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Cloud-top phase (CTP) impacts cloud albedo and pathways for ice particle nucleation, growth, and fallout within extratropical cyclones. This study uses airborne lidar, radar, and Rapid Refresh analysis data to characterize CTP within extratropical cyclones as a function of cloud-top temperature (CTT). During the 2020, 2022, and 2023 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign deployments, the Earth Resources 2 (ER-2) aircraft flew 26 research flights over the northeast and midwest United States to sample the cloud tops of a variety of extratropical cyclones. A training dataset was developed to create probabilistic phase classifications based on Cloud Physics Lidar measurements of known ice and liquid clouds. These classifications were then used to quantify dominant CTP in the top 150 m of clouds sampled by the Cloud Physics Lidar in storms during IMPACTS. Case studies are presented illustrating examples of supercooled liquid water at cloud top at different CTT ranges (−3° < CTTs < −35°C) within extratropical cyclones. During IMPACTS, 19.2% of clouds had supercooled liquid water present at cloud top. Supercooled liquid was the dominant phase in extratropical cyclone cloud tops when CTTs were >−20°C. Liquid-bearing cloud tops were found at CTTs as cold as −37°C. Significance Statement: Identifying supercooled liquid cloud tops' frequency is crucial for understanding ice nucleation mechanisms at cloud top, cloud radiative effects, and aircraft icing. In this study, airborne lidar, radar, and model temperature data from 26 research flights during the NASA IMPACTS campaign are used to characterize extratropical cyclone cloud-top phase (CTP) as a function of cloud-top temperature (CTT). The results show that liquid was the dominant CTP present in extratropical cyclone cloud tops when CTTs were >−20°C with decreasing supercooled liquid cloud-top frequency at temperatures < −20°C. Nevertheless, liquid was present at CTTs as cold as −37°C. [ABSTRACT FROM AUTHOR]

On 7 February 2020 a relatively deep cyclone (∼980 hPa) with midlevel frontogenesis produced heavy snow (20–30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud, while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) Model simulations at 2- and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24-h runs realistically produced a broad sloping region of frontogenesis at midlevels typically; however, there were relatively large (20%–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (<10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20%–30% have too little ice aloft and at low levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5%–10% more precipitation than the 2-km grid. Significance Statement: Heavy snowfall from U.S. East Coast winter storms can cause major societal problems, yet few studies have investigated these storms using field observations and model data. This study focuses on the 7 February 2020 event, where 20–40 cm of snow fell over west-central New York. Our analysis shows a broad region of ascent, rather than a concentrated region favoring a well-defined snowband was the primary process contributing to snowfall. Last, model microphysics were validated within this storm using the in situ aircraft data. Errors in the snow generation aloft and snow growth at low levels likely contributed to the simulated surface precipitation underprediction, but most of the forecast uncertainty is from initial conditions for this short-term (∼24-h lead time) forecast. [ABSTRACT FROM AUTHOR]

On 7 February 2020, precipitation within the comma-head region of an extratropical cyclone was sampled remotely and in situ by two research aircraft, providing a vertical cross section of microphysical observations and fine-scale radar measurements. The sampled region was stratified vertically by distinct temperature layers and horizontally into a stratiform region on the west side, and a region of elevated convection on the east side. In the stratiform region, precipitation formed near cloud top as side-plane, polycrystalline, and platelike particles. These habits occurred through cloud depth, implying that the cloud-top region was the primary source of particles. Almost no supercooled water was present. The ice water content within the stratiform region showed an overall increase with depth between the aircraft flight levels, while the total number concentration slightly decreased, consistent with growth by vapor deposition and aggregation. In the convective region, new particle habits were observed within each temperature-defined layer along with detectable amounts of supercooled water, implying that ice particle formation occurred in several layers. Total number concentration decreased from cloud top to the −8°C level, consistent with particle aggregation. At temperatures > −8°C, ice particle concentrations in some regions increased to >100 L−1, suggesting secondary ice production occurred at lower altitudes. WSR-88D reflectivity composites during the sampling period showed a weak, loosely organized banded feature. The band, evident on earlier flight legs, was consistent with enhanced vertical motion associated with frontogenesis, and at least partial melting of ice particles near the surface. A conceptual model of precipitation growth processes within the comma head is presented. Significance Statement: Snowstorms over the northeast United States have major impacts on travel, power availability, and commerce. The processes by which snow forms in winter storms over this region are complex and their snowfall totals are hard to forecast accurately because of a poor understanding of the microphysical processes within the clouds composing the storms. This paper presents a case study from the NASA IMPACTS field campaign that involved two aircraft sampling the storm simultaneously with radars, and probes that measure the microphysical properties within the storm. The paper examines how variations in stability and frontal structure influence the microphysical evolution of ice particles as they fall from cloud top to the surface within the storm. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

