Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar
Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band
profiling radar (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-frequency radar approaches had limited success. On the other hand, a
joint analysis of DWR, mean 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 in DWR but a 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 in DWR. This approach will
improve quantitative estimations of snow amount and microphysical quantities
such as rime mass fraction. The study suggests that triple-frequency
measurements are not always necessary for in-depth ice microphysical studies
and that dual-frequency polarimetric and Doppler measurements can
successfully be used to gain insights into ice hydrometeor microphysics.
Introduction
Millimeter-wavelength (i.e., operating at 35 and 94 GHz) radars have been
widely used for the study of liquid and ice precipitation clouds, utilizing
the radars' high sensitivity to smaller particles due to Rayleigh scattering and
excellent spatiotemporal resolution (Kollias et al., 2007). Cloud radars at
35 and 94 GHz have been routinely operated at surface-based
observatories during the last 2 decades (e.g., the European Union CloudNet
project and the US Atmospheric Radiation Measurement (ARM) facility;
Illingworth et al., 2015; Stokes and Schwartz, 1994; Mather and Voyles,
2013; Kollias et al., 2014, 2016) and from a variety of ship-based (e.g.,
Lewis et al., 2012) and airborne (e.g., Battaglia et al., 2016;
Tridon et al., 2019) platforms. Millimeter-wavelength radars are particularly suitable
for the study of hydrometeor properties (e.g., particle 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 shorter-wavelength reflectivity,
is affected by the differential backscatter 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), ice water
content (IWC), snowfall rate (e.g., Matrosov, 1998), and identification of
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 in identifying ice
crystals, aggregates, and rimed particles, when considering well-separated
triple radar frequencies (i.e., X, Ka, and W bands) so that each frequency
experiences different scattering regimes. The DWR of X-band to Ka-band
reflectivity (DWRXKa) versus the DWR of Ka-band to W-band reflectivity
(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 frequency combinations such as S, X, and W bands and Ku, Ka, and W
bands (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., 2021).
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
handful of surface observatories.
Another limitation of the triple-frequency measurements for ice particle
identification is that the triple frequencies should be well separated from
each other so that magnitudes of non-Rayleigh scattering are 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 trends corresponding to different hydrometeor types may
not be sufficiently separated from each other. For instance, Ka-band (around
35 GHz) frequency and K-band (around 24 GHz) frequency, which have been
employed by a widely used, low-power, low-cost, high-quality precipitation
profiler, the 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 diagram from the
scattering calculations (detailed descriptions of the scattering
calculations are available in the Appendix). Similarly, Fig. 1c is a DWRXKa-versus-DWRXW 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, while the X–Ka–W DWR space has
larger dynamic ranges that are likely enough to discern the particle types, as
presented in 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).
DWR of K-band (24 GHz) reflectivity to Ka-band (35.5 GHz)
reflectivity versus that of K-band reflectivity to W-band (94 GHz)
reflectivity from (a) the self-similar Rayleigh–Gans
approximation (SSRGA) scattering property database and (b)
Matrosov et al. (2019), accounting for particle aspect ratio (AR). (c) and
(d) are the same as (a) and (b), respectively, but for the DWR of X-band (10.7 GHz) reflectivity to Ka-band reflectivity versus that of X-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 shape parameter (μ) of the PSD (particle size distribution) equal to 0 and 4, respectively. The color of
circles in (a) represents the water-equivalent mass-weighted volume diameter
(Dm) of each PSD used to calculate DWRs; here, Dm values for the
particle models of unrimed aggregates with
μ= 0 (solid red line) and aggregates with a high rime degree (solid
blue line) of Leinonen and Szyrmer (2015) 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 μ= 0 and median volume particle size (color of
circles) ranging from 0.2 to 6.0 mm.
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 the minor
to major particle dimensions). For particles 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 pairs' diagram (Fig. 1), however, are much
smaller than those on individual DWRs for a given frequency pair. To
illustrate this point, Fig. 1b and d show DWRKKa–DWRKW and
DWRXKa–DWRXW correspondences, respectively, for a “soft”
spheroidal particle model with aspect ratios of 0.3 and 0.8. The much weaker
particle shape influence on the DWRKKa–DWRKW field (compared to
individual DWRs) is explained, in part, by the fact that both DWRKKa
and DWRKW increase/decrease as particles become more/less spherical. A
similar feature is found in the DWRXKa–DWRXW field. Particle
populations with similar characteristic sizes (colored circles in Fig. 1b and
d) 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 velocity (MDV). Although the MDV is affected by the
vertical air motion, the cloud Doppler radar 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 and
Hobbs, 1974; Kajikawa, 1989; Mason et al., 2018, 2019).
