We have developed a new method to determine ice nucleating particle (INP)
concentrations observed by the Texas A&M University continuous flow diffusion
chamber (CFDC) under a wide range of
operating conditions. In this study, we evaluate differences in particle
optical properties detected by the Cloud and Aerosol Spectrometer with
POLarization (CASPOL) to differentiate between ice crystals, droplets, and
aerosols. The depolarization signal from the CASPOL instrument is used to
determine the occurrence of water droplet breakthrough (WDBT) conditions in
the CFDC. The standard procedure for determining INP concentration is to
count all particles that have grown beyond a nominal size cutoff as ice
crystals. During WDBT this procedure overestimates INP concentration, because large droplets are miscounted as
ice crystals. Here we design a new analysis method based on depolarization
ratio that can extend the range of operating conditions of the CFDC. The
method agrees reasonably well with the traditional method under non-WDBT
conditions with a mean percent error of
Ice clouds cover approximately 40 % of the Earth's atmosphere (Wylie and Menzel, 1999). Because of their complicated microphysical properties, ice and mixed-phase clouds pose challenges in understanding our global radiative budget and precipitation (Wendisch et al., 2005; Pinto, 1998; Yang et al., 2015; Korolev, 2007). Despite several decades of effort by the atmospheric community to study ice clouds, there are still large gaps in our understanding of the impacts they have on our climate (Boucher et al., 2013). While experimental chambers have been used to study ice nucleation processes and ice nucleating particle (INP) concentrations for more than 30 years, INP measurement techniques are still under development.
Ice nucleation measurements are challenging for several reasons. The
concentration of effective INPs is typically 0.1 to 1000 L
At temperatures below
Composition, surface structure, and size are important factors in determining the ice nucleating ability of an aerosol particle (Zolles et al., 2015; Niemand et al., 2012; Hoose and Möhler, 2012). Measurements suggest that K-feldspar, a common component of soil dust aerosol, may account for a large fraction of Earth's INPs (Atkinson et al., 2013; Yakobi-Hancock et al., 2013). Recent investigations of other aerosols have identified aromatic pollutant aerosols, secondary organic aerosols, marine aerosols, and aerosols produced from biomass burning as effective INPs (Brooks et al., 2014; DeMott et al., 2016; McCluskey et al., 2014, 2016; Levin et al., 2016; Collier and Brooks, 2016).
Optical techniques have been used to detect and characterize ambient ice
crystals (Mishchenko and Sassen, 1998; Yoshida et al., 2010; Noel and
Sassen, 2005). For example, lidar observations
use the depolarization ratio to distinguish cloud particle type (i.e., ice
crystals or water droplets). In traditional lidar applications, the
depolarization ratio is calculated using Eq. (1):
Several previous studies have designed new analysis methods for ice chambers that utilize the depolarization ratio measured by optical particle counters (OPCs) (Glen and Brooks, 2014; Nicolet et al., 2010; Clauss et al., 2013; Garimella et al., 2016). Nicolet et al. (2010) accurately quantified ice crystals in the presence of water droplets in a chamber by using the peak intensity of the depolarization ratio to discriminate between ice crystals and droplets with the Ice Optical DEtector (IODE). Rather than using the peak intensity of the depolarization signal, Clauss et al. (2013) used the width of the pulse detected in the depolarization channel of the Thermo-stabilized Optical Particle Spectrometer for the detection of Ice (TOPS-ice) for phase discrimination. Alternatively, Garimella et al. (2016) used a machine learning technique with scattering signals, including linear depolarization signals detected by an OPC installed in the SPectrometer for Ice Nuclei (SPIN, Droplet Measurement Technologies, Inc.) to determine INP concentration.
A continuous flow diffusion chamber (CFDC) designed to measure ice nucleation was originally developed by Rogers (1988) at the University of Wyoming and was later modified and rebuilt at Colorado State University (CSU). Several other ice nucleation chambers have been developed since then including the CFDC at Texas A&M University (TAMU) used in this study. Many enhancements have been made to ice nucleation chambers (e.g., Rogers et al., 2001; Creamean et al., 2013; DeMott et al., 2015; Prenni et al., 2013; Coluzza et al., 2017; Kanji et al., 2017), including replacement of the TAMU CFDC's standard optical detector (CLIMET, model no. CI-3100), which uses particle size to distinguish ice crystals from water droplets and aerosols, with the Cloud and Aerosol Spectrometer with POLarization (CASPOL, Droplet Measurement Technologies, Inc.). The CASPOL detects forward scattering, backward scattering, and depolarization on a single particle basis. In addition, the CASPOL has been used to differentiate between ice crystals and various types of dust and soil particles based on backward scattering and depolarization signals (Glen and Brooks, 2013, 2014).
