During the 2012 Deep Convective Clouds and Chemistry
(DC3) experiment the National Science Foundation/National Center for
Atmospheric Research Gulfstream V (GV) aircraft sampled the upper anvils of
two storms that developed in eastern Colorado on 6 June 2012. A cloud
particle imager (CPI) mounted on the GV aircraft recorded images of ice
crystals at altitudes of 12.0 to 12.4 km and temperatures (
A new algorithm that uses the circle Hough transform technique was developed
to automatically identify the number, size, and relative position of element
frozen droplets within FDAs. Of the FDAs, 42.0 % had two element frozen
droplets with an average of
Deep convective systems, such as thunderstorms and mesoscale convective systems (MCSs), play an important role in Earth's climate system, for example, by conveying ice crystals to the upper troposphere and lower stratosphere, redistributing latent heat, controlling precipitation, and regulating the Earth's radiation budget (Jensen et al., 1996; Stephens, 2005; de Reus et al., 2009; Frey et al., 2011; Feng et al., 2011, 2012; Gayet et al., 2012; Taylor et al., 2016). Clouds formed by deep convection show several distinct features. Vigorous turrets associated with deep convection generate intense precipitation that influences the hydrological cycle and large anvil shields that modulate radiation due to their extensive spatial and temporal coverage (Feng et al., 2011, 2012; Wang et al., 2015). Overshooting tops associated with strong updrafts are responsible for stratosphere–troposphere exchange (Homeyer et al., 2014; Frey et al., 2015) and can be an indicator of the severity of a thunderstorm (Proud, 2015).
Clouds formed by deep convection have three thermodynamic phases: liquid, mixed, and ice. The cloud particles also have different shapes, sizes, and concentrations that vary in the horizontal and vertical causing horizontal and vertical variability in radiative properties. For example, precipitating cores of tropical convective clouds reveal a negative impact on radiation balance, whereas non-precipitating anvils have a positive impact (Hartmann and Berry, 2017). A numerical simulation (Fu et al., 1995) showed that the spatial extent of an anvil cloud is influenced by moisture advection from the convective turret, radiative effects, and small-scale convection occurring within the anvil (Lilly, 1988). The relationships between the spatial and temporal coverage of convectively generated clouds and their radiative impact are still not well understood and affect the representation of cloud feedbacks in numerical models (Bony et al., 2015, 2016; Hartmann, 2016; Hartmann and Berry, 2017).
Despite the high height of the tropopause and the remote regions where some of these cloud systems occur, there have been in situ measurements of the microphysical and scattering properties of ice crystals in anvil tops (e.g., Heymsfield, 1986; McFarquhar and Heymsfield, 1996; Stith et al., 2002, 2004, 2014, 2016; Connolly et al., 2005; Gallagher et al., 2005; Heymsfield et al., 2005; May et al., 2008; Jensen et al., 2009; Lawson et al., 2010; Frey et al., 2011; Gayet et al., 2012; Barth et al., 2015; Jensen et al., 2016). Although in situ aircraft measurements have some limitations as crystals are not observed where they form (Um et al., 2015), they along with laboratory experiments (e.g., Bailey and Hallett, 2004, 2009) provide information on how crystal habit varies with temperature and humidity.
One distinct characteristic of anvil clouds is the frequent occurrence of plate type crystals and their aggregates, which is different from ice crystals found in non-convective cirrus, where bullet rosettes and their aggregates are most common (McFarquhar and Heymsfield, 1996; Stith et al., 2002; Lawson et al., 2003; Connolly et al., 2005; Um and McFarquhar, 2009; Järvinen et al., 2016). As plate type crystals form at warmer temperatures (Bailey and Hallett, 2004, 2009) than the typical temperatures at anvil tops, these plate crystals must form at lower altitudes and be transported to upper altitudes by convection. It has been hypothesized that the “chain-like” shaped aggregates frequently observed in convective clouds may be produced by high electric fields within clouds (Saunders and Wahab, 1975; Stith et al., 2002, 2004; Lawson et al., 2003; Connolly et al., 2005; Um and McFarquhar, 2009). These shapes differ from aggregates observed in non-convective cirrus where aggregates of bullet rosettes are more common (Um and McFarquhar, 2007) and chain-like structures are not commonly seen.