However, using only MDV and reflectivity measurements would not be enough to
distinguish between aggregation and the 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 degrees of riming. Figure 2 shows
DWRKaW as a function of MDV and differential MDV (dMDV = Ka-band
MDV–W-band MDV). The MDV–DWR correspondence is also sensitive to particle
size distribution (PSD) details and rime degree. Figure 2 indicates only a
weak dependency on PSD, which can be advantageous for distinguishing
particle types as PSD influences are minimized.
(a) DWR of Ka-band (35.5 GHz) reflectivity to W-band (94 GHz)
reflectivity versus Ka-band mean Doppler velocity from the SSRGA scattering
database with particle fall velocity models of Heymsfield and Westbrook (2010). (b) DWR of Ka-band reflectivity to W-band reflectivity versus
difference between Ka-band MDV and W-band MDV. Negative Doppler velocity in
(a) represents a downward motion. Color scale and line legends are the same
as in Fig. 1. These particle models can be classified into particles of a low (red and
magenta), middle (yellow and cyan), and high (blue and green) rime degree.
Similarly to DWR and MDV, polarimetric radar observables are also sensitive to
microphysical 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 for identifying depositional, aggregation, and riming growths (e.g.,
Oue et al., 2016; Matrosov et al., 2020). The most common characteristics of
the polarimetric observables representing the depositional growth are
enhancements of differential reflectivity (ZDR) and the specific
differential phase (KDP) in a dendritic/plate-like growth regime (e.g.,
around a 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 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; Kumjian
et al., 2016; Griffin et al., 2018; Matrosov et al., 2020). Similar vertical
changes in the polarimetric variables have often been 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
increases particle aspect ratios. Mean particle aspect ratios can be
quantitatively estimated using proxies for 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) 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 the early stage of riming is still
challenging even though MDV and polarimetric variables are jointly used due
to their similar signatures (e.g., Oue et al., 2016).
Winter storms in the northeast USA 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 have resulted in
large uncertainty 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 these processes in the observations.
To facilitate studies of cloud microphysics and dynamics in northeast
USA, the Stony Brook Radar Observatory (SBRO) was established in March 2017 in Stony Brook University, Stony Brook, NY (Fig. 3). The flagship radar
of the SBRO is a very sensitive, sophisticated, and well-calibrated Ka-band
(35 GHz) scanning fully polarimetric radar (KASPR). The radar measurements
are complemented by two profiling radar systems operating at the W band (94 GHz,
ROGER) and K band (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 complemented by
polarimetric variables from KASPR for a snowstorm observed on 20 February 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 in distinguishing ice particle types and
growth processes rather than the DWR-only diagrams from triple-frequency
measurements. This study first illustrates the capability and advantage of
the use of MDV and polarimetric radar observables in conjunction with DWR
measurements to identify particle types and growth processes in winter
storms.
(a) Location of the Stony Brook Radar Observatory. (b) Instruments
deployed at SBRO. The big yellow circle in (a) represents the KASPR 30 km
observation range.
Data
SBRO has been in operation since March 2017 (40.897∘ N, 73.127∘ W;
∼22 km west of a National Weather Service (NWS) sounding site at
Upton, NY; https://you.stonybrook.edu/radar/, last access: 24 June 2021). KASPR, ROGER,
and MRRPro at SBRO collected triple-frequency data during a snow event on
20 February 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 camera (MASC; Garrett et al., 2012) 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.
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 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 are available including the 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 the Doppler velocity and spectral
width. The specific differential phase (KDP) is estimated using an
iterative algorithm proposed by Hubbert and Bringi (1995). The data
post-processing details are described in Oue et al. (2018). KASPR was
calibrated using a corner reflector technique, providing reliable
reflectivity data to calibrate the data of the other two radars. The detailed
configurations are also available in Kumjian et al. (2020) and Kollias et
al. (2020b).
