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.

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.

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.

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.

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.