Combination Analysis of Multi-Wavelength, Multi-Parameter Radar Measurements for Snowfall

Abstract. Radar dual wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band Scanning Polarimetric Radar (KASPR, 35 GHz), a profiling W-band (94 GHz) and a next generation K-band (24-GHz) Micro Rain Radar (MRRPro) were exploited for ice particle identification using triple frequency approaches. The results indicated that two of the radar frequencies (K- and Ka-band) are not sufficiently separated, thus, the triple radar frequency approaches had limited success. On the other hand, a joint analysis of DWR, mean vertical Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details.We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase of DWR, but the 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases of DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction.



Introduction
Millimeter-wavelength (i.e., operating at 35-and 94-GHz) radars have been widely used for the 40 study of liquid and ice precipitation clouds, utilizing their high sensitivity to smaller particles due to Rayleigh scattering and excellent spatiotemporal resolution (Kollias et al., 2007). Cloud radars at 35 GHz and 94 GHz have been routinely operated at surface-based observatories the last two decades (e.g., the European Union CloudNet project and the U.S. Atmospheric Radiation Measurement (ARM) facility, Illingworth et al., 2015;Stokes and Schwartz, 1994;Mather and 45 Voyles, 2013; Kollias et al., 2014; and from a variety of ship-based (e.g., Lewis et al., 2012) and airborne platforms (e.g., Battaglia et al., 2016;Tridon et al., 2019). Millimeter wavelength radar are particularly suitable for the study of hydrometeors properties (mass, size) using the presence of non-Rayleigh scattering signals and their higher sensitivity to attenuation. For example, the dual-wavelength ratio (DWR), the ratio of the longer-wavelength reflectivity to the 50 shorter-wavelength reflectivity, is affected by the differential scattering and/or attenuation and depends on the particle size, type, orientation, rime fraction, and radar beam path. DWRs have been used in multi-wavelength radar measurements for microphysical retrievals such as estimations of liquid water content (e.g., Hogan et al., 2005;Huang et al., 2009;Tridon et al., 2013;Zhu et al., 2019) and ice water content (IWC, e.g., Matrosov, 1998) and identification of 55 particle types (e.g., Kneifel et al., 2015;Leinonen and Moisseev, 2015;Moisseev et al., 2015;Sinclair et al., 2016;Matrosov et al., 2019). Kneifel et al. (2015) illustrated the effectiveness of DWRs to identify ice crystals, aggregates, and rimed particles, when considering well separated triple radar frequencies (i.e., X, Ka,and W 60 bands) so that each frequency experiences different scattering regimes. The DWR of X-band to Ka-band reflectivities (DWRXKa) versus DWR of Ka-band to W-band reflectivities (DWRKaW) diagrams indicated different dependencies on particle type and size. Those curves were in good agreement with the observed particle types (Kneifel et al., 2016). The triple-frequency capabilities have been used for different frequencies such as S, X, and W bands and Ku, Ka, and W bands 65 (e.g., Leinonen and Moisseev, 2015;Mason et al., 2019), or even shorter-wavelength radars (e.g., Ka, W, and G bands, Lamer et al. 2020). While the triple-frequency approach is a powerful technique for microphysics research, it requires accurate calibration of the radars, reliable attenuation correction, careful beam matching, and sufficiently high sensitivities at all frequencies.
These conditions are satisfied only in a hand-full of surface observatories. 70 Another limitation of the triple frequency measurements for ice particle identification is that the triple frequencies should be well-separated from each other so magnitudes of non-Rayleigh scattering is different for various DWR pairs and the curves representing a particle type in the DWR correspondence diagram can be distinguished. If the frequencies are too close, then the DWR 75 trends corresponding to different hydrometeor types may not be sufficiently separated from each other. For instance,  frequency and K-band (around 24 GHz) frequency, which has been employed by a widely-used, low-power, low-cost, high-quality precipitation profiler, micro rain radar (MRR, e.g., Peters et al., 2002), are rather close, producing similar trends when coupled with a third frequency as shown in Fig. 1. Figure 1a is a DWRKKa versus DWRKW 80 diagram from the scattering calculations (detailed descriptions of the scattering calculations are available in Appendix). Similarly, Fig. 1c is a DWRXKa versus DWRKaW diagram. These diagrams show that the two-DWR space from the three frequency radars exhibits a dependency on ice particle types, specifically size and rime fraction. However, considering modeling uncertainties and measurement noise, it would be hard to discern the particle types in the K-Ka-W DWR space, 85 while the X-Ka-W DWR space has larger dynamic ranges likely enough to discern the particle types as presented in the previous studies. This is, in part, due to the fact that the K-band frequency (~24 GHz) is not sufficiently separated from the Ka-band frequency (~35 GHz).
It has been shown (e.g., Matrosov et al., 2019) that the DWR also depends on particle shapes (i.e., aspect ratios defined as the ratio of particle minor and major dimensions). For particles 90 preferentially oriented with their major dimensions in the horizontal plane, the DWR dependence on particle shapes is usually strongest for vertically pointing radar measurements and relatively weak for slant radar viewing (Matrosov, 2021). The impacts of particle shape on the two DWR pair's diagram (Fig. 1), however, is much smaller than that on individual DWRs for a given frequency pair. To illustrate this point, Figs. 1b and 1d show DWRKKa-DWRKW and DWRXKa-95 DWRKaW correspondences, respectively, for a "soft" spheroidal particle model with aspect ratios 0.3 and 0.8. A much weaker particle shape influence on this the DWRKKa-DWRKW field (compared to individual DWRs) is explained, in part, by the fact that both DWRKKa and DWRKW increase/decrease as particle become more/less spherical. The similar feature is found in the DWRXKa-DWRKaW field. Particle populations with similar characteristic sizes (color circles in 100 Figs. 1b and 1d) but different aspect ratios (0.3 vs 0.8), however, produce quite different values of DWR for both frequency pairs. It is worth also mentioning that a "soft" spheroid particle model produces DWRKKa-DWRKW correspondences that are similar to those with more sophisticated models ( Fig. 1a vs Fig. 1b).
In addition to power measurements, profiling cloud radar can also measure the mean Doppler 105 velocity (MDV). Although the MDV is affected by the vertical air motion, the community has developed robust methodologies to use MDV to improve discrimination between the particle types and ice growth processes (e.g., Orr and Kropfli, 1999;Luke et al., 2010;Protat and Williams, 2011;Kalesse et al., 2013;Schrom and Kumjian, 2016;Oue et al., 2018). Particle fall speed, which is sensitive to rime fraction, is a valuable variable to use to identify particle types (e.g., Locatelli 110 and Hobbs, 1974;Kajikawa, 1989;Mason et al., 2019). However, using only MDV and reflectivity measurements would not be enough to distinguish between aggregation and early stage of riming, because both are associated with very similar fall speeds (e.g., Oue et al., 2016). This study, first introduces, the use of DWR coupled with MDV to identify particle types that have different degree of riming. Figure 2 shows DWRKaW as a function of MDV and differential MDV (dMDV =  band MDV-W-band MDV). The MDV-DWR correspondence is also sensitive to particle size distribution (PSD) details and rime degree. Fig. 2 indicates only a weak dependency on PSD, which can be advantageous for distinguishing particle types as PSD influences are minimized.
