Wintertime mixed-phase orographic cloud (MPC) measurements were
conducted at the Storm Peak Laboratory (SPL) during the Storm Peak Lab Cloud
Property Validation Experiment (StormVEx) and Isotopic Fractionation in
Snow (IFRACS) programs in 2011 and 2014, respectively. The data include 92 h
of simultaneous measurements of supercooled liquid cloud droplet and
ice particle size distributions (PSDs). Average cloud droplet number
concentration (CDNC), droplet size (NMD), and liquid water content (LWC) were
similar in both years, while ice particle concentration (Ni) and ice water
content (IWC) were higher during IFRACS. The consistency of the liquid cloud
suggests that SPL is essentially a cloud chamber that produces a consistent
cloud under moist, westerly flow during the winter. A variable cloud
condensation nuclei (CCN)-related inverse relationship between CDNC and NMD
strengthened when the data were stratified by LWC. Some of this variation is
due to changes in cloud base height below SPL. While there was a weak
inverse correlation between LWC and IWC in the data as a whole, a stronger
relationship was demonstrated for a case study on 9 February 2014 during
IFRACS. A minimum LWC of 0.05 gm-3 showed that the cloud was not
completely glaciated on this day. Erosion of the droplet distribution at
high IWC was attributed to the Wegener–Bergeron–Findeisen process as the
high IWC was accompanied by a 10-fold increase in Ni. A relationship between
large cloud droplet concentration (25–35 µm) and small ice particles
(75–200 µm) under cold (<-8∘C) but not warm
(>-8∘C) conditions during IFRACS suggests primary
ice particle production by contact or immersion freezing. The effect of
blowing snow was evaluated from the relationship between wind speed and Ni
and by comparing the relative (percent) ice particle PSDs at high and low
wind speeds. These were similar, contrary to expectation for blowing snow.
However, the correlation between wind speed and ice crystal concentration
may support this explanation for high crystal concentrations at the surface.
Secondary processes could have contributed to high crystal concentrations
but there was no direct evidence to support this. Further experimental work
is needed to resolve these issues.
Introduction
Aerosols and their effects on cloud microphysical properties have been shown
to alter precipitation formation and distribution over complex terrain
(e.g., Pruppacher and Klett, 1997; Borys et al., 2003; Rosenfeld and Givati,
2006; Lowenthal et al., 2011; Saleeby et al., 2013). Higher concentrations
of cloud condensation nuclei (CCN) produce more numerous but smaller cloud
droplets (Twomey et al., 1984; Peng et al., 2002; Lowenthal et al., 2002).
This leads to decreased riming efficiency and decreased precipitation on
the windward slope (Borys et al., 2000, 2003) and has been shown to
redistribute precipitation over mountain barriers in modeling studies
(Saleeby et al., 2009, 2013).
There are numerous studies and reviews of ice nucleation theory,
measurements, and modeling (Vali, 1996, 1999; Diehl et al., 2006; Hoose and
Möhler, 2012; Ladino Moreno et al., 2013; Murray et al., 2012; Knopf and
Alpert, 2013; Kanji et al., 2017; Knopf et al., 2018). In mixed-phase clouds
(MPCs), a small fraction of aerosols can act as heterogeneous ice-nucleating
particles (INPs) and produce ice through four known freezing modes:
deposition, immersion, condensation, and contact freezing. Contact freezing
has been found to occur at higher temperatures than immersion freezing for a
given INP (Pitter and Pruppacher, 1973; Lohmann and Diehl, 2006; Nagare et
al., 2016). Biological INPs have been found to produce ice at relatively
higher temperatures than non-biological INPs (Levin and Yankofsky, 1983; Du
et al., 2017).
Secondary ice production (SIP) processes were reviewed by Field et al. (2017).
Sullivan et al. (2018) modeled SIP by rime splintering
(Hallett–Mossop process), droplet shattering, and collisional breakup with
ice particle enhancement depending on temperature, updraft velocity, and INP
concentration. Rime splintering is thought to occur when a supercooled
droplet with a diameter larger than ∼25µm freezes
onto an ice particle or other surface and shatters at temperatures between
-8 and -3∘C (Hallett and Mossop, 1974; Mossop, 1985). Keppas et
al. (2017) found evidence for rime splintering in warm (-6 to 0 ∘C)
frontal clouds. Here, lollie-shaped crystals formed by riming of
columnar crystals by droplets larger than 100 µm were associated with
high concentrations of small columnar crystals. Rangno and Hobbs (2001)
concluded that shattering of freezing droplets larger than 50 µm
could have accounted for high observed ice particle concentrations in Arctic
stratus.
At mountaintop observatories, ice crystal concentrations frequently exceed
aircraft measurements by an order of magnitude or more (Rogers and Vali,
1987; Geerts et al., 2015; Lloyd et al., 2015; Beck et al., 2018).
Lloyd et al. (2015) considered blowing snow, rime splintering, and
detachment of surface frost (Bacon et al., 1998) as sources of high ice
particle concentrations at the Jungfraujoch Sphinx Observatory (JFJ). They
ultimately favored the latter mechanism by process of elimination, albeit
with no direct evidence. In contrast, Beck et al. (2018) suggested that the
enhanced ice crystal concentrations at the Sonnblick Observatory (SBO) were
due to blowing snow, turbulence near the mountain surface, or convergence of
ice crystals near mountaintop due to orographic lifting.
Several studies have shown a link between cloud droplet size and ice
particle concentrations (e.g., Hobbs and Rangno, 1985; Rangno and Hobbs,
2001; Lance et al., 2011; de Boer et al., 2011). Hobbs and Rangno (1985)
found a strong relationship between the width of cloud droplet spectra and
ice particle concentrations in cumuliform and stratiform clouds where cloud
top temperature ranged between -36 and -6∘C. Lance et al. (2011)
found higher concentrations of ice particles larger than 400 µm in
clean Arctic clouds with larger droplets sizes than in polluted Arctic
clouds with smaller but more numerous drops.
Previous studies have furthered our understanding of precipitation formation
and distributions in complex terrain from dynamical and microphysical
perspectives but have been unable to establish a link between cloud
microphysics aloft and at the surface. Rogers and Vali (1987) observed cloud
microphysics at the Elk Mountain Observatory (EMO) located in the Medicine
Bow Mountains of southern Wyoming and from the University of Wyoming Queen
Air (UWQA) aircraft. Comparisons between crystal concentrations at EMO and
on the UWQA routinely showed higher crystal concentrations at the surface.
The authors attributed higher surface concentrations to an unspecified
process of ice crystal production in supercooled orographic clouds in
contact with snow-covered mountain surfaces. However, blowing snow can also
introduce the potential for artifacts in observed ice crystal concentrations
at mountaintop locations (Roger and Vali, 1987; Geerts et al., 2015).
The Storm Peak Lab Cloud Property Validation Experiment (StormVEx) was
conducted from 15 November 2010 to 25 April 2011 at the Desert Research
Institute's (DRI) Storm Peak Laboratory (SPL) to produce a correlative data
set to validate cloud retrievals using in situ measurements at SPL (Mace et
al., 2010; Matrosov et al., 2012). The Isotopic Fractionation in Snow
(IFRACS) study was conducted at SPL from 20 January to 27 February 2014 to
explore the impacts of microphysical processes in wintertime orographic
clouds on the water isotopic composition of falling snow (Lowenthal et al.,
2016; Moore et al., 2016). This paper examines microphysical properties of
wintertime orographic MPC at SPL using data collected during StormVEx and
IFRACS. A large record of concurrent measurements of ice and supercooled
liquid water was produced by these studies. These data enable exploration of
statistical relationships among microphysical properties, the temporal
variation of cloud properties over two winters at this site, the
relationship between the ice and liquid phases, and ice production
mechanisms. Potential measurement artifacts due to instrumental
characteristics and blowing snow are evaluated.
