentration of the aerosols. In addition, continental and maritime cases were simulated for the purpose of validating the aerosol–cloud model and for investigating the critical microphysical and dynamical mechanisms of aerosol indirect effects from anthropogenic solute and solid aerosols, focusing mainly on glaciated clouds. The simulations showed that increased solute aerosols reduced cloud particle sizes by about 5 μm and inhibited warm rain processes. Cloud fractions and their optical thicknesses were increased quite substantially in both cases. Although liquid mixing ratios were boosted, there was however a substantial reduction of ice mixing ratios in the upper troposphere owing to the increase in snow production aloft. These results are detailed in the subsequent parts of this study.

Homogeneous freezing of supercooled droplets occurs in convective systems in low and midlatitudes. This droplet-freezing process leads to the formation of a large amount of small ice particles, so-called frozen droplets, that are transported to the upper parts of anvil outflows, where they can influence the cloud radiative properties. However, the detailed microphysics and, thus, the scattering properties of these small ice particles are highly uncertain. Here, the link between the microphysical and optical properties of frozen droplets is investigated in cloud chamber experiments, where the frozen droplets were formed, grown, and sublimated under controlled conditions. It was found that frozen droplets developed a high degree of small-scale complexity after their initial formation and subsequent growth. During sublimation, the small-scale complexity disappeared, releasing a smooth and near-spherical ice particle. Angular light scattering and depolarization measurements confirmed that these sublimating frozen droplets scattered light similar to spherical particles: that is, they had angular light-scattering properties similar to water droplets. The knowledge gained from this laboratory study was applied to two case studies of aircraft measurements in midlatitude and tropical convective systems. The in situ aircraft measurements confirmed that the microphysics of frozen droplets is dependent on the humidity conditions they are exposed to (growth or sublimation). The existence of optically spherical frozen droplets can be important for the radiative properties of detraining convective outflows.

A Bayesian algorithm to retrieve profiles of cloud ice water content (IWC), ice particle size (Dme), and relative humidity from millimeter-wave/submillimeter-wave radiometers is presented. The first part of the algorithm prepares an a priori file with cumulative distribution functions (CDFs) and empirical orthogonal functions (EOFs) of profiles of temperature, relative humidity, three ice particle parameters (IWC, Dme, distribution width), and two liquid cloud parameters. The a priori CDFs and EOFs are derived from CloudSat radar reflectivity profiles and associated ECMWF temperature and relative humidity profiles combined with three cloud microphysical probability distributions obtained from in situ cloud probes. The second part of the algorithm uses the CDF/EOF file to perform a Bayesian retrieval with a hybrid technique that uses Monte Carlo integration (MCI) or, when too few MCI cases match the observations, uses optimization to maximize the posterior probability function. The very computationally intensive Markov chain Monte Carlo (MCMC) method also may be chosen as a solution method. The radiative transfer model assumes mixtures of several shapes of randomly oriented ice particles, and here random aggregates of spheres, dendrites, and hexagonal plates are used for tropical convection. A new physical model of stochastic dendritic snowflake aggregation is developed. The retrieval algorithm is applied to data from the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) flown on the ER-2 aircraft during the Tropical Composition, Cloud and Climate Coupling (TC4) experiment in 2007. Example retrievals with error bars are shown for nadir profiles of IWC, Dme, and relative humidity, and nadir and conical scan swath retrievals of ice water path and average Dme. The ice cloud retrievals are evaluated by retrieving integrated 94 GHz backscattering from CoSSIR for comparison with the Cloud Radar System (CRS) flown on the same aircraft. The rms difference in integrated backscattering is around 3 dB over a 30 dB range. A comparison of CoSSIR retrieved and CRS measured reflectivity shows that CoSSIR has the ability to retrieve low-resolution ice cloud profiles in the upper troposphere.

Snow particle size distributions (particle size >400 µm) in the western Arctic measured with in situ aircraft instrumentation during the Surface Heat Budget of the Arctic/First ISCCP Regional Experiment - Arctic Clouds Experiment and MixedPhase Arctic Cloud Experiment are analysed. Three cases of shallow, precipitating mixed-phase boundary-layer clouds and two cases of deep, precipitating frontal clouds are examined. Overall, the shallow cases had much lower values of particle concentration and ice water content than the deep cases, indicating large differences in ice initiation and growth between these regimes. Within a given case for both the shallow and deep frontal systems, and for the dataset as a whole, crystal concentration had little correlation with temperature (height), despite an active aggregation process that was indicated by large aggregates (>5 mm) observed in four out of the five cases. Exponential size distributions are fitted to the observations, allowing a direct comparison with the snow particle size distributions that are represented with exponential functions in many bulk microphysics schemes used in weather and climate models. Values of the fitted intercept parameter N0 are generally 2 –10 times smaller for the shallow compared to the deep frontal cases as a result of differences in crystal concentration between these regimes. Values of N0 ∼ 10 7 m −4 specified for snow in many bulk microphysics schemes are broadly consistent with fitted N0 for the deep cases but larger than values for the shallow cases. The deep frontal cases also exhibit a relationship between N0 and temperature consistent with previous observations of midlatitude frontal systems. However, there are no consistent differences in N0 between the shallow and deep cases when partitioned by ice water content. Fitted values of slope parameter λ for the shallow and deep cases are generally consistent with previous studies of lower-latitude cloud systems. Copyright c � 2011 Royal Meteorological Society

