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  • View in gallery
    Fig. 1.

    Differences in area ratios α of particles in liquid phase between M-PACE and ISDAC as functions of cumulative Dmax and focus. Each point represents α difference between M-PACE and ISDAC with particles larger than the given size and focus.

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    Fig. 2.

    The frequency of occurrence of frames with the indicated number of particles per frame (circles) measured by the CPI in (left) ice-, (center) mixed-, and (right) liquid-phase conditions during (top) M-PACE and (bottom) ISDAC. The calculated occurrence frequency of these multiparticle frames (diamonds) with error bars that are based on a random distribution of particles following Poisson statistics with the given number concentrations is also plotted. The E+XX codes associated with the n values indicate that the preceding number should be multiplied by 10+XX.

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    Fig. 3.

    Mean and standard deviation of area ratio α as a function of Dmax (0 < Dmax < 200 μm, with 10-μm interval) in (left) ice-, (center) mixed-, and (right) liquid-phase conditions during M-PACE (black) and ISDAC (gray). Each row corresponds to the indicated number of particles per frame. Each point is plotted when at least five particles were present in a given size range, phase, and particles per frame. The CPIView thresholds of focus > 45 and cutoff = 0 were used to generate the statistics.

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    Fig. 4.

    Mean and standard deviation of (top) α, (top middle) Dmax, (bottom middle) LWC (filled circles) and IWC (times signs), and (bottom) total number concentration of liquid (filled circles) and ice (times signs) as a function of the number of particles per frame in (left) ice-, (center) mixed-, and (right) liquid-phase conditions during M-PACE (black) and ISDAC (gray). Particles with 35 < Dmax < 60 μm measured by the CPI were used to determine α and Dmax. Each point is plotted when at least five particles were present in a given phase in each frame. The CPIView thresholds of focus > 45 and cutoff = 0 were used to generate the statistics. Particles measured by forward-scattering probes, optical array probes, and bulk water content probes were used to determine the water contents and concentrations as discussed in the text.

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    Fig. 5.

    Examples of cloud particles imaged by the CPI in mixed-phase clouds (a) at 2127:40 UTC 6 Oct 2004 (M-PACE) at an altitude of 1.2 km, temperature of −7.4°C, and LWC/TWC = 0.178; (b) at 2200:40 UTC 8 Oct 2004 (M-PACE) at 0.9 km, −8.7°C, and LWC/TWC = 0.801; (c) at 2344:30 UTC 24 Apr 2008 (ISDAC) at 2.7 km, −11.8°C, and LWC/TWC = 0.120; and (d) at 2232:17 UTC 1 Apr 2008 (ISDAC) at 1.3 km, −3.4°C, and LWC/TWC = 0.864.

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    Fig. 6.

    Magnified images of four particles shown in Fig. 5. The Dmax (μm) and α values are embedded in each image; the actual Dmax (white arrow) and the circumscribed circle (dashed line) used to calculate α are also shown in each image.

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    Fig. 7.

    Examples of ice crystals imaged during M-PACE and ISDAC as a function of α. Values of Dmax (μm) and α are embedded in each image.

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    Fig. 8.

    The αmean as a function of LWC/TWC for all mixed-phase clouds sampled during M-PACE; αmean is computed by averaging α for all particles with 35 < Dmax < 60 μm in the given 10-s intervals.

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    Fig. 9.

    Fraction of particles with 35 < Dmax < 60 μm with given α (white space indicates α < 0.8) to total number of particles with 35 < Dmax < 60 μm as a function of LWC/TWC for all mixed-phase clouds sampled during M-PACE.

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    Fig. 10.

    As in Fig. 8, but for all data obtained in mixed-phase clouds during ISDAC.

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    Fig. 11.

    As in Fig. 9, but for all data obtained in mixed-phase clouds during ISDAC.

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    Fig. 12.

    Normalized frequency distribution of rel computed for all mixed-phase clouds sampled during M-PACE and ISDAC.

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    Fig. 13.

    As in Figs. 8 and 10, except different plots correspond to relation between αmean and LWC/TWC from M-PACE and ISDAC for (a) T < −12°C and rel < 5 μm, (b) T ≥ −12°C and rel < 5 μm, (c) T < −12°C and rel ≥ 5 μm, and (d) T ≥ −12°C and rel ≥ 5 μm.

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    Fig. 14.

    Average mass distribution measured by either CDP or FSSP in mixed-phase conditions during ISDAC and M-PACE for T < −12°C and rel < 5 μm (dashed line), T ≥ −12°C and rel < 5 μm (solid line), T < −12°C and rel ≥ 5 μm (solid line with squares), and T ≥ −12°C and rel ≥ 5 μm (dashed line with squares).

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Small Cloud Particle Shapes in Mixed-Phase Clouds

Greg M. McFarquharDepartment of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Junshik UmDepartment of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Robert JacksonDepartment of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Abstract

The shapes of cloud particles with maximum dimensions Dmax between 35 and 60 μm in mixed-phase clouds were studied using high-resolution particle images collected by a cloud particle imager (CPI) during the Mixed-Phase Arctic Cloud Experiment (M-PACE) and the Indirect and Semi-Direct Aerosol Campaign (ISDAC). The area ratio α, the projected area of a particle divided by the area of a circle with diameter Dmax, quantified particle shape. The differing optical characteristics of CPIs used in M-PACE and ISDAC had no effect on derived α provided that Dmax > 35 μm and CPI focus > 45. The fraction of particles with 35 < Dmax < 60 μm with α > 0.8 increased with the ratio of liquid water content (LWC) to total water content (TWC). The average αmean of small particles in each 10-s interval in mixed-phase clouds was correlated with LWC/TWC with a correlation coefficient of 0.60 for M-PACE and 0.43 for ISDAC. The stronger correlation seen during M-PACE was most likely associated with the presence of more liquid droplets that were larger than the CPI detection threshold contributing to αmean; the modal effective radius was larger (11 vs 6 μm), and drops with D > 35 μm had concentrations during M-PACE that were 6 times as large as those of ISDAC. This study hence suggests that area ratio can be used to identify the phase of particles with 35 < Dmax < 60 μm and questions the assumption used in previous studies that all particles in this size range are supercooled droplets.

