Observations of supercooled liquid water are nearly ubiquitous
within wintertime orographic-layer clouds over the Intermountain West;
however, observations of regions containing supercooled drizzle drops
(SCDDs) are much rarer and the factors controlling SCDD development and
location less well understood. As part of the Seeded and Natural Orographic
Wintertime clouds – the Idaho Experiment (SNOWIE) and its goal of improving
understanding of natural cloud structure, this study examines the role of
fine-scale (sub-kilometer) vertical velocity fluctuations on the
microphysical evolution and location of SCDDs within the observed
mixed-phase, wintertime orographic clouds from one research flight in
SNOWIE.
For the case examined, SCDDs developed in an elevated, postfrontal-layer
cloud with cold cloud tops (T<-30∘C) and low number
concentrations of both ice (less than 0.5 L-1) and cloud droplets (less
than 30 cm-3). Regions of supercooled drizzle at flight level extended
more than a kilometer along the mean wind direction and were first located
at and below layers of semi-coherent vertical velocity fluctuations (SCVVFs)
embedded within the cloud and subsequently below cloud top. The
microphysical development of SCDDs in this environment is catalogued using
size and mass distributions derived from in situ probe measurements. Regions
corresponding to hydrometeor growth are determined from radar reflectivity
profiles retrieved from an airborne W-band cloud radar. Analysis suggests
that SCVVF layers are associated with local SCDD development in response to
the kinematic perturbation pattern. This drizzle development and subsequent
growth by collision–coalescence is inferred from vertical reflectivity
enhancements (-20 dBZ km-1), with drizzle production confirmed by in
situ measurements within one of these SCVVF layers. The SCDD production and
growth occurs embedded within cloud over shallow (km or less) layers before
transitioning to drizzle production at cloud top further downwind,
indicating that wind shear and resultant vertical velocity fluctuations may
act to enhance or speed up SCDD development compared to classic cloud top
broadening mechanisms in orographic (or similarly sheared) cloud
environment(s).
Introduction
Over the last 40 years, there have been numerous field campaigns either
directly or indirectly examining mixed-phase, orographic-layer clouds over
the American Intermountain West (Hobbs, 1975; Cooper and Saunders,
1980; Heggli and Reynolds, 1985; Rasmussen et al., 1992; Ikeda et al., 2007;
Rosenfeld et al., 2013). At cloud top temperatures between 0 and -20∘C, these clouds frequently contain extensive regions of
supercooled liquid water (SLW), especially near cloud top, making such
clouds a prime meteorological environment for aircraft icing (Hindman, 1986; Ashenden et al., 1996; Marwitz et al., 1997). In some instances,
SLW mass may be distributed entirely across cloud droplets, i.e., those liquid
hydrometeors that are relatively small and have not attained appreciable
fall speeds, and taken to have diameters less than 50 µm for the purpose of
this study. On the other hand, drizzle drops, with diameters 50 to 500 µm, have appreciable fall velocities (0 to 2 m s-1) relative to cloud
droplets and can consequently grow rapidly via collision–coalescence in the
presence of cloud droplets (Lamb and Verlinde, 2011). Supercooled drizzle
drops (SCDDs) are of special concern in aircraft icing because of the
collection and subsequent freezing of these drops on aircraft wings aft of
deicing devices, such as pneumatic boots (Ashenden et al., 1996). This
study aims to catalogue the effect of local, kilometer-scale kinematic
perturbation patterns on the development and location of SCDDs for one such
mixed-phase cloud system.
Recent climatologies (Rauber et al., 2000; Bernstein et al., 2007) describe
SCDD development as occurring predominantly through collision–coalescence
growth in supercooled clouds largely devoid of ice hydrometeors. Studies
explicitly examining the microphysical development of SCDDs with in situ
aircraft data confirmed the primacy of the collision–coalescence growth
mechanism (Cober et al., 2001), as opposed to the “classical”
mechanism – which sees ice hydrometeors melt as they fall through an
embedded warm layer (T>0∘C) before subsequent
supercooling as fully melted drizzle drops. Wintertime orographic-layer
clouds are frequently too shallow and too cold (outside of cold air damming
events on the east coast of the US) to support a warm nose (Rauber et al.,
2000) – therefore, the climatologies suggest that collision–coalescence is
the dominant SCDD development mechanism in the clouds of interest in this
study.
Collision–coalescence growth is favored in clouds with low cloud droplet
number concentrations. For clouds with similar condensate supply rates,
those with fewer cloud droplets will more quickly produce droplets of larger
diameter (D∼30–40 µm) that approach sizes with
appreciable terminal fall velocities, subsequently stimulating further
growth through collision–coalescence compared to clouds with more numerous
droplets. For this reason, clouds formed in clean air masses (i.e., with
lower numbers of cloud condensation nuclei, CCN) or in less vigorous
updrafts (where saturation ratio remains nearer unity with fewer activated
CCN) are kinetically favored for drizzle formation (Freud and Rosenfeld,
2012). In agreement, the conditions of limited CCN abundance and gradual
ascent are linked to high frequency of SCDD observation (Rauber et al.,
2000; Bernstein et al., 2007). Regions that see shallow clouds form from
warm, moist air gradually lifted over an arctic cold front or orography
frequently see SCDD formation that is faster and more extensive if the clouds
form in clean, maritime air masses (Rasmussen et al., 2002). A region that
has uplift mechanisms in both orography and surface frontal passage, as well
as the required cloud level moisture supply, is the American Intermountain
West (IMW) during the winter storm season.
The presence and amount of ice act as additional factors influencing SCDD
development in mixed-phase orographic clouds. Ice-phase hydrometeors
typically acquire mass more rapidly than liquid species, owing to both a
greater diffusional vapor pressure gradient and increased individual linear
growth rates due to crystal geometry and riming. This places an upper limit
on active ice-nucleating particle (INP) and ice crystal number for SCDD
formation, otherwise ice will more rapidly scavenge the available vapor and cloud
water, inhibiting growth of cloud droplets to drizzle sizes (Rasmussen et
al., 2002; Geresdi and Rasmussen, 2005). A byproduct is that SCDD
observations are infrequent in clouds with cloud tops colder than -15∘C, with few observations of SCDD formation found in the
literature, with cloud tops colder than -20∘C (Lawson et al.
2001; Korolev et al., 2002; Rosenfeld et al., 2013; Silber et al., 2019).
Collision–coalescence initiation and growth often depend on broadening
mechanisms for the largest droplets to begin collection of smaller droplets
in the population in all but the cleanest of clouds (Wood et al., 2018), and
this is true regardless if clouds are supercooled. Steady condensational
growth alone leads to a narrowing of the drop size distribution (DSD) around
a large drop mode (D∼30–40 µm), such that DSD
broadening mechanisms (e.g., turbulent or isobaric mixing and eddy hopping) are necessary to provide the differential fall speed conducive to
collision–coalescence onset and subsequent rapid collective growth. Pobanz
et al. (1994) found that when SCDDs formed in clouds with cloud droplet
number concentrations of more than 100 cm-3, layers of cloud top shear
were correlated with vertical location of drizzle development, presumably
due to turbulent broadening or mixing. Shear-induced turbulent mixing,
especially at cloud top, is thought to be responsible for relatively rapid
DSD broadening (Grabowski and Abade, 2017). Any isobaric mixing of different
temperature parcels near the cloud boundary (e.g., for clouds with a strong
capping inversion) is expected to further accelerate this process. This is
why the warm rain process is understood to start at or near cloud top, with
drizzle mass principally increasing with cloud layer depth (Comstock et al., 2007).