This study summarizes recent improvements in the development of bulk scattering/absorption models at solar wavelengths. The approach combines microphysical measurements from various field campaigns with single-scattering properties for nine habits including droxtals, plates, solid/hollow columns, solid/hollow bullet rosettes, and several types of aggregates. Microphysical measurements are incorporated from a number of recent field campaigns in both the Northern and Southern Hemisphere. A set of 12 815 particle size distributions is used for which Tcld ≤ −40°C. The ice water content in the microphysical data spans six orders of magnitude. For evaluation, a library of ice-particle single-scattering properties is employed for 101 wavelengths between 0.4 and 2.24 μm. The library includes the full phase matrix as well as properties for smooth, moderately roughened, and severely roughened particles. Habit mixtures are developed for generalized cirrus, midlatitude cirrus, and deep tropical convection. The single-scattering properties are integrated over particle size and wavelength using an assumed habit mixture to develop bulk scattering and absorption properties. In comparison with global Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) data, models built with severely roughened particles compare best for all habit mixtures. The assumption of smooth particles provided the largest departure from CALIOP measurements. The use of roughened rather than smooth particles to infer optical thickness and effective diameter from satellite imagery such as the Moderate Resolution Imaging Spectroradiometer (MODIS) will result in a decrease in optical thickness and an increase in particle size.

An ensemble model of cirrus ice crystals combined with a parametrized particle size distribution function (PSD) is used to predict the ice water content (IWC), column-integrated IWC (ice water path, IWP), volume extinction coefficient, and the total solar optical depth, for five tropical cirrus cases. The PSD is estimated from the IWC and in-cloud temperature, and comparisons are presented between the ensemble model predictions and in situ estimates of these microphysical and macrophysical quantities. The in situ estimates were obtained during the Aerosol and Chemical Transport In tropical conVection (ACTIVE) campaign between November-December 2005 and January-February 2006, based at Darwin, Australia. The microphysical instrumentation deployed on the Airborne Australia Egrett research aircraft were the SPEC Cloud Particle Imaging (CPI) probe, Cloud and Aerosol Spectrometer (CAS) and Cloud Imaging Probe (CIP). The CPI was used to measure ice crystal size from about 5 to 1800 mu m, ice crystal number concentration, and to estimate ice crystal shape, IWC, IWP, volume extinction coefficient and the total solar optical depth. The CIP instrument was also used to measure ice crystal size from about 25 to 1550 mu m, ice crystal number concentration and to estimate IWC. For all flights the limited CPI shape recognition algorithm recorded that about 80\% or greater of the ice crystal populations were composed of small irregular or `quasi-spherical' ice crystals. The CPI- and CIP-estimated IWC distributions are compared against each other and it is shown that the distributions are not significantly different at the 95\% level of confidence. The CPI- estimated averaged IWC ranged between approximately 5.3 and 98.2 mg m(-3). The CPI- estimated IWP and total solar optical depth ranged between similar to 1.0 +/- 0.5 and 35.0 +/- 17 g m(-2) and between 0.1 +/- 0.05 and 1.46 +/- 0.73, respectively. To predict the IWC and IWP, an ensemble model effective density-size relationship is derived, and it is shown that the uncertainty in the model predictions are generally within the uncertainty of the CPI estimates for all cases considered. It is also demonstrated that, when the CPI- estimated total solar optical depth is greater than unity, the ensemble model combined with the PSD scheme predicts an uncertainty in the volume extinction coefficient and total solar optical depth that is within the CPI experimental range of uncertainty. However, for total solar optical depths much less than unity, the ensemble model combined with the PSD scheme does not generally predict an uncertainty in the volume extinction coefficient and total solar optical depth that is within the lower range of the CPI uncertainty; the physical reason for this is further explored. The paper demonstrates that there is predictive value in combining an ensemble model of ice crystals with a universal PSD scheme to predict the microphysical and macrophysical properties of importance to radiative transfer through tropical cirrus. Moreover, in the case of very low IWC tropical cirrus, further characterization of the PSD is required using a number of in situ instruments. Copyright (C) Royal Meteorological Society and Crown Copyright, 2011

The mass–dimensional relationship put forth by Brown and Francis has been widely used for developing parameterizations for representing ice cloud microphysical properties. This relationship forms the cornerstone for past and forthcoming retrievals of ice cloud properties from ground-based and spaceborne active and passive sensors but has yet to be rigorously evaluated. This study uses data from six field campaigns to evaluate this mass–dimensional relationship in a variety of ice cloud types and temperatures and to account for the deviations observed with temperature and size, based on properties of the ice particle ensembles. Although the Brown and Francis relationship provides a good match to the observations in a mean sense, it fails to capture dependences on temperature and particle size that are a result of the complex microphysical processes operative within most ice cloud layers. Mass–dimensional relationships that provide a better fit to the observations are developed.