Methods
SPL (3210 m a.s.l.; 40.456570∘ N,
106.739948∘ W) is located on the summit of Mt. Werner in the Park
Range near Steamboat Springs, Colorado (Wetzel et al., 2004). In wintertime,
SPL is in snowing, supercooled liquid cloud roughly 25 % of the time
(Borys and Wetzel, 1997). Storms occur roughly weekly under a variety of
synoptic conditions (Rauber and Grant, 1986; Rauber et al., 1986; Borys and
Wetzel, 1997). As noted by Lowenthal et al. (2016), given sufficient
moisture during winter, a cloud forms and produces persistent snowfall at
SPL. Winds are generally from the west or northwest during snowfall events.
Clouds and snowfall can be inhibited by blocking from the Flat Top range
(maximum elevation 3768 m a.s.l.) under flow from the southwest.
Cloud microphysical properties were measured with the same instruments
during StormVEx and IFRACS. The cloud probes were mounted on a rotating wind
vane (to orient them into the wind) located on the west (upwind) railing of
the roof approximately 6 m above the snow surface (Fig. 1). Cloud droplet
number concentrations (CDNCs) and particle size distributions (PSDs) from
2 to 47 µm were measured with an aspirated Particle Measurement Systems
(PMS), Inc. (Boulder, CO) FSSP-100 forward-scattering spectrometer probe
that was electronically modified by Droplet Measurement Technologies (DMT),
Inc. (Boulder, CO). Liquid water content was calculated from the FSSP-100
PSDs. During IFRACS, the FSSP-100 inlet was equipped with a “scarf tube”,
which narrows and accelerates the flow in the sample volume to 25 ms-1
according to PMS. The air speed at the center of the inlet was measured at
9.4 ms-1, which corresponds to a velocity of 26.7 ms-1 in the
sample volume. The scarf tube was removed during StormVEx such that the air
speed at the inlet should have been the same as that in the sample volume.
Attempts were made to measure the air speed at the inlet during StormVEx but
these were inconsistent. Therefore, StormVEx FSSP-100 concentrations were
calculated using the face velocity of 9.4 ms-1 measured during IFRACS.
Recent picture of SPL probe stand with FSSP-100 in
the foreground, Cloud Imaging Probe (CIP) in the background, and sonic anemometer on
top (a); view facing west over the railing (b).
Average of concurrent 1 min CIP and FSSP-100 measurements during
StormVEx and IFRACS. The values in parentheses are the coefficients of
variation.
a Cloud Imaging Probe (CIP) concentration from 75 to 200 µm.
b CIP concentration ≥400µm. c CIP concentration ≥75µm.
d Ice water content.
e Cloud droplet number concentration.
f Cloud liquid water content.
g Cloud droplet number-weighted mean diameter.
h TAS is the horizontal wind speed.
i Number of 1 min observations in the average.
Ice particle PSDs were measured with a DMT Cloud Imaging Probe (CIP; 25–1600 µm)
optical array probe (OAP) with 64 size channels and a resolution
of 25 µm. An array diode is triggered when a particle obscures
>50 % of the incident laser energy on the diode. During
IFRACS, an Applied Technologies, Inc. (ATI) (Longmont, CO) SATI three-axis sonic
anemometer supplied the wind speed along the horizontal axis of the CIP
probe. For aircraft measurements, this is referred to as true air speed
(TAS). This terminology is adopted to refer to horizontal air speed. During
StormVEx, a Lufft Ventus UMB two-axis sonic anemometer was substituted for the
ATI instrument after 8 February 2011. Data were collected at 1 Hz. The cloud
probes were calibrated and serviced at DMT prior to each field campaign.
The 2-D CIP images from StormVEx and IFRACS were processed using the Optical
Array Shadow Imaging Software (OASIS) program developed at the University of
Manchester (Crosier et al., 2011; Lloyd et al., 2015) and marketed by DMT
(http://www.dropletmeasurement.com/optical-array-shadow-imaging-software-oasis, last access: 15 April 2019).
The CIP depth of field was corrected as a function of particle size
(Baumgardner and Korolev, 1997). Ice particle shattering on the probe tips
was found to be insignificant based on particle interarrival time (Field et
al., 2006). This is consistent with relatively low wind speeds at the
surface compared with aircraft speeds (∼100ms-1).
Concentrations in the first two CIP channels (nominally smaller than 62.5 µm)
were ignored because of sizing uncertainties (Korolev et al.,
1998; Strapp et al., 2001) and because some of these particles are likely to
be cloud droplets in MPC. The total CIP concentration excluding the first
two channels is referred to as Ni. The center-in approach, which includes
particles that obscure an end diode, was used to identify particles and
estimate the sample volume (Heymsfield and Parrish, 1978). Particle size was
described as the area-equivalent diameter, i.e., the diameter of a circle
with the same area as the particle, as determined from the number of
shadowed pixels and the probe resolution. Ice water content (IWC) was
estimated by OASIS using the approach of Brown and Francis (1995). This
estimate is uncertain because mass-dimensional relationships vary
significantly with ice particle habit, riming extent, aggregation, and
temperature (Mitchell, 1996; Schmitt and Heymsfield, 2010).
In aircraft studies, the volume of air sampled by cloud probes is
proportional to TAS. At aircraft speeds, particles are sampled along the
horizontal axes of, and perpendicular to, the sample area of the cloud probes.
This is not necessarily the case with ground-based sampling, even when the
probes are mounted on a wind vane such as those used at SPL or JFJ, where
cloud probes were mechanically oriented into the wind based on sonic
anemometer measurements (Lloyd et al., 2015). If the particle trajectory is
not as described above, the particles can appear misshapen but not
necessarily incorrectly sized according to the area-equivalent diameter. CIP data
used in the following analysis were constrained as follows: (1) 1 s TAS
>1 and <20ms-1. A lower limit is needed to
ensure that particles traversed the CIP diode array as close to horizontally
as possible. Note that the updraft near the mountain tends to impart a
horizontal trajectory on falling ice particles (Borys et al., 2000). An
upper limit is needed to guard against contamination by blowing snow. During
StormVEx and IFRACS, snow and supercooled cloud water samples were
collected in bags and on cloud sieves (Borys et al., 2000). Such sampling is
not practical at wind speeds above 15 ms-1, where snow may blow out of
the bags and the cloud sieves may become overloaded. For the January and
February period during StormVEx, TAS was >20ms-1 during
34 out of 492 995 (0.007 %) 1 s CIP measurements. The corresponding
frequency during IFRACS was 3663 out of 338 230 (1.1 %). The 5 min average
temperature, pressure, and humidity were measured by the SPL weather
station. Water vapor concentration and isotopic composition were measured
during IFRACS with a Picarro L2130-i water vapor isotopic analyzer
(Lowenthal et al., 2016).
Results and discussion
The full StormVEx program lasted nearly 6 months, from November 2010 to
April 2011, while IFRACS was designed as a 6-week field project in January and
February 2014. During IFRACS, the Picarro analyzer began collecting data
on 20 January; however, the weather was clear until 27 January (Lowenthal et
al., 2016). For a consistent comparison between the two studies, StormVEx
data are limited to January and February 2011. Cloud probe measurements were
made on 30 d during StormVEx and 15 d during IFRACS. Measurement
periods during StormVEx were intended for comparison with ground-based
remote sensing instruments. The probes were turned on when it started
snowing but were not necessarily turned off if SPL was not in MPC.
Measurements during IFRACS were started only when SPL was in MPC to sample
liquid and ice for isotopic analysis. While there were twice as many
sampling days during StormVEx, the CIP probe measured particles for 101.4
and 77.2 h during StormVEx and IFRACS, respectively. The 1 s data
were averaged to 1 min with a 75 % (at least 45 s) data
completeness requirement. To ensure that the measurements represented MPC,
only seconds when Ni was >0, LWC was >0.01gm-3 and CDNC was ≥10cm-3 were included. With these
constraints, there were 49.2 and 43 h of concurrent MPC measurements
during StormVEx and IFRACS, respectively.
