This study demonstrates that linear depolarization ratio (LDR) values obtained from zenith-pointing Ka-band radar Doppler velocity spectra are sufficient for detecting columnar ice crystals. During a deep precipitating system over the Arctic on 7 December 2013, the radar recorded LDR values up to −15 dB at temperatures corresponding to the columnar ice crystal growth regime. These LDR values were also consistent with scattering calculations for columnar ice crystals. Enhancements in LDR were suppressed within precipitation fallstreaks because the enhanced LDR values of columnar ice crystals were masked by the returns from the particles within the fallstreaks. However, Doppler velocity spectra of LDR within the fallstreak distinguished populations of slower-falling particles with high LDR (>−15 dB) and faster-falling particles with much lower LDR, suggesting that columnar ice crystals with high LDR coexisted with larger isometric particles that produced low LDR while dominating the total copolar reflectivity, thereby decreasing LDR. The measurements suggest that the columnar ice crystals originated in liquid-cloud layers through secondary ice production.

Understanding phase transitions in mixed-phase clouds is of great importance because the hydrometeor phase controls the lifetime and radiative effects of clouds. In high latitudes, these cloud radiative effects have a crucial impact on the surface energy budget and thus on the evolution of the ice cover. For a springtime low-level mixed-phase stratiform cloud case from Barrow, Alaska, a unique combination of instruments and retrieval methods is combined with multiple modeling perspectives to determine key processes that control cloud phase partitioning. The interplay of local cloud-scale versus large-scale processes is considered. Rapid changes in phase partitioning were found to be caused by several main factors. Major influences were the large-scale advection of different air masses with different aerosol concentrations and humidity content, cloud-scale processes such as a change in the thermodynamical coupling state, and local-scale dynamics influencing the residence time of ice particles. Other factors such as radiative shielding by a cirrus and the influence of the solar cycle were found to only play a minor role for the specific case study (11–12 March 2013). For an even better understanding of cloud phase transitions, observations of key aerosol parameters such as profiles of cloud condensation nucleus and ice nucleus concentration are desirable.

Microphysical processes in shallow Arctic precipitation clouds are illustrated using measurements of differential reflectivity ZDR from the U.S. Department of Energy Atmospheric Radiation Measurement Program polarimetric X-band radar deployed in Barrow, Alaska. X-band hemispheric range height indicator scans used in conjunction with Ka-band radar and lidar measurements revealed prolonged periods dominated by vapor depositional, riming, and/or aggregation growth. In each case, ice precipitation fell through at least one liquid-cloud layer in a seeder–feeder situation before reaching the surface. A long period of sustained low radar reflectivity ZH (<0–5 dBZ) and high ZDR (6–7.5 dB) throughout the depth of the cloud and subcloud layer, coinciding with observations of large pristine dendrites at the surface, suggests vapor depositional growth of large dendrites at low number concentrations. In contrast, ZDR values decreased to 2–3 dB in the mean profile when surface precipitation was dominated by aggregates or rimed dendrites. Small but consistent differences in zenith Ka-band radar Doppler velocity and lidar depolarization measurements were found between aggregation- and riming-dominated periods. The clean Arctic environment can enhance ZDR signals relative to more complex midlatitude cases, producing higher values.

The study of Arctic ice and mixed‐phase clouds, which are characterized by a variety of ice particle types in the same cloudy volume, is challenging research. This study illustrates a new approach to qualitative and quantitative analysis of the complexity of ice and mixed‐phase microphysical processes in Arctic deep precipitating systems using the combination of Ka‐band zenith‐pointing radar Doppler spectra and quasi‐vertical profiles of polarimetric radar variables measured by a Ka/W‐band scanning radar. The results illustrate the frequent occurrence of multimodal Doppler spectra in the dendritic/planar growth layer, where locally generated, slower‐falling particle populations are well separated from faster‐falling populations in terms of Doppler velocity. The slower‐falling particle populations contribute to an increase of differential reflectivity (ZDR), while an enhanced specific differential phase (KDP) in this dendritic growth temperature range is caused by both the slower and faster‐falling particle populations. Another area with frequent occurrence of multimodal Doppler spectra is in mixed‐phase layers, where both populations produce ZDR and KDP values close to 0, suggesting the occurrence of a riming process. Joint analysis of the Doppler spectra and the polarimetric radar variables provides important insight into the microphysics of snow formation and allows the separation of the contributions of ice of different habits to the values of reflectivity and ZDR.