Multifrequency airborne radars have become instrumental in evaluating the performance of satellite retrievals and furthering our understanding of ice microphysical properties. The dual-frequency ratio (DFR) is influenced by the size, density, and shape of ice particles, with higher values associated with the presence of larger ice particles that may have implications regarding snowfall at the surface. The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign involves the coordination of remote sensing measurements above winter midlatitude cyclones from an ER-2 aircraft to document the fine-scale precipitation structure spanning four radar (X-, Ku-, Ka-, and W-band) frequencies and in situ microphysical measurements from a P-3 aircraft that provide additional insight into the particle size distribution (PSD) behavior and habits of the hydrometeors related to the DFR. A novel approach to identify regions of prominently higher Ku- and Ka-band DFR at the P-3 location for five coordinated flights is presented. The solid-phase mass-weighted mean diameter (Dm) was 58% larger, the effective density (ρe) 37% smaller, and the liquid-equivalent normalized intercept parameter (Nw) 74% lower in regions of prominently higher DFR. Microphysical properties within a triple-frequency framework suggest signatures consistent with aggregation and riming as in previous studies. Last, a pretrained neural network radar retrieval is used to investigate the vertical structure of microphysical properties associated with the larger DFR signatures and provides the spatial context for inferring certain microphysical processes. Significance Statement: The purpose of this study is to better understand what radar measurements from multiple frequencies can tell us about the sizes, shapes, and concentrations of ice particles within winter snowstorms, and how these observations are related to banded precipitation structures since they can have implications for snowfall at the surface. Our results show that ice particles are on average larger and less dense when the reflectivity difference between two radars operating at different wavelengths is larger and supports the process by which crystals aggregate to form larger particles. These findings aim to improve how satellites and forecasting models represent precipitation in the cloud and at the surface. [ABSTRACT FROM AUTHOR]

Maritime boundary‐layer clouds over the Southern Ocean (SO) have a large shortwave radiative effect. Yet, climate models have difficulties in representing these clouds and, especially, their phase in this observationally sparse region. This study aims to increase the knowledge of SO cloud phase by presenting in‐situ cloud microphysical observations from the Southern Ocean Clouds, Radiation, Aerosol, Transport Experimental Study (SOCRATES). We investigate the occurrence of ice in summertime marine stratocumulus and cumulus clouds in the temperature range between 6 and −25°C. Our observations show that in ice‐containing clouds, maximum ice number concentrations of up to several hundreds per liter were found. The observed ice crystal concentrations were on average one to two orders of magnitude higher than the simultaneously measured ice nucleating particle (INP) concentrations in the temperature range below −10°C and up to five orders of magnitude higher than estimated INP concentrations in the temperature range above −10°C. These results highlight the importance of secondary ice production (SIP) in SO summertime marine boundary‐layer clouds. Evidence for rime splintering was found in the Hallett‐Mossop (HM) temperature range but the exact SIP mechanism active at lower temperatures remains unclear. Finally, instrument simulators were used to assess simulated co‐located cloud ice concentrations and the role of modeled HM rime‐splintering. We found that CAM6 is deficient in simulating number concentrations across the HM temperature range with little sensitivity to the model HM process, which is inconsistent with the aforementioned observational evidence of highly active SIP processes in SO low‐level clouds. Plain Language Summary: Clouds in the Southern Ocean are important for climate but not well represented in climate models. Observations in this remote region have been rare. This study presents results from a recent airborne campaign that took place in the Southern Ocean where low‐ and mid‐level clouds were investigated by detecting individual cloud particles within the clouds. Although large fraction of the observed clouds did not contain ice crystals, occasionally high amounts of ice crystals were observed that cannot be explained by ice formation on aerosol particles but were result of multiplication of existing ice crystals. We tested the capability of a commonly used climate model to represent the observed ice concentrations and their sensitivity to one ice multiplication process parameterized in the model. These investigations revealed that the in the model the ice multiplication process was not responsible for generation of ice, which is in contradiction with the observations. Key Points: Ice concentrations several orders of magnitude higher than ice nucleating particle concentrations were observedSecondary ice production was believed to be responsible for the observed high ice number concentrationsComparison with climate model indicated that secondary ice processes are still inadequately represented in the model [ABSTRACT FROM AUTHOR]