An analysis of two days of in situ observations of ice particle size spectra, in convectively generated cirrus, obtained during NASA’s Tropical Composition, Cloud, and Climate Coupling (TC4) mission is presented. The observed spectra are examined for their fit to the exponential, gamma, and lognormal function distributions. Characteristic particle size and concentration density scales are determined using two (for the exponential) or three (for the gamma and lognormal functions) moments of the spectra. It is shown that transformed exponential, gamma, and lognormal distributions should collapse onto standard curves. An examination of the transformed spectra, and of deviations of the transformed spectra from the standard curves, shows that the lognormal function provides a better fit to the observed spectra.

Anvils produced by vigorous tropical convection contribute significantly to the earth’s radiation balance, and their radiative properties depend largely on the concentrations and sizes of the ice particles that form them. These microphysical properties are determined to an important extent by the fate of supercooled droplets, with diameters from 3 to about 20 microns, lofted in the updrafts. The present study addresses the question of whether most or all of these droplets are captured by ice particles or if they remain uncollected until arriving at the −38°C level where they freeze by homogeneous nucleation, producing high concentrations of very small ice particles that can persist and dominate the albedo. Aircraft data of ice particle and water droplet size distributions from seven field campaigns at latitudes from 25°N to 11°S are combined with a numerical model in order to examine the conditions under which significant numbers of supercooled water droplets can be lofted to the homogeneous nucleation level. Microphysical data were collected in pristine to heavily dust-laden maritime environments, isolated convective updrafts, and tropical cyclone updrafts with peak velocities reaching 25 m s−1. The cumulative horizontal distance of in-cloud sampling at temperatures of −20°C and below exceeds 50 000 km. Analysis reveals that most of the condensate in these convective updrafts is removed before reaching the −20°C level, and the total condensate continues to diminish linearly upward. The amount of condensate in small (

In situ measurements of ice crystal concentrations and sizes made with aircraft instrumentation over the past two decades have often indicated the presence of numerous relatively small (< 50 μm diameter) crystals in cirrus clouds. Further, these measurements frequently indicate that small crystals account for a large fraction of the extinction in cirrus clouds. The fact that the instruments used to make these measurements, such as the Forward Scattering Spectrometer Probe (FSSP) and the Cloud Aerosol Spectrometer (CAS), ingest ice crystals into the sample volume through inlets has led to suspicion that the indications of numerous small-crystals could be artifacts of large-crystal shattering on the instrument inlets. We present new aircraft measurements in anvil cirrus sampled during the Tropical Composition, Cloud, and Climate Coupling (TC4) campaign with the 2-Dimensional Stereo (2D-S) probe, which detects particles as small as 10 μm. The 2D-S has detector "arms" instead of an inlet tube. Since the 2D-S probe surfaces are much further from the sample volume than is the case for the instruments with inlets, it is expected that 2D-S will be less susceptible to shattering artifacts. In addition, particle inter-arrival times are used to identify and remove shattering artifacts that occur even with the 2D-S probe. The number of shattering artifacts identified by the 2D-S interarrival time analysis ranges from a negligible contribution to an order of magnitude or more enhancement in apparent ice concentration over the natural ice concentration, depending on the abundance of large crystals and the natural small-crystal concentration. The 2D-S measurements in tropical anvil cirrus suggest that natural small-crystal concentrations are typically one to two orders of magnitude lower than those inferred from CAS. The strong correlation between the CAS/2D-S ratio of small-crystal concentrations and large-crystal concentration suggests that the discrepancy is likely caused by shattering of large crystals on the CAS inlet. We argue that past measurements with CAS in cirrus with large crystals present may contain errors due to crystal shattering, and past conclusions derived from these measurements may need to be revisited. Further, we present correlations between CAS spurious concentration and 2D-S large-crystal mass from spatially uniform anvil cirrus sampling periods as an approximate guide for estimating quantitative impact of large-crystal shattering on CAS concentrations in previous datasets. We use radiative transfer calculations to demonstrate that in the maritime anvil cirrus sampled during TC4, small crystals indicated by 2D-S contribute relatively little cloud extinction, radiative forcing, or radiative heating in the anvils, regardless of anvil age or vertical location in the clouds. While 2D-S ice concentrations in fresh anvil cirrus may often exceed 1 cm−3, and are observed to exceed 10 cm−3 in turrets, they are typically ~0.1 cm−3 and rarely exceed 1 cm−3 (

