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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]

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]

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]

Since the advent of dual-polarization radar, methods of classifying hydrometeors by type from measured polarization variables have been developed. The deterministic approach of existing hydrometeor classification algorithms of assigning only one dominant habit to each radar sample volume does not properly consider the distribution of habits present in that volume, however. During the Profiling of Winter Storms field campaign, the "NSF/NCAR C-130" aircraft, equipped with in situ microphysical probes, made multiple passes through the comma heads of two cyclones as the Mobile Alabama X-band dual-polarization radar performed range-height indicator scans in the same plane as the C-130 flight track. On 14-15 February and 21-22 February 2010, 579 and 202 coincident data points, respectively, were identified when the plane was within 10s (~1 km) of a radar gate. For all particles that occurred for times within different binned intervals of radar reflectivity ZHH and of differential reflectivity ZDR, the reflectivity-weighted contribution of each habit and the frequency distributions of axis ratio and sphericity were determined. This permitted the determination of habits that dominate particular ZHH and ZDR intervals; only 40% of the ZHH-ZDR bins were found to have a habit that contributes over 50% to the reflectivity in that bin. Of these bins, only 12% had a habit that contributes over 75% to the reflectivity. These findings show the general lack of dominance of a given habit for a particular ZHH and ZDR and suggest that determining the probability of specific habits in radar volumes may be more suitable than the deterministic methods currently used. [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]

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]

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]

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 liquid 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 and 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 labelled 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 78% 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 °C and 5 °C, there are roughly equal numbers of frozen and liquid mid-size particles (0.17 < Deq < 0.33 mm) and larger particles (Deq > 0.33 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]

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]

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]

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]

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]

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]