During the radar measurements on 20 February 2019, KASPR executed a
scanning strategy that consisted of surveillance (plan position indicator, PPI) scans at a
15∘ elevation angle, a zenith-pointing PPI, hemispheric
range–height indicator (HSRHI) scans at four azimuth angles, and a 5 min
vertically pointing (VPT) mode during which Doppler spectrum data were
collected. This pattern was repeated and took approximately 15 min to
complete. During a 15 min cycle, two 15∘ PPI scans were included,
so 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 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 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.
The system was initially developed as an airborne radar and was integrated
on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin
Otter aircraft (Mead et al., 2003). In 2017, the system was refurbished by
installing 61 cm parabolic dish antennas and upgrading all the C-FMCW 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.
This radar system is capable of collecting Doppler spectra with
spatiotemporal resolutions similar to those of 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 4 s at 30 m vertical
spacing with a beamwidth of 0.3∘ (Table 1).
Specifications for KASPR, ROGER, and MRRPro.
Ka-band scanning polarimetric radar(KASPR)W-band profiling radar(ROGER)K-band Micro Rain Radar Pro (MRRPro)Frequency35.29 GHz (wavelength ∼8.5 mm)94.8 GHz (wavelength ∼3.2 mm)24.23 GHz (wavelength ∼12.4 mm)Range resolutionConfigurable between 15–200 m; 15 m in VPT mode, 30 m in HSRHI and PPI for this study5–150 m, 30 m for thisstudy>10 m, 60 m for this studyBeamwidth0.32∘0.3∘1.5∘Maximum rangeConfigurable; 15 km in VPT mode, 30 km in RHI and PPI for this studyConfigurable; 18.5 km forthis studyConfigurable; 7 km for this studyVelocity resolutionConfigurable; 0.04 m s-1 for this studyConfigurable; 0.08 m s-1for this studyConfigurable between0.05–6.00 m s-1; 0.19 m s-1 for this studyObservablesReflectivity, Doppler velocity, full set of polarimetric variables, Doppler spectraReflectivity, Doppler velocity, Doppler spectraReflectivity, Dopplervelocity, Doppler spectraK-band (24 GHz) 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 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 4 s 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.
Ground-based in situ measurements
A Parsivel optical disdrometer measures terminal velocity and the horizontal
size of individual 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 cm2. The measured size and velocity are
classified into 1 of 32 size bins ranging from 0.062 to 24.5 mm and 32
velocity bins ranging from 0.04 to 20.5 m s-1 every minute.
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 toward the focal point about 10 cm away
(Garrett et al., 2012; Garrett and Yuter, 2014). On top of each camera rests a 2700 lm light-emitting diode. The focal point lies within a ring that has two
near-infrared emitter–detector pairs arranged in arrays that are separated
vertically by 32 mm. The arrangement of the emitter–detector pairs allows
for a trigger depth of field of 3100 mm2 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.
MethodReflectivity calibration and DWR estimation
KASPR reflectivity measurements were well calibrated using a corner
reflector technique (Lamer et al., 2021). Therefore, systematic offsets for
the MRRPro and ROGER total reflectivities have been corrected by comparing
them with the KASPR reflectivity at cloud bases from a different
non-precipitating 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 s time spacings).
Gaseous attenuation needs to be considered and corrected when using
short-wavelength radars (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 daily (00:00 and 12:00 Z) at
Upton, 21 km east of the observatory. The estimated column-integrated
two-way 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). Although the attenuation in oxygen may not be as
large as that in water vapor, it may be non-negligible. 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.
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 US
northeast coast, indicating the presence of significant amounts of
supercooled cloud water in the snowstorms (e.g., Colle et al., 2014).
However, it was 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 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 should then be equal
to the DWR plateau near the cloud top. We applied the technique of Tridon et al. (2020) to the DWR from KASPR and ROGER measurements to find the DWR plateau
near the cloud top as follows:
The measured DWRKaW values are averaged over 450 m (30 gates) and 20 s
(20 rays). The range window was adapted for this study.
The DWR variance within the moving windows defined above must be lower than
4 dB2. Because the DWR data were still noisy after the averaging, we
used a larger window size (450 m) than in Tridon et al. (2020).
KASPR reflectivity and its variance (within the same moving windows) must be
lower than 5 dBZ and 2.5 dB2, 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.