Corresponding author address: Prof. Greg McFarquhar, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S. Gregory Street, MC 223, Urbana, IL 61801. E-mail: mcfarq@atmos.uiuc.edu

Abstract

The shapes of cloud particles with maximum dimensions Dmax between 35 and 60 μm in mixed-phase clouds were studied using high-resolution particle images collected by a cloud particle imager (CPI) during the Mixed-Phase Arctic Cloud Experiment (M-PACE) and the Indirect and Semi-Direct Aerosol Campaign (ISDAC). The area ratio α, the projected area of a particle divided by the area of a circle with diameter Dmax, quantified particle shape. The differing optical characteristics of CPIs used in M-PACE and ISDAC had no effect on derived α provided that Dmax > 35 μm and CPI focus > 45. The fraction of particles with 35 < Dmax < 60 μm with α > 0.8 increased with the ratio of liquid water content (LWC) to total water content (TWC). The average αmean of small particles in each 10-s interval in mixed-phase clouds was correlated with LWC/TWC with a correlation coefficient of 0.60 for M-PACE and 0.43 for ISDAC. The stronger correlation seen during M-PACE was most likely associated with the presence of more liquid droplets that were larger than the CPI detection threshold contributing to αmean; the modal effective radius was larger (11 vs 6 μm), and drops with D > 35 μm had concentrations during M-PACE that were 6 times as large as those of ISDAC. This study hence suggests that area ratio can be used to identify the phase of particles with 35 < Dmax < 60 μm and questions the assumption used in previous studies that all particles in this size range are supercooled droplets.

Corresponding author address: Prof. Greg McFarquhar, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S. Gregory Street, MC 223, Urbana, IL 61801. E-mail: mcfarq@atmos.uiuc.edu

1. Introduction

Mixed-phase clouds, in which supercooled water droplets and ice crystals coexist in the same volume of air, occur throughout the troposphere. Although there is ambiguity in the volume used to define where ice and water coexist and consequently in defining exactly what constitutes a mixed-phase cloud, many remote sensing (e.g., Shupe et al. 2001; Intrieri et al. 2002a,b) and in situ (e.g., Cober et al. 2001b; Lawson et al. 2001, Fleishauer et al. 2002; Korolev et al. 2003; Field et al. 2004; McFarquhar et al. 2007b, 2011) observations have established that mixed-phase clouds are prevalent. Because ice has a lower equilibrium vapor pressure than water, ice crystals would be expected to grow at the expense of supercooled water drops in water subsaturated environments because of the Bergeron–Findeisen mechanism. This process should lead to the quick glaciation of mixed-phase clouds. Mixed-phase clouds persist for long periods of time, however, especially over the Arctic (Pinto 1998; Zuidema et al. 2005; Verlinde et al. 2007; Shupe 2011).

Modeling studies suggest that they persist because of a delicate balance between cloud-top radiative cooling, microphysical heating, ice sedimentation, and large-scale forcing (Pinto 1998; Harrington et al. 1999), a balance that depends on assumptions about ice fall speeds, single-scattering properties, and primary and secondary ice nucleation mechanisms (Jiang et al. 2000; Harrington and Olsson 2001; Lohmann 2002; Morrison et al. 2003). Information on the sizes, shapes, and phases of cloud particles is needed to improve the representation of such processes in models.

Because mixed-phase clouds are radiatively significant (Dong et al. 2001; Dong and Mace 2003; Zuidema et al. 2005) and thermodynamic phase affects cloud radiative properties (Sun and Shine 1994), knowledge of phase is critical for understanding the role of mixed-phase clouds in the climate system. The phases of small particles with maximum dimensions Dmax of less than 60 μm are especially important because particles in this size range can contribute more than 50% to the total projected area and hence to the extinction of Arctic stratus (McFarquhar et al. 2007b). Knowledge of the phase of small cloud particles can also be important for identifying nucleation processes occurring in mixed-phase clouds.

When calculating the single-scattering properties or developing model representations of mixed-phase clouds, it has been commonly assumed that small particles are supercooled water droplets and larger particles are ice crystals (McFarquhar and Cober 2004; McFarquhar et al. 2007b). This assumption has been based on visual inspection of particle images, analysis of the shape of size distributions measured by forward-scattering probes detecting particles with Dmax of less than 50 μm, and analysis of icing-probe detector data. Because small ice particles are quasi spherical (e.g., Nousiainen and McFarquhar 2004) and cloud droplets are spherical, the phase of particles can be determined by examining the shapes of particles. In fact, algorithms that are based on particle shape have been used to determine the phases of larger ice particles measured by optical array probes (e.g., Cober et al. 2001b) and of smaller ice particles measured by the Small Ice Detector (SID; Field et al. 2004).

This paper explores the degree to which high-resolution (2.3 μm) observations of cloud particles with 35 < Dmax < 60 μm imaged by a cloud particle imager (CPI) can be used to differentiate whether small cloud particles are composed of water or ice using data collected during two recent Arctic field campaigns, the Mixed-Phase Arctic Cloud Experiment (M-PACE; Verlinde et al. 2007) and the Indirect and Semi-Direct Aerosol Campaign (ISDAC; McFarquhar et al. 2011). These data are also used to explore the relationship between cloud particle shape/phase and bulk microphysical properties, such as the ratio of liquid water content (LWC) to total water content (TWC). This approach expands upon the earlier study of Field et al. (2004) who showed that particle sphericity measured by a SID could be used as a proxy for particle phase by using high-resolution particle images in the analysis, by using more reliable measures of bulk mass content, and by exploring reasons for the existence or absence of correlations between particle shape/phase and bulk microphysical properties. A second-generation SID (Cotton et al. 2010) also uses spatial scattering from individual particles smaller than the resolution of many optical imaging probes to define particle shape, from which particle phase is assessed, and a polar nephelometer gives scattering patterns from single cloud particles (Shcherbakov et al. 2006). Such probes are not always available during field experiments, however.