In this study, we demonstrate how differences in particle optical properties can be used to differentiate between ice crystals, droplets, and aerosols detected by the CASPOL. In addition, we present a new method to quantify INP concentrations detected by the TAMU CFDC using depolarization ratio. Finally, INP concentrations obtained using the new method are compared with results obtained through the traditional analysis method that primarily uses particle size to identify INP as well as INP concentrations reported by another ice nucleation chamber, the CSU CFDC.
The TAMU CFDC was custom built in our laboratory at Texas A&M University and has been operated in previous laboratory and field campaigns to take temperature- and supersaturation-resolved INP concentration measurements (Glen and Brooks, 2014; McFarquhar et al., 2011). Additional details on CFDC and CFDC-CASPOL instrument design and operation are provided in our previous work (Glen and Brooks, 2013, 2014; Glen, 2014). Hereafter, CFDC refers to the TAMU CFDC unless otherwise stated.
During operation, sample aerosols pass through a diffusion dryer to remove
moisture from the air and before they enter the CFDC. Typically, aerosol
flow is directed through a BGI Sharp Cut Cyclone impactor (model 0.732)
prior to entering the CFDC in order to remove aerosols with a diameter
greater than
Next, aerosols enter the CFDC processing chamber where temperature and
supersaturation are controlled. The processing chamber consists of two
concentric cylindrical walls coated with ice. Separate refrigeration units on
each wall can be controlled to create a temperature gradient in the chamber
that imposes a region of supersaturation with respect to ice (SS
Before measurements can be taken with the CFDC, the processing chamber must
be prepared. First, a vacuum pump is used to evacuate the chamber for
approximately 30 min in order to eliminate ambient aerosols that may
have infiltrated the chamber and to remove moisture that may cause the walls
to accumulate an uneven coating of ice or allow ice to accumulate in other
sensitive regions. The walls are then cooled to a temperature of
At the base of the processing chamber, particles pass through a detector to
determine INP concentration. In previous TAMU CFDC studies, either an OPC (Climet, Inc.) or the CASPOL were employed (Glen and Brooks, 2014; McFarquhar et al.,
2011). During FIN-02, the CASPOL was the chosen detector. Two mass flow
controllers downstream of the CASPOL are used to set the total flow and
recirculating sheath flow through the CFDC-CASPOL. The difference between the
total and sheath flows determines the sample flow. For this campaign, the
total flow was set to values ranging from 6 to 9 L min
The CASPOL (Droplet Measurement Technologies, Inc.) is a prototype
particle-by-particle counter. Laser light (680 nm) is scattered by single
particles entering the CASPOL and detected by three detectors that give
information about the optical properties: a forward scatter detector, a
backward scatter detector with a parallel polarized filter, and a backward
scatter detector with a perpendicular polarized filter. Particles are sized
according to the intensity of light, which reaches the CASPOL's forward
scatter detector, as in a traditional OPC. The forward scattering detector
of the CASPOL registers particles on an individual basis and sorts those
particles into a series of size bins ranging from 0.6 to 50
The depolarization ratio derived from CASPOL measurements is defined as
follows (Glen and Brooks, 2014):
The second phase of FIN-02 took place at the Institute of Meteorology and
Climate Research: Atmospheric Aerosol Research (IMK-AAF) facility at the
Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany (DeMott et
al., 2017). Two specialized chambers at KIT were used in this campaign: the
Aerosols Interaction and Dynamics in the Atmosphere (AIDA) chamber and the
Aerosol Preparation and Characterization (APC) chamber. The AIDA chamber can
be used to simulate atmospheric conditions that give rise to cloud particle
formation and growth and has been used in many previous campaigns and
instrument intercomparisons to examine the ice nucleating ability of various
aerosols (Amato et al., 2015; Schnaiter et al., 2016; Wagner et al., 2015;
DeMott et al., 2011). The AIDA chamber is a three-story, 84 m
During the campaign groups from 22 institutions sampled both the AIDA and APC chambers using a variety of online and offline ice nucleation measurement techniques. For verification of the TAMU CFDC-CASPOL measurements and new analysis method, we compare our results to the measurements of the CSU CFDC. In order to test the CASPOL detector response to ice and non-ice particles, auxiliary measurements of olive oil droplets, ambient aerosols, and homogeneously frozen ice crystals are also evaluated and compared to the TAMU CFDC-CASPOL heterogeneous nucleation data collected during FIN-02.