FSSP-100 and CIP particle size distributions
Average PSDs calculated from concurrent 1 min average FSSP-100 and CIP
measurements are shown in Fig. 2a and b for StormVEx and IFRACS,
respectively. The average PSDs were similar in the two studies.
Corresponding averages of 1 min CIP and FSSP-100 concentrations are
summarized in Table 1, which shows that LWC and CDNC were similar in the two
studies. Average IWC during IFRACS was twice that during StormVEx. Small
(75–200 µm, referred to as Conc75-200) and large (>400µm)
ice particle concentrations were also higher during IFRACS. The
average LWC at SPL was more than an order of magnitude lower than LWC
observed in the Sierra Nevada (1.5 gm-3) and Cascade (2 gm-3)
mountains, respectively (Lamb et al., 1976; Hobbs, 1975). The ratios of
average Conc75-200 to average Ni were 91 % and 83 % during StormVEx and
IFRACS, respectively. Based on their coefficients of variation, liquid cloud
properties (CDNC and LWC) were much less variable than Conc75-200, large ice
particles, and Ni at SPL.
Average of concurrent 1 min FSSP-100 and CIP particle size
distributions (PSDs) from StormVEx (a) and IFRACS (b).
While the first CIP channel, nominally 12.5–37.5 µm, lines up with
the FSSP-100 PSD at ∼25µm in both studies (Fig. 2),
concentrations of FSSP-100 particles larger than 25 µm undershot the
CIP PSD during StormVEx, and to a lesser extent, during IFRACS. The FSSP-100
reported non-zero concentrations of particles larger than 25 µm for
14 % and 56 % of 1 s measurements during StormVEx and IFRACS,
respectively. During these periods, average CDNC, LWC, and NMD were similar,
i.e., 200 cm-3, 0.105 gm-3, and 9.1 µm, respectively,
during StormVEx, and 210 cm-3, 0.103 gm-3, and 9.2 µm,
respectively, during IFRACS. Average TAS was 6.1 ms-1 during StormVEx
and 6.0 ms-1 during IFRACS. At an FSSP-100 sampling flow speed of 9.4 ms-1
at the inlet and an average TAS of ∼6ms-1,
sampling is super-isokinetic, leading to undersampling of larger droplets.
Gerber et al. (1999) demonstrated inertial enhancement of large drop
concentrations in the aspirated FSSP fitted with a flow accelerator (scarf
tube). Thus, the loss of large droplets caused by super-isokinetic sampling
may have been partially offset by inertial concentration of large droplets
by the scarf tube during IFRACS. However, it is difficult to see how
undersampling would have totally eliminated large droplets when they were
present.
Spherical liquid drops and ice particles can be distinguished with image
analysis; however, this is only possible for particles with area-equivalent
diameters larger than about 110 µm for the CIP with 25 µm
resolution (Crosier et al., 2011). The average of 1 s CIP PSDs in
mixed-phase (wet) cases were compared with dry cases when Conc75-200 was
>0 and LWC was 0 (no particles detected by the FSSP-100).
Figure 3 shows the ratio of the average of 1 s wet to average dry CIP
concentrations as a function of size for StormVEx and IFRACS. In both
studies, the ratio was elevated in the first CIP channel only. The ratio
decreased significantly and was flat between the third and eighth CIP
channels, i.e., Conc75-200. This suggests that, on average, the CIP
measurements were only affected by cloud droplets in the first CIP channel.
Average Conc75-200 was higher under wet than dry conditions: 78 versus 49 L-1
during StormVEx and 118 versus 21 L-1 during IFRACS. Average
TAS values under wet and dry conditions were similar, i.e., 5.9 and 6.5 ms-1,
respectively, during StormVEx and 5.9 and 5.2 ms-1, respectively,
during IFRACS. The potential impact of ice particles on FSSP-100
measurements cannot be observed directly with these instruments. However,
the magnitude of the ratio of wet/dry concentrations in CIP channel 1
constrains the effect of ice particles on the FSSP-100 measurements. The
relative fraction of crystals in CIP channel 1 can be estimated from the
ratio of wet/dry in CIP channel 1 to the average of the ratios of wet/dry in
CIP channels 3–8, where droplets were absent and where the ratios of
wet/dry
were constant. These values, 2.3 and 6 for StormVEx and IFRACS,
respectively, imply that 43 % (1/2.3) and 16.7 % (1/6) of particles in
CIP channel 1 were ice crystals during StormVEx and IFRACS, respectively.
Because of the sizing uncertainty for particles which triggered a single
diode (CIP channel 1), it is impossible to know precisely which FSSP-100
channels were impacted by ice crystals.
Ratio of average mixed-phase (LWC>0.01gm-3,
CDNC>10cm-3) wet to dry (LWC=0) PSDs for StormVEx (a) and
IFRACS (b).
Frequency distribution of Conc75-200, wind speed, and temperature
as a function of wind direction.
a Temperature based on 5 min average measurements.b There were 2893/2955 and 2459/2580 1 min measurements when the wind
vane was not frozen during StormVEx and IFRACS, respectively.
The distributions of Conc75-200, wind speed, and temperature as a function of
wind direction during StormVEx and IFRACS are summarized in Table 2. During
StormVEx, mostly all of the NNW (300 to 360∘) cases were on 22 January 2011. The
5 min
average wind direction was exactly the same (351.9∘) for
3.5 h. It is not likely that a 5 min average value could be the same
to a tenth of a degree for two consecutive 5 min periods, much less
18. During IFRACS, many of the NNW wind directions exhibited the same
value for 30 min or more. The reason is that the wind vane can
become iced by riming and does not move. The data were screened for repeated
5 min wind speeds and these were eliminated. This reduced the number of
1 min observations by 2 % and 4.7 % during StormVEx and IFRACS,
respectively. Winds were from the NW sector ∼75.3 % and 57 %
of the time during StormVEx and IFRACS, respectively. There was one
5 min
period during IFRACS when the wind direction was 11∘. High Conc75-200
values were seen in the NW sector in both studies but the highest concentrations
were seen in the NNW sector, albeit at low frequency. When segregated by
wind direction, there was no relationship between Conc75-200 and temperature
or wind speed in either study.
Supercooled liquid cloud microphysics
In non-precipitating warm clouds, an increase in CCN should increase CDNC
while decreasing droplet size at constant LWC (Albrecht, 1989). Smaller
drops may inhibit collision coalescence and precipitation and increase LWC
(Zheng et al., 2010). Borys et al. (2000) demonstrated a direct relationship
between clear-air-equivalent sulfate concentration (a surrogate for
pre-cloud CCN) and CDNC and an inverse relationship between CDNC and droplet
size (NMD) in MPC at SPL. In such clouds, the droplet distribution may be
impacted by riming of ice particles and by transitions between the liquid
and ice phases. Figure 4 presents the relationship between 1 min droplet
NMD and CDNC in MPC during StormVEx (Fig. 4a) and IFRACS (Fig. 4c). The
relationship is stronger when the data are stratified by LWC. The average
NMD and CDNC were calculated for each of the four ranges of LWC in Fig. 4
and are plotted in the figures as a function of LWC. NMD and CDNC increased
monotonically with LWC in both studies. This is consistent with enhanced
growth of droplets as cloud base drops below SPL. However, for CDNC to
increase with LWC, either the supersaturation must increase or CCN aerosols
must become entrained in the cloud between cloud base and SPL. Figure 4b
and d present average FSSP-100 PSDs for low (0.05–0.1 gm-3) and high
(0.2–0.3 gm-3) LWC, corresponding to Fig. 4a and c, respectively.
The distributions are shifted to larger sizes at high LWC and the increase
in CDNC is evident for droplet sizes larger than 10 µm. Note that the
shift in the PSDs to larger sizes at high LWC stops at about 35 µm; i.e., the concentration of very large drops is higher at low LWC.