Similar to DWR and MDV, polarimetric radar observables are also sensitive to microphysical 120 properties such as particle type, characteristic size, rime fraction, aspect ratio, canting angle, and complexity of shape (e.g., Myagkov et al., 2016). These properties provide a constraint on the particle shapes (i.e., aspect ratio) and help to mitigate the uncertainty in the DWR analysis mentioned above. The polarimetric variables are particularly suitable to identify depositional, aggregation, and riming growths (e.g., Oue et al., 2016). The most common characteristics of the 125 polarimetric observables representing the depositional growth are enhancements of differential reflectivity (ZDR) and specific differential phase (KDP) in a dendritic/plate-like growth regime (e.g., around temperature of -15°C), where the ice crystals with small aspect ratios are formed by depositional growth. The ZDR values decrease with decreasing height in a region of aggregation, while (KDP) often has a maximum just below the ZDR peak. With further height decrease, the aspect 130 ratios of the individual particles increase (e.g., Vivekanandan et al., 1994;Ryzhkov et al., 1998;Kennedy and Rutledge, 2011;Andrić et al., 2013;Bechini et al., 2013;Schrom et al., 2015;Griffin et al., 2018;Matrosov et al., 2020). Similar vertical changes of the polarimetric variables have been often found in the rime-dominated regions (e.g., Zawadzki et al., 2001;Oue et al., 2016;Giangrande et al., 2016;Kumjian and Lombardo, 2017), as heavy riming 135 increases particle aspect ratios. Mean particle aspect ratios can be quantitatively estimated using proxies of radar circular depolarization ratios (e.g., Matrosov et al., 2017). Radar depolarization ratios can also be used to distinguish among ice hydrometeor types effectively separating oblate (e.g., plates, dendrites) from prolate (e.g., columns, needles) habits (e.g., Matrosov, 1991; Reinking et al., 2002;Matrosov et al., 2012;Oue et al., 2015). Schrom and Kumjian (2016) 140 suggested that a complementary use of mean vertical Doppler velocity could help to distinguish the riming process from aggregation-dominated regions. A joint analysis of polarimetric variables and Doppler spectra by Oue et al. (2018) illustrated a capability of particle type identification in Arctic mixed-phase clouds. However, distinguishing between aggregation and early stage of riming is still challenging even though MDV and polarimetric variables are jointly used due to the 145 similar signatures (e.g., Oue et al., 2016).
Winter storms in the Northeast U.S often effect heavy snowfall and destruction of life and property. The lack of understanding of ice microphysical processes in the storms and poor representation of the ice microphysics parameterizations in numerical cloud models has resulted in large uncertainty 150 in forecasting snowfall. The ice microphysical processes including depositional, riming, and aggregation growths often coexist in the snowstorm cloudy volumes (e.g., Kumjian and Lombardo, 2017;Colle et al., 2014), making it difficult to identify the processes in the observations. To facilitate studies of cloud microphysics and dynamics in the Northeast U.S., the Stony Brook Radar Observatory (SBRO) was established in March in 2017 in Stony Brook University, Stony Brook, 155 NY (Fig. 3). The flagship radar of the SBRO is a very sensitive, sophisticated, and well-calibrated  scanning fully-polarimetric radar (KASPR). The radar measurements are complemented by two profiling radar systems operating at W-band (94-GHz, ROGER) and Kband (24-GHz, MRRPro) and ground-based in-situ sensors. The SBRO radar systems have collected vertically pointing triple frequency reflectivity and Doppler velocity data which were 160 complemented by polarimetric variables from KASPR for a snowstorm observed on February 20, 2019. The triple frequency measurements showed that the DWR from the dual-wavelength measurements in conjunction with MDV and polarimetric observations had a higher efficiency to distinguish ice particle types and growth processes rather than the DWR-only diagrams from triplefrequency measurements. This study first illustrates the capability and advantage of the use of 165 MDV and polarimetric radar observables in conjunction with DWR measurements to identify particle types and growth processes in winter storms.  (d) are the same as (a) and (b), respectively, but for DWR of X-band (10.7 GHz) reflectivity to Ka-band reflectivity versus that of Ka-band reflectivity to W-band reflectivity. Line colors in (a) and (c) represent particle models listed in Table A1. Solid and dashed lines in (a) and (c) represent the PSD's shape parameter ( ) equal to 0 and 4, respectively. Color of circles in (a) represents water-equivalent mass-weighted volume diameter (Dm) of each PSD used to calculate DWRs; 180 here, Dm values for the particle models of Leinonen and Szyrmer's (2015) unrimed aggregates with = 0 (solid red line) and aggregates with high rime degree (solid blue line) are presented. Solid and dashed lines in (b) and (d) represent AR=0.8 and AR=0.3, respectively. The DWRs in (b) and (d) were calculated for PSDs with = 0 and median volume particle size (color of circles) ranging from 0.2 to 6.0 mm. 185  ROGER, and MRRPro at SBRO collected triple frequency data during a snow event on February 20, 2019. This is so far the only case where the high-quality, well-calibrated triple frequency measurements together with ground-based in-situ measurements for snow particles for the evaluation are available. The SBRO site also has ground-based in-situ observation instruments.
The in-situ instruments including a Parsivel optical disdrometer and Multi-Angle Snowflake 210 Camera (MASC, Garrett et al. 2014) were used to evaluate the radar-based particle identifications. The SBRO operates ceilometers at the SBRO and Brookhaven National Laboratory sites. Ceilometer backscatter is sensitive to cloud liquid droplets embedded in ice precipitation clouds. A microwave radiometer was also installed at the SBRO site; however, it was not functional during the precipitation in this study due to significant snow accumulation on the sensor. 215 2.1. Ka-band Scanning Polarimetric Radar (KASPR) KASPR, a state-of-the-art cloud scanning radar, is capable of collecting Doppler spectra and radar moments through alternate transmission of horizontally (H) and vertically (V) polarized waves 220 and simultaneous reception of co-polar and cross-polar components of the backscattered wave with the beamwidth of 0.32°. Therefore, a full set of polarimetric radar observables is available including radar reflectivity (ZHH), differential reflectivity (ZDR), differential phase (φDP), co-polar correlation coefficient (ρhv), linear depolarization ratio (LDR), and cross-polar correlation coefficient (ρhx), along with Doppler velocity and spectral width. Specific differential phase (KDP) 225 is estimated using an iterative algorithm proposed by Hubbert and Bringi (1995). The data postprocessing details are described in Oue et al. (2018). The KASPR was calibrated using a corner reflector technique, providing reliable reflectivity data to calibrate the other two radar data. The detailed configurations are also available in Kumjian et al. (2020) and Kollias et al. (2020b).