Abstract. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-meansquare differences between the two sources are of the order of 1 and 2 m s −1 , respectively, and time-height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s −1 . The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. The results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.

Characteristics of graupel in an Arctic deep mixed-phase cloud on 7 December 2013 were identified with observations from an X-band scanning polarimetric radar and a Ka-band zenith-pointing radar in conjunction with scattering calculations. The cloud system produced generating cells and strongly sheared precipitation fall streaks. The X-band radar hemispheric RHI observables revealed spatial sorting of polarimetric signatures: decreasing (with increasing range) differential propagation phase shift φDP, negative specific differential phase KDP collocated with negative differential reflectivity ZDR in the upper half of the fall streak, and increasing or near-constant φDP with positive ZDR at the bottom edge of the fall streak. The negative KDP and ZDR, indicating prolate particles with vertically oriented maximum dimensions, were consistent with small, slow-falling conical graupel coexisting with low concentrations of more isometric graupel. The observed negative KDP values were best matched by scattering calculations for small, dense conical graupel with 30°–40° cone angles. The positive KDP and ZDR and the Doppler spectra indicate that large isometric graupel coexisted with a second population of slower-falling rimed platelike particles in the lower half of the fall streak. Through the core of the fall streak, φDP decreased in range while ZDR was slightly positive, indicating that the prolate conical graupel dominated φDP while the isometric larger graupel dominated reflectivity (and thus ZDR). These results demonstrate the capability of polarimetric observables and Doppler spectra to distinguish different growth stages of rimed particles, allowing for the improvement of hydrometeor classification methods.

A simple numerical experiment was performed to investigate the result published in many papers that measurements indicate that aggregates may be well represented as oblate spheroids with mean aspect ratio (semiminor axis to semimajor axis length) of 0.6. The aspect ratio measurements are derived from two-dimensional projections of complex three-dimensional aggregates. Here, aggregates were modeled as ellipsoids with semiprincipal axes of length a, b, and c, which include oblate spheroids (a = b) as a class, and the projected aspect ratios of large numbers of two-dimensional projections of them were sampled. When sampling oblate spheroids with aspect ratio 0.6 over random orientations, the mean projected aspect ratio is 0.746. A mean projected aspect ratio of 0.6 is obtained for an oblate spheroid with aspect ratio of 0.33. When sampling randomly oriented ellipsoids with semiminor axes (b, c) varying from 0.10 to 1.00 in steps of 0.01, representing many complex shapes, the mean projected aspect ratio is 0.595, close to the measured mean projected aspect ratio of aggregates of 0.6. These experiments demonstrate that the conclusion one may safely draw from the projected aspect ratio measurements is that the mean aspect ratio of aggregates is lower than 0.6. Moreover, the projected aspect ratio distributions from measurements suggest a mixture of aggregate shapes, rather than only oblate spheroids as is often assumed.

Abstract. Ground-based observatories use multisensor observations to characterize cloud and precipitation properties. One of the challenges is how to design strategies to best use these observations to understand these properties and evaluate weather and climate models. This paper introduces the Cloud-resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution cloud-resolving models (CRMs) to emulate multiwavelength, zenith-pointing, and scanning radar observables and multisensor (radar and lidar) products. CR-SIM allows for direct comparison between an atmospheric model simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmospheric model. CR-SIM has the flexibility to easily incorporate additional microphysical modules, such as microphysical schemes and scattering calculations, and expand the applications to simulate multisensor retrieval products. In this paper, we present several applications of CR-SIM for evaluating the representativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar–lidar integrated cloud products and multi-Doppler wind retrievals, and optimizing radar sampling strategy using observing system simulation experiments. These applications demonstrate CR-SIM as a virtual observatory operator on high-resolution model output for a consistent comparison between model results and observations to aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientific community.