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.

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, 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. 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.

Scattering models of precipitation‐size ice particles have shown that aggregates and spheroidal particles occupy distinct regions of the K_u‐K_a‐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.

The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rain and snow with an emphasis on the evaluation of snowfall retrieval. Disdrometer measurements of rain show support for the two separate prescribed relations within the GPM-DPR algorithm between the precipitation rate (R) and the mass weighted mean diameter ( $D_m$ ) with a mean absolute percent error ( $MAPE$ ) on R of $\mathrm{29\%}$ and $\mathrm{47\%}$ and a mean bias percentage ( $MBP$ ) of $-\mathrm{6\%}$ and $-\mathrm{20\%}$ for the stratiform and convective relation, respectively. Ground-based disdrometer measurements of snow show higher MAPE and MBP values in the retrieval of R, at $\mathrm{77\%}$ and $-\mathrm{52\%}$ , respectively, compared to the stratiform rain relation. An investigation using the disdrometer-measured fall velocity and mass in the calculation of R and $D_m$ illustrates that the variability found in hydrometeor mass causes a poor correlation between R and $D_m$ in snowfall. The results presented here suggest that $R-D_m$ retrieval is likely not optimal in snowfall, and other retrieval techniques for R should be explored.

With the launch of the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data synthetically derived from state-of-the-art ice particle scattering models and measured in-situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass weighted mean diameter (D_ml) and the liquid equivalent normalized intercept parameter (N_wl). Evaluations against a test dataset showed statistically significant improved ice water content (IWC) retrievals compared to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were –0.7%, +2.6% and +1% for D_ml, N_wl and IWC, respectively. An evaluation on three case-studies with co-located radar observations and in-situ microphysical data show that the NN retrieval has MPE of –13%, +120% and +10% for D_ml, N_wl and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals compared to the default algorithm, removing the default algorithm’s ray-to-ray instabilities, and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.

Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form $m=a{D}^b$ are widely used and usually universally applied across the complete range of particle sizes. For the first time, the dependence of a and b coefficients in m–D relationships on median mass diameter (D_mm) is studied. Using combined cloud microphysical data collected during the Olympic Mountains Experiment and coincident observations from Airborne Precipitation Radar Third Generation, D_mm-dependent (a, b) coefficients are derived and represented as surfaces of equally plausible solutions determined by some tolerance in the chi-squared difference ${\chi }^2$ that minimizes the difference between observed and retrieved radar reflectivity. Robust dependences of a and b on D_mm are shown with both parameters significantly decreasing with D_mm, leading to smaller effective densities for larger D_mm ranges. A universally applied constant m–D relationship overestimates the mass of large aggregates when D_mm is between 3–6 mm and temperatures are between −15–0 °C. Multiple m–D relations should be applied for different D_mm ranges in retrievals and simulations to account for the variability of particle sizes that are responsible for the mass and thus for the variability of particle shapes and densities.