The remainder of the paper is divided as follows. Section 2 describes the data collected during M-PACE and ISDAC used in the analysis. Section 3 describes the method for determining the shape and phase of small cloud particles in mixed-phase clouds from CPI images. Section 4 describes the shapes of small cloud particles observed during M-PACE and ISDAC and their dependence on the ratio of LWC to TWC, and it identifies reasons why the shapes of small cloud particles exhibit different correlations with LWC/TWC under different conditions. The principal findings and their implications for determination of mixed-phase cloud properties are described in sections 5 and 6.

2. M-PACE and ISDAC

A focused set of ground-based and aircraft in situ observations of mixed-phase clouds was collected over the North Slope of Alaska between September and October of 2004 during M-PACE to advance the understanding of dynamical and microphysical processes in mixed-phase clouds, including radiative transfer through clouds (Verlinde et al. 2007). Although these data proved invaluable for process-oriented investigations and evaluation of model simulations and remote sensing retrievals, the data were limited in that observations were only obtained in pristine fall conditions and in that there was a lack of reliable aerosol and small ice crystal measurements. Thus, the follow-on campaign ISDAC was conducted over the North Slope of Alaska in March and April of 2008, with a major goal being to determine the extent to which different properties of Arctic aerosol during the spring relative to the autumn produce differences in the cloud microphysical and macrophysical properties and the surface energy budget (McFarquhar et al. 2011). Data from both campaigns are used in this analysis.

Data collected by the University of North Dakota (UND) Citation aircraft during M-PACE and by the National Research Council of Canada (NRC) Convair-580 aircraft during ISDAC are used. The UND Citation collected 35 h of data in boundary-level mixed-phase stratus on 13 different flights, including flights profiling single and multilayer clouds (Verlinde et al. 2007). The NRC Convair 580 collected data on 12 different days, again in boundary-layer stratus occurring in both single and multiple layers (McFarquhar et al. 2011). As expected, more polluted conditions were generally encountered during ISDAC than during M-PACE. There were considerable variations in aerosol loading among ISDAC flights (Earle et al. 2011), however, with some flights classified as pristine and others as polluted, with a 300-cm−3 threshold aerosol concentration (cf. Peng et al. 2002) used to divide the categories.

Data were collected by 20 instruments that measured size-resolved and bulk cloud properties during ISDAC and by 8 instruments during M-PACE. There is sufficient information to compute the bulk cloud properties from both experiments. Table 1 summarizes the cloud instrumentation from both projects used in this analysis. McFarquhar et al. (2007b) describe the manner in which the data were processed and cloud parameters were determined using M-PACE data. Similar procedures were performed to process the data and determine cloud parameters using ISDAC data (Jackson et al. 2012).

Table 1.

Summary of operating principles and measured parameters of microphysical probes used in the presented analysis. Here, PSD is particle size distribution and Nt is number concentration.

Table 1.

Because Korolev and Isaac (2005), Korolev et al. (2011), Lawson (2011), and others have recently suggested that measurements of cloud particles can be biased by the shattering of large ice crystals on probe tips and inlets, it is important to discuss the techniques used to minimize the presence of artificially generated small particles in the data used. Although new tips designed to mitigate shattering were not extensively used during M-PACE and ISDAC, data from the optical array probes [i.e., two-dimensional cloud probe (2DC), two-dimensional precipitation probe (2DP), cloud-imaging probe (CIP), and two-dimensional stereo probe (2DS)] were processed using algorithms that filtered out shattered artifacts by rejecting particles with interarrival times of less than 10−4 s as in Field et al. (2006). The forward-scattering spectrometer probe (FSSP) and cloud droplet probe (CDP) used during M-PACE and ISDAC, respectively, also did not have shatter-mitigating tips and hence may measure artificially high concentrations in ice (e.g., Gayet et al. 1996; Field et al. 2003, 2006; McFarquhar et al. 2007a). Although interarrival times for the FSSP and CDP were not recorded, the influence of shattered particles on measured size distributions is used to identify cloud phase because peaked distributions signify the presence of liquid drops (Cober et al. 2001b) for which shattering is not believed to be a problem.

Figure 2 of McFarquhar et al. (2007b) shows the scheme used to identify the phase of each 10-s cloud penetration during M-PACE. The Rosemount Icing Detector (RICE) was the primary probe used to detect the presence of supercooled water. When supercooled water droplets collide and accrete on the surface of a vibrating cylinder, corresponding changes in its vibrating frequency induce a voltage change (Mazin et al. 2001; Cober et al. 2001a), signifying the presence of liquid. Because McFarquhar and Cober (2004) and McFarquhar et al. (2007b) showed that forward-scattering probes with inlets or conventional tips measure flat distributions in the presence of ice particles and peaked distributions in the presence of small liquid drops, information about the breadth of the distribution is also used to identify phase. Data from other probes were also used in the algorithm as indicated in Fig. 2 of McFarquhar et al. (2007b). A similar phase identification scheme was developed for ISDAC with the exceptions that the CDP was used instead of the FSSP for determining the number concentrations of small cloud particles and that the number concentrations of larger-sized particles were determined from a combination of the 2DS, 2DC, and 2DP rather than exclusively from the 2DC as in McFarquhar et al. (2007b). Only cloud penetrations identified as mixed phase are used in the analysis presented in this paper.

The TWC was measured by a counterflow virtual impactor (CVI) during M-PACE and by a counterflow spectrometer and impactor probe (CSI) and deep-cone Nevzorov probe during ISDAC. The use of the CSI, CVI, and deep-cone Nevzorov probe improves upon the use of the earlier shallow-cone Nevzorov probe in the Field et al. (2004) study of mixed-phase conditions because the bouncing of ice crystals out of the shallow cone (Korolev et al. 2008) causes an underestimate of TWC. Electrical interference caused by an unknown factor produced an uncorrectable baseline offset in the Nevzorov probe TWC during ISDAC for some time periods, however. The CSI also suffered from an occasional spurious uncorrectable baseline offset and sometimes did not detect TWC in the presence of 2DS, 2DC, 2DP, or CIP images. Thus, differences in the CSI and Nevzorov probe TWCs sometimes exceeded an order of magnitude. Jackson et al. (2012) used time periods in ice-phase clouds when the CSI and Nevzorov TWC differed by less than 50% to show that habit-dependent mass–Dmax relations applied to size–shape distributions derived from a combination of CPI, 2DS, 2DC, and 2DP data provided the best agreement with the bulk TWCs. McFarquhar et al. (2007b) also derived mass–Dmax relations that gave TWCs estimated from the 2DC and high-volume precipitation sampler (HVPS) data most consistent with the bulk TWCs in ice-phase conditions. The mass contained in ice crystals was thus determined by applying the relevant mass–Dmax relations to the measured size distributions.