230
During the radar measurements on February 20, 2019, KASPR executed a scanning strategy that consisted of surveillance (PPI) scans at a 15° elevation angle, a zenith pointing PPI, hemispheric range-height indicator (HSRHI) scans at four azimuth angles, and a 5-minute vertically pointing mode (VPT) during which Doppler spectrum data were collected. This pattern was repeated and took approximately 15 minutes to complete. During a 15-min cycle, two 15°-PPI scans were 235 included, so that we had the 15°-PPI scans every ~7 min which were used to produce quasi-vertical profile products. The PPI and HSRHI scans were performed in a full polarimetry mode with scan speeds of 6 s -1 and 2 s -1 , respectively, to collect data with a 30-m range-gate spacing, 0.6° PPI azimuthal spacing and 0.3° HSRHI elevation spacing. The VPT mode was executed with only horizontally polarized waves transmitted and both horizontally and vertically polarized waves 240 received. During the VPT mode, the Doppler spectra were collected every second with a 15-m range-gate spacing and 0.04 m s -1 velocity bin spacing. The zenith PPI scans were used to estimate a systematic bias of ZDR. The ZDR values presented in this study were corrected for the systematic biases. 245

94-GHz (W-band) Frequency Modulated Continuous Wave (FMCW) profiling radar (ROGER)
The system was initially developed as an airborne radar and was integrated on the Center for 250 Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter aircraft (Mead et al., 2003). In 2017, the system was refurbished with installing 24-inch parabolic dish antennas, and all the CFMCW electronics, including a new metal frame to hold the antennas, the server computer and the power supplies, to make it suitable for ground-based observations and easy shipping. 255 This radar system is capable of collecting Doppler spectra with spatiotemporal resolutions similar to KASPR (Table 1) and located next to KASPR, which allows good beam matching and reliable DWR measurements. The data during the cases were collected every four seconds at 30-m vertical spacing with a beamwidth of 0.3° (Table 1). 260

Micro Rain Radar Pro (MRRPro)
The MRRPro is the latest version of the MRR developed by Metek GmBH that has evolved to be a powerful standalone profiler for investigations of precipitation and cloud dynamics with very 265 low installation and logistics effort. The MRRPro features a high-performance processing unit which significantly improves the options in the operating parameters (Table 1). During the observation in this study, the MRRPro collected Doppler spectra at a 60 m range-gate spacing every four seconds up to the maximum observation range of 7 km. The Nyquist velocity was 12.08 m s -1 during the observations producing the velocity bin spacing of 0.192 m s -1 . 270

Ground-based in-situ measurements
A Parsivel optical disdrometer measures terminal velocity and horizontal size of individual 275 precipitation particles passing through a sheet of light (30-mm wide, 1-mm high, and 180-mm long) with a 650-nm laser diode with a power of 3 mW (Löffler-Mang and Blahak, 2001). The total measuring surface has an area of 54 cm 2 . The measured size and velocity are classified into one of 32 size bins ranging from 0.062 to 24.5 mm and 32 velocity bins ranging from 0.04 to20.5 m s -1 every 1 minute. 280 The Multi-Angle Snowflake Camera (MASC) is located adjacent to the Parsivel. The MASC consists of three cameras that are separated by an angle of 36°, each pointed towards the focal point about 10 cm away (Garrett et al., 2012;. On top of each camera rests a 2700 lumen light emitting diode. The focal point lies within a ring that has two near-infrared-emitter-detector-285 pairs arranged in arrays that are separated vertically by 32 mm. The arrangement of the emitterdetector pairs allows for a trigger depth of field of 3100 mm 2 but because of the camera field of view and depth of focus, only about 11% of the images taken are in focus. Falling hydrometeors larger than 0.1 mm are recorded and their fall speed is calculated as the time difference between triggering each emitter-detector pair. 290

Reflectivity calibration and DWR estimation
KASPR reflectivity measurements were well calibrated using a corner reflector technique (Lamer et al. 2020). Therefore, systematic offsets for the MRRPro and ROGER total reflectivities have 300 been corrected by comparing them with the KASPR reflectivity at cloud bases from a different non-precipitation cloud case. The MRRPro and ROGER reflectivity and mean Doppler velocity data were interpolated into the KASPR VPT data resolution (15-m range and 1-sec time spacings).
Gaseous attenuation needs to be considered and corrected when using short-wavelength radars 305 (Lamer et al., 2021). The MRRPro's K-band (24 GHz) frequency is the lowest in the present study, however, the 24 GHz frequency is very close to a peak in the water vapor absorption spectrum (e.g., Liebe et al., 1993;Rosenkranz, 1998). Therefore, the water vapor attenuation for MRRPro could also be significant. We corrected the MRRPro, KASPR, and ROGER reflectivities for water vapor attenuation based on the Rosenkranz (1998) results, using sounding profiles launched twice 310 daily (00 and 12 Z) at Upton, 21 km east of the observatory. The estimated column-integrated twoway attenuations at K, Ka, and W bands for our case study were up to 0.7, 0.2, and 1.2 dB, respectively.
Another source of the gaseous attenuation we should consider is oxygen (e.g., Liebe et al., 1993).