Supersaturation history provides an analytical framework for understanding
several mechanisms (e.g., vertical velocity fluctuations, turbulent eddy
hopping, mixing events) that may be responsible for the rapid spectral
broadening and subsequent collision–coalescence enhancement in warm
stratiform clouds (Cooper, 1989; Politovitch and Cooper, 1988; Korolev and Mazin, 1993; Korolev, 1995). For instance, Korolev found that when
modeled cloud parcels are subjected to repeated vertical velocity
fluctuations, DSDs broaden and may even see a second small-diameter droplet
mode develop from interstitial CCN activation (hereafter, secondary droplet
activation). Turbulence and wave motions were both suggested as possible
sources for these vertical velocity fluctuations, but the lack of
parcel-following in situ measurements made validating these behaviors an
observational challenge (Pobanz et al., 1994).
Between the orographic SLW case studies (Rauber and Grant, 1986; Rauber,
1992), SCDD climatologies (Rauber et al., 2000; Bernstein et al., 2007),
mechanistic understandings of SCDD production (Rosenfeld et al., 2013), and
exceptional cases (Korolev and Isaac, 2000; Pobanz et al., 1994), a clear
picture of SCDD formation develops: clouds formed in gradual updrafts within
low CCN and INP populations are most likely to produce SCDD. The frequency,
spatial extent, and thermodynamic extremity of SCDD production is a function
of CCN and INP abundance (Rosenfeld et al., 2013). Wind shear and dynamic
instability appear to lead to SCDD development in clouds with exceptionally
high CCN concentrations given low enough ice concentrations (Korolev and
Isaac, 2000; Pobanz et al., 1994). Mixed-phase clouds throughout the western
US in which the phase partitioning is mostly liquid are common even well
away from the coast (Hindman, 1986). Such clouds must contain low
concentrations of cloud droplets and ice to develop SCDDs (Saleeby et al., 2011).
Where encountered in orographic environments, these supercooled, relatively
clean clouds are expected to encounter vertical and turbulent motions at
both broadscales and fine scales (Houze and Medina, 2005).
This study examines an individual case from a field campaign located in the
IMW in which SCDDs developed in a winter orographic cloud system despite
cold cloud tops (T∼-30∘C), which are typically
associated with more active ice nucleation and more abundant natural ice
(DeMott et al., 2010). Persistently low droplet number concentrations (less
than 50 cm-3) and frequent SCDD observations from about half of the
cases throughout the field campaign (Tessendorf et al., 2018) inspired this
analysis and are consistent with the climatological maxima of wintertime
SCDD frequency that stretches from the coastal barrier mountains into Idaho
(Bernstein et al., 2007). The analysis focuses on the spatial kinematic
patterns and their effect on the liquid-phase precipitation development in
these mixed-phase clouds.
Study area and data
The Seeded and Natural Orographic
Wintertime clouds – the Idaho
Experiment (SNOWIE) was designed to observe and analyze the evolving
wintertime orographic cloud structure in a series of prescribed airborne
cloud-seeding experiments (Tessendorf et al., 2018). As part of this
process, it was necessary to establish the evolution of the natural cloud
structure and microphysics as a baseline for evaluating cloud-seeding
effects. Separately, the extensive dataset and state-of-the-art measurements
were expected to yield new insights toward the natural cloud structure,
microphysical evolution, and precipitation patterns of mixed-phase winter
orographic clouds. Understanding how fine-scale (km or less) dynamical
processes impact cloud microphysical development and spatial distribution,
amount, and phase of observed precipitation in such clouds is at the
forefront of the remote sensing and cloud microphysics observational
literature (e.g., Houze and Medina, 2005) and further provides valuable
insight to cloud modeling and microphysical parameterizations.
To characterize and describe the development of precipitation hydrometeors
(e.g., SCDDs) at flight level requires direct measurements of cloud
hydrometeor spectra, thermodynamic and dynamic conditions (which govern the
development of the spectra), and characterization of the spatial variability
of each of these variables. Remote profiling radar, in situ cloud probes, temperature and
humidity sensors, and gust probes, on board the University of Wyoming King
Air (UWKA) research aircraft, catalogued the evolving cloud structure and
precipitation patterns for repeated fixed flight legs oriented along the
mean wind direction through cloud (Fig. 1), at as low an altitude as
practical. UWKA legs, anchored above the Packer John (PJ; see Fig. 1) ground
site, recurrently sampled coincident spatial cross sections through the
evolving orographic cloud structure, often between the -10 to -15∘C level. Flight legs (blue line in Fig. 1) were generally no
longer than 100 km, with the western end located over the valley and the
eastern end extending over the Sawtooth Range. Soundings launched
at Crouch, ID (KCRH, Fig. 1), before and during each flight were used to
characterize bulk thermodynamic and dynamic conditions.
SNOWIE experimental setup, showing a plan view schematic for an
example case of westerly winds. Blue squares (□) correspond to the Snowbank
(SB) and Packer John (PJ) ground sites and the plus sign (+) indicates the
Crouch (KCRH) sounding launch site. The rendered topography domain is the
same as in the orange inset in the upper right-hand corner of the figure. The
black bounding box indicates the target seeding domain.
Measurements from the W-Band Wyoming Cloud Radar (WCR) documented the
orographic cloud structure above and below flight level and provided context
for the in situ cloud microphysics measurements (as in Vali et al., 1998;
Wang and Geerts, 2003; Wang et al., 2012). Previous studies demonstrated
that the WCR resolves fine-scale details of orographic clouds
(∼30 m spatial resolution), observing aspects of their
dynamical and microphysical structure technologically impossible in previous
decades (Aikins et al., 2016). The WCR is sensitive to cloud droplets and
drizzle in the Rayleigh regime, with Mie effects starting at around 600 µm and reflectivity increasing monotonically with diameter up to
millimetric sizes (D>0.95 mm). Radar reflectivity for volumes
containing even large drizzle drops was therefore dominated by the
contribution of the largest drops, and throughout SNOWIE no drizzle drops
larger than 0.5 mm diameter were observed, such that Mie effects were
nonexistent for purely liquid volumes. Doppler velocity measurements from
the WCR captured the near-vertical, reflectivity-weighted motions of the
distributed hydrometeor targets. In the data presented here, no attempt has
been made to separate hydrometeor terminal fall velocity with vertical air
motions. Since the antennas point nearly vertically, the influence of
horizontal wind in the Doppler measurements is negligible for straight and
level flight.