Another unique feature of ice crystals in deep convective clouds is the high
concentration of small frozen droplets (Gayet et al., 2012; Baran et al.,
2012; Stith et al., 2014). These are no doubt generated from the freezing of
supercooled droplets that have been observed at temperatures as low as
Although not plentiful, there are some observations of the shapes of these
small ice crystals at the tops of anvils and convective towers. Gayet et
al. (2012) reported up to 70 cm
The radiative properties (e.g., albedo) of convective cloud systems depend strongly on both the concentrations and shapes of crystals in the anvil-cloud layer. In order to better understand the role of continental convective clouds in Earth's radiation budget, the fractional contributions of different habits must be quantified, and the scattering properties of the habits determined. This is complicated by a couple of issues. First, several idealized crystal models representing shapes of small crystals have been proposed (McFarquhar et al., 2002; Yang et al., 2003; Nousiainen and McFarquhar, 2004; Nousiainen et al., 2011; Um and McFarquhar, 2011, 2013; Järvinen et al., 2016), but it is not known which best characterizes the shapes. Second, few in situ aircraft observations of continental convective clouds have been made due to their high altitudes and the difficulty of flying through or near strong updrafts. In this study, 22 393 crystals imaged by a cloud particle imager (CPI) on 6 June 2012 in anvil clouds over eastern Colorado during DC3 are analyzed to determine the morphological properties of single frozen droplets and FDAs (e.g., size and number of element) and their radiative impacts. Although previous studies (Gayet et al., 2012; Baran et al., 2012; Stith et al., 2014, 2016) have analyzed FDAs observed in continental deep convective clouds, the dimensions and three-dimensional (3-D) shapes of FDAs that are important for radiative implication were not determined as is done in this study.
The remainder of this paper is organized as follows. Section 2 summarizes the in situ aircraft measurements made during DC3. In Sect. 3, a habit classification scheme that distinguishes FDAs from other crystals is introduced along with the methodology used to identify the number and size of element frozen droplets within FDAs. The morphology of FDAs and their reconstructed 3-D shapes are shown in Sect. 4. Two different parameters that describe the 3-D shapes of aggregate particles, fractal dimension and aggregation index, are also introduced. Furthermore, the characteristics of the shapes of FDAs are compared against those of black carbon aggregates in Sect. 4. The significance of this study and concluding remarks are made in Sect. 5.
The 2012 DC3 experiment investigated the impacts of deep midlatitude continental convective clouds on upper tropospheric chemistry and composition in the US Midwest (Barth et al., 2015). The National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) Gulfstream V (GV), the National Aeronautics and Space Administration (NASA) DC-8, and the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Falcon aircraft were deployed during DC3.
In this study, in situ measurements were acquired from the GV equipped with a
Stratton Park Engineering Company Inc. (SPEC) 3V-CPI instrument, a cloud
droplet probe (CDP, manufactured by Droplet Measurement Technologies, DMT),
and a specially modified Particle Measuring Systems (PMS) optical array probe
(2DC), which uses high-speed electronics and a 64-element
25
The shattering of large cloud particles on the shrouds, tips, or inlets of
cloud probes can cause artificial increases in in situ measured
concentrations of small particles. Thus, the impacts of shattering must be
prevented or removed (Field et al., 2003, 2006; McFarquhar et al., 2007;
Korolev et al., 2011; Lawson, 2011; Jackson and McFarquhar, 2014; Jackson et
al., 2014; Korolev and Field, 2015). The CDP used during DC3 did not have a
shroud; thus, shattering is not expected to be substantial (Stith et al.,
2014). Anti-shattering tips (Korolev et al., 2011) were installed on the 2DC,
and post-processing methods of removing particles with small interarrival
times (Field et al., 2003, 2006) were applied. 2DC measurements of only
particles with
Segregated time periods of the 6 June flight and contributions (%) of
crystal habit to the total number (total projected area) of ice crystals for
the given time period. The average and standard deviation of temperature (
During the 6 June 2012 flight, the GV sampled the upper anvils of two storms
that developed in eastern Colorado between 22:10:00 and 22:30:00 UTC near
the CSU-CHILL radar (40.45
Based on CPI crystal images obtained in tropical ice clouds, Um and McFarquhar (2009) developed a classification scheme to sort crystals into eleven habits: small, medium, and large quasi-spheres, columns, plates, bullet rosettes, aggregates of columns, aggregates of plates, aggregates of bullet rosettes, capped columns, and unclassified. To represent other crystal habits commonly found in midlatitude and Arctic clouds, the capability of sorting into more habits (i.e., dendrite, needle, aggregates of needles, and FDAs) has been added to the scheme (McFarquhar et al., 2017). Thus, this habit classification scheme now sorts crystals into 15 different categories in a quasi-automatic manner that requires some manual intervention.