The bulk LWC was measured by a King probe during each project. An FSSP measured droplets with 3 < Dmax < 50 μm during M-PACE. Several forward-scattering probes measured droplets in this size range during ISDAC. The CDP is used to characterize these droplets because postproject investigations showed that it gave sizes that were the most consistent with those of the beads used in its calibration. This was confirmed by comparing the bulk LWC measured by the King probe against that derived by integrating the mass contained in the FSSP or CDP size distributions. McFarquhar et al. (2007b) showed that the LWC derived from the M-PACE FSSP size distributions was within 25% of the King LWC for liquid-phase conditions. For ISDAC, a slope of 1.17 was obtained when fitting the CDP LWC as a function of the King probe LWC. In this paper, the LWC is estimated as the bulk value measured by the King probe. The TWC is then merely the sum of the LWC and ice water content (IWC) estimated from the ice size distributions as discussed above. The effective radius of supercooled droplets rel is the third moment of the FSSP or CDP size distributions divided by the second moment. It is questionable whether contributions from liquid drizzle drops with Dmax of greater than 50 μm should be included in the calculation of rel for cloud droplets. Because drops with Dmax of greater than 50 μm were present only 4.1% of the time during ISDAC (Jackson et al. 2012) and 18% of the time during M-PACE (McFarquhar et al. 2007b), the omission of such drops does not have a big impact on the calculation of rel.

3. Determining shape and phase of small cloud particles

High-resolution cloud particle images were obtained using a CPI installed on the UND Citation and on the NRC Convair-580 during M-PACE and ISDAC, respectively. The CPI records images of cloud particles on a 1-million-pixel charge-coupled device (CCD) using a 25-ns pulsed high-power laser that is fired when at least one particle is detected in the sample volume by two lower-powered particle detection lasers shining on photodiode detectors (Lawson et al. 2001).

Version 1 of the CPI was used during M-PACE, and version 2.0 was used during ISDAC. Although the resolution (2.3 μm) and image processing are identical, the two versions have different optical characteristics that affect how quickly the image quality degrades as particles move away from the position of maximum focus, with the particles imaged by version 1.0 becoming more quickly out of focus than those imaged by version 2.0. Although this characteristic also affects the size-dependent sample volume, only the difference in image quality between probes and its impact on morphological measures of particles is considered in this study because only the shape characteristics, and not the concentrations, of particles are examined.

There is no widely accepted lower detection limit for the CPI. Particles smaller than some threshold either may not be detected or there may not be sufficient resolution for characterizing their shapes and differentiating between water and ice. In this study, a threshold for the lower detection limit of the CPI was investigated using images of cloud particles with Dmax of less than 100 μm measured in liquid-phase clouds, which are expected to be spherical in shape. The area ratio, the projected area of a particle divided by a circumscribed circle with diameter Dmax (McFarquhar and Heymsfield 1996; Um and McFarquhar 2011), gives a measure of the sphericity of a particle. The area ratio α of each particle was determined from the raw CPI data using the “CPIView” software developed by the Stratton Park Engineering Company (SPEC), Inc. Figure 1 shows the difference in area ratio of particles measured in liquid-phase clouds during ISDAC and M-PACE as a function of cumulative Dmax and CPI focus. Liquid particles measured by the CPI, version 1.0, used in M-PACE have higher area ratios than those measured by version 2.0 that was used in ISDAC, with differences being more prominent for focus of less than 45. From this analysis, it was concluded that any difference between the optical characteristics of the different versions could be ignored for particles with Dmax > 35 μm and focus > 45. Subsequent analysis is restricted to this subset of particles.

Fig. 1.
Fig. 1.

Differences in area ratios α of particles in liquid phase between M-PACE and ISDAC as functions of cumulative Dmax and focus. Each point represents α difference between M-PACE and ISDAC with particles larger than the given size and focus.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

a. Removing shattered artifacts from CPI data

Although prior studies have shown that shattering may artificially amplify the concentrations of ice crystals measured by forward-scattering probes and optical array probes, there have been no comprehensive investigations on the extent to which crystals imaged by the CPI represent naturally occurring ice crystals or shattered artifacts. To minimize the effects of shattering on cloud properties derived from forward-scattering or optical array probes, two approaches have been used. The first is postprocessing to remove shattered particles (e.g., Cooper 1977; Field et al. 2003, 2006) and the second is a redesign of probe tips (Korolev et al. 2011). Because a redesign of the CPI tips is not within the scope of this study, an attempt was made to identify and remove any shattered particles from the collected CPI data through postprocessing.

Field et al. (2003, 2006) showed that the distributions of particle interarrival times in the sample volumes of a fast FSSP (FFSSP) and of a two-dimensional array probe had bimodal shapes. The first mode, corresponding to the arrival of shattered remnants, occurred at interarrival times of less than 10−3 s for the FFSSP (Field et al. 2003) and less than 10−4 s for the optical array probe (Field et al. 2006). It is not as easy to filter out shattered remnants from CPI data using interarrival times, however, because data are not recorded with such high temporal resolution. For example, the maximum frame rate of the CPI, version 2.0, used in ISDAC is 74 frames per second, which corresponds to a time resolution of only ~10−2 s. Thus, different methods were required for identifying the presence of shattered particles in the CPI data.