The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a NASA-sponsored field campaign to study wintertime snowstorms focusing on East Coast cyclones. This large cooperative effort takes place during the winters of 2020–23 to study precipitation variability in winter cyclones to improve remote sensing and numerical forecasts of snowfall. Snowfall within these storms is frequently organized in banded structures on multiple scales. The causes for the occurrence and evolution of a wide spectrum of snowbands remain poorly understood. The goals of IMPACTS are to characterize the spatial and temporal scales and structures of snowbands, understand their dynamical, thermodynamical, and microphysical processes, and apply this understanding to improve remote sensing and modeling of snowfall. The first deployment took place in January–February 2020 with two aircraft that flew coordinated flight patterns and sampled a range of storms from the Midwest to the East Coast. The satellite-simulating ER-2 aircraft flew above the clouds and carried a suite of remote sensing instruments including cloud and precipitation radars, lidar, and passive microwave radiometers. The in situ P-3 aircraft flew within the clouds and sampled environmental and microphysical quantities. Ground-based radar measurements from the National Weather Service network and a suite of radars located on Long Island, New York, along with supplemental soundings and the New York State Mesonet ground network provided environmental context for the airborne observations. Future deployments will occur during the 2022 and 2023 winters. The coordination between remote sensing and in situ platforms makes this a unique publicly available dataset applicable to a wide variety of interests. [ABSTRACT FROM AUTHOR]

Mixed-phase Southern Ocean clouds are challenging to simulate, and their representation in climate models is an important control on climate sensitivity. In particular, the amount of supercooled water and frozen mass that they contain in the present climate is a predictor of their planetary feedback in a warming climate. The recent Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) vastly increased the amount of in situ data available from mixed-phase Southern Ocean clouds useful for model evaluation. Bulk measurements distinguishing liquid and ice water content are not available from SOCRATES, so single-particle phase classifications from the Two-Dimensional Stereo (2D-S) probe are invaluable for quantifying mixed-phase cloud properties. Motivated by the presence of large biases in existing phase discrimination algorithms, we develop a novel technique for single-particle phase classification of binary 2D-S images using a random forest algorithm, which we refer to as the University of Washington Ice–Liquid Discriminator (UWILD). UWILD uses 14 parameters computed from binary image data, as well as particle inter-arrival time, to predict phase. We use liquid-only and ice-dominated time periods within the SOCRATES dataset as training and testing data. This novel approach to model training avoids major pitfalls associated with using manually labeled data, including reduced model generalizability and high labor costs. We find that UWILD is well calibrated and has an overall accuracy of 95 % compared to 72 % and 79 % for two existing phase classification algorithms that we compare it with. UWILD improves classifications of small ice crystals and large liquid drops in particular and has more flexibility than the other algorithms to identify both liquid-dominated and ice-dominated regions within the SOCRATES dataset. UWILD misclassifies a small percentage of large liquid drops as ice. Such misclassified particles are typically associated with model confidence below 75 % and can easily be filtered out of the dataset. UWILD phase classifications show that particles with area-equivalent diameter (Deq) < 0.17 mm are mostly liquid at all temperatures sampled, down to - 40 ∘C. Larger particles (Deq>0.17 mm) are predominantly frozen at all temperatures below 0 ∘C. Between 0 and 5 ∘C , there are roughly equal numbers of frozen and liquid mid-sized particles (0.170.33 mm) are mostly frozen. We also use UWILD's phase classifications to estimate sub-1 Hz phase heterogeneity, and we show examples of meter-scale cloud phase heterogeneity in the SOCRATES dataset. [ABSTRACT FROM AUTHOR]

