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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]

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]