Um and McFarquhar (2011) suggested that multiple particles imaged in the same CPI frame were more likely remnants of a large particle shattering on the CPI housing or inlet than a single particle captured in one frame because shattered remnants have shorter interarrival times. They showed that over 98.4% of small ice crystals imaged in tropical cirrus during the Tropical Warm Pool International Cloud Experiment were the only particles in the CPI frame, suggesting shattering was not a problem because of the low ice concentrations and small particle sizes observed during that project. On the other hand, frames with single particles represented only 42.7% and 74.8% of the total frames acquired during M-PACE and ISDAC, respectively. There is some probability that multiple particles could occur in the same sample volume measured by a CPI, however, especially under conditions of high number concentrations. Korolev and Isaac (2005) calculated the probability of multiple particles occurring in any sample volume, assuming particles were randomly distributed following Poisson statistics, as
e1
where n is the number concentration of particles and m is the number of particles in the sample volume SV.
The sample volume SV of a CPI is given by
e2
where AF = (2.3 × 10−6)2 m2 is an area of one pixel on the CCD array and x and y are the pixel coordinates. The depth of field (DOF) changes with pixel position and can be determined empirically. For simplicity, an average DOF of 1.4 mm, obtained during a calibration of version 2.0 of the CPI (Um et al. 2012), is used so that the SV of the CPI is approximately 7.766 × 10−6 L.

Figure 2 plots the frequency of occurrence of frames with the indicated number of particles per frame during M-PACE and ISDAC for ice-, mixed-, and liquid-phase cases separately. The expected frequency distribution was calculated using Poisson statistics and normalized to the number of frames with n given as the maximum concentration for all time periods during the specific project with the indicated cloud phase. The n was estimated from combinations of forward-scattering probes (Dmax < 50 μm) and optical array probes (Dmax > 50 μm). Because n may also include contributions from shattered particles, the calculated probability is thus an upper estimate on the frequency at which multiple particle frames should occur. In reality, shattering depends on many other factors in addition to total concentration, such as particle habit, amount of riming, particle size, angle of attack, probe geometry, and mounting location on an aircraft. Nevertheless, Fig. 2 gives some indication on the expected occurrence frequency of multiple particle frames.

Fig. 2.
Fig. 2.

The frequency of occurrence of frames with the indicated number of particles per frame (circles) measured by the CPI in (left) ice-, (center) mixed-, and (right) liquid-phase conditions during (top) M-PACE and (bottom) ISDAC. The calculated occurrence frequency of these multiparticle frames (diamonds) with error bars that are based on a random distribution of particles following Poisson statistics with the given number concentrations is also plotted. The E+XX codes associated with the n values indicate that the preceding number should be multiplied by 10+XX.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

For ice-phase clouds sampled during M-PACE and ISDAC, n is less than 200 L−1, and hence the calculated probability of multiple particles occurring in a CPI frame is less than 0.1%. The observed frequency of how often multiple particle images occur is much higher. Hence, images from frames with multiple particles are assumed to be artifacts. In mixed-phase clouds, the observed occurrence frequency of frames with more than three (M-PACE) and seven (ISDAC) particles is greater than those expected from Poisson statistics. This result suggests that, although there may be some shattering of particles on the CPI housing and inlet in mixed-phase clouds, shattering does not occur as frequently for mixed-phase clouds as for ice-phase clouds. For liquid-phase clouds, the rate at which multiple particle frames occur is about the same as that expected from random sampling for both ISDAC and M-PACE, showing that shattering is not likely a problem for liquid-phase clouds.

With this background, an investigation of how the morphology of imaged particles varied with the number of particles imaged on a frame was conducted, from which it can be hypothesized how the characteristics of small shattered ice crystals differ from those of naturally occurring crystals. Figure 3 shows how the mean and standard deviation of α depend on Dmax as a function of the number of particles imaged in a frame for ice-, mixed-, and liquid-phase clouds. When only one particle was imaged (Fig. 3, top row) in ice-phase clouds during both M-PACE and ISDAC, α rapidly decreased with Dmax for Dmax of less than ~50 μm and thereafter was almost constant for ~50 < Dmax < 200 μm. The initial decrease in α with Dmax may be associated with a change in ice crystal shapes as the crystals grew because ice crystals evolve from small, quasi-spherical crystals when they first form to larger more nonspherical crystals as they grow (Heymsfield and McFarquhar 2002). McFarquhar et al. (1999) show relationships between α and Dmax for different idealized habits, which can be used for interpreting results in Fig. 3. For larger crystals (Dmax > 50 μm), even though the mean α was almost constant, the standard deviations were large because of the huge variety of shapes that nonspherical crystals can have. Similar trends have been noted in ice clouds over the Arctic and the Great Lakes (Korolev and Isaac 2003).

Fig. 3.
Fig. 3.

Mean and standard deviation of area ratio α as a function of Dmax (0 < Dmax < 200 μm, with 10-μm interval) in (left) ice-, (center) mixed-, and (right) liquid-phase conditions during M-PACE (black) and ISDAC (gray). Each row corresponds to the indicated number of particles per frame. Each point is plotted when at least five particles were present in a given size range, phase, and particles per frame. The CPIView thresholds of focus > 45 and cutoff = 0 were used to generate the statistics.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

For mixed-phase clouds with single particle images, the mean α showed little variation with Dmax for Dmax of less than ~60 μm but decreased with increasing Dmax for larger particles. In these mixed-phase clouds, a large fraction of particles with Dmax of less than ~60 μm were probably supercooled water droplets, which are spherical and hence have α of greater than 0.8. On the other hand, particles with Dmax of greater than ~60 μm are more frequently nonspherical ice crystals with lower α, explaining the decrease in mean α with Dmax. For liquid-phase clouds, the mean α slightly increases with increasing Dmax for Dmax of less than ~50 μm, and there is no clear trend in how it varies with Dmax for Dmax of greater than ~50 μm. For liquid time periods before, after, or between periods identified as mixed phase, it is possible that there were a few ice crystals with Dmax of greater than 70 μm; manual verification of the results of the phase identification should have minimized the frequency of any erroneous phase identification, however. When two particles are present in a frame (Fig. 3, second row), similar trends in how the mean and standard deviation of α vary with Dmax are seen for ice-, mixed-, and liquid-phase clouds. As greater numbers of particles were seen in a frame (Fig. 3, lower rows), the trends become weaker.