For a given cloud, whether the cloud top is predominately made up of ice crystals or supercooled liquid droplets plays a large role in the clouds overall radiative effects. This study uses collocated airborne radar, lidar, and thermodynamic data from 12 high‐altitude flight legs during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) to characterize Southern Ocean (SO) cold sector cloud top phase (i.e., within 96 m of top) as a function of cloud top temperature (CTT). A training data set was developed to create probabilistic phase classifications based on High Spectral Resolution Lidar data and Cloud Radar data. These classifications were then used to identify dominant cloud top phase. Case studies are presented illustrating examples of supercooled liquid water at cloud top at different CTT ranges over the SO (−3°C < CTTs < −28°C). During SOCRATES, 67.4% of sampled cloud top had CTTs less than 0°C. Of the subfreezing cloud tops sampled, 91.7% had supercooled liquid water present in the top 96 m and 74.9% were classified entirely as liquid‐bearing. Liquid‐bearing cloud tops were found at CTTs as cold as −30°C. Horizontal cloud extent was also determined as a function of median cloud top height. Plain Language Summary: Low‐level clouds over the Southern Ocean have a large effect on the region's radiation budget. The radiation budget is strongly influenced by the phase (liquid or ice) of cloud tops, which is where most solar radiation is reflected, and most infrared radiation is radiated to space. For this reason, identifying the phase of cloud tops is important. In this study, airborne radar, lidar, and temperature data from 12 high‐altitude flight legs during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) are used to characterize Southern Ocean cloud top phase as a function of cloud top temperature. The results show that liquid is the dominant phase present in clouds over the Southern Ocean, with liquid present at cloud top temperatures as cold as −30°C. Key Points: Airborne radar, lidar, and thermodynamic data are used to determine Southern Ocean cloud top phase as a function of cloud top temperatureConsidering only clouds with subfreezing cloud top temperatures, the dominant phase at cloud top in 74.9% of clouds was liquidLiquid‐bearing cloud tops were even found at temperatures as cold as −30°C [ABSTRACT FROM AUTHOR]

An atmospheric river (AR) impacting Tasmania, Australia, and the Southern Ocean during the austral summer on 28–29 January 2018 during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study campaign is analyzed using a modeling and observational approach. Gulfstream‐V dropsonde measurements and Global Precipitation Measurement radar analyses were used in conjunction with Weather Research and Forecasting model simulations with water vapor tracers to investigate the relative contributions of tropical and midlatitude moisture sources to the AR. Moisture associated with a monsoonal tropical depression became entrained into a midlatitude frontal system that extended to 60°S, reaching the associated low‐pressure system 850 km off the coast of Antarctica—effectively connecting the tropics and the polar region. Tropical moisture contributed to about 50% of the precipitable water within the AR as the flow moved over the Southern Ocean near Tasmania. The tropical contribution to precipitation decreased with latitude, from >70% over Australia, to ~50% off the Australian coast, to less than 5% poleward of 55°S. The integrated vapor transport (IVT) through the core of the AR reached above 500 kg m−1 s−1 during 1200 UTC 28 January to 0600 UTC 29 January, 1.29 times the average amount of water carried by the world's largest terrestrial river, the Amazon. The high IVT strength might be attributed to the higher water vapor content associated with the warmer temperatures across Australia and the Southern Ocean in austral summer. Plain Language Summary: Atmospheric rivers (ARs) are long, narrow corridors of strong horizontal water vapor transport, normally associated with a low‐level jet stream ahead of a cold front. They play a critical role in the global hydrologic cycle and have been studied extensively over the northeast Pacific. This and a companion paper are the first to analyze an AR event impacting Tasmania, Australia, and the Southern Ocean using a modeling and observational approach. The AR extended from the tropics over northern Australia southeastward to the southern Australian coast and over the Southern Ocean to 60°S. The flow at higher altitudes in the AR was primarily composed of moisture originating in the tropics, while at lower altitudes and warmer temperatures, the moisture was primarily sourced from middle latitudes as a result of the circulation associated with a cold front. The tropical contribution to precipitation decreased with latitude, from >70% over Australia, to ~50% off the Australian coast, to less than 5% poleward of 55°S. Water vapor transported through the core of the AR was more than the average amount of water carried by the world's largest river, the Amazon, during the same time interval. Key Points: The contribution of moisture from the tropics to precipitation within an Australian summer atmospheric river is documentedOver 50% of the moisture arriving at the Southern Ocean at altitudes above the 0°C isotherm was sourced from tropical latitudesThe tropical contribution to precipitation decreased with latitude, >70% over Australia, ~50% off Australia, and <10% poleward of 60°S [ABSTRACT FROM AUTHOR]