In situ probes on the UWKA-measured cloud hydrometeors with diameters from a
few micrometers to several millimeters (Table 1). Two probe types were used to
collect these data – a forward-scattering cloud probe (i.e., the Cloud
Droplet Probe, CDP), and two optical array probes (OAPs) for larger
hydrometeors. The CDP (Lance et al., 2010) provided 5 Hz cloud droplet (1 to
50 µm) size spectra in bins 1 to 2 µm wide. The CDP rms accuracy
of mean droplet diameter of 0.7 µm was determined after the campaign
using the University of Wyoming droplet generator (Faber et al., 2018).
The OAPs image larger hydrometeors (D>∼50µm) as particles pass through an illuminated sample volume and shadow
individual members of a linear photodiode array. The 2D stereo probe (2DS;
Lawson et al., 2006) imaged particles at a 10 µm resolution across a
1.28 mm diode array, accurately resolving the hydrometeor spectra for
particles 50µm<D<1 mm. The 2D precipitation probe
(2DP) measured hydrometeors larger than a millimeter, with an image
resolution of 200 µm. The data from the OAPs were processed using the
University of Illinois OAP Processing Software (Jackson et al., 2014; Finlon
et al., 2016), to perform standard image rejection and dimension
corrections. Image-derived size and particle timing information and a sample
volume estimate following Heymsfield and Parrish (1978) were used to produce
particle size distributions. Shattering artifacts were avoided using
anti-shattering tips on the 2DS and by filtering of particles with a short,
static inter-arrival time threshold in the software processing.
From these 1 Hz particle size spectra, several integrated water content
metrics were calculated to estimate the mass distribution within certain
drop size categories. The total liquid water content – i.e., across the
entire measured liquid hydrometeor size spectrum – was integrated from the
combined CDP and 2DS size spectra and is hereafter referred to as
LWCtot. The cloud water content (CWC) and drizzle water content (DWC)
metrics contain the mass from the 2 to 50 µm and 50 µm to 1 mm
parts of the cloud hydrometeor spectrum, respectively, and hence sum to
LWCtot. The calculated LWCtot was compared to the bulk estimate
from the Rosemount icing probe, which is sensitive to all sizes of SLW
drops. A comparison performed over two flight legs validated these
estimation methods. The only remarkable disagreement between the metrics
came for LWC values of the Rosemount greater than 0.4 g m-3, where the
integrated LWCtot was larger compared to the Rosemount icing probe
measurement. This may be an overestimation of LWCtot related to
mis-sizing of drizzle drops that are near the edge of the depth-of-field in
the 2DS and appear as hollow images. Conversely, this may also be due to an
underestimation from the Rosemount probe due to splashing of SCDDs that are
not completely captured by the probe's icing rod. Regardless, the error is
almost certainly associated with the liquid mass of SCDDs and the Rosemount
and integrated LWCtot estimates provide a lower and upper bound,
respectively.
The following results and analysis produced from the WCR profiles, in situ
bulk probes, and cloud microphysics datasets from the first UWKA flight in
SNOWIE highlight the role of sub-kilometer vertical velocity fluctuations
in the spatiotemporal distribution of SCDDs and the inferred cloud
microphysical response.
Results
The results presented are from the period of 02:45 to 04:05 UTC (legs 1, 2,
and 5) during the first flight of the field campaign on 8 January 2017. Two
distinct layer clouds developed in the wake of a precipitating frontal cloud
system. Of these two clouds, the elevated cellular cloud layer contained
both low background number concentrations of ice and cloud droplets and
embedded kilometer or longer regions of SCDDs that formed in a larger
pattern of orographic lift.
Synoptic and thermodynamic context
The UWKA research flight followed the passage of a deep snow band associated
with a weak jet streak in the 500 mb wind field. The deep, saturated
atmosphere present in the upstream sounding during the heavily precipitating
period roughly 4 h prior to the start of leg 1 (Fig. 2a) experienced
mid-tropospheric drying and veering and strengthening of the winds above 8 km m.s.l. This led to lowered cloud tops and a pronounced dry slot from 7 to 9 km in the preflight sounding just 3 h later (Fig. 2b). This dry layer
contained thin layers of expected dynamic instabilities – defined by bulk
Richardson number from 0 to 0.5 (Fig. 2b; blue shading). The layer below,
between 4 and 7 km, saw several vertical humidity variations accompanied by
evaporational cooling of the radiosonde upon exiting cloud layer tops,
resembling conditional instabilities (orange shading). These layers were not
expected to correspond to real convective motions in cloud.
Thermodynamic and dynamic profiles from radiosondes launched at
Crouch, ID (KCRH; Fig. 1). Shaded levels correspond to relaxed critical
values of the bulk Richardson number, Ribulk<0.5, after 10 pt
(∼50 m) vertical smoothing of the field. Orange shading
indicates negative bulk Richardson values – corresponding to static
instability – and blue corresponds to purely dynamic instability, 0<Ribulk<0.5. Relative times (T±) reference the
02:45 UTC leg 1 start time.
By the start of the first flight leg at 02:45 UTC, a shallow orographic cloud
layer persisted over the study region on the western end of the flight
track, with cloud tops around 4 km m.s.l. (Fig. 3a) – matching the top of the
lower saturated layer in the preflight sounding (Fig. 2b). This orographic
cloud layer was capped on the eastern end by a layer of broken cellular
cloud structures roughly 1 to 3 km wide – hereafter the elevated cellular
layer – resembling, at times, either coherent Kelvin–Helmholtz (K-H) billows or incoherent
generating cells. This elevated cellular layer was consistently strongest in
terms of layer depth and highest radar reflectivities over the highest
terrain at the eastern end of the leg.
The final upstream sounding, launched 1 h after the start of leg 1 (Fig. 2c), indicated a deeper saturated layer through 6.5 km and further
strengthening and veering of the wind above, with more vertically
homogeneous, near-zonal winds between 3 and 6 km. This shear profile
resulted in several layers of possible dynamic instability within 500 m, both
above and below the top of the saturated layer, and matched well with the 6
to 6.5 km cloud tops observed with the WCR during flight legs 4 and 5 (Fig. 3d and e).
Terrain-referenced W-band radar reflectivity cross sections for
all 10 flight legs. All distances are relative to Packer John Mountain, with
positive (negative) values downwind (upwind). Leg start and end times are in
UTC, with (a) through (j) corresponding to legs 1 through 10, respectively.
Variations in humidity and wind, superimposed on the background zonal winds
and low-level orographic clouds, appeared responsible for an elevated cloud
layer that was at times unstable and variable in vertical location and depth
(Fig. 2b). Additionally, a surface inversion and attendant low-level static
stability was present in all the upstream soundings around the time of the
flight (Fig. 2a, b and c). As a result, calculated bulk Froude numbers were
consistent with blocked flow below 2 km m.s.l., matching the overall low-level
static stability pattern that was present through much of the field campaign
(Tessendorf et al., 2018). The stability from this surface inversion may
have helped to decouple the surface air mass from the free troposphere above
the Sawtooth Range barrier.