Figure 4 shows how the mean and standard deviation of α for particles with 35 < Dmax < 60 μm (Fig. 4, top row) varied as a function of the number of particles per frame. The α are largest for liquid-phase clouds and smallest for the ice-phase clouds. In ice and liquid clouds, α does not depend on number of particles per frame in a statistically significant sense, suggesting that shattered particles are not distinguishable from naturally occurring particles in CPI images. A clear decrease in α for mixed-phase clouds sampled during ISDAC with the number of particles in a frame is noted, however. A corresponding increase in mean Dmax and IWC and decrease in LWC are also shown. Because the liquid (label N_l) and ice crystal (label N_i) concentrations did not vary appreciably with the numbers of particles per frame, the change in Dmax is associated with the larger IWCs. The larger particles, which are more nonspherical and have lower α, are also more prone to shattering.

Fig. 4.
Fig. 4.

Mean and standard deviation of (top) α, (top middle) Dmax, (bottom middle) LWC (filled circles) and IWC (times signs), and (bottom) total number concentration of liquid (filled circles) and ice (times signs) as a function of the number of particles per frame in (left) ice-, (center) mixed-, and (right) liquid-phase conditions during M-PACE (black) and ISDAC (gray). Particles with 35 < Dmax < 60 μm measured by the CPI were used to determine α and Dmax. Each point is plotted when at least five particles were present in a given phase in each frame. The CPIView thresholds of focus > 45 and cutoff = 0 were used to generate the statistics. Particles measured by forward-scattering probes, optical array probes, and bulk water content probes were used to determine the water contents and concentrations as discussed in the text.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

Thus, the comparison of single- and multiparticle CPI images suggests that multiple particles could have occurred in the same CPI sample volume without any influence from shattering for the high number concentrations associated with mixed- and liquid-phase clouds during M-PACE and ISDAC. The concentrations observed in ice-phase clouds were too low to make the observed occurrence of multiparticle frames plausible from a random distribution of particles, however. Therefore, only particle images from single-particle frames are used in the analysis presented henceforth to avoid any possible contamination due to shattering on a CPI.

b. Analysis of crystal images

Previous analysis of in situ data acquired in mixed-phase clouds (Cober et al. 2001b; Korolev et al. 2003; McFarquhar and Cober 2004) showed that the TWC of mixed-phase clouds was typically dominated by contributions from either supercooled water droplets or large ice crystals. Figure 5 shows examples of cloud particles imaged by the CPI in ice-dominated (LWC/TWC < 0.5) mixed-phase clouds with LWC/TWC = 0.178 (Fig. 5a) and 0.120 (Fig. 5c) on 6 October 2004 (M-PACE) and 24 April 2008, respectively (ISDAC) and in water-dominated (LWC/TWC > 0.5) mixed-phase clouds with LWC/TWC = 0.801 (Fig. 5b) and 0.864 (Fig. 5d) on 8 October 2004 (M-PACE) and 1 April 2008 (ISDAC), respectively. Large needle or column crystals, together with many small particles, some of which are round and likely are supercooled water drops, seen in the CPI data, confirm that these and many other time periods with similar distributions of images are mixed phase.

Fig. 5.
Fig. 5.

Examples of cloud particles imaged by the CPI in mixed-phase clouds (a) at 2127:40 UTC 6 Oct 2004 (M-PACE) at an altitude of 1.2 km, temperature of −7.4°C, and LWC/TWC = 0.178; (b) at 2200:40 UTC 8 Oct 2004 (M-PACE) at 0.9 km, −8.7°C, and LWC/TWC = 0.801; (c) at 2344:30 UTC 24 Apr 2008 (ISDAC) at 2.7 km, −11.8°C, and LWC/TWC = 0.120; and (d) at 2232:17 UTC 1 Apr 2008 (ISDAC) at 1.3 km, −3.4°C, and LWC/TWC = 0.864.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

In addition to differing numbers of large ice crystals in the water- and ice-dominated scenes, the small cloud particle shapes also appear different. This is illustrated in Fig. 6, which shows a magnified image of four particles depicted in Fig. 5, two of which (Figs. 6a,c) were measured in ice-dominated conditions and two of which (Figs. 6b,d) were measured in water-dominated conditions. The area ratios of the small particles in the ice-dominated conditions are substantially lower than those in the water-dominated conditions. Manual inspection of similar images in other ice- and water-dominated clouds confirmed that the shapes of small particles vary in mixed-phase clouds and seemed to depend on whether the data were collected in ice- or water-dominated conditions. Because an important ramification of the shape and phase of small particles varying in mixed-phase clouds is that it cannot be assumed that all particles with Dmax of less than 60 μm are liquid (McFarquhar and Cober 2004; McFarquhar et al. 2007b), it was necessary to investigate further how α of the cloud particles varied with cloud conditions in mixed-phase clouds.

Fig. 6.
Fig. 6.

Magnified images of four particles shown in Fig. 5. The Dmax (μm) and α values are embedded in each image; the actual Dmax (white arrow) and the circumscribed circle (dashed line) used to calculate α are also shown in each image.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

Figure 7 shows examples of cloud particles that were imaged by the CPI during MPACE and ISDAC, corresponding to different ranges of α. A definite trend is seen, with the particles appearing more circular for 0.9 < α < 1.0 and less circular for α < 0.8. For 0.8 < α < 0.9, the particles may appear as less circular than those with α > 0.9, which are hypothesized to be liquid, but more circular than those with α < 0.8, which are hypothesized to be ice. In past studies, such particles have been termed quasi spherical (e.g., Nousiainen and McFarquhar 2004). In this study, the ambiguous terms spherical, quasi-spherical, and aspherical are not used, with particles subsequently being categorized only according to α. In the next section, analysis to investigate how the average area ratio αmean of all particles with 35 < Dmax < 60 μm in each 10-s interval is shown to explore how particle shapes and phases vary with LWC/TWC under different conditions.

Fig. 7.
Fig. 7.