Relationships among 1 min average mean cloud droplet diameter
(NMD) and concentration (CDNC), segregated by liquid water content (LWC, gm-3),
as shown by colors in the legend, during StormVEx (a) and IFRACS (c).
Corresponding average PSDs for low (0.01–0.05 gm-3) and high
(0.2–0.3 gm-3) LWC are shown in panels (b) and (d). The error bars in
panels (b) and (d) are standard errors.
Relationship between LWC and IWC
As noted above with respect to Table 1, liquid cloud microphysical
properties at SPL were less variable than those of the ice phase. One reason
for this is that the ice phase is impacted by processes occurring upwind and
at higher altitude. Lowenthal et al. (2011, 2016) estimated that most of the
snow mass was formed within 1 km above SPL. This does not preclude ice
nucleation at higher altitudes, as small, freshly nucleated crystals
contribute little to IWC. Even though riming occurs, most efficiently for
large droplets, it is not apparent from Fig. 2 that the liquid cloud was
impacted by the ice phase. Indeed, the Pearson and Spearman rank
(non-parametric) correlations between all concurrent 1 min average IWC
and LWC were only -0.18 and -0.10, respectively, during StormVEx and
-0.13
and -0.16, respectively, during IFRACS. The effect of outliers,
characteristic of skewed distributions, is reduced with the non-parametric
statistic. Henceforth, the Spearman rank correlation is displayed in
parenthesis after the Pearson correlation. Scatter plots of IWC versus LWC
are shown in Fig. 5a and b for StormVEx and IFRACS, respectively. The edge
in the data suggests that there were periods when IWC and LWC were more
strongly anti-correlated. If only days with at least 2 h of valid,
1 min average data are considered, there were 4 out of 11 and 3 out of 11 d
during StormVEx and IFRACS, respectively, where the Pearson and
Spearman rank correlations between IWC and LWC were less than -0.5.
Relationships between LWC and IWC during StormVEx (a) and IFRACS (b).
A sampling day during IFRACS with relatively high average IWC (0.23 gm-3)
and LWC (0.182 gm-3) was identified for closer examination.
Figure 6 presents time series of 1 min average IWC and LWC on 9 February 2014.
In this case, the correlation between IWC and LWC was -0.59 (-0.60),
suggesting interaction between the ice and liquid phases. The minimum
1 min average LWC was 0.05 gm-3and there were no “dry” (LWC=0)
1 s sample periods on this day. To contrast periods with high and low
IWC, average FSSP-100 PSDs were calculated for a high-ice period between
12:45 and 13:17 MST (Fig. 6) and for low-ice periods outside of that
interval with the additional constraint that the LWC/IWC ratio was greater
than 2. These PSDs are presented in Fig. 7. Figure 8a and b present CIP
images from the high- and low-ice periods, respectively. Note the relatively
higher concentration of “dots” in Fig. 8b (low IWC, high LWC). These
represent cloud droplets that occluded a single CIP diode. The average IWC
and LWC were 0.72 and 0.088 and 0.054 and 0.25 gm-3 for the high- and
low-ice periods, respectively. The average IWC and LWC during the high-ice
periods were 3.7 and 1.98 times higher, respectively, than the study-wide
averages (Table 1). Compared with the low-ice period, the high-ice FSSP-100
PSD displays a marked loss of particles with diameters between
∼5 and 23 µm. The corresponding loss of liquid water
was 0.181 gm-3 (Fig. 7). The most obvious explanation is evaporation
of droplets (Wegener–Bergeron–Findeisen process). The loss of LWC is much
lower than the more-than-order-of-magnitude difference in IWC for the two
cases. The high-ice period is characterized by an order of magnitude higher
Ni concentration (525 L-1) compared with 50 L-1 during the low-ice
period. The correlation between IWC and Ni was 0.98 (0.98). There were no
relationships between LWC or IWC and either temperature or water vapor
concentration, which were relatively invariant, i.e., -5.4±0.3∘C and 8064±204ppmv, respectively.
Time series of LWC and IWC on 9 February 2014 during IFRACS. The
high-ice period is from 12:45 to 13:17 MST. The low-ice periods are
indicated by the shaded areas.
Average PSDs for high-ice (12:45–13:17 MST) and low-ice (<12:45 or
>13:17 MST and LWC/IWC>2) periods in Fig. 6. The
values in the middle of the plot are the differences between the high (red)
and low (black) cumulative LWC in the three sections of the distributions
defined by the vertical dotted lines. The error bars are standard errors.
CIP images from 9 February 2014: (a) 13:12:19 MST, high-ice and
low-LWC, and (b) 12:29:09 MST, low-ice and high-LWC periods. The vertical bars
contain all of the images sampled in 1 s. The width of each bar
corresponds to 1600 µm.
Liquid-mediated ice production
In this section, the hypothesis that ice production in MPC at SPL was
related to large droplet concentration is examined. Large droplets are
defined as CDNC25-35 with diameters between 25 and 35 µm. Because of
the paucity of CDNC25-35 concentrations >0 during StormVEx, the
analysis is confined to IFRACS. The 30 s averages were calculated for
periods with CDNC25-35 >0 and Conc75-200 >0 using
the 75 % data completeness criterion. The relationships between
30 s
average CDNC25-35 and Conc75-200 were examined under cold (<-8∘C)
and warm (>-8∘C) conditions. This is
intended to distinguish cold and warm primary or secondary (e.g.,
Hallett–Mossop rime splintering) ice production processes. Figure 9a and b
present relationships for IFRACS under cold and warm conditions,
respectively. The average temperatures for the cold and warm periods were
-11.2±1.5 and -5.8±0.8∘C, respectively. Figure 9a
shows a moderate relationship (r=0.72 [0.73]) between CDNC25-35 and
Conc75-200 at cold temperatures but no relationship at warm temperatures
(r=0.161 [-0.165]).
Relationships between 30 s average concentrations of large
cloud droplets (CDNC25-35) and small ice crystals (Conc75-200) during IFRACS
under cold conditions (<8∘C) and warm (>8∘C)
conditions. The number of observations and the Pearson (Spearman
rank) correlations are shown.
Given the relationships between large droplet and small ice crystal
concentrations, is the temperature range at SPL consistent with immersion
and/or contact freezing? This appears to be the case at colder temperatures
(<-8∘C) at SPL for contact freezing, as seen in Figs. 7
and 13 in Ladino Moreno et al. (2013) and for immersion freezing, particularly for
biological INPs (Levin and Yankofsky, 1983; Du et al., 2017; Kanji et al.,
2017). The lack of a relationship at warm temperatures would appear to
preclude secondary ice formation by the Hallett–Mossop process. As noted
above, the FSSP-100 cannot distinguish liquid droplets from ice crystals. It
is possible that the relationship between CDNC25-35 and Conc75-200 represents
an autocorrelation between two segments of the ice crystal distribution. Two
factors argue against this: (1) Fig. 3 suggests that ice particles are 6
times more prevalent than droplets in the large droplet size range; and (2) the
relationship does not exist at >-8∘C.
Higher-resolution instruments, such as the holographic imagers used by Beals
et al. (2015) and Beck et al. (2018), should be used to address this issue.