General cloud structure and vertical motions
There were several differences between the orographic cloud layer (4.5 km m.s.l. and below) and the cellular layer above. The orographic cloud layer
persisted over the nearest 1 to 2 km above the terrain, with cloud tops that
rose slightly (no more than 500 m) from west to east with the average height
of the topography beneath (e.g., Fig. 3a). The cellular layer, however, was
transient – discrete layers of cells advected into the target area at
varying altitudes. Some of these layers appeared coupled to the lower
orographic cloud layer (as in legs 1, 2, 4, and 5), while others appeared
totally separate (as in legs 3, 9, and 10). This behavior is consistent with
the large vertical variations in wind shear and humidity between the three
soundings in this layer (Fig. 2), including several dynamically unstable
layers. Consistent with this, several of the elevated layers appeared to
contain overturning (or breaking) cells in the reflectivity profiles, for
example, within the elevated cellular layer of leg 4 from 10 to 15 km
downwind of PJ (Fig. 3d).
Across the entire research flight, the radar reflectivity within the upper
cloud layer was less than -5 dBZ, except for discrete, individual fall
streaks. This behavior suggests mostly liquid cloud species in the elevated
layer, confirmed by the 99th percentile of precipitation-sized ice
number (integrated from the 2DP probe) for each of the first four legs
remaining below 0.1 L-1 and being only marginally higher for leg 5, with a
99th percentile value of 0.3 L-1. Some of the higher-reflectivity
fall streaks, especially towards the end of the flight, may have
corresponded to seeding lines (French et al., 2018; Tessendorf et al., 2018;
Hatt, 2019) after the seeding period started at the end of leg 2 but are
otherwise beyond the scope of this study. The radar reflectivity within the
lower orographic cloud layer, by comparison, was greater than in the
cellular layer above. Large regions within 1 km of the surface contained
reflectivity greater than 5 dBZ, suggesting the presence of ice below the
orographic cloud top. This conjecture is consistent with a significant
reduction of about 4 m s-1 in downward Doppler velocity in the lowest
∼1 km above the surface (Fig. 4). This reduction often
occurred at a level corresponding to an increase in the radar reflectivity.
The inferred relative abundance of ice in this shallow orographic layer may
be due to more abundant aerosol (and INP) presumed to reside below the
strong surface inversion (Fig. 2b) or from secondary ice multiplication in
the warm (-5<T<-15∘C) temperatures in the
lower layer, but no direct in situ measurements were available in cloud
below flight level.
Terrain-referenced W-band Doppler velocity spatiotemporal cross
sections for all 10 flight legs. All distances are relative to Packer John
Mountain, with positive (negative) values downwind (upwind). Positive values
of Doppler velocity indicate upward motion. Leg start and end times are in
UTC, with (a) through (j) corresponding to legs 1 through 10, respectively.
Mean reflectivity-weighted, near-vertical Doppler velocities (hereafter,
hydrometeor vertical velocities or Doppler velocities) were available from
the WCR to quantify cloud vertical motions (i.e., the convolution of vertical
air motions and reflectivity-weighted population terminal fall speed). Unfortunately, complex
dynamics at sub-kilometer scales and hydrometeor size and phase
inhomogeneity convoluted the observed Doppler velocities, making assumptions
about a constant hydrometeor fall speed specious. In fact, the spread of
fall speeds associated with observed hydrometeor size distributions were
greater than the spread of air motions observed in the dynamic structures of
focus (<1.5 m s-1 amplitude, where sampled at flight level).
Despite this complexity, there were several obvious and consistent trends in
the observed Doppler velocities: nearly all legs showed a distinct
terrain-induced vertical velocity couplet centered roughly 24 km downwind of
Packer John and directly above a pronounced north–south ridge, oriented
perpendicular to the mean wind and flight direction (Fig. 4). This couplet
consisted of up to 2 m s-1 upward Doppler velocities over the upwind
slope, immediately followed by as much as 4 m s-1 downward Doppler
velocities on the downwind side, and frequently extended up to cloud top (as
in leg 5). Despite the wave-like signatures present in the reflectivity
profiles, Doppler velocity couplets away from flight level and phase
relationships at flight level between perturbation kinematic and
thermodynamic quantities (not shown) were inconsistent with K-H waves. For
this reason, care was taken separately in (1) quantifying the effects of
spatial variations in hydrometeor fall speed and (2) adopting the label of
semi-coherent vertical velocity fluctuations (SCVVFs) to distinguish layers
of these regularly spaced, vertically oriented velocity perturbations from the
more isotropic turbulent motions found elsewhere. Probable meteorological
sources for SCVVFs in this environment include K-H waves, shear-driven
mechanical overturning (Houze and Medina, 2005), and shallow convective
overturning with some regular triggering mechanism; however, the actual
sources did not seem to uniquely affect the microphysics and therefore
remain undistinguished. What follows are descriptions of how SCVVFs affected
the evolution and spatial distribution of precipitation in the elevated
cellular cloud layer, significant for where drizzle development deviated
from the expectation of starting at cloud top and collecting through the
depth of SLW cloud.
Comparisons between drizzling legs (1, 2, and 5)
The three flight legs of interest, 1, 2, and 5 (Table 2), were flown at
altitudes ranging from 3.9 to 4.5 km m.s.l. During each of the legs the UWKA
encountered kilometer-or-longer stretches of SCDD measured at flight level
within the elevated cellular cloud layer. Significantly larger drops were
observed on the first two legs compared to leg 5 despite similar cloud water
contents across all three. The regions containing SCDDs were all located at
or downwind of Packer John Mountain (PJ; the start of prominent terrain
features along this transect), where reflectivities and cloud layer
thicknesses were consistently near the leg maxima. Above the windward slope
of the Sawtooth Range, from 10 to 25 km downwind of PJ, was a broad region
of ascent observed on most legs (0 to 1 m s-1 hydrometeor upward
velocities) which contributed to the relatively high reflectivities and
cloud layer thicknesses compared to cloud further upwind (Fig. 4). From 10
to 60 km downwind of PJ, where SCDDs were encountered on all three legs,
flight level vertical velocities varied from -0.5 to 2 m s-1, with
perturbation magnitudes on legs 1 and 2 of up to 0.6 m s-1 and less
than 0.2 m s-1 for leg 5 (Table 2). The flight level temperatures on
these legs ranged from -16∘C on legs 2 and 5 to -11∘C for the lowest altitude leg 5.
Flight level cloud characterization information between legs 1, 2,
and 5.
Leg125Altitude (m)450048003900–4200Temperature (∘C)-14.5-16-11Gust probe vertical velocity (m s-1)-0.5 to 2-0.2 to 1.7-0.5 to 1.5Flight level perturbation vertical velocity magnitude (m s-1)<0.5<0.7<0.2Cloud water content (g m-3)<0.6<0.4<0.6DWC in plumes (g m-3)0.2 to 0.80.1 to 1.00.1 to 0.4Cloud droplet number concentration (cm-3)2 to 303 to 308 to 35Mean volume diameter (µm)<80<70<4599th percentile number concentration of precipitation-sized ice (L-1)0.10.10.3
Cloud water content (CWC) measured at flight level was similar for these
drizzling sections of cloud across all three flight legs, with maximum
values approaching 0.6 g m-3 in legs 1 and 5. Slightly lower maximum
CWCs were measured in the drizzling sections of leg 2, i.e., only as high as 0.4 g m-3, possibly reduced due to scavenging and removal of cloud water by
drizzle in the time between legs 1 and 2 (Table 2). Cloud droplet number
concentration measured at flight level during all three legs never exceeded
35 cm-3 and decreased to values less than 5 cm-3 within portions
of cloud in which there appeared significant SCDD sedimentation from above.