Examples of ice crystals imaged during M-PACE and ISDAC as a function of α. Values of Dmax (μm) and α are embedded in each image.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

4. The dependence of small particle shape/phase on LWC/TWC

Figure 8 shows the variation of αmean with LWC/TWC for all mixed-phase cases sampled during M-PACE. A linear regression showed that
e3
with a correlation coefficient of 0.60. Each point in Fig. 8 includes contributions from all particles with 35 < Dmax < 60 μm that were imaged in a given 10-s period. Thus, there are contributions from cloud particles with several different habits and phases in the computation of αmean.
Fig. 8.
Fig. 8.

The αmean as a function of LWC/TWC for all mixed-phase clouds sampled during M-PACE; αmean is computed by averaging α for all particles with 35 < Dmax < 60 μm in the given 10-s intervals.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

To better visualize how small particle shapes vary with LWC/TWC, Fig. 9 plots the ratio of the total number of small particles with 35 < Dmax < 60 μm with α in the indicated range to the total number of small cloud particles as a function of LWC/TWC. For LWC/TWC of less than 0.4, the majority of small cloud particles have α of less than 0.8. Only approximately 4% of small cloud particles have α > 0.9 for LWC/TWC < 0.1, with this percentage approaching 20% for LWC/TWC of 0.5. Particles with α of greater than 0.8 make up 25% of the total small particles for LWC/TWC of less than 0.2, but this fraction increases to 80% for an LWC/TWC of 0.5. For LWC/TWC between 0.9 and 1.0, the percentage of particles with α of greater than 0.8 is up to 80%. Because particles with α > 0.8 or 0.9 are likely to be liquid and particles with lower α are probably ice, this result shows that some fraction of small cloud particles are indeed ice in mixed-phase clouds, with the fraction decreasing as LWC/TWC increases.

Fig. 9.
Fig. 9.

Fraction of particles with 35 < Dmax < 60 μm with given α (white space indicates α < 0.8) to total number of particles with 35 < Dmax < 60 μm as a function of LWC/TWC for all mixed-phase clouds sampled during M-PACE.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

Figure 10 shows the relationship between αmean and LWC/TWC for 10-s averages for all mixed-phase clouds penetrated during ISDAC. A linear regression showed that
e4
but with a correlation coefficient of only 0.43, lower than the 0.60 correlation seen in Fig. 8 for the M-PACE data. Figure 11 shows how the fractional contributions of particles with α in different ranges varied with LWC/TWC ratio for the ISDAC mixed-phase cases. Despite some increase in the fraction of particles with 0.8 < α < 0.9 and 0.9 < α < 1.0 with LWC/TWC for LWC/TWC < 0.3, the trend in how the fraction of particles with α in different ranges varies with LWC/TWC in Fig. 11 is not as clear as in Fig. 9. Section 5 discusses reasons why there was some variation in the relationship between α and LWC/TWC for the M-PACE and ISDAC datasets.
Fig. 10.
Fig. 10.

As in Fig. 8, but for all data obtained in mixed-phase clouds during ISDAC.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

Fig. 11.
Fig. 11.

As in Fig. 9, but for all data obtained in mixed-phase clouds during ISDAC.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

5. Discussion

Observations during ISDAC were typically, but not exclusively, made in more polluted environments than those during M-PACE (Jackson et al. 2012). Enhanced concentrations of aerosols associated with polluted environments can affect cloud microphysical properties through a number of hypothesized mechanisms. For example, Curry (1995) hypothesized that there was enhanced ice-phase precipitation in pristine Arctic cases because of an abundance of ice-forming nuclei, whereas Girard et al. (2005) claimed that freezing was inhibited by the presence of sulfate in more polluted conditions. Given that the numbers of cloud condensation nuclei generally outnumber those of ice nuclei by several orders of magnitude (DeMott et al. 2010), however, it is likely that the enhanced pollution has the biggest impact on mixed-phase cloud properties through processes that involve increases in cloud condensation nuclei (Rangno and Hobbs 2001; Borys et al. 2003) rather than increases in ice nuclei (Lohmann 2002).

Aircraft and satellite observations have indicated that cloud droplet number concentrations increase and rel decreases in both cumuli and stratocumuli that are affected by aerosols (e.g., Han et al. 1994; Martin et al. 1994; Brenguier et al. 2003). Differences in rel are important for the study of small particle shape in mixed-phase clouds because of the low detection threshold of the CPI. When rel is lower, there are more small droplets that might not be imaged by the CPI and hence would not be included in the computation of αmean. Therefore, an investigation was made to determine whether differences in rel between ISDAC and M-PACE could explain the differences in trends observed in Figs. 8 and 10 and in Figs. 9 and 11.

Figure 12 plots the normalized frequency of occurrence of rel for both ISDAC and M-PACE. The rel values measured during ISDAC are significantly lower than those sampled during M-PACE, with a peak occurrence of 5 μm as compared with 11 μm. Given that the CPI is only able to effectively measure particles with radii of approximately 15 μm and given that αmean was computed only from the contributions of particles with 35 < Dmax < 60 μm, it suggests that a much greater fraction of the liquid particles would have been detected by the CPI during M-PACE than during ISDAC and hence αmean would have contributions from a much greater fraction of the liquid particles. This situation could explain the greater correlation of αmean with LWC/TWC for M-PACE than for ISDAC.

Fig. 12.
Fig. 12.

Normalized frequency distribution of rel computed for all mixed-phase clouds sampled during M-PACE and ISDAC.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

To better illustrate how differences in the M-PACE and ISDAC rel distributions affect the relations between αmean and LWC/TWC, all mixed-phase clouds sampled during M-PACE and ISDAC were divided into four categories according to their temperature T and rel: T < −12°C and rel < 5 μm, T ≥ −12°C and rel < 5 μm, T < −12°C and rel ≥ 5 μm, and T ≥ −12°C and rel ≥ 5 μm. For each category, the relation between αmean and LWC/TWC is shown in Fig. 13. Regardless of temperature, αmean is much more strongly correlated with LWC/TWC for rel ≥ 5 μm than for rel < 5 μm. When liquid drop sizes are larger, a greater fraction of the drops is detected by the CPI and hence is included in calculations of αmean, explaining the correlation. For smaller drop sizes, most liquid drops are not detected by the CPI and hence there is no reason to expect a correlation between αmean and LWC/TWC. The reason for the weak dependence of the relation between αmean and LWC/TWC on temperature is not known.