Blowing snow
Blowing snow can cause significant artifacts in ice crystal measurements at
surface locations. Rogers and Vali (1987) found higher ice crystal
concentrations at the Elk Mountain Observatory compared with those observed
aloft on the University of Wyoming Queen Air but discounted blowing snow as
the explanation for this difference. Lloyd et al. (2015) concluded that high
ice crystal concentrations at JFJ were not caused by blowing snow. Geerts et
al. (2015) compared CIP concentrations (≥75µm) at SPL with
those measured aboard the University of Wyoming King Air (UWKA) during the
Colorado Airborne Multiphase Cloud Study (CAMPS) when the aircraft was in
the vicinity of SPL. Concentrations were considerably higher at SPL when the
maximum wind speed associated with 5 min average measurements was above
about 4 ms-1. This was attributed to blowing snow. However, a valid
comparison between aircraft and surface measurements depends on the
assumption that both platforms measure the same ice crystal population. This
would require establishing crystal trajectories from a point upwind aloft to
a point downwind at the surface. Even if a direct link between the PSDs
aloft and at the surface could be demonstrated, the falling crystal PSD is
likely to be modified by depositional growth at ice supersaturation in the
low-level liquid cloud, riming and aggregation, or sublimation in
subsaturated regions. Beck et al. (2018) reported a large increase in Ni
when the maximum wind speed increased from 14–16 to ≥16ms-1 at
the Sonnblick Observatory in Rauris, Austria, when winds were from the south.
Relationships between maximum
1 s TAS (MTAS) (a) and 1 min average TAS (b) and Ni for high-ice, low-ice, and intermediate-ice
(all other 1 min periods) periods on 9 February 2014.
Figure 10a plots the 1 s maximum TAS (MTAS) during a 1 min period
and the corresponding 1 min average TAS (Fig. 10b) against
1 min average Ni for high-ice, low-ice, and all other (intermediate-ice) periods
on 9 February 2014. MTAS was highly correlated with TAS [0.90 (0.90] over
the course of the day. The highest Ni values correspond to the highest MTAS (and
TAS), and vice versa. Average MTAS was 16.6±2.4, 8.9±2.0, and 11.3±2.8ms-1 during high-, low-, and intermediate-ice periods,
respectively. This could imply that high Ni resulted from blowing snow when
the winds were higher in the early afternoon. However, contrary to results
reported by Beck et al. (2018), there was no step function in Ni
corresponding to a threshold in MTAS. Further, there appears to be an
inverse relationship between 1 min MTAS and 1 minNi, especially for
the high- and low-ice regimes. Beck et al. (2018) noted that a correlation
between MTAS and blowing snow could be reduced if the averaging time was too
long or obscured because of an (indeterminate) lag between the arrival of
the gust and the particles that may have been lofted by it. Beck et al. (2018)
suggested using an averaging time of 10–15 s. Figure 11 plots
15 s average (using the 75 % data completeness criterion) MTAS
against Ni for the high-ice, low-ice, and intermediate-ice periods on 9 February 2014.
Figure 11 shows that while both MTAS and Ni varied
considerably in each case, there was no apparent wind speed threshold and
the correlations between MTAS and Ni were actually negative under high- and
low-ice conditions. These results are not consistent with the blowing snow
hypothesis.
Relationships between 15 s average Ni and MTAS for high-ice (a), low-ice (b),
and intermediate-ice (c) periods on 9 February 2014.
Relationships between TAS and small ice crystal concentrations
(Conc75-200) during StormVEx and IFRACS. r is the Pearson (Spearman rank)
correlation.
Examining all available data, Table 3 presents average Conc75-200 over
ranges of TAS during StormVEx and IFRACS. Conc75-200 increases
monotonically, if not linearly, with TAS in both studies. If it is assumed
that smaller crystals should be lofted more efficiently from the snow
surface and remain suspended farther downwind than larger ones (Schmidt Jr.,
1982), blowing snow should result in a relative enrichment of small crystals
in the CIP PSD, independent of absolute concentration. Average 1 min CIP
PSDs were calculated, normalized to average Ni, and expressed as
percentages. These are presented for high (8–12 ms-1) and low (1–3 ms-1)
TAS in Fig. 12. During StormVEx, Conc75-200 was 83 % and 93 % of
Ni at low and high TAS, respectively. The corresponding percentages during
IFRACS were 79 % and 87 %, respectively. The relative enrichments of
Conc75-200 at high TAS, i.e., 10 % and 8 %, during StormVEx and IFRACS,
respectively, are consistent with expectations for blowing snow. However,
these percentages cannot explain the large differences in the absolute
concentrations of Conc75-200 at high and low wind speeds, which are factors
of 4.5 and 6.5 during StormVEx and IFRACS, respectively (Table 3). They also
do not explain the large differences between surface and aircraft
measurements observed by Rogers and Vali (1987) and Geerts et al. (2015).
Correlation of wind speed with crystal concentrations does not necessarily
imply blowing snow. In mountain clouds, ice crystal concentrations vary with
synoptic and orographic dynamics. Stronger uplift nucleates more crystals
upwind and above the mountain barrier as droplets continue to grow and
temperatures decrease (e.g., Neiman et al., 2002; Stoelinga et al., 2013).
The correlations between 1 min average TAS and vertical velocity were
0.75 (0.72) and 0.66 (0.67) during StormVEx and IFRACS, respectively.
Averages of 1 min relative (% of Ni) CIP PSDs at low (1–3 ms-1)
and high (8–12 ms-1) TAS during StormVEx (a) and IFRACS (b).
Average TAS values are shown in parentheses.
Secondary ice production
Secondary ice production (SIP) mechanisms have been extensively reviewed
(e.g., Field et al., 2017). Sullivan et al. (2018) modeled SIP by rime
splintering, droplet shattering, and collisional breakup. Rangno and Hobbs
(2001) concluded that shattering of large droplets (>50µm)
upon freezing could have accounted for high observed ice particle
concentrations in Arctic stratus. While there is no evidence of droplets
this large at SPL, they could be present upwind and above SPL. Keppas et al. (2017)
concluded that rime splintering occurred in warm (-6 to 0 ∘C) frontal clouds. Lollie-shaped crystals were taken as evidence of
riming of columnar crystals by droplets larger than 100 µm. Neither
ice lollies nor droplets this large have been observed in MPC at SPL.
Lloyd et al. (2015) considered blowing snow, rime splintering, and
detachment of surface frost (Bacon et al., 1998) as sources of high ice
particle concentrations at JFJ. They ultimately favored the latter process,
albeit with no direct evidence. There is also no evidence regarding surface
frost splinters at SPL. Snow was continually falling during measurement
periods at SPL, leaving no undisturbed icy surface to accumulate frost. Rime
splintering (Hallett–Mossop) is thought to occur at temperatures above -8∘C.
During StormVEx, average Conc75-200 was 13.6 and 89 L-1 at temperatures warmer than -8∘C and colder than
-12∘C, respectively. The corresponding average TAS values were
5.8 and 5.2 ms-1, respectively. During IFRACS, average Conc75-200 was
95 and 116 L-1 at temperatures warmer than -8∘C and
colder than -12∘C, respectively. The corresponding average
TAS values were 6.1 and 4.9 ms-1, respectively. While rime splintering may
have occurred, it was not the dominant ice formation mechanism.
Conclusions
Studies of orographic MPCs were conducted at SPL in northwestern Colorado in January and February
during StormVEx (2011) and IFRACS (2014). In total, the data represent
∼92h when SPL was immersed in supercooled liquid cloud
and it was snowing. On average, liquid cloud PSDs, CDNC, NMD, and LWC were
similar between years, while Ni and IWC were 48 % and 114 % higher,
respectively, during IFRACS. Average wind speeds were similar
(∼6ms-1) in both studies, while average temperatures
were colder during StormVEx (-12.8∘C) than IFRACS (-8.2∘C).
Supercooled liquid cloud properties at SPL were consistent
between the two studies. The microphysical properties of ice particles were
more variable as they depend on the structure of the cloud above and
upstream of SPL.