Within these plumes of SCDDs, which appeared only in flight legs 1 and 2,
DWC measured at flight level was at times as high as 1 g m-3. Additionally,
within SCDD plumes, the mean volume diameter of the DSD approached 80 µm (Table 2). Unlike the first two legs, the SCDDs sampled in leg 5 were
much smaller, and the DSD mean volume diameter did not exceed 45 µm.
The primary microphysical differences for these three legs were the smaller
SCDDs in leg 5 relative to legs 1 and 2. The following section provides an
analysis of where SCVVFs may have acted to enhance hydrometeor growth and
the subsequent evolution of cloud downwind.
Semi-coherent vertical velocity fluctuations
The primary structural difference within the elevated cellular cloud layer
across these three legs, which appeared responsible for cloud microphysical
characteristics and SCDD development, were the presence and vertical
location of layers of SCVVFs. A train of these velocity fluctuations were
sampled at flight level during leg 1 from 24 to 35 km downwind of PJ (Fig. 5). The SCVVFs appeared as a series of ±0.5 m s-1 vertical
velocity perturbations with a wavelength of roughly 1 to 2 km (Fig. 5b). The
vertical velocity fluctuations drove both a thermodynamic (Fig. 5e) and
microphysical response (Fig. 5c and d), which saw positive perturbation
vertical velocities paired with lower temperatures, higher cloud droplet
number, and lower CWC relative to the mean trend. Appreciable drizzle mass
was only present in the perturbation downdrafts (Fig. 5c, pink curve).
Detailed radar and in situ measurements for the drizzling portion
of leg 1. Spatiotemporal vertical cross sections of radar reflectivity are
shown in (a). Panels (b) through (e) are derived from flight-level in situ
measurements and show (b) vertical air velocity (w), computed perturbation
vertical velocity (w′), and the variance of the perturbation vertical
velocity (w′w′); (c) liquid water content derived from cloud droplets (CWC),
drizzle drops (DWC), and both combined (LWCtot); (d) cloud droplet
number concentration (Ncld) and DSD mean volume diameter (MVD) for all
hydrometeors with D<1.2 mm; and (e) temperature (T), dew point
(TD), and relative humidity (RH). The CFAD bounds shown in (a)
correspond to the columns for Fig. 8a–c. Perturbation vertical velocities in
(b) were calculated by subtracting a boxcar-smoothed (over 10 s or roughly 1 km) vertical velocity field from the measured vertical velocity and
represent the sub-kilometer vertical velocity perturbations.
From size distributions averaged across individual perturbation updrafts and
downdrafts (Fig. 6), it is apparent that secondary droplet activation was
primarily responsible for the increased droplet number concentration within
perturbation updrafts. DSDs corresponding to perturbation updrafts show that
much of the increased droplet number concentration can be explained by an
increased number of 6 to 8 µm droplets, which are an order of magnitude
more abundant than within the perturbation downdrafts and nearly as abundant
as the number of droplets in the primary mode from 25 to 35 µm. Given
that these legs were flown at a constant altitude, the secondary droplet
activation in perturbation updrafts, paired with a reduction in the CWC, may
indicate kinetically limited parcel behavior and is examined in the
discussion. The perturbation downdrafts contained increased DWC, larger
droplets, and lower total number concentration relative to perturbation
updrafts. The decreased number and increased DWC are likely explained by
scavenging by the larger drops, which were as large as 150 µm (Fig. 6),
and indicate an active collision–coalescence process. Furthermore,
collision–coalescence likely began very near or just above flight level, as
the reflectivity values between -25 and -15 dBZ within the nearest 400 m above flight level are indicative of populations of cloud droplets with
very few, if any, drizzle drops (Fig. 5a).
Bin-width-normalized averaged size distributions for
representative perturbation updrafts and downdrafts within the flight-level-sampled
SCVVF train from leg 1. Table (b) contains calculated distribution
parameters for the curves shown in (a). The corresponding location downwind
of Packer John is given in the legend in (a).
Spatiotemporal cross sections of Doppler velocity (Fig. 7) highlight the
difficulty in identifying layers of SCVVFs away from the aircraft using the
WCR. During leg 1, from 25 to 30 km downwind of PJ, a region where in situ
measurements indicate a regular perturbation velocity pattern with 1 to 2 km
spacing (Fig. 5b), no similar Doppler velocity pattern appears from
the WCR within the nearest few hundred meters of flight level (Fig. 7a).
However, within 200 m of cloud top, from 30 to 35 km downwind of PJ, a clear
train of vertical velocity fluctuations can be seen (Fig. 7a). These Doppler
velocity fluctuations match the crests of the wavelike reflectivity
structures near cloud top in the corresponding reflectivity profile (Fig. 5a, top circled) but do not extend as far downward into cloud as the
reflectivity structures. This perturbation velocity pattern is clearest in
the highest 200 m of cloud, presumably due to the smaller sizes and
resulting lower terminal velocities of scatterers there. In regions lower in
cloud, the radar volumes contain more and larger drizzle drops and the
resulting Doppler velocities become gradually more negative, eventually
dominating the overall Doppler velocity pattern. Very near flight level, it
is possible to estimate the hydrometeor terminal fall speed by subtracting the in situ measured air velocity from the
WCR-measured Doppler velocity in the nearest range gates. Near flight level,
29 km downwind of PJ, we note an increase in hydrometeor terminal velocity
(Fig. 7b, red and blue lines). This matches well with increases in DWC and
DSD mean volume diameter beginning at nearly the same location illustrated
in Fig. 5c and e.
The link between SCVVFs and hydrometeor growth is also apparent in Contoured
Frequency by Altitude Diagrams (CFADs) generated from WCR radar reflectivity
measurements. For the region in leg 1 corresponding to the sampled SCVVF
train at flight level, 25 to 30 km downwind of PJ (Fig. 8a), the median
reflectivity rapidly increased from a roughly constant -25 dBZ above
5 km m.s.l. (∼500 m above flight level) to greater than -15 dBZ just
below flight level, suggesting rapid growth from cloud droplets to drizzle
drop sizes for the low number concentrations observed in these clouds. This
increase was characterized by a roughly -20 dBZ km-1 slope in the
reflectivity CFAD, which appeared consistently within layers of SCVVFs
elsewhere in cloud this day. For example, in a layer of SCVVFs near cloud
top at 6 km m.s.l., located at 30 to 35 km downwind of PJ, a similar
reflectivity slope with altitude is measured (Fig. 8b). The reflectivity
enhancement tied to both of these layers of SCVVFs is discrete in
comparison to the more gradual growth (roughly -7 dBZ km-1) that
occurred further downwind on this leg, starting at cloud top and extending
through the entire cloud layer (Fig. 8c).