CFDC-CASPOL data are sorted into 1 min segments in order to achieve a sufficient sample volume detected by the CASPOL. Temperature, pressure, sample, and sheath flows are used to determine a standard temperature and pressure (273 K, 1013.5 mb) sample volume, which is used to convert the raw count of particles in each 1 min segment to a concentration. Occasionally ice particles may detach from the ice-coated walls. To account for this, a filter is placed upstream of the sample inlet in order to determine background signal of the CFDC chamber. The background period that is closest to a given 1 min sample period is applied by subtracting that background concentration from the total concentration measured by the CASPOL at the sample time.
The traditional analysis method counts INPs based on a nominal size cut of 2
There are several limitations to the traditional analysis method used to
process CFDC data, which relies on size alone to differentiate ice from
water particles (as described in Sect. 2.3). As previously mentioned,
supercooled water droplets may form in the chamber in conditions
supersaturated with respect to water (SS
WDBT is a common issue in continuous flow ice nucleation instruments,
although the point at which WDBT occurs varies between instruments of
differing dimensions and even as a function of operating conditions
(especially temperature) within a single instrument (Rogers et al., 2001;
DeMott et al., 2015; Garimella et al., 2016). CFDCs in use today are
custom-built instruments which vary in physical dimensions and choice of
detector, although all operate under the same basic principles. Due to the
combination of different chamber dimensions, flow rates, operating conditions
(temperature and supersaturation) in the growth and evaporation regions
within the instrument, and the choice of detector and size cutoff, WDBT
varies from instrument to instrument. In some cases, it
can be difficult to determine when WDBT is occurring; if the instrument is
unintentionally operated at supersaturations above WDBT, droplets will be
miscounted as ice crystals. Even within a single instrument, specific
conditions of WDBT vary with operating temperature, the ambient humidity, the
hygroscopicity and the size of sample aerosols, and the sample flow, which
determines the residence time in the instrument. Typically, in the TAMU CFDC
the onset of WDBT occurs at 3 to 4 % SS
In the traditional analysis, any aerosols larger than the nominal size cut
are miscounted as INPs. Operation with an upstream impactor reduces this
problem. However, depending on the flow, 1 to 10 % of particles larger
than 2
Measurements were taken with the CASPOL independent of the CFDC to provide instrument response to various types of particles, which may coincidently reach the detector during CFDC-CASPOL operation.