The inverse
relationship between cloud droplet size (NMD) and concentration
(CDNC) is related to CCN at SPL (Borys et al., 2000). This relationship is
stronger when the data are stratified by LWC. Both CDNC and NMD increase
with increasing LWC, demonstrating droplet growth and enhanced activation
or entrainment of CCN below SPL. Future studies at SPL would benefit from
direct measurement of cloud base height. There was a weak relationship
between LWC and IWC for all data (the correlation was -0.18 (-0.10) and
-0.13 (-0.16) during StormVEx and IFRACS, respectively); however, a stronger
inverse relationship was evident on several days during each study. This was
demonstrated for a case on 9 February 2014, where the correlation between
IWC and LWC was -0.59 (-0.60). During a period of maximum IWC on this day,
the droplet PSD showed a significant loss of liquid water and a decrease in
droplet concentration compared to periods with low IWC and high LWC. As
there was an order of magnitude increase in the ice crystal concentration
(Ni) between the high- and low-ice periods, the loss of LWC was likely due
to crystal growth at the expense of evaporating droplets
(Wegener–Bergeron–Findeisen process).
A relationship between large cloud droplets (CDNC25-35) and small ice
crystals (Conc75-200) during IFRACS suggests that droplet freezing (contact
or immersion) was involved in ice production at SPL. This relationship was
only evident at temperatures below -8∘C. There was no evidence
that secondary ice production mechanisms such as rime splintering, large
droplet freezing, or frost splintering influenced Conc75-200 at SPL. It is
unclear how these processes could have produced the observed correlation
between large droplet and small ice crystal concentrations. Blowing snow can
significantly impact surface ice crystal concentrations and has been invoked
to explain large differences between surface and aircraft ice crystal
measurements. The potential effect of blowing snow on ice crystal
measurements at SPL was evaluated from two perspectives. On 9 February 2014,
during IFRACS, 1 min average Ni increased with both 1 min average TAS
and the 1 s maximum TAS (MTAS), although there was no threshold wind
speed or step function in Ni. However, during high-ice and low-ice periods,
there was an inverse correlation between 15 s average Ni and MTAS over
a wide range of MTAS. This is not consistent with blowing snow. For the
entire data set, Ni also increased with wind speed. To test the hypothesis
that this was caused by blowing snow, it was assumed that blowing snow
should preferentially enhance the relative abundance of small crystals
(Conc75-200) in the CIP PSD. Comparison of the relative (expressed as
percentages of Ni) ice crystal PSDs at high (8–12 ms-1) and low (1–3 ms-1)
TAS showed that Conc75-200 was enriched by 8 %–10 % at higher TAS.
However, this level of enrichment cannot explain the factor of 4.5–6.5
higher Conc75-200 at high TAS at SPL or previously reported orders of
magnitude differences between surface and aircraft measurements. Stronger
dynamics, especially orographic and/or convective uplift, also contribute to
ice production upwind and above the mountain. It is possible that both
primary production and blowing snow were active at SPL. These results
highlight the need for targeted experiments to quantify the contributions of
blowing snow to ice crystal concentrations at mountaintop locations. They
also demonstrate the limitations of instrumentation such as the FSSP-100 and
CIP (2-D optical array probe) for distinguishing liquid droplets from small
ice crystals in mixed-phase clouds. Higher-resolution instruments are
required for this purpose.
Data availability
Data are available at https://www.dri.edu/doug-lowenthal-research-reviews, last access: 15 April 2019.
Author contributions
DHL is Professor Emeritus at DRI, was the principal investigator on IFRACS,
worked on the IFRACS field experiment, analyzed the StormVEx and IFRACS data, and produced the
first draft and subsequent revisions of the manuscript and the responses to the reviewers. AGH
is the director of the Desert Research Institute's Storm Peak Laboratory, was a principal
investigator on StormVEx and co-principal investigator on IFRACS, contributed to the first
draft and subsequent revisions of the manuscript, and provided input on the responses to the
reviewers. ROD was a graduate student at DRI who worked on the IFRACS field experiment, used
the results in his Master's thesis, contributed to the first draft and subsequent revisions
of the manuscript, and provided input on the responses to the reviewers. IBM is the site manager
at Storm Peak Laboratory, supervised the StormVEx and IFRACS field programs, and participated in
the StormVEx and IFRACS field experiments. RDB is Professor Emeritus at DRI, participated in the
IFRACS field experiment, and provided input to the first draft of the manuscript. GGM was a principal
investigator on StormVEx and provided input to the first draft of the
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by Department of Energy Atmospheric System Research
Program grant DE-SC0014304 and by National Science Foundation Division of
Atmospheric Sciences grant AGS-1260462. Logistical assistance from the
Steamboat Ski and Resort Corporation is greatly appreciated. The Desert
Research Institute is an equal opportunity service provider and employer and is a
permittee of the Medicine Bow–Routt National Forests. We would
especially like to thank and acknowledge the hard work of many people who
made the StormVEx project possible, including the many DOE ATSC and ASR
staff, Storm Peak Laboratory (SPL) local volunteers, the Steamboat Ski and
Resort Corporation, the US Forest Service, the Grand Junction National
Weather Service office, and all of the graduate students (Betsy Berry,
Stewart Evans, Ben Hillman, Will Mace, Clint Schmidt, Carolyn Stwertka, Adam Varble, and Christy Wall), who put considerable effort into data collection.
Review statement
This paper was edited by Ottmar Möhler and reviewed by two anonymous referees.
ReferencesAlbrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, 10.1126/science.245.4923.1227, 1989.Bacon, N. J., Swanson, B. D., Baker, M. B., and Davis, E. J.: Breakup of
levitated frost particles. J. Geophys. Res., 103, 13763–13775, 10.1029/98JD01162, 1998.Baumgardner, D. and Korolev, A.: Airspeed corrections for optical array
probe sample volumes, J. Atmos. Ocean. Tech., 14, 1224–1229,
10.1175/1520-0426(1997)014<1224:ACFOAP>2.0.CO;2, 1997.Beals, M. J., Fugal, J. P., Shaw, R. A., Lu, J., Spuler, S. M., and Stith, J.
L.:
Holographic measurements of inhomogeneous cloud mixing at the centimeter
scale, Science, 350, 87–89, 10.1126/science.aab0751, 2015.Beck, A., Henneberger, J., Fugal, J. P., David, R. O., Lacher, L., and Lohmann, U.:
Impact of surface and near-surface processes on ice crystal concentrations measured
at mountain-top research stations, Atmos. Chem. Phys., 18, 8909–8927, 10.5194/acp-18-8909-2018, 2018.Borys, R. D. and Wetzel, M. A.: Storm Peak Laboratory: a research, teaching,
and service facility for the atmospheric sciences, B. Am. Meteorol. Soc.,
78, 2115–2123, 10.1175/1520-0477(1997)078<2115:SPLART>2.0.CO;2, 1997.Borys, R. D., Lowenthal, D. H., and Mitchell, D. L.: The relationships among
cloud microphysics, chemistry, and precipitation rate in cold mountain
clouds, Atmos. Environ., 34, 2593–2602,
10.1016/S1352-2310(99)00492-6, 2000.Borys, R. D., Lowenthal, D. H., Cohn, S. A., and Brown, W. O. J.: Mountaintop and
radar measurements of aerosol effects on snow growth and snowfall rate,
Geophys. Res. Lett., 30, 1538, 10.1029/2002GL016855, 2003.Brown, P. R. A. and Francis, P. N.: Improved measurements of the ice
water-content in cirrus using a total-water probe, J. Atmos. Ocean. Tech.,
12, 410–414, 10.1175/1520-0426(1995)012<0410:IMOTIW>2.0.CO;2, 1995.Crosier, J., Bower, K. N., Choularton, T. W., Westbrook, C. D., Connolly, P. J., Cui, Z. Q.,
Crawford, I. P., Capes, G. L., Coe, H., Dorsey, J. R., Williams, P. I., Illingworth, A. J.,
Gallagher, M. W., and Blyth, A. M.: Observations of ice multiplication in a weakly
convective cell embedded in supercooled mid-level stratus, Atmos. Chem. Phys., 11, 257–273, 10.5194/acp-11-257-2011, 2011.de Boer, G., Morrison, H., Shupe, M. D., and Hildner, R.: Evidence of liquid
dependent ice nucleation in high-latitude stratiform clouds from surface
remote sensors, Geophys. Res. Lett., 38, L01803, 10.1029/2010GL046016,
2011.Diehl, K., Simmel, M., and Wurzler, S.: Numerical sensitivity studies on the
impact of aerosol properties and drop freezing modes on the glaciation,
microphysics, and dynamics of clouds, J. Geophys. Res., 111, D07202,
10.1029/2005JD005884, 2006.Du, R., Du, P., Lu, Z., Ren, W., Liang, Z., Qin, S., Li, Z., Wang, Y., and
Fu, P.: Evidence for a missing source of efficient ice nuclei, Sci. Rep.-UK,
7, 39673, 10.1038/srep39673, 2017.Field, P. R., Heymsfield, A. J., and Bansemer, A.: Shattering and particle
interarrival times measured by optical array probes, J. Atmos. Ocean.