The impact that SCVVF layers had on the broader microphysical character of
cloud during leg 1 was a trend of increasing hydrometeor size with distance
downwind. At the location of broad 0.5 to 1 m s-1 updraft 20 to 25 km
downwind of PJ (Fig. 5b), the DSD contained mostly cloud droplets with
diameters less than 40 µm (Fig. 9a, red). In the region of SCVVFs at
flight level 25 to 35 km downwind of PJ, the diameter of the cloud droplet
mode shifts to larger sizes and the steep exponential tail flattens out into
a drizzle shoulder (Fig. 9a, green and blue). Even further downwind (Fig. 9a, orange and purple), a mature drizzle shoulder (100µm<D<300µm) becomes apparent. Here, downwind of the SCVVFs at
flight level, the sampled drizzle originates from the layer near cloud top.
Observations from flight leg 2 indicate that the SCVVF layers present in leg
1 had broken down into incoherent turbulence. A prominent drizzle
precipitation plume was present from 45 to 53 km downwind of PJ, capped by a
turbulent and variable cloud top height (circled, Fig. 10a). Still present
were juxtaposed perturbation updrafts and downdrafts, especially near cloud
top (Fig. 10b), but these were neither well organized nor layered as observed in
leg 1 and did not have a unifying spatial scale. Within the drizzle plume
clearly evident in the reflectivity field (Fig. 10a), in situ measurements
revealed DWCs in excess of 0.4 g m-3 (Fig. 10d). While several short
wavelength perturbations appeared in the flight level vertical velocity
profile (Fig. 10c), a consistent correlation for either
the thermodynamic (Fig. 10e) or the bulk microphysical measurements (Fig. 10d) did not appear, unlike leg 1.
Doppler velocity and estimated hydrometeor terminal fall speed for
a portion of leg 1. Vertical cross section of Doppler velocity (a) and
reflectivity-weighted hydrometeor population terminal fall speed (b) estimated by flight level gust probe vertical velocity (dashed black)
minus averaged Doppler velocity of the three nearest useable radar gates above
(red) and below (blue) flight level.
CFAD of radar reflectivity for three 5 km wide columns from leg
1, with relative location in km downwind of PJ indicated at the top of each
panel. The dashed red line is the median reflectivity for a vertical level,
and frequency is normalized for each vertical level (same colors at the top as
any other level). Shading indicates the primary inferred-growth regions
within the elevated cellular layer.
Averaged size distributions for legs 1, 2, and 5 (a, b, and c,
respectively) from the CDP, 2DS, and 2DP cloud and precipitation probes.
Each of the blue composite size spectra correspond to the averaged size
distributions at flight level during the CFADs in Fig. 12.
Detailed radar and in situ measurements for the drizzling portion
of leg 2. Spatiotemporal vertical cross sections of radar reflectivity are
shown in (a) and vertical Doppler velocity is shown in (b). Panels (c)
through (e) are derived from flight-level in situ measurements and show (c) vertical air velocity (w), computed perturbation vertical velocity (w′), and
the variance of the perturbation vertical velocity (w′w′); (d) liquid water
content derived from cloud droplets (CWC), drizzle drops (DWC), and both
combined (LWCtot); and (e) temperature (T), dew point (TD), and
relative humidity (RH). The CFAD bounds shown in (a) correspond to Fig. 12d–f. Perturbation vertical velocities in (c) were calculated as
described in Fig. 5.
Leg 5, by comparison, contained a long and shallow layer of SCVVFs located
12 to 33 km downwind of PJ between 4.5 and 4.8 km m.s.l., about 500 to 1000 m
below cloud top (Fig. 11a, circled) and just above the flight level. The
horizontal scale of these fluctuations was smaller than in leg 1, with the
width of a complete up and down perturbation couplet less than 1 km (Fig. 11b).
Perhaps because of both the thinness of the SCVVF layer and its nearness to
flight level, drops were much smaller compared to those observed in leg 1.
The DSD mean volume diameter remained below 45 µm (Table 2) and size
distributions at flight level just below these SCVVFs reveal significantly
lower concentration of drizzle drops with D>100µm
compared to those observed in both legs 1 and 2 (Fig. 9c). Unlike
observations in legs 1 and 2, however, a relatively even
partitioning of mass distribution between CWC and DWC (Fig. 11d) did appear. Also, the
presence of ice was corroborated by 2DS probe images (not shown), indicating
that any vertical reflectivity enhancements from layers of SCVVFs for this
leg are complicated by the increased linear growth rates (and hence
reflectivity response) of ice in a mixed-phase environment.
The same as in Fig. 10 except for the drizzling portion of leg 5. The
CFAD bounds correspond to Fig. 12g–i.
Reflectivity and Doppler velocity CFADs for three 5 km wide drizzling
columns from legs 1, 2, and 5 were generated for comparison (Fig. 12). The
incoherent turbulence at cloud top for leg 2, seen in the large spread of
Doppler velocities in the highest 1 km of cloud (Fig. 12e), produced a
similar vertical reflectivity enhancement pattern as at the eastern end of
leg 1 (Fig. 8c), where reflectivity gradually increases with distance
downward through the elevated cellular layer. This pattern also appears in
drizzling marine stratocumulus clouds where drizzle production typically
occurs at cloud top and drizzle drops grow throughout the entire cloud layer
(e.g., Comstock et al., 2007). The broadening processes associated with
incoherent turbulence and entrainment at cloud top are sufficient for
drizzle production and subsequent accretional growth through the whole cloud
layer. By comparison, the thin embedded layer of SCVVFs present in leg 5 led
to a shallow growth layer with larger reflectivity–altitude gradients (i.e.,
more horizontal slope in the thinner shaded growth region; Fig. 12g) than in
either legs 1 or 2. The larger ice particles present in the tail of the
corresponding size distribution for the column from leg 5 (Fig. 9c) explain
the similar median radar reflectivity up to 0 dBZ at flight level observed
in legs 2 and 5 (Fig. 12d and g), despite the comparatively smaller, more
numerous drizzle drops in leg 5 compared to legs 1 and 2. All three
drizzling columns contained reverse S correlation patterns between
reflectivity and Doppler velocity in the vertical, associated with
hydrometeor growth and fallout over the layer (Fig. 12c, f, and i).
CFADs of reflectivity; Doppler velocity; and their zero-lag
cross-correlation for the legs 1, 2, and 5 (from top row to bottom row, respectively, with
relative distances downwind of PJ indicated at the top of each row). The
dashed red line (left column) is median reflectivity for a vertical level
and frequency is normalized for each vertical level (the same colors at the top as
any other level). Vertical profiles of zero-lag cross correlation between
reflectivity and Doppler velocity are shown in the right column, with
reverse S correlation patterns highlighted in light blue. Shading indicates
the primary inferred-growth regions within the elevated cellular layer.
