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  1. Abstract The discovery of a polarimetric radar signature indicative of hydrometeor refreezing has shown promise in its utility to identify periods of ice pellet production. Uniquely characterized well below the melting layer by locally enhanced values of differential reflectivity ( Z DR ) within a layer of decreasing radar reflectivity factor at horizontal polarization ( Z H ), the signature has been documented in cases where hydrometeors were completely melted prior to refreezing. However, polarimetric radar features associated with the refreezing of partially melted hydrometeors have not been examined as rigorously in either an observational or microphysical modeling framework. Here, polarimetric radar data—including vertically pointing Doppler spectral data from the Ka-band Scanning Polarimetric Radar (KASPR)—are analyzed for an ice pellets and rain mixture event where the ice pellets formed via the refreezing of partially melted hydrometeors. Observations show that no such distinct localized Z DR enhancement is present, and that values instead decrease directly beneath enhanced values associated with melting. A simplified, explicit bin microphysical model is then developed to simulate the refreezing of partially melted hydrometeors, and coupled to a polarimetric radar forward operator to examine the impacts of such refreezing on simulated radar variables. Simulated vertical profiles of polarimetric radar variables and Doppler spectra have similar features to observations, and confirm that a Z DR enhancement is not produced. This suggests the possibility of two distinct polarimetric features of hydrometeor refreezing: ones associated with refreezing of completely melted hydrometeors, and those associated with refreezing of partially melted hydrometeors. Significance Statement There exist two pathways for the formation of ice pellets: refreezing of fully melted hydrometeors, and refreezing of partially melted hydrometeors. A polarimetric radar signature indicative of fully melted hydrometeor refreezing has been extensively documented in the past, yet no study has documented the refreezing of partially melted hydrometeors. Here, observations and idealized modeling simulations are presented to show different polarimetric radar features associated with partially melted hydrometeor refreezing. The distinction in polarimetric features may be beneficial to identifying layers of supercooled liquid drops within transitional winter storms. 
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  2. Abstract Quasi-vertical profiles (QVPs) of polarimetric radar data have emerged as a powerful tool for studying precipitation microphysics. Various studies have found enhancements in specific differential phase K dp in regions of suspected secondary ice production (SIP) due to rime splintering. Similar K dp enhancements have also been found in regions of sublimating snow, another proposed SIP process. This work explores these K dp signatures for two cases of sublimating snow using nearly collocated S- and Ka-band radars. The presence of the signature was inconsistent between the radars, prompting exploration of alternative causes. Idealized simulations are performed using a radar beam-broadening model to explore the impact of nonuniform beam filling (NBF) on the observed reflectivity Z and K dp within the sublimation layer. Rather than an intrinsic increase in ice concentration, the observed K dp enhancements can instead be explained by NBF in the presence of sharp vertical gradients of Z and K dp within the sublimation zone, which results in a K dp bias dipole. The severity of the bias is sensitive to the Z gradient and radar beamwidth and elevation angle, which explains its appearance at only one radar. In addition, differences in scanning strategies and range thresholds during QVP processing can constructively enhance these positive K dp biases by excluding the negative portion of the dipole. These results highlight the need to consider NBF effects in regions not traditionally considered (e.g., in pure snow) due to the increased K dp fidelity afforded by QVPs and the subsequent ramifications this has on the observability of sublimational SIP. Significance Statement Many different processes can cause snowflakes to break apart into numerous tiny pieces, including when they evaporate into dry air. Purported evidence of this phenomenon has been seen in data from some weather radars, but we noticed it was not seen in data from others. In this work we use case studies and models to show that this signature may actually be an artifact from the radar beam becoming too big and there being too much variability of the precipitation within it. While this breakup process may actually be occurring in reality, these results suggest we may have trouble observing it with typical weather radars. 
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  3. Abstract. Radar dual-wavelength ratio (DWR) measurements from the Stony Brook RadarObservatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-bandprofiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rainradar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radarfrequencies (K and Ka band) are not sufficiently separated; thus, thetriple-frequency radar approaches had limited success. On the other hand, ajoint analysis of DWR, mean Doppler velocity (MDV), andpolarimetric radar variables indicated potential in identifying ice particletypes and distinguishing among different ice growth processes and even inrevealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPRprofiling measurements and differential reflectivity (ZDR) and specificdifferential phase (KDP) from the KASPR quasi-vertical profiles. TheDWR-versus-MDV diagrams coupled with the polarimetric observables exhibiteddistinct separations of particle populations attributed to different rimedegrees and particle growth processes. In fallstreaks, the 35–94 GHz DWRpair increased with the magnitude of MDV corresponding to the scatteringcalculations for aggregates with lower degrees of riming. The DWR valuesfurther increased at lower altitudes while ZDR slightly decreased,indicating further aggregation. Particle populations with higher rimedegrees had a similar increase in DWR but a 1–1.5 m s−1 largermagnitude of MDV and rapid decreases in KDP and ZDR. The analysisalso depicted the early stage of riming where ZDR increased with theMDV magnitude collocated with small increases in DWR. This approach willimprove quantitative estimations of snow amount and microphysical quantitiessuch as rime mass fraction. The study suggests that triple-frequencymeasurements are not always necessary for in-depth ice microphysical studiesand that dual-frequency polarimetric and Doppler measurements cansuccessfully be used to gain insights into ice hydrometeor microphysics. 