315
Although the attenuation in oxygen may not be as large as that in water vapor, it may be nonnegligible. We also estimated the attenuation by oxygen (i.e., dry air) for each of the three frequencies using the sounding profiles and corrected the MRRPro, KASPR, and ROGER reflectivities. The estimated column-integrated two-way attenuations for dry air at K, Ka, and W bands were generally 0.1, 0.2, and 0.3 dB, respectively. 320 Liquid water, which was expected to be present in precipitating clouds as supercooled droplets producing riming can also be a cause of significant attenuation. Riming commonly occurs in snowstorms observed along the U.S. North East Coast indicating the presence of significant amounts of supercooled cloud water in the snowstorms (e.g., Colle et al., 2014). However, it was 325 difficult to identify liquid cloud layers and liquid water content and estimate specific attenuation at each range bin in the ice clouds. Moreover, attenuation by ice particles might be significant if the large amount of ice were produced in the clouds and the radar beam passed through the ice layers. Tridon et al. (2020) proposed a relative path-integrated attenuation (PIA) technique to retrieve liquid-water content using DWR profiles. A key idea of this technique is that the DWR 330 from dual frequency radars near cloud tops, where it is expected that small ice crystals are in the Rayleigh scattering regime for both radar wavelengths, is mainly due to the PIA associated with liquid cloud droplets and ice particles. The DWR attributed to the total attenuation cshould then be equal to the DWR plateau near the cloud top. We applied the Tridon's et al. (2020) technique to the DWR from KASPR and ROGER measurements to find DWR plateau near the cloud top 335 with the following adaptations: • the measured DWRKaW are averaged over 450 m (30 gates) and 20 seconds (20 rays).
• The DWR variance within the moving windows defined above must be lower than 4 dB 2 • KASPR reflectivity and its variance (within the same moving windows) must be lower 340 than 5 dBZ and 2.5 dB 2 , respectively. • the DWR plateau is found where the DWR gradient is lower than 1 dB km −1 near the cloud top at each profile. • the masked DWRKaW is averaged at the cloud-top layer, and the DWR value is considered the total PIA. 345 The ROGER reflectivity was corrected for the estimated PIA linearly in the cloud layer from the ice cloud base, so that the total attenuation in the column was consistent with the estimated PIA. This assumption might produce an uncertainty, however, this kind of correction showed reasonable results compared to no correction for PIA, as demonstrated by previous studies (e.g., Dias Neto 350 et al., 2019; Oue et al., 2018). The DWR plateau-based PIA estimation technique requires enough sensitivity to capture cloud tops where Rayleigh scattering is expected for both. The MRRPro is sufficiently sensitive to precipitation ( Fig. 4c) but not to small particles with reflectivity < 0 dBZ. The MRRPro reflectivity near its echo top could still include non-Rayleigh scatterings at Ka or W bands. Because of this, attenuations by hydrometeors in the KASPR and MRRPro reflectivity 355 fields were not accounted for using the DWR plateau-based attenuation correction in this study. Moreover, the presence of supercooled liquid droplets might cause total signal extinction. A microwave radiometer deployed at the SBRO observed liquid water path (LWP) values, which were generally around 150 g m -2 before the precipitation onset. According to Tridon et al. (2020), this amount of liquid should produce a path integrated attenuation less than 1 dB in the KASPR 360 and MRRPro reflectivity measurements.
Another error source of the DWR analysis is radar beam mismatching. The three radars were located at the same observation site; the distance between those radars were less than 5 m, therefore, we expect that the beam mismatch due to location is small. On the other hand, a 365 difference in beamwidths (Table 1) is another possible cause of beam mismatching. The KASPR and ROGER beamwidths are well matched, while MRRPro's beamwidth is 5 times larger than those of the KASPR and ROGER. The beamwidth differences between MRRPro and KASPR, and MRRPro and ROGER might result in larger variabilities of DWRs. 370

Mean Doppler velocity
Similar to the reflectivity measurements, the MRRPro and ROGER mean Doppler velocity data were interpolated into the KASPR VPT data resolution. The observed mean Doppler velocities 375 from the 3 radars were corrected for air density changes based on the sounding profiles and adjusted to the surface.

KASPR polarimetric observables 380
The polarimetric radar observables such as ZDR and KDP are more prominent when they are collected at lower elevation scans, whereas the DWR data were collected by vertically pointing measurements. To compare those two data sets from the different types of scans, we employed a quasi-vertical profile (QVP) technique proposed by Ryzhkov et al. (2016). The QVP technique 385 azimuthally averages polarimetric radar variables for each conical PPI scan at non-zero elevations to produce these variables in a height versus time format. The QVPs have high vertical resolutions allowing for capture important polarimetric radar signatures and their evolution (e.g., Griffin et al., 2018;Kumjian and Lombardo, 2017;Troemel et al., 2019). We use the PPI scans at an elevation angle of 15° every 7-8 min with a scan rate of 6° s -1 . Since the slant range resolution of 390 the 15° PPI data is 30 m, the corresponding QVP data have the vertical spacing of approximately 10 m and the maximum height of 7.8 km. Note that the actual vertical resolution of QVP is determined by the vertical size of the radar resolution volume, which increases with distance from the radar . The use of conical PPI at a higher elevation angle (15°) for QVP reconstruction ensures relatively high horizontal resolution at lower altitudes (11 km at the height 395 of 2 km) that facilitates direct comparison with the DWR profiles from the three radar vertically pointing measurements. The KASPR QVP data were interpolated into the KASPR VPT data resolution, similar to Oue et al. (2018). Because a single PPI scan was performed every 7 min while the KASPR 5-min VPT dwell collecting profile data every second was performed in a 15min interval, a single KASPR QVP corresponds to about 150 DWR profiles. 400

Case Description
During the 2018-2019 and 2019-2020 winter seasons, most of precipitation was non-dry snow 405 including rain, wet snow, refrozen particles, and sleet, and provided very few dry snow events at the ground around Long Island, NY. Those non-dry snow particles caused significant attenuation of radar signals particularly at millimeter wavelengths and accumulation on the radomes. Although the majority of the observed precipitation cases during the winter seasons included the non-dry snow particles near the ground, for a few cases before snow started to accumulate, ice clouds (with, 410 possibly, embedded supercooled cloud layers) were observed aloft. We selected a period from a snow precipitation case on February 20, 2019, when KASPR VPT, ROGER, and MRRPro simultaneously observed snowfall without significant attenuations. A high pressure system at the surface persisted around Long Island from 09 UTC to 21 UTC on 415 February 20, 2019, while two troughs were also identified to the southeast of Long Island: one was elongated from a low pressure system in Tennessee to Pennsylvania, and the other was associated with another low pressure system around the coast of Georgia and lay along the East Coast toward Long Island. Either one of the two or both could be accompanied by a warm frontal-like stratiform precipitation providing snow in Long Island. Snow precipitation started at around 18:00 UTC at 420 SBRO. Based on the MASC-observed particle images and Parsivel-observed particle diameter and fall velocity, dry snow aggregates dominated from the beginning till 23:30 UTC, and then the dominant precipitation included mixed-phase particles and changed into pure rain at around 04:00 UTC on February 21. 425   The KASPR and ROGER reflectivity fields indicated generating cell-like features by 17:10 UTC 455 near the cloud top above 8 km altitude (Fig. 4), as the MDV indicated convection features in the generating cells (Fig. 5). These generating cells produced fallstreaks underneath as reflectivity increased toward the ground and reached the ground by 18:20 UTC. KASPR RHI scans in Fig. 6 showed fallstreaks elongating from the generating cell layer following the wind direction above the 2 km altitude (W-E direction). The KASPR ZDR was enhanced between the fallstreaks while 460 KDP increased in the lower part of the enhanced ZDR layer and just below the enhanced ZDR layer. The enhancement of ZDR generated at a height of 5-6 km, where the temperature was ranging from -20°C to -15°C corresponding to a dendritic growth layer and close to supersaturation with respect to ice from the 12 UTC sounding (Fig. 4e). This is a typical signature of the aggregation and generation of dendritic crystals commonly observed by previous studies (e.g., Kennedy and 465 Rutledge, 2011;Schneebeli et al., 2013;Kumjian et al., 2014;Williams et al., 2015;Oue et al. 2018). The DWRKaW increased toward the ground in the fallstreaks, as reflectivity increased. At times corresponding to the fallstreaks reaching the ground, MASC observed large aggregates.
Starting from 17:50 UTC, precipitation observed at the surface originated at 6 km altitude. The 470 KASPR RHI measurements revealed that cloud aloft was decoupled from below and there were structured generating cells near the lower cloud top at 6 km ( Fig. 6). Large ZDR values were observed between the generating cells and between fallstreaks, while KDP slightly increased just below the generating cell layer but decreased to near zero within the fallstreaks. There was a layer of large DWRKaW at 4-5.5 km altitude from 17:15 to 18:50 UTC, even though the  reflectivity was smaller than that in the former fallstreaks. The large DWRKaW extended toward the ground and reached the surface at 18:30 UTC (Fig. 4d), as the KASPR polarimetric signatures associated with the fallstreaks reached the surface (Figs. 5d and 5e). Corresponding to the time when the fallstreak features reached the surface, MASC observed rimed particles (Fig. 4f). These DWR and polarimetric features likely indicate an ice particle growth, however, it is hard to 480 determine specific ice growth processes (i.e., distinguishing riming and aggregation processes) from the DWRKaW or the polarimetric observables only.
There are several signatures that suggest different types of ice particle growth during the two periods. A distinct difference between the two periods is found in the MDV from the vertically-485 pointing measurements and the KASPR polarimetric observables; they suggest different ice particle fall speeds attributed to the particle types and microphysics. The downward motion within the fallstreaks during the first period gradually increased toward the ground to 1.5 m s -1 , indicative of growth of individual ice particles. The fallstreaks corresponded to the enhanced KDP, but decreased ZDR, suggesting that oblate small particles aggregated within the fall streaks. In contrast, 490 the latter period corresponded to decreased KDP, while ZDR values are enhanced near the 6 km altitude but decreased toward the surface. These KDP and ZDR evolutions suggest that small oblate ice crystals formed at 6 km altitude and aggregated as they fell forming spherical shapes, as many previous polarimetric radar studies observed. The MDV showed faster downward motion compared to the fallstreaks in the first period, suggesting heavy aggregation and/or riming. 495 Another interesting characteristic to be noted is that there was a distinct region of turbulence, which can clearly be seen as a layer with large spectrum width and variability of MDV at around 3 km altitude. This was consistent with large lidar backscatter values suggesting liquid cloud base. The reflectivity and DWR of fall streaks were intensified below the turbulence layer. 500 Although the individual radar parameters suggest a variety of ice particle types and microphysical processes, it is not straightforward to identify the ice particle types and distinguish the processes, in particular aggregation and riming, by a single measurement. 505