Discussion
Much of the previous work describing SCDD development in orographic, mixed-phase cloud systems focused on the necessary conditions for
development – namely the low cloud droplet and ice number concentrations
coupled with condensate supply rates sufficient to support condensational
growth to the droplet sizes required for active collision–coalescence
(Rauber, 1992; Ikeda et al., 2007). Several other studies suggest conditions,
which may be responsible for accelerated drizzle development or for relaxing
these necessary conditions, introducing broadening mechanisms important for
SCDD production in cloud (Pobanz et al., 1994; Korolev and Isaac, 2000). Of
these, the relationship between fine wind shear levels, spatial
supersaturation fluctuations, and SCDD development has yet to be connected
mechanistically by in situ measurements, despite being identified both as
associated with SCDD development (Pobanz et al., 1994) and, separately, as
important for the spectral broadening seen in certain layer clouds (Cooper,
1989; Korolev, 1995; Korolev and Mazin, 1993). The observations here seem to be an
important continuation of the work by Pobanz et al. (1994), which called for
further airborne research investigating the link between layers of strong
wind shear and SCDD development. While their explanation called for
observations of K-H billows to understand the production mechanisms, the
microphysical behavior in layers of SCVVFs here seems to provide similar
insight towards understanding these mechanisms.
Microphysical response to SCVVF layers
The insight provided from sampling one of these SCVVF trains with the in
situ cloud hydrometeor probes (Fig. 5) allows for some characterization of
the microphysical processes in clouds of this type. Based on the flight
level measurements, a conceptual model is presented to consistently describe
the microphysical response to SCVVFs (Fig. 13). The kinematic structure and
LWC response for leg 1 saw positive (negative) perturbation updrafts
(downdrafts) paired with negative (positive) CWC perturbations from the
trend and positive (negative) cloud droplet number concentration
perturbations associated with droplet activation (evaporation). For these
regular vertical velocity fluctuations in clouds with sufficiently low
concentrations of cloud droplets, the supersaturation response to vertical
velocity fluctuations as described by Korolev (1995) is responsible for
(re)activating interstitial CCN as small (6 to 8 µm) droplets in the
sub-adiabatic perturbation updrafts and separately broadening the primary
droplet mode from repeated supersaturation fluctuations. Sub-adiabatic
implies LWC values below what is expected from the adiabatic LWC
formulation,
LWC=ΓLWC⋅z-zCB,
where ΓLWC represents the adiabatic lapse rate of liquid water content
determined by cloud base temperature and pressure (Albrecht et al., 1990)
and z-zCB is the height above cloud base. The mean CWC for the SCVVFs
sampled at flight level was 0.25 g m-3 with regularly spaced
oscillations ±0.05 to 0.08 g m-3 about that mean (Fig. 5c).
Simplified schematic of spatial responses to the perturbation
updraft (blue) and downdraft (red) pattern superimposed on broader
orographic lift (broad blue arrow). The colored trajectories indicate
the approximate path of parcels passing through the kinematic pattern,
following the schema of Houze and Medina (2005). Lines of constant cloud
water content (green) indicating the expected deformations due to
condensational kinetic effects, with line weight corresponding to relative
condensate mass. Cloud parcels circulate within the vertical velocity
perturbation pattern and more and smaller drops are located in perturbation
updrafts than downdrafts. CWC contours appear flat and unperturbed above and
below the vertical velocity fluctuation pattern, as they are determined by
the adiabatic ascent in the broader uplift pattern.
In a well-mixed (i.e., nearly constant equivalent potential temperature; Fig. 2), non-precipitating orographic-layer cloud, the adiabatically constrained
CWC is expected to remain nearly constant at a given altitude, with only
small perturbations that are the result of variations in cloud base
thermodynamic conditions. Back of the envelope calculations estimate the
specific adiabatic CWC lapse rate of this elevated cellular-layer cloud is
about 0.001 g m-4, taking the thermodynamic conditions from the
sounding at the interface between the orographic and elevated cellular
layers as a pseudo cloud base for this upper layer. Given mean CWC of 0.25 g m-3 observed at flight level, this indicates roughly 250 m of ascent
for the cloud parcels sampled at this altitude. Variations of ±5∘C at cloud base would then correspond to ±0.05 g m-3
perturbations in CWC, and variations of ±50 mb would correspond to
±0.01 g m-3 perturbations, respectively. While the orographic
environment does predispose clouds to experience more variation in cloud
base conditions than similar layer clouds associated with fronts or boundary
layers, cloud base thermodynamic variations of this magnitude are not
expected over spatial scales of 0.5 to 2 km and are therefore insufficient
to explain the regular CWC perturbation observed. Instead, the perturbations
of up to 40 % of the mean CWC at a constant altitude were likely the
result of dynamic and/or precipitation processes that were tied to the
SCVVFs.
The primary effect on CWC if only condensational effects are considered and
where drizzle is not falling through parcels from above may be explained by kinetic effect, as described by Korolev (1995). The negative CWC
perturbations in leg 1 were accompanied by local supersaturation sufficient
for secondary droplet activation (i.e., saturation ratio large enough to
activate interstitial CCN), inferred from the presence of small droplets (6
to 8 µm) within perturbation updrafts (Fig. 6a, red and blue curves).
Such sub-adiabatic behavior is linked to the kinetic limitation on
condensational growth. As noted earlier, cloud droplet number concentrations
were less than 30 cm-3 and the “condensational inertia” of droplet
populations to condense excess water vapor supply governed the
supersaturation response, associated CWC response, and secondary droplet
activation behavior. For the droplet populations less than 30 cm-3 and
mean count diameter of between 20 and 30 µm, the corresponding phase
relaxation time is around 10 s (using estimation methodology by Fukuta and
Walter, 1970; Polotivitch and Cooper, 1988; Korolev, 1995). This phase
relaxation time corresponds to expected perturbations from the adiabatic
mean of as much as 0.02 g m-3 at flight level, indicating that while
the kinetic effect cannot fully explain the perturbation magnitude in the
CWC field, it acts in the proper observed direction and explains the primary
adiabatic (i.e., closed parcel) effect in these clouds. This zero-lag
anticorrelation between vertical velocity and CWC perturbations results in
the spatial pattern illustrated in Fig. 13.
The remaining magnitude of CWC variation is likely related to the
precipitation dynamics. Removal of cloud water by scavenging from drizzle in
perturbation updrafts would lead to lower CWCs and reduced cloud droplet
number. While the lower CWCs are indeed observed, and this may account for
the greater magnitude reduction expected from the kinetic–adiabatic model
alone, cloud droplet number concentrations increase. However, the increase
in activation due to the kinetic limitation as noted previously is likely
greater than the reduction in number concentration due to scavenging. Within
the interspersed perturbation downdrafts, greater DWC and larger drizzle
drops are observed, indicating active collision–coalescence. CWC and cloud
droplet number concentrations are therefore expected to be further depressed
relative to the mean than expected from the kinetic condensational effect
alone. These regions are the likely origin of drizzle fall streaks observed
in the WCR profiles and are represented by slightly larger drizzle drops in
the downdraft region in Fig. 13.
Reflectivity-inferred hydrometeor growth in SCVVF layers
Comparisons between vertical reflectivity, Doppler velocity, and their cross
correlation suggest two main microphysical behaviors within layers of
SCVVFs. The first is rapid, and often discrete, drop growth in the vertical
tied to layers of vertical velocity fluctuations and not confined to cloud
top. This vertical growth rate appears as large for these SCVVF layers in
leg 1 as for the drizzle production at cloud top in leg 2. The second
behavior is a reverse S cross-correlation pattern (see Vali et al., 1998) in
layers of SCVVFs, irrespective of hydrometeor phase differences, which
further corroborates the local hydrometeor growth and fallout tied to these
layers.