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 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 particle populations
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
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 of less than 1 dB in the KASPR and MRRPro reflectivity
measurements.
Height–time cross sections of (a) KASPR VPT reflectivity, (b)
ROGER reflectivity, (c) MRRPro reflectivity, and (d) DWR of KASPR
reflectivity to ROGER reflectivity on 20 February 2019; (e) vertical profiles
of temperature (solid line) and dew point temperature (dashed line) from the
NWS Upton sounding measurements at 12:00 UTC on 20 February (black color) and 00:00 UTC on 21 February (blue color) 2019; and (f) examples of snowflake images
captured by the MASC. Boxes in (d) represent analysis regions used for Figs. 7–10. Gray and blue shades in (e) represent regions of supersaturation with
respect to ice for 12:00 UTC on 20 February and 00:00 UTC on 21 February, respectively.
Each image in (f) displays observation time and maximum dimension in
parentheses (unit is mm).
Another error source of the DWR analysis is radar beam mismatching. The
three radars were located at the same observation site; the distances 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 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 KASPR and ROGER. The beamwidth differences between MRRPro and
KASPR and between MRRPro and ROGER might result in larger variabilities in DWRs.
Mean Doppler velocity
Similarly 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 from the three radars were corrected for air
density changes based on the sounding profiles and adjusted to the surface.
KASPR polarimetric observables
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 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 of important polarimetric radar signatures and their evolution
(e.g., Griffin et al., 2018, 2020; 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 the 15∘ PPI data is 30 m, the corresponding
QVP data have vertical spacing of approximately 10 m and a 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 (Ryzhkov et al., 2016). The use of
conical PPI at a higher elevation angle (15∘) for QVP
reconstruction ensures relatively high horizontal resolutions at lower
altitudes (11 km at the height 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,
similarly to in 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 at a 15 min interval, a single KASPR QVP corresponds to about
150 DWR profiles.
Case description
During the 2018/19 and 2019/20 winter seasons, most precipitation was
non-dry snow including rain, wet snow, refrozen particles, and sleet, with
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, possibly, embedded supercooled
cloud layers) were observed aloft. We selected a period from a snow
precipitation case on 20 February 2019, when KASPR in VPT mode, ROGER, and MRRPro
simultaneously observed snowfall without significant attenuations.
A high-pressure system at the surface persisted around Long Island from 09:00 to 21:00 UTC on 20 February 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 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 21 February.
Figures 4 and 5 show the time–height curtain images of the reflectivities
from the three radars and KASPR and ROGER MDV together with KASPR
polarimetric QVPs. The triple-frequency measurements started at 15:41 UTC.
The cloud base descended until the lidar backscatter signal reached KASPR's lowest gate (0.4 km altitude) at 19:00 UTC. The cloud top attained a
10 km altitude, but the cloud top was decoupled from the ice precipitation
from 17:45 UTC onwards.
Height–time cross sections of (a) MDV from the KASPR VPT
measurements, (b) MDV from the ROGER measurements, (c) spectrum width from
the KASPR VPT measurements, (d) QVP of KASPR ZDR, (e) QVP of KASPR
KDP, and (f) ceilometer backscatter on 20 February 2019. Black dots in (f)
represent cloud base heights.
The KASPR and ROGER reflectivity fields indicated generation of cell-like
features by 17:10 UTC near the cloud top above an 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 KDP increased
in the lower part of the enhanced ZDR layer and just below the enhanced
ZDR layer. The enhancement of ZDR was generated at a height of 5–6 km,
where the temperature ranged from -20 to -15∘C,
corresponding to a dendritic growth layer and close to supersaturation with
respect to ice from the 12:00 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 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, the MASC observed large aggregates.
KASPR (a, d) reflectivity, (b, e)ZDR, and (c, f)KDP from
RHI measurements (a, b, c) at 17:39 UTC at an azimuth angle of 99∘ and (d, e, f) at 18:22 UTC at an azimuth angle of 0∘.
Starting from 17:50 UTC, precipitation observed at the surface originated at a
6 km altitude. The 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 Ka-band
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 (Fig. 5d and e). Corresponding to the time
when the fallstreak features reached the surface, the MASC observed rimed
particles (Fig. 4f). These DWR and polarimetric features likely indicate
ice particle growth; however, it is hard to 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 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 fallstreaks. In contrast, 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 an 6 km altitude and aggregated as
they fell, forming more spherical shapes, as many previous polarimetric radar
studies have observed. The MDV showed faster downward motion compared to the
fallstreaks in the first period, suggesting heavy aggregation and/or riming.