One population of interest is water droplets. The Vibrating Orifice Aerosol
Generator (VOAG) (TSI, Inc., model 3450) was used with olive oil solutions to
produce monodisperse spherical droplets of chosen sizes as a proxy for water
droplets that form in the CFDC. Though the index of refraction of olive oil
(1.44 to 1.47) is slightly higher than water (1.33) (Hecht and Zajac, 2002),
these droplets are a reasonable approximation for the depolarization ratio
signal of water droplets because they are uniform spheres. As reported in
Glen and Brooks (2013), the uncertainty in sizing due to differences in the
complex refractive indices of oil and water are up to 30 % based on a
comparison of VOAG oil droplet calibrations of CASPOL to water-based
calibrations performed by the manufacturer. For this project, droplets were
generated with the diameters of 2
For VOAG droplet generation, a separate olive oil and 2-propanol solution is
prepared for each desired size. The VOAG's vibration frequency and
dispersion and dilution flows are set according to computed specifications
as detailed in the VOAG manual and as previously performed (Glen and Brooks,
2013, 2014). Downstream of the VOAG, the sample droplets travels through a
charge neutralizer (Aerosol Neutralizer 3054A, TSI Inc.) to prevent particle
loss since charged particles tend to be attracted to the walls of sample
tubing. Following the neutralizer, sample flow is split between flow to the
CASPOL, controlled by a mass flow controller and a Gast air pump on the
downstream side, and a dump line which allows for excess flow generated from
the VOAG to be expelled from the system. For each size, data are collected
for roughly 15 min during which approximately 10 000 droplets are
sampled. It was observed that a mode of small (submicron diameter) residual
2-propanol do not evaporate but remain in the sample flow and are detected
by the CASPOL. For this reason, all particles less than 1
The CASPOL's response to a second population of interest, ambient aerosol, was also evaluated for the new analysis method. Aerosol was sampled at the Storm Peak Laboratory (SPL) in Steamboat Springs, CO during the third phase of FIN-03 in September 2015. The use of a diverse aerosol population is necessary to ensure that the new analysis method be successful at discriminating ice crystals in the CFDC from a wide range of aerosols. SPL is an ideal sampling location because the aerosol population comes from many sources including mineral dust, organics from deciduous and coniferous forests, biomass burning aerosols that have been transported from forest fires in the western United States, and sulfates that are produced by two coal burning power plants that are located approximately 50 and 100 km from the laboratory. Ambient aerosol sampling at SPL was accomplished by connecting the CASPOL directly to an ambient sample inlet in the laboratory for a total time of 92 h over a 7-day period.
Thirdly, a population of ice crystals was needed for the new method.
CFDC-CASPOL measurements were taken under conditions that approached those
needed for homogeneous freezing, thus generating higher concentrations of ice
crystals in the absence of activated liquid droplets. These measurements are
detailed in Glen and Brooks (2014). For these measurements, the sample flow was
conditioned with a pre-cooler, which was set to
For clarity, the CASPOL measurements of the VOAG droplets, ambient aerosols collected at SPL, and ice crystals generated in homogeneous conditions are referred to as droplet, aerosol, and ice crystal training datasets, respectively.
Optical signatures of training data populations: ice
crystals
This analysis used optical differences between ice crystals, droplets, and aerosols in order to identify and quantify ice crystals that form in the CFDC. The CASPOL has been used previously to discriminate between different aerosol populations using an empirical tool known as an optical signature (Glen and Brooks, 2013). In an analogous method, optical signatures produced from CALIPSO satellite backscatter and depolarization data have been used to identify cloud phase (Hu et al., 2009).
In Fig. 1a–c, CASPOL optical signatures for ice, droplet and aerosol
training data are shown, respectively. The
signatures show depolarization ratio (as defined in Eq. 2) versus total
backscatter. The signatures are generated by defining a 50
In Fig. 1d–f, optical signatures normalized with respect to forward scatter,
Consistent with the findings of Glen and Brooks (2013), CASPOL optical signatures can be used as an empirical tool to detect differences in the bulk optical properties of different particle populations. However, in order to design a new analysis method, it is necessary to gain a quantitative understanding of how the CASPOL detects single particles as opposed to bulk populations of particles.
Model calculations can provide insight on how particles depolarize light in
the CASPOL. To perform model calculations, we first must define the relation
between the CASPOL depolarization ratio (Eq. 2) and the scattering phase
matrix. The CASPOL laser emits an incident beam that propagates along the
To compute the scattering phase matrices of these models with specific sizes at CASPOL wavelength, we apply so-called improved geometric optics method (IGOM) for particle with relatively large size and the invariant imbedding T-matrix method (II-TM) for particles with relatively small sizes (Yang and Liou, 1996; Bi et al., 2013; Bi and Yang, 2014; Johnson, 1988). The combination of these two methods is chosen because of the different size parameters of the aerosol and ice crystal populations. The T-matrix method is a highly accurate method for calculating scattering properties of atmospheric particles (Koepke et al., 2015; Brooks et al., 2004). However, it becomes impractical for large particles due to its excessive demands on the computational power. In contrast, the IGOM is accurate over the range of particle sizes over which the particle size to be much larger than the incident wavelength (Xu et al, 2017).
Three idealized ice crystal habits were modeled: a hexagonal column, a hexagonal plate, and a droxtal. These shapes represent generalizations of common ice crystal habits (Bailey and Hallett, 2009). An idealized dust-like particle with fractal facets was used to model aerosols (Liu et al., 2013). These particles are nonspherical and thus will yield different measured depolarization ratios depending on their orientation in the CASPOL. The model provides the mean depolarization ratio over all orientations with respect to the laser beam. In contrast, the theoretical depolarization of water droplets is zero at all sizes.