Tech., 23, 1357–1371, 10.1175/JTECH1922.1,
2006.Field, P. R., Lawson, R. P., Brown, P. R. A., Lloyd, G., Westbrook, C.,
Moisseev, D., Miltenberger, A., Nenes, A., Blyth, A., Choularton, T.,
Connolly, P., Buehl, J., Crosier, J., Cui, Z., Dearden, C., DeMott, P.,
Flossmann, A., Heymsfield, A., Huang, Y., Kalesse, H., Kanji, Z. A., Korolev,
A., Kirchgaessner, A., Lasher-Trapp, S., Leisner, T., McFarquhar, G.,
Phillips, V., Stith, J., and Sullivan, S.: Secondary ice production: current
state of the science and recommendations for the future, Meteorological
Monographs, 58, 7.20, 10.1175/AMSMONOGRAPHS-D-16-0014.1, 2017.Geerts, B., Pokharel, B., and Kristocich, D. A. R.: Blowing snow as a natural
glaciogenic cloud seeding mechanism, Mon. Weather Rev., 143, 5017–5033,
10.1175/MWR-D-15-0241.1, 2015.Gerber, H., Frick, G., and Rodi, A. R.: Ground-based FSSP and PVM
measurements of liquid water content, J. Atmos. Ocean. Tech., 16,
1143–1149, 10.1175/1520-0426(1999)016<1143:GBFAPM>2.0.CO;2, 1999.Hallett, J. and Mossop, S. C.: Production of secondary particles during the
riming process, Nature, 249, 26–28, 10.1038/249026a0, 1974.Heymsfield, A. and Parrish, J. L.: A computational technique for increasing
the effective sampling volume of the PMS 2-D particle size spectrometer, J.
Appl. Meteorol., 17, 1566–1572, 10.1175/1520-0450(1978)017<3C1566:ACTFIT>2.0.CO;2, 1978.Hobbs, P. V.: The nature of winter clouds and precipitation in the Cascade
Mountains and their modification by artificial seeding: Part I. Natural
conditions, J. Appl. Meteorol., 14, 783–804,
10.1175/1520-0450(1975)014<0783:TNOWCA>2.0.CO;2, 1975.Hobbs, P. V. and Rangno, A. L.: Ice particle concentration in clouds, J.
Atmos. Sci., 42, 2523–2549,
10.1175/1520-0469(1985)042<2523:IPCIC>2.0.CO;2, 1985.Hoose, C. and Möhler, O.: Heterogeneous ice nucleation on atmospheric aerosols:
a review of results from laboratory experiments, Atmos. Chem. Phys., 12, 9817–9854, 10.5194/acp-12-9817-2012, 2012.Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo,
D. J., and Kramer, M.: Overview of ice nucleating particles, Meteorological
Monographs, 58, 1.1–1.33, 10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017.Keppas, S. C., Crosier, J., Choularton, T. W., and Bower, K. N.: Ice lollies:
An ice particle generated in supercooled conveyor belts, Geophys. Res.
Lett., 44, 5222–5230, 10.1002/2017GL073441, 2017.Knopf, D. A. and Alpert, P. A.: A water activity based model of heterogeneous
ice nucleation kinetics for freezing of water and aqueous solution droplets,
Faraday Discuss., 165, 513–534, 10.1039/c3fd00035d, 2013.Knopf, D. A., Alpert, P. A., and Wang, B.: The role of organic aerosol in
atmospheric ice nucleation: a review, ACS Earth Space Chem., 2, 168–202,
10.1021/acsearthspacechem.7b00120, 2018.Korolev, A., Strapp, J., and Isaac, G.: Evaluation of the accuracy of PMS
Optical Array Probes, J. Atmos. Ocean. Tech., 15, 708–720,
10.1175/1520-0426(1998)015<0708:EOTAOP>2.0.CO;2, 1998.Ladino Moreno, L. A., Stetzer, O., and Lohmann, U.: Contact freezing: a review of
experimental studies, Atmos. Chem. Phys., 13, 9745–9769, 10.5194/acp-13-9745-2013, 2013.Lamb, D., Nielsen, K. W., Klieforth, H. E., and Hallett, J.: Measurement of
liquid water content in cloud systems over the Sierra Nevada, J. Appl. Meteorol.,
15, 763–775, 10.1175/1520-0450(1976)015<0763:MOLWCI>2.0.CO;2, 1976.Lance, S., Shupe, M. D., Feingold, G., Brock, C. A., Cozic, J., Holloway, J. S.,
Moore, R. H., Nenes, A., Schwarz, J. P., Spackman, J. R., Froyd, K. D., Murphy, D. M.,
Brioude, J., Cooper, O. R., Stohl, A., and Burkhart, J. F.: Cloud condensation nuclei
as a modulator of ice processes in Arctic mixed-phase clouds, Atmos. Chem. Phys., 11, 8003–8015, 10.5194/acp-11-8003-2011, 2011.Levin, Z. and Yankofsky, S. A.: Contact versus immersion freezing of freely
suspended droplets by bacterial ice nuclei, J. Appl. Meteorol. Climatol.,
22, 1964–1966, 10.1175/1520-0450(1983)022<1964:CVIFOF>2.0.CO;2,
1983.Lloyd, G., Choularton, T. W., Bower, K. N., Gallagher, M. W., Connolly, P. J.,
Flynn, M., Farrington, R., Crosier, J., Schlenczek, O., Fugal, J., and Henneberger, J.:
The origins of ice crystals measured in mixed-phase clouds at the high-alpine
site Jungfraujoch, Atmos. Chem. Phys., 15, 12953–12969, 10.5194/acp-15-12953-2015, 2015.Lohmann, U. and Diehl, K.: Sensitivity studies of the importance of dust
ice nuclei for the indirect aerosol effect on stratiform mixed-phase clouds,
J. Atmos. Sci., 63, 968–982, 10.1175/JAS3662.1,
2006.Lowenthal, D. H., Borys, R. D., and Wetzel, M. A.: Aerosol distributions and
cloud interactions at a mountaintop laboratory, J. Geophys. Res., 107,
4345, 10.1029/2001JD002046, 2002.Lowenthal, D. H., Borys, R. D., Cotton, W., Saleeby, S., Cohn, S. A., and
Brown, W. O. J.: The altitude of snow growth by riming and vapor deposition in
mixed-phase orographic clouds, Atmos. Environ., 45, 519–522,
10.1016/j.atmosenv.2010.09.061, 2011.Lowenthal, D. H., Hallar, A. G., McCubbin, I., David, R., Borys, R., Blossey,
P., Muehlbauer, A., Kuang, Z., and Moore, M.: Isotopic fractionation in
wintertime orographic clouds. I: isotopic measurements, J. Atmos. Ocean.
Tech., 33, 2663–2678, 10.1175/JTECH-D-15-0233.1, 2016.Mace, J., Matrosov, S., Shupe, M., Lawson, P., Hallar, G., McCubbin, I.,
Marchand, R., Orr, B., Coulter, R., Sedlacek, A., Avallone, L., and Long,
C.: StormVEx: The Storm Peak Lab Cloud Property Validation Experiment
science and operations plan, U.S. Department of Energy Tech Rep.