Another interesting characteristic to be noted is that there was a distinct
region of turbulence, which can clearly be seen as a layer with a large
spectrum width and variability in MDV at around a 3 km altitude. This was
consistent with large lidar backscatter values suggesting the presence of a
liquid cloud base. The reflectivity and DWR of fallstreaks were intensified
below the turbulence layer.
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.
Results and discussionsDWRs from the three frequencies
Based on the DWRKaW height–time plots, we selected four regions as
shown in Fig. 4d, each of which had similar characteristics in terms of DWRs, MDV,
and polarimetry 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 (smaller DWRKW at a given DWRKKa), while
those from Region C and Region D were distributed toward the higher-rime-degree regions (larger DWRKW at a given DWRKKa). 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 overlap, 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 separated in the frequency domain.
(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 Appendix A. The color line legend is the same as in
Fig. 1a, and black lines are same as black lines in Fig. 1b.
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., 2021). The most likely cause is unaccounted for attenuations particularly
at Ka and W bands due to 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 clouds or by underlying liquid layers
themselves. Ice particles could also cause signal attenuation (Battaglia et
al., 2020; 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 ROGER reflectivity.
Secondly, the beam mismatch could be significant when the radar beams
penetrate fine narrow fallstreaks, even though the radars were collocated
within 5 m of each other. As mentioned previously, the KASPR and
ROGER beamwidths are well matched (0.3∘), while the MRRPro's
beamwidth is 5 times larger (1.5∘). The radar sampling volumes,
which are larger at higher altitudes, cannot resolve the small-timescale and small-spatial-scale phenomena, and the difference in beamwidth is a source of
uncertainty. Moreover, the ice particle models may not represent the whole
gamut of ice particles possibly present in the clouds.
DWRs coupled with MDV and polarimetric variables
Observed MDV is mainly attributed to the vertical air motion and the
particle fall speeds, which are 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 from Region A are less than 7 dB and are located between the middle-rime-degree
(yellow and cyan) and low-rime-degree (red and magenta) lines, suggesting
light riming of small aggregates (Fig. 8a). These particles could grow
keeping a similar degree of riming by aggregation, as the data points from
Region B are shifted toward larger DWRKaW values between the middle-rime-degree and
low-rime-degree lines (Fig. 8b). It is possible that the turbulence layer at
around a 3 km altitude (Fig. 5b and c) contributed to light riming. The
turbulence also contributed to the wide distribution of MDV.
(a)–(d) Diagrams of KASPR VPT MDV versus DWRKaW for regions A, B,
C, and D, respectively. (e) Diagram of KASPR VPT MDV versus DWRKW for
Region D and (f) of KASPR VPT MDV versus DWRKKa for Region D.
Color shades represent normalized frequency. Lines in each panel represent
the SSRGA calculations using different particle models and PSDs described in
Appendix A. The legend for the lines is as in Fig. 1.
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
DWRKaW). These data clusters suggest that the aggregation dominated,
but some particles started riming. Region D, which is located below Region
C, also has 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 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 MDV
changed from -1.8 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, as reported by Locatelli and Hobbs (1974). These characteristics suggest that aggregates produced near the
cloud top at 6 km rimed during the falling 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 shown not only in
the fields of DWRKaW versus KASPR MDV but also in the DWRKW-versus-MDV (Fig. 8e) and DWRKKa-versus-MDV (Fig. 8f) diagrams.
Adding polarimetric information supports this interpretation and gives
further insights into particle microphysics in terms of their shapes.
Figure 9a–d and e–h are the same as Fig. 8a–d, but
the color shades represent KASPR QVP ZDR (Fig. 9a–d) and KDP
(Fig. 9e–h), respectively. For the data following the low-rime-degree lines
for Region C and Region D, ZDR values decreased as DWR increased (Fig. 9c, d), while KDP values slightly decreased by approximately 0.2∘ km-1 (Fig. 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
into large crystals as DWR increased. On the other hand, the 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 a
median volume size of 1 and 2 mm, respectively (Matrosov, 2021). The increase
in DWRKaW in the diagrams can include both the particle size and the particle shape
effects.