Figure 2 shows the depolarization ratios as a function of size for the three
ice crystal habits, dust-like aerosol, and water droplets. For hexagonal
columns, hexagonal plates, and droxtals, the depolarization ratio increases
from less than 0.05 to as high as 0.35 as the optical diameter increases from
0.5 to 8
Depolarization ratio vs. diameter for modeled particles: droplets, aerosols, hexagonal column ice crystals, hexagonal plate ice crystals, and droxtals.
Similar to ice crystals, depolarization ratios of the modeled dust aerosols
increase with particle diameter. At most sizes, the aerosol data fall within
the range of depolarizations ratios reported for the three ice crystal shapes.
This indicates that the use of depolarization ratios will not make an
improvement in differentiating between aerosols and ice crystals.
Fortunately, the traditional CFDC method incorporates the use of an impactor
to physically remove aerosols greater than 1.75
In this section, we empirically test the assertion that the CASPOL depolarization ratio can be used to discriminate ice crystals from aerosols and water droplets. To accomplish this, the training datasets of droplets, aerosols, and ice crystals shown above (Fig. 1) are examined further. The lognormal size distributions (shown as a percent of population) observed by the CASPOL for the droplet, aerosol, and ice crystal training data are shown in Fig. 3a. Each VOAG size in the droplet training dataset is treated as a separate population and plotted as a separate line in the figure. As seen in Fig. 1a, the size distributions of droplets, aerosols, and ice crystals overlap. This demonstrates the primary disadvantage to using particle diameter as the sole criteria to identify ice crystals.
For each training dataset, the frequency distribution of depolarization
ratio reported as a percentage of the total particles in the dataset is
shown in Fig. 3b. As seen in the figure, droplets have depolarization ratios
up to 0.3. Therefore, we visually assign 0.3 as the nominal depolarization
threshold cutoff for differentiating between ice crystals and non-ice
particles. The choice on 0.3 is further evaluated in Sect. 3.7.
Unfortunately, a small percentage of aerosols do have depolarizations greater
than this threshold. However, since aerosols with sizes above
1.75
In Fig. 3c, the percent of particles that achieve a depolarization ratio
As discussed in Sect. 2.4, WDBT can be difficult to identify when relying on
the traditional analysis method. To better determine periods when WDBT
conditions are occurring in the CFDC, particle size distributions and mean
depolarization ratio can be considered. Here, the onset of water droplet
breakthrough is analytically defined as the time period where a continuous
size distribution extends from the small size bins past the 2
In Fig. 4, the CFDC begins sampling at relatively low supersaturations.
During this time period, the few ice crystals nucleate in the chamber as
particles are mostly larger than 5
In this section, the frequency distribution of depolarization ratios of
particle populations present in the CFDC are investigated for comparison to
the training datasets. First, all data from the FIN-02 campaign were
classified as WDBT conditions or normal operating conditions. Then particle
diameters were used to determine the particle type. Aerosol particles during
the FIN-02 campaign were generally smaller than 2
Date and time (CET), the composition of aerosol sampled, and the
CFDC operating temperature (
Figure 5 shows the depolarization ratio distributions of the CFDC populations
interpreted to be ice crystals, water droplets, and aerosols. For the
analysis completed to produce Fig. 5, 19 normal operating condition periods
and 17 WDBT periods with variable time lengths were classified. Ice crystals
achieve higher depolarization ratios than water droplets and aerosol;
13.5 % of ice crystals in the CFDC achieve a depolarization ratio larger
than 0.3, compared to 1.5 % percent of water droplets and 0.3 % of
aerosols. These values are very similar to the percentages of training data
particles that achieve a depolarization ratio greater than 0.3. Ice crystals
achieve depolarization ratios larger than 0.3 more than 10 times more
frequently than aerosol or water droplets. One interesting feature in the
CFDC observations are the two Snomax® cases
(cases 13 and 14 in Table 1 at
Frequency distribution of depolarization ratios for CFDC
populations: ice crystal periods (19 periods classified), WDBT periods (17
periods classified), and aerosol periods (19 periods classified). Mean
temperatures of periods included range from
Mean depolarization ratios vs. particle diameter for modeled and observed particles. Observed error bars provide a standard deviation on the depolarization ratios of particles at each reported size. No error bars are reported for model calculations.