DWRs from the three frequencies 510
Based on the DWRKaW height-time plots, we selected four regions as shown in Fig. 4d, each of which had similar DWRs, MDV, and polarimetry features to identify ice particle types and their growth processes. We first present traditional triple-frequency DWR-DWR diagrams (DWRKW versus DWRKKa in Fig. 7) for each selected region. The DWRs from Region A and Region B tend to be distributed toward the model low rime degree lines (larger DWRKKa at a given DWRKW), 515 while those from Region C and Region D were distributed toward the higher rime degree regions (smaller DWRKKa at a given DWRKW). These are consistent with MASC ice particle observations. Although the distribution of the DWRs for each region seems to be significantly separated, most of the data are overlapped, making it hard to distinguish the growth processes and types. This is, in part, because K-band (24 GHz) and Ka-band (35 GHz) measurements are not sufficiently 520 separated in the frequency domain.
Besides the insufficient frequency separation, there are data points that deviate from the model lines in the DWRKW versus DWRKKa field in each region. There are several causes of such deviations (e.g., Lamer et al., 2020). The most likely cause is unaccounted attenuations particularly 525 at Ka and W bands due to either supercooled cloud water or ice or both. The ceilometer backscatter measurements shown in Fig. 5f, in addition to the MWR LWP measurements, suggest that thin supercooled liquid cloud layers were indeed present at least around the large Doppler spectrum width layer. Unfortunately, the ceilometer backscatter information is insufficient to provide a complete mapping of such layers because of complete signal extinction caused either by the ice 530 clouds or by underlying liquid layers themselves. Ice particles could also cause signal attenuation Tridon et al., 2020) particularly for the shorter-wavelength radars. Although the DWR plateau-based PIA technique has corrected the ROGER reflectivity for those attenuations related to the KASPR reflectivity (Sect. 3.1), the attenuation in the KASPR reflectivity itself cannot be accounted for in this study. This factor also causes underestimation of the PIA-corrected 535 ROGER reflectivity.
Secondly, the beam mismatch could be significant when the radar beams penetrate narrow, fine fallstreaks, even though the radars were collocated within 5 meters from each other. As mentioned previously, the KASPR and ROGER beamwidths are well matched (0.3°), while the MRRPro's 540 beamwidth is 5-times larger (1.5°). The radar sampling volumes, which are larger at higher altitudes, cannot resolve the small time and spatial scale phenomena, and the difference in beamwidth is a source of uncertainty. Moreover, the ice particle models may not represent all the gamut of ice particles possibly present in the clouds. 545 Figure 7: (a-d) DWRKKa versus DWRKW diagrams for Regions A, B, C, and D, respectively. Color shades represent normalized frequency. Lines in each panel represent the SSRGA calculations using particle type and PSD models described in Sect. 3.4. The color line legend is the same as in Figure 1a, and black lines are same as black lines in Fig. 1b. 550