An atmospheric river affecting Australia and the Southern Ocean on 28–29 January 2018 during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) is analyzed using nadir‐pointing W‐band cloud radar measurements and in situ microphysical measurements from a Gulfstream‐V aircraft. The AR had a two‐band structure, with the westernmost band associated with a cold frontal boundary. The bands were primarily stratiform with distinct radar bright banding. The microphysical evolution of precipitation is described in the context of the tropical‐ and midlatitude‐sourced moisture zones above and below the 0°C isotherm, respectively, identified in Part I. In the tropical‐sourced moisture zone, ice particles at temperatures less than −8°C had concentrations on the order of 10 L−1, with habits characteristic of lower temperatures, while between −8°C and −4°C, an order of magnitude increase in ice particle concentrations was observed, with columnar habits consistent with Hallett‐Mossop secondary ice formation. Ice particles falling though the 0°C level into the midlatitude‐sourced moisture region and melting provided "seed" droplets from which subsequent growth by collision‐coalescence occurred. In this region, raindrops grew to sizes of 3 mm and precipitation rates averaged 16 mm hr−1. Plain Language Summary: Atmospheric rivers (ARs) are long, narrow zones of enhanced horizontal poleward water vapor transport that have profound effects on the global hydrological cycle. This and a companion study are the first to analyze an AR affecting Australia and the Southern Ocean through an observational and modeling approach, respectively. There were two precipitation bands associated with the AR, which were primarily stratiform in character with an enhancement of radar reflectivity near the melting level. Part I showed that moisture in the upper part of the AR (above the 0°C level) was primarily sourced from the tropics, while moisture in the lower part (below the 0°C level) was primarily sourced from the middle latitudes. The microphysical evolution of precipitation through these two zones from cloud top to the ground within this AR is investigated. Ice particles forming and growing at altitudes above the 0°C level served as the "seeds" that allowed for further growth below the 0°C level as they melted and collided with droplets. The concentrations, shapes, and size distributions of the ice particles falling through each zone are presented and related to microphysical growth processes occurring within the bands. Key Points: The physical structure and microphysical properties of a summer austral atmospheric river are describedIce particle concentrations increased to >100 L−1 in the zone from −8°C to −4°C, consistent with secondary ice formationIce particles falling from the tropical‐sourced moisture stream aloft trigger coalescence below the 0°C isotherm [ABSTRACT FROM AUTHOR]

The bulk microphysical properties and number distribution functions (N(D)) of supercooled liquid water (SLW) and ice inside and between ubiquitous generating cells (GCs) observed over the Southern Ocean (SO) during the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES) measured by in situ cloud probes onboard the NCAR/NSF G‐V aircraft are compared. SLW was detected inside all GCs with an average liquid water content of 0.31 ± 0.19 g m−3, 11% larger than values between GCs. The N(D) of droplets (maximum dimension D < 50 μm) inside and between GCs had only slight differences. For ice particles, on the other hand, the mean concentration (median mass diameter) with D > 200 μm inside GCs was 2.0 ± 3.3 L−1 (323 ± 263 μm), 65% (37%) larger than values outside GCs. As D increases, the percentage differences became larger (up to ~500%). The more and larger ice particles inside GCs suggest the GC updrafts provide a favorable environment for particle growth by deposition and riming and that mixing processes are less efficient at redistributing larger particles. The horizontal scale of observed GCs ranged from 200 to 600 m with a mean of 395 ± 162 m, smaller than GC widths observed in previous studies. This study expands knowledge of the microphysical properties and processes acting in GCs over a wider range of conditions than previously available. Key Points: Supercooled liquid water was detected in all generating cells (GCs) characterized by small horizontal widths over the Southern OceanThe liquid water content and number concentration of droplets inside GCs were slightly larger than values between the GCsThe ice particle sizes, number concentrations, and dispersions inside GCs were larger than those between GCs [ABSTRACT FROM AUTHOR]