Fig. 13.
Fig. 13.

As in Figs. 8 and 10, except different plots correspond to relation between αmean and LWC/TWC from M-PACE and ISDAC for (a) T < −12°C and rel < 5 μm, (b) T ≥ −12°C and rel < 5 μm, (c) T < −12°C and rel ≥ 5 μm, and (d) T ≥ −12°C and rel ≥ 5 μm.

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

Figure 14 shows the average FSSP/CDP mass distribution functions for each of the four categories in Fig. 13. For rel < 5 μm, less than 1% of the mass is contained in drops with Dmax > 35 μm, showing that almost none of the liquid was measured by the CPI in such cases. On the other hand, for rel ≥ 5 μm the median of the distribution occurs for Dmax > 20 μm, with 4.3% (22.3%) of the mass contained in droplets with D > 35 μm for T < −12°C (≥−12°C), the threshold at which the CPI unambiguously resolves the shape of small particles. Because the rel values were much larger for the mostly pristine cases measured during M-PACE than for the more polluted cases measured during ISDAC, the differing trends for the ISDAC and M-PACE datasets are hence explained. This result shows a limitation of the CPI for detecting liquid water, especially for cases with rel < 5 μm. With the availability of other instrumentation such as forward-scattering probes, however, it is still possible to detect and measure such particle sizes.

Fig. 14.
Fig. 14.

Average mass distribution measured by either CDP or FSSP in mixed-phase conditions during ISDAC and M-PACE for T < −12°C and rel < 5 μm (dashed line), T ≥ −12°C and rel < 5 μm (solid line), T < −12°C and rel ≥ 5 μm (solid line with squares), and T ≥ −12°C and rel ≥ 5 μm (dashed line with squares).

Citation: Journal of Applied Meteorology and Climatology 52, 5; 10.1175/JAMC-D-12-0114.1

6. Summary

Analysis of high-resolution (2.3 μm) images of cloud particles with maximum dimensions Dmax between 35 and 60 μm collected by a cloud particle imager in mixed-phase clouds containing both ice crystals and supercooled water droplets during the Mixed-Phase Arctic Cloud Experiment and the Indirect and Semi-Direct Aerosol Campaign was used to determine how the shapes and phases of particles varied with the ratio of the liquid water content to the total water content. There are six principal findings of this study:

  1. A comparison of the frequency at which multiple particles are observed in a CPI frame against that computed by assuming that particles are randomly distributed according to Poisson statistics shows that multiple particles in single CPI frames in ice-phase conditions are probably artifacts produced from the shattering of large ice crystals on the CPI inlet or housing. On the other hand, multiple particles in CPI frames in mixed- or liquid-phase clouds are likely naturally occurring cloud particles.

  2. Analysis of particles with Dmax < 100 μm imaged in liquid-phase clouds by version 1.0 (M-PACE) and version 2.0 (ISDAC) of the CPI showed no difference in morphological characteristics, provided that Dmax > 35 μm and CPI focus > 45. Therefore, such thresholds are proposed as limits for future analysis of CPI particle images.

  3. The shapes of cloud particles with 35 < Dmax < 60 μm in ice-phase conditions show no statistical difference in area ratio α (the projected area of the particle divided by that of a circumscribed circle with diameter Dmax) for multiple particles imaged in the same CPI frame when compared with single crystals in a CPI frame. This suggests that the shapes of small ice crystals might not permit distinguishing shattered crystals from naturally occurring ice crystals.

  4. Particles with 35 < Dmax < 60 μm in mixed-phase clouds had higher α in water-dominated mixed-phase clouds (defined as mixed-phase clouds with LWC/TWC > 0.5) than in ice-dominated clouds. In water-dominated clouds, α above 0.9 occurred frequently, whereas α was typically less than 0.9 in ice-dominated clouds.

  5. During M-PACE, the mean roundness αmean of small particles with 35 < Dmax < 60 μm in each 10-s interval was linearly correlated with LWC/TWC, with a correlation coefficient of 0.60. During ISDAC, the correlation coefficient between αmean and LWC/TWC was 0.43.

  6. The modal liquid effective radius rel of the more polluted clouds sampled during ISDAC was 5 μm as compared with 12 μm for the more pristine clouds sampled during M-PACE. For clouds measured during both projects with rel < 5 μm, less than 1% of the liquid mass was contained in drops with Dmax > 35 μm; for clouds with rel ≥ 5 μm, about 20% of the liquid mass was contained in drops with Dmax > 35 μm. Given a CPI detection threshold of approximately 35 μm, the lack of correlation between αmean and LWC/TWC for ISDAC is likely explained by the fact the CPI probably did not detect the majority of liquid droplets during that project.

Perhaps one of the most important findings of this study is the result that not all small particles in mixed-phase clouds appear to be supercooled water. This conclusion may have important ramifications for the development of future model parameterization schemes that are needed to physically represent single-scattering and fallout processes that are important for determining the radiative influence of mixed-phase clouds. Further, its impact on the development and evaluation of retrieval schemes from ground- and satellite-based remote sensors is important.

Future work should put the image analysis presented here in the context of observations collected by other probes. In particular, direct observations of the scattering phase function in mixed-phase clouds produced by an instrument such as a polar nephelometer (e.g., Gayet et al. 1997) could be compared with those calculated from assumed size, shape, and phase distributions of cloud particles to give a more comprehensive view on the shapes and phases of small cloud particles.

Acknowledgments

Data were obtained from the Atmospheric Radiation Measurement Program archive, sponsored by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER) Environmental Science Division. This research was supported by the DOE Office of Science (BER) under Grants DE-FG02-02ER63337, DE-FG02-07ER64378, DE-FG02-09ER64770, DE-SC0001279, and DE-SC0008500. We are grateful for the efforts of personnel at the University of North Dakota for collection of the M-PACE data and at Environment Canada, National Research Council of Canada, and SPEC, Inc., for collection of the ISDAC data. Gong Zhang helped with preparation of some preliminary versions of the figures. The comments of three anonymous reviewers improved the quality of the paper.

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