DWRs coupled with MDV and polarimetric variables
Observed MDV is attributed to mainly the vertical air motion and the particle fall speeds, which 555 is sensitive to particle size, rime degree, size distribution, and type, and can provide additional information to distinguish ice types and processes. Kneifel and Moisseev (2020) demonstrated that MDV is a function of rime fraction. We further illustrate that MDV coupled with DWR shows a good indicator of degree of riming. Figure 8 shows the observed DWRKaW as a function of KASPR MDV together with the model plot with different rime degrees (lines). Most of DWRKaW values 560 from Region A are less than 7 dB and are located between the middle (yellow and cyan) and low (red and magenta) rime degree lines, suggesting light riming of small aggregates (Fig. 8a). These particles could grow keeping the similar degree of riming by aggregation, as the data points from Region B are shifted toward larger DWRKaW values between the middle and low rime degree lines (Fig. 8b). It is possible that the turbulence layer at around 3 km altitude (Figs. 5b and 5c) 565 contributed to light riming. The turbulence also contributed to the wide distribution of MDV.
DWRKaW from Region C generally follows the low-rime degree particle lines; DWRKaW increased from near zero to 10 dB while MDV changed from near zero to -0.8 m s -1 . Some data points are shifted toward the middle-rime degree particle lines (i.e. faster downward motion at a given 570 DWRKaW). These data clusters suggest that the aggregation dominated, but some particles started riming. Region D, which is located below Region C, has also generally two data clusters. A smaller data cluster closely follows the low-rime degree lines, as the DWRKaW increased 2 to 9 dB while the MDV changed from -0.6 m s -1 to -1.3 m s -1 . The other population, which has higher occurrence, is generally along the middle rime degree lines; the DWRKaW increased from 3 to 12 dB while the 575 MDV changed from -1.8 m s -1 to -2.5 m s -1 in the middle of the population. The left edge of the second data population is closer to the higher riming degree (blue and green) lines. Those downward MDVs belonging to the two populations are consistent with fall velocities of aggregates and heavily-rimed particles, respectively, studied by Locatelli and Hobbs (1974). These characteristics suggest that aggregates produced near the cloud top at 6 km rimed during the falling 580 as particle fall speeds quickly increased. These distinct separations of the particle populations associated with the particle growth processes are not clearly found in the triple-frequency DWR field in Fig. 7, whereas these are also shown in the DWRKW versus KASPR MDV (Fig. 8e) and DWRKKa versus KASPR MDV (Fig. 8f) diagrams. 585 Adding polarimetric information supports this interpretation and gives further insights into particle microphysics in terms of their shapes. Figures 9a-d (Figs. 9g,h). It can be interpreted that small ice particles producing near zero DWRs were horizontally-oriented oblate particles in the dendritic crystal growth zone (temperature of -15 to -10°C), which produced large ZDR values and then aggregated to be large 600 size as DWR increased. On the other hand, individual frequency pair DWR for vertically pointing measurements also strongly depends on particle aspect ratios (Matrosov et al. 2019). The impacts of particle aspect ratio on DWRKaW values could be as high as ~3 and ~5 dB for particle distributions with median volume size of 1 and 2 mm, respectively (Matrosov 2021). The increase of DWRKaW in the diagrams can include both the particle size and shape effects. 605 The DWR-MDV diagrams suggest that as DWRKaW increased, the MDV corresponding to the low rime degree particle populations in both Region C and Region D reached ~-1 m s -1 , consistent with the fall speeds of low rime degree aggregates. This effect is more likely due to the increase of size rather than aspect ratio. During the aggregation process, the size distribution of snowflakes evolves 610 in such a way that the concentration of smaller, higher-density particles decreases whereas the number of larger, lower density snowflakes increases. This is a primary reason for the reduction of both ZDR and KDP due to aggregation, although the increase of the average aspect ratio and possibly more chaotic orientation additionally contribute to such a reduction. The KDP values could also be accounted for by changes in the number concentration of the horizontally-oriented oblate 615 particles (with aspect ratio < 1); its increase contributes to increasing KDP.
In the DWR-MDV data clusters following the high-rime degree particle lines in Region C, ZDR and KDP quickly decreased as the DWRKaW and the magnitude of MDV increased; ZDR values decreased from 2 dB to 0.5 dB, and the KDP values decreased from 0.4 ° km -1 to near zero (Figs. 620 9c,g). Although the increase of the DWRKaW includes the effects of both size and aspect ratio as discussed above, the increase of the magnitude of MDV can represent the increase of size. The ZDR and KDP values are lower than those from the cluster along the low-rime degree models at a given DWRKaW. Lower KDP and ZDR values suggest a particle growth by heavier riming, which tend to produce more spherical particles. 625 These ZDR and KDP characteristics shown in both low and high rime degree particle data groups in Region C are very similar to those in Region D, but the KDP and ZDR values in Region D are generally lower at a given DWRKaW, with a mean MDV of -3.5 m s -1 (Figs. 9d,h). The lower KDP and ZDR in Region D represent further particle growth which is accompanied by the decrease of 630 their density, increase of their aspect ratios, and more random orientation.
It is interesting that for DWRKaW values less than 5 dB in Region C, the observed ZDR values with faster fall speeds (corresponding to the high-rime degree particle lines) are larger than those with slower fall speeds (corresponding to the low-rime degree particle lines) at a given DWRKaW (Fig.  635 9c). This suggests that riming first worked to fill the gaps of branches of dendrite crystals, resulting in increasing the mass of individual crystals without significant change in their aspect ratio and thus ZDR increased. This type of riming would not significantly contribute to the increase of KDP (Fig. 9g), likely due to low concentration of such particles. This characteristic is consistent with the early stage of riming reported by previous studies (e.g., Oue et al., 2016;Li et al., 2018). 640 5a-c results in an uncertainty in the particle identification for Region A, particularly when DWRKaW values are smaller than 5 dB and MDV is varying between -3 and 0 m s -1 . Adding polarimetric variables together with temperature information facilitates the interpretation of the microphysics. ZDR values in Region A are positive, but smaller than 1 dB, and 655 smaller than those from the later fallstreaks (Region C and Region D, for a given DWR), suggesting an aggregation process which was accompanied by a decrease of particle density, an increase of their aspect ratios, and more random particle orientations compared to Region C and Region D. In contrast, KDP is larger than that from the later fallstreaks. The large KDP and smaller ZDR values in Region A suggest aggregation intensified by higher number concentration of ice 660 crystals. The increase of ice number concentration can be explained by two processes. One possible cause is that near the dendrite growth regime (around -15°C), dendritic ice crystals were nucleated. The dendritic branches could work to facilitate interlocking (Pruppacher and Klett, 2010). This is a well-known characteristic in winterstorms reported by many previous studies using polarimetric radar measurements (e.g., Kennedy and Rutledge, 2011). Another process is seeding from above 665 (e.g., Griffin et al, 2018;Oue et al. 2018), which is more likely to contribute to the increase of ice concentration for this case. The cloud top height during observations in Region A and Region B reached 10 km, approximately 4-km higher than in Region C and Region D (Figs. 5 and 6). This fact suggests that higher concentration of ice particles aloft seeded in Region A. Moreover, a possible light riming in the turbulence region could increase the mass of individual particles, hence 670 KDP, as the cluster extended to the middle rime degree included large KDP values.
The particles were further growing at lower altitudes as DWRKaW increased with decreasing ZDR in Region B. However, a sublimation process near the ground could also be plausible. The nearest soundings at Upton (12 Z, black lines in Fig. 4e) showed a dry air condition at the lower altitudes. 675 This sounding time was ~5-6 hours before the radar observation, but the dry air condition could still be present near the ground, thus favouring sublimation in the lower altitudes of Region B. Due to sublimation some branches and/or edges of aggregate particles could have disappeared, resulting in decreasing the mean volume diameter. The classical aggregation process could have stopped with KDP remaining relatively large because it usually decreases proportionally to the mean volume 680 diameter. Decrease of IWC attributed to the sublimation might have been minor with any noticeable impact on KDP. These processes related to the sublimation are represented by a cluster with high KDP in Region B, where the DWRKaW values slightly increased while KDP values kept high compared to Region A. The sublimation also contributed to decreasing particle fall speed, as shown by a minor decrease of the magnitude of MDV in the data group, but the MDVprobably 685 resulted from some balance between the fall speed increase due to aggregation and its decrease due to sublimation. The classical diabatic sublimation cools and moistens the ambient air. Therefore, the sublimation subsided as the cloud base descended with time ( Fig. 5f) and snow particles in the fallstreaks eventually reached the ground. 690