Diagrams of KASPR VPT MDV versus DWRKaW for (a, e) Region A,
(b, f) Region B, (c, g) Region C, and (d, h) Region D. Color shades in (a)–(d)
and (e)–(h) represent KASPR QVP ZDR and KDP, respectively, averaged
at each MDV–DWR bin. Lines in each panel represent the SSRGA calculations
using particle type and PSD models described in Appendix A. The legend for the
lines is the same as in Fig. 1.
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 in size rather than in aspect ratio. During the aggregation process,
the size distribution of snowflakes evolves 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 in both ZDR and KDP due to aggregation,
although the increase in 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 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 to 0.5 dB, and the KDP values decreased from 0.4∘ km-1 to near
zero (Fig. 9c, g). Although the increase in the DWRKaW includes the
effects of both size and aspect ratio as discussed above, the increase in
the magnitude of MDV can represent the increase in size. The ZDR and
KDP values are lower than those from the cluster following the low-rime-degree model's lines at a given DWRKaW. Lower KDP and ZDR values
suggest particle growth by heavier riming, which tends to produce more
spherical particles.
These ZDR and KDP characteristics shown in both low-rime-degree 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 (Fig. 9d, h). The
lower KDP and ZDR in Region D represent further particle growth
which is accompanied by the decrease in their density, increase in their
aspect ratios, and more random orientation.
It is interesting that for DWRKaW values of 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. 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 in 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).
Compared to Region C and Region D, the polarimetric observables in Region A
and Region B (Fig. 9a, b, e, and f) do not show clear trends with
changes in rime degree, and the dynamical oscillation shown in Fig. 5a–c
results in an uncertainty in the particle identification for Region A,
particularly when DWRKaW values are smaller than 5 dB and MDV varies 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 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 in particle density, an increase in 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 a higher number concentration of ice crystals. The increase in
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 winter storms that has been reported by many previous studies using
polarimetric radar measurements (e.g., Kennedy and Rutledge, 2011). Another
process is seeding from above (e.g., Griffin et al., 2018; Oue et al., 2018),
which is more likely to contribute to an increase in 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 a 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
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 (12:00 Z, black lines in Fig. 4e) showed a dry air condition at the lower
altitudes. This sounding time was ∼5–6 h before the radar
observation, but the dry air condition could still have been present near
the ground, thus favoring 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 diameter. Decrease in 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
remained high compared to in Region A. The sublimation also contributed to
decreasing particle fall speed, as shown by a minor decrease in the
magnitude of MDV in the data group, but the MDV probably 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.
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 types on the diagrams of DWRKaW versus
differential MDV (dMDV = KASPR MDV - ROGER MDV) for Region C and
Region D, together with the scattering calculations using the particle
models. Similarly to the DWR–MDV diagram in Fig. 9, Region C includes a
cluster with a higher number of occurrences along the particle lines of low-to-middle rime
degrees, and a lower-frequency cluster extends toward the
high-rime-degree particle lines. Region D has more data points for the
high-rime-degree particle population.
Difference in KASPR VPT MDV versus DWRKaW and ROGER MDV versus DWRKaW
diagrams for (a) Region C and (b) Region D. Color shades represent
normalized frequency. Panel (c) is the same as (b), but the color shades represent
KASPR QVP ZDR. Lines in panels (a) and (b) represent the SSRGA
calculations using particle type and PSD models described in Appendix A. The
legend for the lines is the same as in Fig. 1.
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 large dMDV values (Fig. 10c) are approximately 0.7 dB, suggesting that
the particles were non-spherical, possibly contributing to the decrease in
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 a few
hundredths of 1 m s-1 and exact range-time-bin gate matching for lower
DWRKaW (<5 dB). Although the vertical air motion contributions
in MDV from each radar are canceled out in dMDV, subgrid-scale turbulence,
the wide range of particle fall speeds, and different sampling times for the
observations (1 s for KASPR in VPT mode and 4 s for ROGER) can all be sources of
uncertainties. This limitation may affect the scatterplot distributions,
e.g., with some points clustering outside the envelope of the model's lines.
This limitation also affects Region A (not shown).