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  4. Kneifel, Stefan (Ed.)
    Observations collected during the 25-February-2020 deployment of the Vapor In-Cloud Profiling Radar at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research, something that until now was only discussed in theory. The field experiment, which coordinated an X-, Ka, W- and G-band radar, revealed that the Ka-G pairing can generate differential reflectivity signal several decibels larger than the traditional Ka-W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes. The observations also showed that G-band signals experience non-Rayleigh scattering in regions where Ka- and W-band signal don’t, thus demonstrating the potential of G-band radars for sizing sub-millimeter ice crystals and droplets. Observed peculiar radar reflectivity patterns also suggest that G-band radars could be used to gain insight into the melting behavior of small ice crystals. G-band signal interpretation is challenging because attenuation and non-Rayleigh effects are typically intertwined. An ideal liquid-free period allowed us to use triple frequency Ka-W-G observations to test existing ice scattering libraries and the results raise questions on their comprehensiveness. Overall, this work reinforces the importance of deploying radars with 1) sensitivity sufficient to detect small Rayleigh scatters at cloud top in order to derive estimates of path integrated hydrometeor attenuation, a key constraint for microphysical retrievals, 2) sensitivity sufficient to overcome liquid attenuation, to reveal the larger differential signals generated from using G-band as part of a multifrequency deployment, and 3) capable of monitoring atmospheric gases to reduce related uncertainty 
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  5. null (Ed.)
    Abstract Fully polarimetric scanning and vertically pointing Doppler spectral data from the state-of-the-art Stony Brook University Ka-band Scanning Polarimetric Radar (KASPR) are analyzed for a long-duration case of ice pellets over central Long Island in New York from 12 February 2019. Throughout the period of ice pellets, a classic refreezing signature was present, consisting of a secondary enhancement of differential reflectivity Z DR beneath the melting layer within a region of decreasing reflectivity factor at horizontal polarization Z H and reduced copolar correlation coefficient ρ hv . The KASPR radar data allow for evaluation of previously proposed hypotheses to explain the refreezing signature. It is found that, upon entering a layer of locally generated columnar ice crystals and undergoing contact nucleation, smaller raindrops preferentially refreeze into ice pellets prior to the complete freezing of larger drops. Refreezing particles exhibit deformations in shape during freezing, leading to reduced ρ hv , reduced co-to-cross-polar correlation coefficient ρ xh , and enhanced linear depolarization ratio, but these shape changes do not explain the Z DR signature. The presence of columnar ice crystals, though apparently crucial for instigating the refreezing process, does not contribute enough backscattered power to affect the Z DR signature, either. 
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  6. null (Ed.)
    Abstract An engaged scholarship project called “Snowflake Selfies” was developed and implemented in an upper-level undergraduate course at The Pennsylvania State University (Penn State). During the project, students conducted research on snow using low-cost, low-tech instrumentation that may be readily implemented broadly and scaled as needed, particularly at institutions with limited resources. During intensive observing periods (IOPs), students measured snowfall accumulations, snow-to-liquid ratios, and took microscopic photographs of snow using their smartphones. These observations were placed in meteorological context using radar observations and thermodynamic soundings, helping to reinforce concepts from atmospheric thermodynamics, cloud physics, radar, and mesoscale meteorology courses. Students also prepared a term paper and presentation using their datasets/photographs to hone communication skills. Examples from IOPs are presented. The Snowflake Selfies project was well received by undergraduate students as part of the writing-intensive course at Penn State. Responses to survey questions highlight the project’s effectiveness at engaging students and increasing their enthusiasm for the semester-long project. The natural link to social media broadened engagement to the community level. Given the successes at Penn State, we encourage Snowflake Selfies or similar projects to be adapted or implemented at other institutions. 
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  7. null (Ed.)
    Abstract. Ground-based observatories use multisensor observations to characterize cloud and precipitation properties. One of the challenges is how to designstrategies 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 atmosphericmodel simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmosphericmodel. 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 therepresentativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar–lidar integrated cloud products and multi-Dopplerwind 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 aidinterpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-basedmeasurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientificcommunity. 
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