The link between stratiform precipitation microphysics and multifrequency radar observables is thoroughly investigated by exploiting simultaneous airborne radar and in situ observations collected from two aircraft during the OLYMPEX/RADEX (Olympic Mountain Experiment/Radar Definition Experiment 2015) field campaign. Above the melting level, in situ images and triple‐frequency radar signatures both indicate the presence of moderately rimed aggregates. Various mass‐size relationships of ice particles and snow scattering databases are used to compute the radar reflectivity from the in situ particle size distribution. At Ku and Ka band, the best agreement with radar observations is found when using the self‐similar Rayleigh‐Gans approximation for moderately rimed aggregates. At W band, a direct comparison is challenging because of the non‐Rayleigh effects and of the probable attenuation due to ice aggregates and supercooled liquid water between the two aircraft. A variational method enables the retrieval of the full precipitation profile above and below the melting layer, by combining the observations from the three radars. Even with three radar frequencies, the retrieval of rain properties is challenging over land, where the integrated attenuation is not available. Otherwise, retrieved mean volume diameters and water contents of both solid and liquid precipitation are in agreement with in situ observations and indicate local changes of the degree of riming of ice aggregates, on the scale of 5 km. Finally, retrieval results are analyzed to explore the validity of using continuity constraints on the water mass flux and diameter within the melting layer in order to improve retrievals of ice properties. Key Points: Signature of moderately rimed aggregates in triple‐frequency radar reflectivity measurements is confirmed by in situ microphysicsSelf‐similar ice crystal models coupled with Rayleigh‐Gans approximation ensure consistency between in situ and radar observationsProfiles of solid and liquid precipitation microphysics are retrieved by combining triple‐frequency radars [ABSTRACT FROM AUTHOR]

Mass–dimension (m – D) relationships determining bulk microphysical properties such as total water content (TWC) and radar reflectivity factor (Z) from particle size distributions are used in both numerical models and remote sensing retrievals. The a and b coefficients representing m=aDb relationships, however, can vary significantly depending on meteorological conditions, particle habits, the definition of particle maximum dimension, the probes used to obtain the data, techniques used to process the cloud probe data, and other unknown reasons. Thus, considering a range of a,b coefficients may be more applicable for use in numerical models and remote sensing retrievals. Microphysical data collected by two-dimensional optical array probes (OAPs) installed on the University of North Dakota (UND) Citation aircraft during the Mid-latitude Continental Convective Clouds Experiment (MC3E) were used in conjunction with TWC data from a Nevzorov probe and ground-based S -band radar data to determine a and b using a technique that minimizes the chi-square difference between the TWC and Z derived from the OAPs and those directly measured by a TWC probe and radar. All a and b values within a specified tolerance were regarded as equally plausible solutions. Of the 16 near-constant-temperature flight legs analyzed during the 25 April, 20 May, and 23 May 2011 events, the derived surfaces of solutions on the first 2 days where the aircraft-sampled stratiform cloud had a larger range in a and b for lower temperature environments that correspond to less variability in N(D) , TWC, and Z for a flight leg. Because different regions of the storm were sampled on 23 May, differences in the variability in N(D) , TWC, and Z influenced the distribution of chi-square values in the (a,b) phase space and the specified tolerance in a way that yielded 2.8 times fewer plausible solutions compared to the flight legs on the other dates. These findings show the importance of representing the variability in a,b coefficients for numerical modeling and remote sensing studies, rather than assuming fixed values, as well as the need to further explore how these surfaces depend on environmental conditions in clouds containing ice hydrometeors. [ABSTRACT FROM AUTHOR]

We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX- RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triplefrequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of highdensity snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions. [ABSTRACT FROM AUTHOR]