DWR coupled with differential MDV
The MDV measurements also have frequency dependencies because of the complex interplay between non-Rayleigh effects and the PSDs. . Figure 10 shows dependencies of the ice particle 695 types on the DWRKaW versus differential MDV (dMDV = KASPR MDV -ROGER MDV) diagrams for Region C and Region D, together with the scattering calculations using the particle models. Similar to the DWR-MDV diagram in Fig. 9, Region C includes a cluster with a higher number of occurrences along the low-to-middle rime degree particle lines, and a lower frequency cluster extends toward the high-rime degree particle lines. Region D has more data points for the 700 high-rime degree particle population.
Region D also includes large dMDV values greater than 0.6 m s -1 for DWRKaW values between 3 and 10 dB (Fig. 10b). It is possible that the larger values of dMDV correspond to an increase in the particle sizes and not to changes in the degree of riming. The ZDR values corresponding to these 705 large dMDV values (Fig. 10c) are approximately 0.7 dB, suggesting that the particles were nonspherical, possibly contributing to the decrease of DWRKaW compared to the spherical particles.
As the scattering calculations show, distinguishing among different degrees of riming requires accurate measurements of MDV with an error of few hundredths of 1 m s -1 and exact range-time-710 bin gate matching for lower DWRKaW (< 5 dB). Although the vertical air motion contributions in MDV from each radar are cancelled out in dMDV, subgrid scale turbulence, the wide range of particle fall speeds, and different sampling times for the observations (1 s for KASPR VPT and 4 s for ROGER) can all be sources of uncertainties. This limitation may affect the scatterplot distributions, e.g. with some points clustering oustide the envelop of the model's lines. This 715 limitation also affects Region A (not shown). Region C and (b) Region D. Color shades represent normalized frequency. (c) is the same as (b), but the color shade represents KASPR QVP ZDR. Lines in (a) and (b) panels represent the SSRGA calculations using particle type and PSD models described in Sect. 3.4. The legend of the lines is same as Figure 1. 725

Evaluation using ground-based in-situ measurements
The particle properties retrieved from the ground-based measurements including fall speed, size, aspect ratio, and area ratio are the result of ice growth processes in the clouds aloft. The Parsivel 730 and MASC observations allowed us to evaluate the radar-based particle characteristics described above. The Parsivel and MASC collocated with the radars collected precipitation particles after 18:13 UTC and 18:16 UTC, respectively. The snow images from MASC were quantified by measurements of aspect ratio and area ratio, and their time series were presented in Fig. 11. Figures 11a and 11b present frequencies (color shade) together with median values (black line) observed 735 for a 20-min time range every 1 minute.
We also estimated mass-weighted mean diameter for Parsivel-measured PSD. The ice particle mass was estimated using a methodology proposed by von . The methodology is based on a theory that individual particle mass can be expressed based on a hydrodynamic theory 740 derived by Böhm (1989) using the Reynold's number and the Best number (e.g., Mitchell, 1996;Mitchell and Heymsfield, 2005;Heymsfield and Westbrook, 2010). The equation of mass (Eq. 5 of von  indicates that the mass can be a function of fall velocity, area ratio, and size. In the present study, the area ratio is derived from the MASC images, and the fall velocity and size are estimated from the Parsivel measurements. The Parsivel-observed particle diameter 745 and fall speed are fitted to a form of = ! where and are constants using the 20-min integrated data. Previous studies pointed out that Parsivel's velocity and even size measurements for snow include large uncertainties owing to the sampling limitation (Battaglia et al. 2010). Before estimating the relationships, we removed unrealistic velocity values as follows: 1) data having a small diameter (<1 mm) and too large velocity (>1.5 m s -1 ) according to Locatelli and Hobbs 750 (1974), and 2) data outside upper and lower boundaries of the V-D relationships. The upper boundary was determined based on Locatelli and Hobbs's (1974) V-D relationships for rimed aggregates, and the lower boundary was determined based on Szyrmer and Zawadzki's (2010) V-D relationships for unrimed aggregates. The Parsivel-measured size was adjusted to the maximum dimension using a technique proposed by von . Figure 11c presents the time 755 series of the estimated water-equivalent mass-weighted mean diameter from the Parsivel-measured PSD.
These time seriesare consistent with the fallstreaks reaching the ground. Aspect ratio represents oblateness of particles relating to ZDR and partly contributing to KDP. Isaac (2003) 760 andJian et al. (2017) suggested that mean aspect ratio of observed snowflakes can be around 0.6 or a bit smaller, while that of heavy-rimed particles such as graupel increases toward 1. Radar depolarization-based retrievals of snowflake aspect ratios near the ground indicated mean aspect ratios of about 0.4 -0.5 (e.g., Matrosov et al., 2020). Area ratio in the current study is defined as the ratio of the area of the snowflake, which is found by counting all white pixels in a black and 765 white image, to the area of the circumscribing circle defined by the maximum diameter from MASC. The area ratio increases with riming ).
The aspect ratio was relatively low before 18:30 UTC, where the median value was less than 0.6. At the same time, the area ratio was also relatively low, where the median area ratio was smaller 770 than 0.5. This period corresponds to time where fall streaks included in Region A and Region B reached near the ground, consistent with the radar MDV-DWR characteristics. The mass-weighted mean size was approximately 0.4 mm, consistent with the scattering model calculations shown in Fig. 2a. It should be noted also that aspect ratio estimates from in situ data (e.g., from Parsivel and/or MASC measurements) are inferred from 2D particles projections, so these estimates usually 775 overestimate actual aspect ratios, which are defined as true minor-to-major particle dimension ratios (Jiang et al., 2017;Matrosov et al., 2017).
The median in situ aspect ratio exceeded 0.6 between 18:30 and 19:00 UTC, while area ratio also increased. The water-equivalent mass-weighted mean diameter increased after 18:33 UTC, as it 780 exceeded 1.3 mm between 18:38 and 19:48 UTC except 18:58, 19:22, and 19:28) UTC. Those large diameter periods correspond to times where fall streaks included in Region D reached the ground. The ground-based characteristics suggests that snowflakes were larger aggregates, which heavily rimed having faster fall speeds, consistent with the observed characteristics of the radar MDV-DWR coupled with the polarimetric observable. 785 Figure 11: Time series of (a) aspect ratio and (b) area ratio of snow particles measured by the MASC and (c) water-equivalent mass-weighted mean size of Parsivel-measured PSDs. Color shades and black lines in (a,b) represent normalized frequency and median values, respectively, 790 for snow particles collected during a 15-min window every 1 minute.