DOE/SCARM-10-021, 45 pp., available at:
https://www.arm.gov/publications/programdocs/doe-sc-arm-10-021.pdf (last access: 7 August 2018), 2010.Matrosov, S. Y., Mace, G. G., Marchand, R., Shupe, M. D., Hallar, A. G., and
McCubbin, I. B.: Observations of ice crystal habits with a scanning
polarimetric W-band radar at slant linear depolarization ratio mode, J.
Atmos. Ocean. Tech., 29, 989–1008, 10.1175/jtech-d-11-00131.1, 2012.Mitchell, D. L.: Use of mass- and area-dimensional power laws for determining
precipitation particle terminal velocities, J. Atmos. Sci., 53, 1710–1723,
10.1175/1520-0469(1996)053<1710:UOMAAD>2.0.CO;2, 1996.Moore, M., Blossey, P. N., Muehlbauer, A., and Kuang, K.: Microphysical
controls on the isotopic composition of wintertime orographic precipitation,
J. Geophys. Res., 121, 7235–7253, 10.1002/2015JD023763, 2016.Mossop, S. C.: Secondary ice particle production during rime growth: The
effect of drop size distribution and rimer velocity, Q. J. Roy. Meteorol. Soc.,
111, 1113–1124, 10.1002/qj.49711147012, 1985.Murray, B. J., O'Sullivan, D., Atkinson, J. D., and Webb, M. E.: Ice nucleation
by particles immersed in supercooled cloud droplets, Chem. Soc. Rev., 41,
6519–6554, 10.1039/c2cs35200a, 2012.Nagare, B., Marcolli, C., Welti, A., Stetzer, O., and Lohmann, U.: Comparing contact
and immersion freezing from continuous flow diffusion chambers, Atmos. Chem. Phys., 16, 8899–8914, 10.5194/acp-16-8899-2016, 2016.Neiman, P. J., Ralph, F. M., White, A. B., Kingsmill, D. E., and Persson, P. O.
G.:
The statistical relationship between upslope flow and rainfall in
California's coastal mountains: Observations during CALJET, Mon. Weather Rev.,
130, 1468–1492, 10.1175/1520-0493(2002)130<1468:TSRBUF>2.0.CO;2, 2002.Peng, Y., Lohmann, U., Leaitch, R., Banic, C., and Couture, M.: The cloud
albedo-cloud droplet effective radius relationship for clean and polluted
clouds from RACE and FIRE.ACE, J. Geophys. Res., 107, 4106,
10.1029/2000JD000281, 2002.Pitter, R. L. and Pruppacher, H. R.: A wind tunnel investigation of freezing
of small water drops falling at terminal velocity in air, Q. J. Roy.
Meteorol. Soc., 99, 540–550, 10.1002/qj.49709942111, 1973.Pruppacher, H. R. and Klett, J. D.: Microphysics of clouds and
precipitation, 2nd edn., Kluwer Academic Publishers, Boston, 954 pp.,
10.1007/978-0-306-48100-0, 1997.Rangno, A. L. and Hobbs, P. V.: Ice particles in stratiform clouds in the
Arctic and possible mechanisms for the production of high ice
concentrations, J. Geophys. Res., 106, 15065–15075, 10.1029/2000JD900286,
2001.Rauber, R. M. and Grant, L. O.: The characteristics and distribution of cloud
water over the mountains of northern Colorado during wintertime storms. Part
II: Microphysical characteristics, J. Clim. Appl. Meteorol., 25, 489–504, 10.1175/1520-0450(1986)025<0489:TCADOC>2.0.CO;2, 1986.Rauber, R. M., Grant, L. O., Feng, D., and Snider, J. B.: The characteristics
and distribution of cloud water over the mountains of northern Colorado
during wintertime storms. Part I: temporal variations, J. Clim. Appl.
Meteorol., 25, 468–488, 10.1175/1520-0450(1986)025<0468:TCADOC>2.0.CO;2 1986.Rogers, D. and Vali, G.: Ice crystal production by mountain surfaces, J.
Clim. Appl. Meteorol., 26, 1152–1168, 10.1175/1520-0450(1987)026<1152:ICPBMS>2.0.CO;2, 1987.Rosenfeld, D. and Givati, A.: Evidence of orographic precipitation
suppression by air pollution-induced aerosols in the western United States,
J. Appl. Meteor. Climatol., 45, 893–911, 10.1175/JAM2380.1, 2006.Saleeby, S. M., Cotton, W. R., Lowenthal, D., Borys, R. D., and Wetzel, M. A.:
Influence of cloud condensation nuclei on orographic snowfall, J. Appl.
Meteor. Climatol., 48, 903–922, 10.1175/2008JAMC1989.1, 2009.Saleeby, S. M., Cotton, W. R., Lowenthal, D., and Messina, J.: Aerosol Impacts
on the Microphysical Growth Processes of Orographic Snowfall, J. Appl.
Meteor. Climatol., 52, 834–852, 10.1175/JAMC-D-12-0193.1, 2013.Schmidt Jr., R. A.: Properties of blowing snow, Rev. Geophys. Space Phys.,
20, 39–44, 10.1029/RG020i001p00039, 1982.Schmitt, C. G. and Heymsfield, A. J.: The dimensional characteristics of ice
crystal aggregates from fractal geometry, J. Atmos. Sci., 67, 1605–1616,
10.1175/2009JAS3187.1, 2010.Stoelinga, M. T., Stewart, R. E., Thompson, G., and Thériault, J. M.: Microphysical
processes within winter orographic cloud and precipitation systems, in: Mountain Weather Research and Forecasting, edited by:
Chow,
F., De Wekker, S., and Snyder, B.,
Springer Atmospheric Sciences, Springer, Dordrecht, 10.1007/978-94-007-4098-3_7, 2013.Strapp, J. W., Albers, F., Reuter, A., Korolev, A. V., Maixner, U., Rashke,
E., and Vukovic, Z.: Laboratory measurements of the response of a PMS
OAP-2DC, J. Atmos. Ocean. Tech., 18, 1150–1170, 10.1175/1520-0426(2001)018<1150:LMOTRO>2.0.CO;2, 2001.Sullivan, S. C., Hoose, C., Kiselev, A., Leisner, T., and Nenes, A.: Initiation of secondary
ice production in clouds, Atmos. Chem. Phys., 18, 1593–1610, 10.5194/acp-18-1593-2018, 2018.Twomey, S., Piepgrass, M., and Wolfe, T. L.: An assessment of the impact of
pollution on global cloud albedo, Tellus, 36B, 356–366,
10.3402/tellusb.v36i5.14916, 1984.
Vali, G.: Ice nucleation – a review, in: Nucleation and Atmospheric Aerosols
1996, presented at the 14th International Conference on Nucleation and
Atmospheric Aerosols, Helsinki, 26–30 August 1996.Vali, G.: Ice nucleation – theory, a tutorial, presented at the NCAR/ASP
1999 Summer Colloquium, available at: http://www-das.uwyo.edu/~vali/nucl_th.pdf (last access: 7 August 2018), 1999.Wetzel, M., Meyers, M., Borys, R., McAnelly, R., Cotton, W., Rossi, A.,
Frisbie, P., Nadler, D., Lowenthal, D., Cohn, S., and Brown, W.: Mesoscale
snowfall prediction and verification in mountainous terrain, Wea.
Forecast., 19, 806–828, 10.1175/1520-0434(2004)019<0806:MSPAVI>2.0.CO;2, 2004.Zheng, X., Albrecht, B., Minnis, P., Ayers, K., and Jonson, H. H.: Observed
aerosol and liquid water path relationships in marine stratocumulus,
Geophys. Res. Lett., 37, L17803, 10.1029/2010GL044095, 2010.