Mass-dimension (m-D) relationships determining bulk microphysical properties such as total water content (TWC) and radar reflectivity factor (Z) from particle size distributions are used in both numerical models and remote sensing retrievals. The a and b coefficients representing m = aDb relationships, however, can vary significantly depending on meteorological conditions, particle habits, definition of particle maximum dimension, the probes used to obtain the data, techniques used to process the cloud probe data, and other unknown reasons. Thus, considering a range of a,b coefficients may be more applicable for use in numerical models and remote sensing retrievals. Microphysical data collected by two-dimensional optical array probes (OAPs) installed on the University of North Dakota Citation aircraft during the Mid-latitude Continental Convective Clouds Experiment (MC3E) were used in conjunction with TWC data from a Nevzorov probe and ground-based S-band radar data to determine a and b using a technique that minimizes the chi-square difference between TWC and Z derived from the OAPs and that directly measured by a TWC probe and radar. All a and b within a specified tolerance were regarded as equally plausible solutions. Of the 16 near-constant temperature flight legs analyzed during the 25 April, 20 May, and 23 May 2011 events, the derived surfaces of solutions on the first two days where the aircraft sampled stratiform cloud had a larger range in a and b for lower temperature environments that corresponded to less variability in N(D), TWC, and Z for a flight leg. Because different regions of the storm were sampled on 23 May, differences in the variability of N(D), TWC, and Z influenced the distribution of chi-square values in (a,b) phase space and the specified tolerance in a way that yielded 6.7 times fewer plausible solutions compared to the flight legs on the other dates. These findings show the importance of representing the variability in a,b coefficients for numerical modeling and remote sensing studies rather than assuming fixed values, as well as the need to further explore how these surfaces depend on environmental conditions in ice and mixed phase clouds. [ABSTRACT FROM AUTHOR]

Abstract: Scattering models of precipitation‐size ice particles have shown that aggregates and spheroidal particles occupy distinct regions of the Ku‐Ka‐W‐band dual‐frequency ratio (DFR) plane. Furthermore, past ground‐based observations suggest that particle bulk density and characteristic size can be retrieved from the DFR plane. This study, for the first time, evaluates airborne DFR observations with coincident airborne microphysical measurements. Over 2 hr of microphysical data collected aboard the University of North Dakota Citation from the Olympic Mountains Experiment are matched with Airborne Precipitation and cloud Radar Third Generation triple‐frequency radar observations. Across all flights, 31% (63%) of collocated data points show nonspheroidal (spheroidal) particle scattering characteristics. DFR observations compared with in situ observations of effective density and particle characteristic size reveal relationships that could potentially be used to develop quantitative dual‐ and triple‐frequency DFR ice property retrievals. [ABSTRACT FROM AUTHOR]

We have developed an algorithm that retrieves the microphysical properties of falling snow from multi-frequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multi-frequency airborne radar observations from the OLYMPEX/RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar, and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and test the sensitivity of the algorithm to the prior assumptions. The results suggest that multi-frequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars, and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. [ABSTRACT FROM AUTHOR]

An analysis of the microphysical structure of elevated convection within the comma head region of two winter cyclones over the midwestern United States is presented using data from the Wyoming Cloud Radar (WCR) and microphysical probes on the NSF/NCAR C-130 aircraft during the Profiling of Winter Storms campaign. The aircraft penetrated 36 elevated convective cells at various temperatures T and distances below cloud top Zd - The statistical properties of ice water content (IWC), liquid water content (LWC), ice particle concentration with diameter > 500 μm N>500, and median mass diameter Dmm, as well as particle habits within these cells were determined as functions of zd and T for active updrafts and residual stratiform regions originating from convective towers that ascended through unsaturated air. Insufficient data were available for analysis within downdrafts. For updrafts stratified by zd, distributions of IWC, N>500, and Dmm for all zd between 1000 and 4000 m proved to be statistically indistinct. These results imply that turbulence and mixing within the updrafts effectively distributed particles throughout their depths. A decrease in IWC and N>500 in the layer closest to cloud top was likely related to cloud-top entrainment. Within residual stratiform regions, decreases in IWC and N>500 and increases in Dmm were observed with depth below cloud top. These trends are consistent with particles falling and aggregating while entrainment and subsequent sublimation was occurring. [ABSTRACT FROM AUTHOR]