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 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 and 18:16 UTC, respectively. The
snow images from the MASC were quantified by measurements of aspect ratio and
area ratio, and their time series were presented in Fig. 11. Figure 11a and
b present frequencies (color shades) together with median values (black
line) observed for a 20 min time range every 1 min.
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, for snow
particles collected during a 15 min window every 1 min.
We also estimated the mass-weighted mean diameter for Parsivel-measured PSD. The
ice particle mass was estimated using a methodology proposed by von Lerber
et al. (2017). The methodology is based on a theory that individual particle
mass can be expressed based on a hydrodynamic theory derived by Böhm (1989) using 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 Lerber et al., 2017) 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 and fall speed are fitted to a form of V=aDb, where a and b
are constants using the 20 min integrated data. Previous studies have 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 the following apparently
unrealistic velocity values: (1) those exceeding 1.5 m s-1 associated with
particles having a diameter of less than 1 mm in agreement with Locatelli and
Hobbs (1974) and (2) data outside upper and lower boundaries of the V–D
relationships. The upper boundary was determined based on Locatelli and
Hobbs (1974) V–D relationships for rimed aggregates, and the lower
boundary was determined based on Szyrmer and Zawadzki (2010) V–D
relationships for unrimed aggregates. The Parsivel-measured size was
adjusted to the maximum dimension using a technique proposed by von Lerber
et al. (2017). Figure 11c presents the time series of the estimated
water-equivalent mass-weighted mean diameter from the Parsivel-measured PSD.
These time series are consistent with the fallstreaks reaching the ground.
Aspect ratio represents oblateness of particles relating to ZDR and
partly contributing to KDP. Korolev and Isaac (2003) reported that mean
aspect ratios of ice hydrometeors observed in situ from aircraft sampling
are often around 0.6, while those of heavily rimed particles such as graupel
increase toward 1. Depolarization-based radar retrievals of snowflake
aspect ratios near the ground indicated mean intrinsic 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-white image, to the area of the
circumscribing circle defined by the maximum diameter from the MASC. The area
ratio increases with riming (von Lerber et al., 2017).
The aspect ratio was relatively low before 18:30 UTC, when the median value
was less than 0.6. At the same time, the area ratio was also relatively low,
with the median area ratio smaller than 0.5. This period corresponds to a
time when fallstreaks 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 also be noted that aspect ratio
estimates from in situ data (e.g., from Parsivel and/or MASC measurements
or aircraft-based particle probes) are inferred from 2D particle
projections, so these estimates usually overestimate actual (i.e.,
intrinsic) 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 exceeded 1.3 mm between 18:38 and
19:48 UTC except at 18:58, 19:22, and 19:28 UTC. Those large-diameter periods
correspond to times where fallstreaks included in Region D reached the
ground. The ground-based characteristics suggest that the snowflakes were
heavily rimed, larger aggregates, consistent with the observed
characteristics of the radar MDV, DWR, and polarimetric observables.
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 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.
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 2019/20. We successfully collected
triple-frequency data from vertically pointing measurements for a snowstorm
along the US northeast coast on 20 February 2019. Quasi-vertical profile
(QVP) height-versus-time data were also obtained from KASPR PPI scans at an
elevation angle of 15∘. We investigated all pairs of DWRs 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 the Ka band, 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 showing the impact of different
ice crystal types on DWR–MDV–polarimetric variables.
A schematic DWR–MDV–polarimetric variable diagram based on the
observation in this study.
Regions that included fallstreaks were dominated by the aggregation process,
where the DWRKaW increased with the magnitude of MDV corresponding to
the scattering calculations for aggregate particles of low to middle rime degrees (e.g., marked 1 in Fig. 12; regions A and B in Fig. 4). 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 aggregation. Larger KDP in the
fallstreaks were attributed to high-number-concentration ice particles
generated aloft that facilitated aggregation. Alternatively enhanced
KDP regions could have been generated in turbulent regions by light
riming causing an increase in the mass of individual particles (e.g., marked
2 in Fig. 12; Region B in Fig. 4). Finally sublimation active near the
ground at the beginning of precipitation might have resulted in dissipating
the branches of the large aggregates and, consequently, decreasing the mean
volume diameters. This caused little increase in 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 m s-1.