Summary
DWRs from triple-frequency measurements are useful to identify ice particle types and processes as proposed in previous studies . For the technique to be effective the radar frequencies need to 800 be well separated. This requirement limits applications when using 24, 35, and 94 GHz frequency radars, like in this study. Despite this limitation, MDV and polarimetric variables can be used complementarily to identify ice particle types and distinguish among different ice growth processes and even reveal additional microphysical details. 805 We conducted triple frequency measurements using the MRRPro (24 GHz), the Ka-band scanning polarimetric radar (KASPR, 35 GHz), and the W-band profiling radar (ROGER, 94 GHz) at the Stony Brook University Radar Observatory in the winter season of 2019-2020. We successfully collected the triple frequency data from vertically-pointing measurements for a snowstorm along the U.S. North East coast on February 20, 2019. Quasi-vertical profile (QVP) height-versus-time 810 data were also obtained from KASPR PPI scans at an elevation angle of 15°. We investigated all pairs of DWR from the triple frequencies (i.e., DWRKKa, DWRKW, and DWRKaW) in conjunction with MDV from the KASPR vertically-pointing measurements and ZDR and KDP from the KASPR QVPs. Overall, it was challenging to discern the precipitation particle types in the DWRKKa-versus-DWRKW diagram only, likely due to insufficient separation of the K-band frequency from Ka band,815 whereas the DWR-versus-MDV diagrams for all DWR pairs exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. Figure 12 presents a schematic DWR-MDV-polarimetric variable diagram for this case.
Regions that included fallstreaks were dominated by the aggregation process, where the DWRKaW 820 increased with the magnitude of MDV corresponding to the scattering calculations for low-to middle-rime degree aggregate particles (e.g., marked 1 in Fig. 12). The DWRKaW values further increased at lower altitudes of the fallstreaks as reflectivity increased. ZDR and KDP values were 0.6 dB and 0.8 ° km -1 , respectively. The small ZDR values in the lower region in conjunction with the MDV and Doppler spectrum width measurements suggested further ice growth produced by 825 aggregation. Larger KDP in the fallstreaks represented high number concentration ice particles generated aloft that facilitated aggregation. A possible light riming in a turbulence region could increase the mass of individual particles, hence KDP (e.g., marked 2 in Fig. 12). A sublimation process apparent near the ground at the beginning of precipitation might result in dissipating branches and/or edges of aggregates and decreasing the mean volume diameters. This caused little 830 increase of DWR and kept KDP large.
Characteristics of riming were discerned in other regions where several different particle populations were expected. Associated with a population of lower rime aggregates, DWRKaW increased from near zero to 10 dB while the magnitude of MDV increased from near zero to 0.8 835 m s -1 . KDP and ZDR slightly decreased as DWRKaW increased, which were consistent with aggregate particles accompanied by the decrease of their density, increase of their aspect ratios, and more random orientation (e.g., marked 1 in Fig. 12). Another particle population which was expected to have larger degrees of riming was distinguished from the particle populations with smaller degrees of riming using the DWRKaW-versus-MDV diagram (e.g., marked 3 in Fig. 12); it had an increase 840 of DWRKaW similar to that for aggregates with lower riming but the magnitude of MDV was around 2-2.5 m s -1 (approximately 1-1.5 m s -1 larger than that for the former particle population). KDP and ZDR rapidly decreased to near zero when DWRKaW increased suggesting a rapid particle growth. Although DWRKaW also strongly depends on particle shape (in addition to dependence on particle size), the increase of the magnitude of MDV was likely attributed to the ice particle growth. 845 In the lower altitudes, the occurrence of the higher rime degree particle populations increased as the magnitude of MDV reached 3.5 m s -1 , while KDP and ZDR at a given DWRKaW were smaller as compared to the upper region. These characteristics suggest further riming and increase of aspect ratios. The DWRKaW-MDV diagrams also depicted the early stage of riming where ZDR increased while the magnitude of MDV increased collocated with small increases of DWRKaW and KDP (e.g., 850 marked 2 in Fig. 12). The other DWRs (i.e., DWRKKa and DWRKW) as a function of MDV as well as coupling with the polarimetric variables also showed consistent characteristics, indicating that they are very useful to distinguish between riming and aggregation processes.
This study illustrated the capabilities of DWR measurements coupled with MDV and polarimetric 855 measurements to discern riming and aggregation processes, which have been often observed by single-frequency radar measurements but not well separated. This approach will improve quantitative estimations of snow amount (i.e., IWC, snow rate) and microphysical quantities such as rime mass fraction (e.g., Moisseev et al., 2017;Li et al., 2018). Dual-frequency measurements coupled with MDV -typically available from all cloud radar systems-not only would be more 860 practical than the triple frequency measurements (since they only involve two radars)) but they are more effective in separating the two processes as well. Such systems, when used in synergy with polarimetric observations, common in research and weather networks (e.g. Kollias et al., 2020a, NWS WSR-88D radars) can reveal complex microphysics as presented in this study. Shorter wavelength radars and lidars as well as microwave radiometers can be complementarily used for 865 better capturing the presence of supercooled liquid droplets and the riming process (e.g., Lamer et al., 2020;Tridon et al., 2020). Appendix 875 Calculations of DWR and mean Doppler velocity for aggregated snowflakes using the selfsimilar Rayleigh-Gans approximation To evaluate the observed DWRs and mean Doppler velocity, we calculated the radar reflectivities and mean Doppler velocities at the three frequencies (i.e., 24.0 GHz, 35.5 GHz, and 94.0 GHz) 880 using the radar backscattering cross section database obtained from the self-similar Rayleigh-Gans approximation (SSRGA) method proposed by Hogan and Westbrook (2014). The SSRGA uses the Rayleigh-Gans approximation and its extension for an ensemble of particles, for which horizontal orientation with no canting was employed. The SSRGA employs simple mathematical formulation which is very efficient in the numerical implementation and produces more realistic 885 scattering properties compared to spheres/spheroids models, taking into account the internal structure of aggregates (e.g., Hogan and Westbrook, 2014;Hogan et al., 2017;Tyynelä et al., 2013;Leinonen et al., 2013;Tridon et al., 2019).
In this work, the SSRGA was adopted to calculate radar backscattering cross sections at a vertical 890 incident angle for individual aggregate particles with different rime degree (i.e. effective liquid water path) and size modeled by Leinonen and Szyrmer (2015) and Hogan and Westbrook (2014), similar to Tridon et al. (2019). Table A1 lists the particle models with different rime degrees used in the present study. To compute the radar reflectivity from the radar backscatter signals from the database, we used a gamma distribution as a particle size distribution (PSD), where water-895 equivalent mass-weighted diameter (Dm) varied from 0.1 mm to 2.5 mm with a fixed shape parameter ( ) of 0 and 4.
Mean Doppler velocity at 1000 hPa was computed for each particle model and each PSD using the radar backscatter signals and a particle terminal velocity model by Hogan and Westbrook (2014). 900 For the all MDV presented in this study, negative values represent downward motions.  Leinonen and Szyrmer (2015) unrimed aggregate model (model A) LS15A0.0kg/m 2 Leinonen and Szyrmer (2015)  905 Data availability. The SBRO radar data are available at the SBU Academic Commons.
Author contributions. Data collection and analysis were made by MO. Conceptualization of the 910 method, interpretation, and writing were shared between MO, PK, SM, AR, and AB. Scattering calculation using SSRGA was made by the AB's group. processing the MASC and Parsivel data and retrieving particle properties. We also thank Matthew Miller and Sandra Yuter of North Carolina State University for supporting the MASC observations and providing its data.

Financial supports. 925
This research was supported by the National Science Foundation grant # 1841246.