Using airborne data from several recent field projects, the authors have taken another look at the properties of exponential ice particle size distributions (PSDs) when the PSDs are broad. Two primary questions are addressed: for what ranges of ice water content (IWC) and equivalent radar reflectivity (Ze) do exponentials produce accurate estimates of these higher moments of the PSD, and why is there a lower limit to the value to the slope of exponential fits to PSD, λ, as has been found from airborne measurements? Earlier studies at temperatures primarily above −10°C have found that λ measured in snow during spiral descents through deep ice cloud layers decreases to about 9 cm−1 and then remains there. Several physical processes have been advanced to explain these observations. If reliable, the data could be used to improve retrieval of ice cloud properties through remote sensing and for cloud model representations of ice cloud microphysical properties. For data acquired from 2D probes, recent evidence indicates shattering of large ice particles ahead of, but attributable to, the probe’s sensing area, generating small crystals that the probe then senses. Shattered artifacts have been objectively removed from the data. Comparisons of size distributions before and after removal of suspected shattered particles suggest that the reported minimum may have been due to shattering and/or other instrument errors. The revised PSDs indicate that for λ < 40 cm−1, 0.1 g m−1 < IWC, and 5 dB < Ze, moments two (IWC) through four (Ze) of the PSD are dominated by particles larger than a few hundred microns. Analytical representations with more variables than exponentials (e.g., gamma PSD) are not required to accurately derive these moments from the PSD. In these situations, the intercept parameter of the exponential PSD, N0 ≈ 1 cm−4, is 5 to 30 times larger than assumed earlier.

The ventilation factor and capacitance used in numerical models to represent ice crystal aggregates directly affects the growth rate of the ice crystal aggregates, and consequently the sink of atmospheric water vapor. Currently, numerical models that prognose ice water content (IWC) and water vapor mixing ratio represent the capacitance and ventilation factor of precipitation-sized particles with simplified geometries, such as hexagonal plates. The geometries of actual precipitation-sized particles are often more complex, and a test of the values being employed is needed. Aircraft observations obtained during a Lagrangian spiral descent through the sublimation zone of a tropical anvil cloud have been used to determine an estimate of combined dimensionless capacitance and ventilation factor for the nonpristine geometries exhibited by ice crystal aggregates. By combining measurements of bulk ice water content, the particle size distribution, and environmental subsaturation, the change in ice water content was modeled throughout the spiral descent and compared with observations of the change in ice water content. Uncertainties resulting from potential systematic biases in the measurements and parameterizations used in the analysis were investigated with sensitivity tests. Most of the uncertainty was related to an assumed maximum potential bias in the measurement of IWC of ±45%. The resulting combined ventilation factor and dimensionless capacitance value was 1.3 (with a range of 0.6–1.9, defined by 68% of sensitivity test trials) for a particle size–weighted mean value of (Sc)1/3(Re)1/2 = 14.9 ± 1.7, where Sc is the Schmidt number and Re is the Reynolds number. Results from commonly adopted combinations of ventilation factor relations and capacitances are compared with the observations presented here, and, finally, surrogate dimensionless capacitances are suggested that when combined with commonly used ventilation factor relations are consistent with the results presented herein.

Many microphysical process rates involving snow are proportional to moments of the snow particle size distribution (PSD), and in this study a moment estimation parameterization applicable to both midlatitude and tropical ice clouds is proposed. To this end aircraft snow PSD data were analyzed from tropical anvils [Tropical Rainfall Measuring Mission/Kwajelein Experiment (TRMM/KWAJEX), Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE)] and midlatitude stratiform cloud [First International Satellite Cloud Climatology Project Research Experiment (FIRE), Atmospheric Radiation Measurement Program (ARM)]. For half of the dataset, moments of the PSDs are computed and a parameterization is generated for estimating other PSD moments when the second moment (proportional to the ice water content when particle mass is proportional to size squared) and temperature are known. Subsequently the parameterization was tested with the other half of the dataset to facilitate an independent comparison. The parameterization for estimating moments can be applied to midlatitude or tropical clouds without requiring prior knowledge of the regime of interest. Rescaling of the tropical and midlatitude size distributions is presented along with fits to allow the user to recreate realistic PSDs given estimates of ice water content and temperature. The effects of using different time averaging were investigated and were found not to be adverse. Finally, the merits of a single-moment snow microphysics versus multimoment representations are discussed, and speculation on the physical differences between the rescaled size distributions from the Tropics and midlatitudes is presented.