Layers of SCVVFs in legs 1 and 5 were responsible for vertical reflectivity
enhancements similar in magnitude, roughly -20 dBZ km-1, as those produced by
the drizzling cloud in leg 2 where layers of SCVVFs were not present.
However, these SCVVF layers, especially in the relatively upwind cloud
elements closer to PJ, were responsible for discrete growth layers that did
not begin at cloud top. This indicates that the vertical velocity
fluctuations were likely responsible for the initiation of
collision–coalescence and drizzle production that occurred earlier and at a
different location in cloud compared to the classic idea of production at
cloud top. Further downwind, corresponding to later in time from the upwind
edge, drizzle production and growth did occur at cloud top and subsequent
growth of the SCDDs occurred through the depth of the SLW layer, even
without the presence of SCVVFs. This was most apparent in the transition
between legs 1 and 2 from discrete growth at the level of these SCVVFs to
growth over the entire layer, starting at cloud top, in leg 2. While
qualitative, this observation suggests the importance of SCVVFs in other
layered liquid clouds where embedded shear or shallow layers of static
instabilities may be responsible for enhancing the collision–coalescence
process. Layers of SCVVFs may also be important in clouds where
condensational growth and cloud top spectral broadening occur too slowly for
active warm rain production, although with the caveat that any
condensational kinetic effects are bound to be smaller than reported here.
This, however, agrees with the observations of both Pobanz et al. (1994) and
Korolev and Isaac (2005).
A distinct feature of the layers of SCVVFs is the bimodal DSD with
populations of large (D>30µm) and small (D<10µm) droplets of similar number that were not present elsewhere in cloud. This
small droplet mode contains much less mass compared to the large droplet
mode, and collisions between the large and small droplets are likely
inefficient (E∼1 % to 3 % for drops of these sizes in
laminar flow; Rogers and Yau, 1996), but the effect of such numerous
possible collision events, especially given the large fall speed
separations, in a turbulent environment may be enough to break the colloidal
stability of the narrow large drop mode for a few lucky drops, such that
subsequent self-collection within this mode becomes favored. Furthermore,
parcel model results (Korolev, 1995) have shown that repeated supersaturation
variations driven by vertical velocity fluctuations produce a local
broadening about the larger droplet mode. This broadening may provide enough
fall speed separation for self-collection without the need for larger
droplets to physically interact with the newly activated smaller droplets.
The increases in drop size and drizzle mass with distance downwind within
SCVVF layers where parcels have undergone repeated supersaturation
fluctuations are in qualitative agreement with this hypothesis.
A reverse S cross correlation pattern between reflectivity and Doppler
velocity with altitude across these SCVVF layers further corroborates the
drop growth in these layers. Vali et al. (1998) demonstrated this pattern in
drizzling coastal stratus as the result of upward transport of drizzle and
dilution of downward-moving parcels near cloud top (region of positive
correlation) that transitioned to the dominance of precipitation terminal
fall speed increases below (region of negative correlation). Here the same
trend is present in leg 5 (Fig. 12g), where the very low background
reflectivities (-25 dBZ) above the growth layer transition to rapid
reflectivity increases below 5 km m.s.l. correlated with positive Doppler
velocities (Fig. 12i). As the Doppler velocities become more negative below
this layer (Fig. 12h), the pattern reverses to the falling drizzle (and ice)
dominating the reflectivity signature – with strong anticorrelation between
reflectivity and Doppler velocity. This strong anticorrelation is dominated
by the terminal fall speed–size relationship (e.g., terminal fall speed is
proportional to the square of the diameter for drizzle drops). At the top of
the growth layer, where weaker positive correlation exists between
reflectivity and Doppler velocity, it is important to consider both the
contribution of hydrometeor terminal velocity and air motion to the observed
Doppler velocities. For the populations just above the growth layer,
terminal velocities for the largest cloud droplets are much lower than the
magnitude of the vertical velocity perturbations (±0.5 to 1.0 m s-1) and therefore the Doppler velocity signal is dominated by air
motions. This suggests that the regions of upward relative air motion are
correlated with higher reflectivities near the top of SCVVF layers, though
without in situ measurements nearer the top of these layers it is impossible
to determine whether this is due primarily size or concentration. A more
expansive conceptual model (cf. Fig. 13) would incorporate the vertical
gradient of these growth and fallout effects across the SCVVF layer but is
too conjectural without more penetrations through SCVVF trains at different
altitudes.
Conclusions
Low cloud droplet number concentrations of less than 30 cm-3 and
precipitation-sized ice number concentrations of less than 0.5 L-1,
despite cold cloud top temperatures (T<-30∘C),
provided favorable conditions for the development of SCDDs in a postfrontal orographic-layer cloud forming over the Sawtooth Range in the American
Intermountain West. This cloud, while transient and variable in vertical
location and depth, was consistently strongest over the prominent terrain
features downwind of Packer John Mountain and frequently contained layers
of SCVVFs. Where present, SCVVFs were associated with local enhancement of
the development and growth of SCDDs in response to the kinematic
perturbation pattern. This was demonstrated by strong vertical enhancements
in CFADs of reflectivity, on the order of -20 dBZ km-1, and attributed
to hydrometeor growth through collision–coalescence. This drizzle production
and growth occurred embedded within cloud and over relatively shallow layers
before transitioning to drizzle production at cloud top and growth over the
entire elevated cellular-layer cloud. Compared to quiescent clouds, those
containing SCVVFs will have more active DSD broadening processes and larger
CWC gradients coincident with regions of probable turbulent mixing. This
appears to explain the observation that initial SCDD production can be
enhanced by SCVVF layers and can lead to SCDD production in vertical regions
other than just cloud top.
Data availability
Data presented here were collected during the NSF SNOWIE Campaign and are publicly available through the SNOWIE data archive website maintained by the Earth Observing Laboratory at the National Center for Atmospheric Research, flight level aircraft data can be accessed at: 10.15786/M2MW9F (University of Wyoming – Research Flight Center, 2017). The microphysical size spectra data can be accessed at: 10.5065/D6GT5KXK (French and Majewski, 2017) and WCR L2 data can be accessed at: 10.15786/M2CD4J (Haimov and Tripp, 2017).
Author contributions
AM performed the analysis and prepared the manuscript. JRF contributed to
interpretation of results and provided critical edits in preparing the
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to acknowledge the contributions of both
Coltin Grasmick and Phil Bergmaier for both feedback on the ideas present
herein and the shared access to their IDL libraries used in several figures.
Finally, the feedback and suggestions from the SNOWIE principal
investigators and senior scientists (Sarah Tessendorf, Lulin Xue, Kyoko
Ikeda, and Roy Rasmussen of NCAR; Katja Friedrich of CU Boulder; and Bob
Rauber of the University of Illinois) were invaluable in honing in on the
important elements of this analysis.
Financial support
Observations from and participation in the SNOWIE field campaign were funded through NSF AGS (grant no. 1547101), with UWKA participation supported by NSF AGS (grant no. 1441831).
Review statement
This paper was edited by Ottmar Möhler and reviewed by two anonymous referees.
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