In this section, modeled and observed particles discussed in the preceding
results section are compared. Figure 6 shows modeled and observed mean
depolarization ratios of particles as a function of diameter. The modeled
results (green) are shown with the same shape conventions as Fig. 2. Observed
results include training (blue shapes) and CFDC (red shapes) ice crystals
(pentagrams), aerosols (squares), and droplets/WDBT particles (circles).
Observed values are accompanied by error bars representing the standard
deviation of depolarization ratios of particles at the respective diameters
plotted. The CFDC populations presented here include particles sampled from
all FIN-02 experiments, and not only those discussed in Sect. 3.5 above. The
same conventions are used here to process these particles: CFDC ice crystals
are those larger than 2
In Fig. 6, both the model calculations and the observed results indicate that
ice crystals have higher mean depolarization ratios than water droplets and
aerosols on average at diameters above 5
In Sect. 3.5, the complex WDBT population was discussed. WDBT particles
consist of both water droplets and ice crystals. Diffusional growth theory
dictates that ice crystals will grow to larger sizes in the CFDC than water
droplets (Pruppacher and Klett, 2010). Figure 6 shows an increase in the
depolarization ratio from
There are significant differences between modeled particles and their
observed counterparts. Observations show water droplets depolarizing light,
but the observed mean depolarization ratio of water droplets is almost zero
(
The observations are qualitatively consistent with the model in that ice crystals depolarize more light than water droplets and aerosols. However, the discrepancies between the observed and modeled mean depolarization ratios and the wide distributions of observed depolarization ratios dictate that we cannot rely on a mean modeled depolarization ratio to identify and quantify ice crystals in the CFDC. Rather than designing a theoretical model based on model calculations, we move forward by designing an empirical model based on the CASPOL observed signals.
The results above show that counting ice crystals in the CFDC using
depolarization ratio can be challenging since only
To obtain a more accurate INP concentration, we used a linear regression model to fit the number of particles with depolarization ratios above the threshold (0.3) to the number of ice crystals in the CFDC. Linear regressions are frequently used to interpret the signal(s) of new instrumentation or new techniques by validating the signal with a “ground truth” measurement (e.g., Li et al., 2016; Zimmerman et al., 2017; Brunner et al., 2016; Choi et al., 2016).
In our case, ground truth is provided by the aerosol-only (Storm Peak), ice-only (homogeneous), and droplet-only (VOAG) training data populations discussed above. To create a linear regression model which relates the number of particles with depolarization ratios above the threshold (0.3) to ice crystals concentration, a CASPOL dataset containing a known number of ice crystal and non-ice particles is required. Here, aerosol-only, ice-only, and droplet-only data are added together to create artificial datasets in which the number of each type of particle is known. The aerosol, ice crystal, and droplet training datasets are randomized in time before particles are selected from each population to create the simulated datasets. (This analysis is possible because the data point for each individual particle detected by the CASPOL includes forward scattering, backward scattering, and depolarization).
In total 50 simulated datasets are generated. Table S1 in the Supplement
50 the concentration of ice crystals, water droplets, and aerosols in
each dataset. Each simulated dataset is divided into 120 segments, containing
a number of ice crystals ranging from 0 to 350. The number of water droplets
and aerosols are constant throughout all segments in a single dataset. All 50
datasets contain segments with the same number of randomly selected ice
crystals. The upper range of
The quantity of aerosols and water droplets in each dataset is determined by
a multiplication factor
Application of depolarization ratio method on three CFDC runs.
Aerosol composition and temperature are labeled in the title.
As discussed above, particles in the INP datasets smaller than the CFDC size
cut of 2
Figure 7 shows the
The linear regression for the 0.3 threshold is provided in Eq. (9):
Individual cases of “ice-only” and “WDBT” INP concentration comparisons with the traditional size cut and depolarization ratio methods. Error bars report the CFDC-CASPOL counting error of 39 %.