KDP and ZDR slightly decreased as DWRKaW increased, which
was consistent with aggregate particles and accompanied by a decrease in
the particles' density, increase in their aspect ratios, and more random particle orientation
(e.g., marked 1 in Fig. 12; regions C and D in Fig. 4). 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; regions
C and D in Fig. 4); it had an increase in 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 rapid particle growth. Although
DWRKaW also strongly depends on particle shape (in addition to
dependence on particle size), the increase in the magnitude of MDV was
likely attributed to the ice particle growth. 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 than those observed in the upper region.
These characteristics suggest further riming and increase in 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 in DWRKaW and KDP (e.g., marked 2 in Fig. 12;
Region C in Fig. 4). 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 the joint analysis of the
DWRs, MDV, and polarimetric variables is very useful to distinguish between
riming and aggregation processes for these frequency pairs as well.
This study illustrated the capabilities of DWR measurements coupled with MDV
and polarimetric measurements to discern riming and aggregation processes,
which have been often observed by single-frequency radar measurements but
not well separated. This study highlights that dual-frequency measurements
coupled with MDV – typically available from all cloud radar systems – not only
are more practical than the triple-frequency measurements (since they only
involve two radars) but 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 and therefore
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). Shorter-wavelength radars and lidars as well as
microwave radiometers can be complementarily used for better capturing the
presence of supercooled liquid droplets and the riming process (e.g., Lamer
et al., 2021; Tridon et al., 2020).
Calculations of DWR and mean Doppler velocity for aggregated snowflakes using the self-similar 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, 35.5, and 94.0 GHz) 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 a simple mathematical formulation which is very efficient in
its numerical implementation and produces more realistic scattering
properties compared to sphere/spheroid models, taking into account the
internal structure of aggregates (e.g., Hogan and Westbrook, 2014; Hogan et
al., 2017; Tyynelä et al., 2011; Leinonen et al., 2013; Tridon et al.,
2019).
Particle models used in the present study.
Particle modelName in figuresLeinonen and Szyrmer (2015) unrimed aggregate model (model A)LS15A0.0kg/m2Leinonen and Szyrmer (2015) rimed aggregate model (model A) with effective liquid water path of 0.5 kg m-2LS15A0.5kg/m2Leinonen and Szyrmer (2015) rimed aggregate model (model A) with effective liquid water path of 2.0 kg m-2LS15A2.0kg/m2Leinonen and Szyrmer (2015) rimed aggregate model (model B) with effective liquid water path of 0.5 kg m-2LS15B0.5kg/m2Leinonen and Szyrmer (2015) rimed aggregate model (model B) with effective liquid water path of 2.0 kg m-2LS15B2.0kg/m2Hogan and Westbrook (2014)HW14
In this work, the SSRGA was adopted to calculate radar backscattering cross
sections at a vertical incident angle for individual aggregate particles
with different rime degrees (i.e., effective liquid water path) and sizes
modeled by Leinonen and Szyrmer (2015) and Hogan and Westbrook (2014),
similarly to in 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 the
water-equivalent mass-weighted diameter (Dm) varied from 0.1 mm to 2.5 mm with a fixed shape parameter (μ) 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 Heymsfield and Westbrook (2010). For all the MDV values presented
in this study, negative values represent downward motions.
Data availability
The SBRO radar data are available at the SBU Academic Commons (https://commons.library.stonybrook.edu/somasdata/11; Oue, 2021).
Author contributions
Data collection and analysis were performed by MO. Conceptualization of the
method, interpretation, and writing were shared between MO, PK, SYM, AVR, and
AB. Scattering calculation using the SSRGA was made by AB's group.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Mariko Oue, Pavlos Kollias, Sergey Y. Matrosov, and Alexander V. Ryzhkov were supported by the
National Science Foundation grant nos. AGS-1841215, 1841246, 1841260, and 1904809. Alessandro Battaglia was
supported by Atmospheric System Research (grant no. DE-SC0017967). We thank
Frederić Tridon of the University of Cologne for providing the lookup tables
of SSRGA scattering properties and Samantha Nebylitsa of the University of Miami
for 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 MASC data.
Financial support
This research has been supported by the National Science Foundation (grant nos. AGS-1841215, 1841246, 1841260, and 1904809) and the Atmospheric System Research (grant no. DE-SC0017967).
Review statement
This paper was edited by Gianfranco Vulpiani and reviewed by two anonymous referees.
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