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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]

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]

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]