INP concentrations were obtained using both the depolarization ratio method
(Eq. 9) and the traditional method on CFDC data collected during the FIN-02
campaign. Three representative CFDC runs of
Snomax® at
Figure 9 summarizes the mean concentrations obtained through the traditional
and new method for all periods when the CFDC was operational during FIN-02.
In total, 27 ice-only periods and WDBT cases are included. A description
of the date and time, aerosol composition, and temperature of each case is
detailed in Table 1. In cases 24, 25, and 26 WDBT did not occur, so no data
are reported. The error bars report the CFDC-CASPOL uncertainty in INP
concentration, which is 39 % based on combined instrumental uncertainties
(Glen and Brooks, 2014, 2013), Fig. 9 shows that in all but 4 cases out of 27
(cases 2, 7, 9, and 23), the mean concentration of the new analysis method is
in agreement with traditional analysis method for the ice-only periods.
Figure 9 also shows that only 9 out of 24 WDBT cases have statistical
agreement between the new and traditional analysis method. At the onset of
WDBT, the impact of water droplets on the INP concentration determined by the
2
To summarize the comparison between our new method and the traditional method
during the ice-only periods, the INP concentrations determined using the
traditional method vs. new method are plotted in Fig. 10. Each point on the
plot represents data for a 1 min segment. The black line in Fig. 10 is a
1 : 1 line. Since the analysis used to generate Fig. 10 only uses data
collected under normal operating conditions (not WDBT), the traditional
concentration can be considered ground truth. The data closely follow the
1 : 1 line, confirming that the depolarization ratio can be used to
reliably retrieve an INP concentration when no or few water droplets and/or aerosols
are larger than 2
Traditional INP concentration vs. new INP concentration with 1 : 1 line for “ice-only” periods.
TAMU CFDC versus CSU CFDC comparison:
The MPE of the method is dependent on the INP concentration.
Due to the high detection limit of concentration for the CASPOL, the MPE of the new method is
Based on Fig. 10, the new analysis method provides very accurate results when
INP concentrations are greater than 50 000 L
As a final test of the new method during water droplet breakthrough periods,
a reliable measure of INP at higher supersaturation conditions (when the TAMU
CFDC is experiencing WDBT) is needed. Due to design and flow rate
differences, the CSU CFDC does not experience the
onset of WDBT until higher supersaturations than the TAMU CFDC, up to
108 % or higher depending on temperature (DeMott et al., 2015). Thus,
inclusion of the CSU data provides a test of the new method at higher
relative humidities under conditions when data obtained through the TAMU
CFDC's traditional method is spurious due to water droplet breakthrough.
Figure 11 shows the comparison of the TAMU CFDC's traditional (2
This paper presents a new analysis method that uses the depolarization ratio
to quantify INP concentrations in the TAMU CFDC in terms of single-particle
depolarization measured by the CFDC's CASPOL detector. Ice crystal, droplet
and aerosol training populations were used to build simulated datasets with
known concentrations of aerosols, droplets, and ice
crystals, respectively. The simulated datasets
were evaluated, assuming a depolarization ratio
threshold of 0.3, above which all particles were classified as ice crystals.
A linear regression fit between ice crystal concentration and number of
particles detected greater than or equal to the depolarization ratio
threshold of 0.3 was determined and applied to CFDC data collected during the
FIN-02 campaign. Concentrations of INP determined by the new analysis method
agree reasonably well with the traditional method (ice detection by size
segregation) under normal operating temperatures and supersaturations (with
no large water droplets present) with a mean percent error of
The data used in this study will be made available in a future publication (DeMott et al., 2017).
The authors declare that they have no conflict of interest.
The authors acknowledge primary support from the National Science Foundation, grant no. ECS-1309854. Ezra J. T. Levin, Kaitlyn J. Suski, and Paul J. DeMott acknowledge support from NSF grant no. AGS-1358495. The FIN-02 and FIN-03 campaigns were supported by NSF grant no. AGS-1339264 and by the US Department of Energy's Atmospheric System Research, an Office of Science, Office of Biological and Environmental Research program, under grant no. DE-SC0014487. Special thanks to Daniel Cziczo and Ottmar Möhler for their roles in coordinating the FIN-02 and FIN-03 studies and to all research teams involved in making those studies possible. Edited by: Mingjin Tang Reviewed by: four anonymous referees