In this study, ice crystals classified as small (SQS), medium (MQS), and large quasi-spheres (LQS) are regarded as single frozen droplets. The FDAs that occur near anvil tops are often classified as bullet rosettes, aggregates of bullet rosettes, or unclassified from the automated part of the algorithm; therefore, an additional manual check was necessary to confirm whether or not these crystals were FDAs. To be classified as FDAs, there must be at least two quasi-circular frozen droplets as elements. Habits that frequently occurred during the 6 June 2012 flight were single frozen droplets (i.e., SQS, MSQ, and LQS) and their aggregates (i.e., FDAs), whereas very few pristine shape crystals, such as plates, columns, and bullet rosettes, were observed (see Table 1 and Fig. 3).
Contributions of ice crystal habits by number (red) and by
projected area (blue) during
The circle Hough transform (CHT, Duda and Hart, 1972) detects circular objects in digital images and is one of many feature-extracting techniques that use the Hough transform (Hough, 1962). Several variants of the Hough transform have been developed, such as, the fast Hough transform (Li et al., 1986), two-stage CHT (Yuen et al., 1990), space saving approach CHT (Albanesi and Ferretti, 1990), and the phase-coding method (Atherton and Kerbyson, 1999). These techniques have been used to detect natural particles with circular shapes in digital images, such as circular nanoparticles in transmission electron microscopy (TEM) images (Bescond et al., 2014; Mirzaei and Rafsanjani, 2017).
CPI images of frozen droplet aggregates (FDAs, left image of each
column) and those with determined element frozen droplets (red circle, right
image of each column). The 46
Prior studies have used such techniques to identify the elemental or primary
particles within black carbon aggregates (e.g., Bescond et al., 2014; China
et al., 2013), most of which are circular. Similar techniques can be applied
to the FDAs observed near the tops of anvil clouds assuming the element
frozen droplets have spherical shapes. The biggest difference between TEM and
CPI images is that the quality of TEM images is, in general, better than that
of CPI images. A CPI image has an inhomogeneous background and debris or
noise, such as impulse noise (i.e., salt-and-pepper noise), which causes
lower quality images. Thus, additional image-quality control was required
before applying the CHT technique to the images. This was accomplished in a
number of steps. First, a median filter that is a nonlinear digital filtering
technique to remove noise is applied to the CPI images classified as FDAs.
The 256-level gray-scale CPI images are then converted to binary images based
on the average intensity of pixels to further remove background noise and
debris. Figure 4 shows example images of CPI FDAs. Two different CHT
techniques, the two-stage CHT and phase-coding method, are then applied to
the images to detect element circles (i.e., frozen droplets) as shown by the
red circles in Figs. 4 and 5. Two different techniques are used because the
performance of each technique varies depending on the CPI image being
classified. The technique used for the subsequent analysis is chosen as that
for which the projected area of the FDAs determined for the element frozen
droplets identified by CHT technique (i.e., area determined by red lines in
Fig. 5) best matches that for the original CPI image (i.e., area enclosed by
green line in Fig. 5). However, the performance of both techniques is quite
similar. For example, the phase-coding technique shows closer agreement with
the imaged area for the FDAs shown in the top row of Fig. 5, while the
two-stage CHT shows closer agreement for the FDAs shown in the bottom row. Although the
phase-coding method provided marginally better results for
Element frozen droplets (red circles) determined using the
phase-coding (left column) and two-stage CHT (middle column) techniques.
Examples of two different FDAs are shown in the top and bottom rows,
respectively. Original CPI images of FDAs are shown in the right column along
with the 46
Figure 3 shows the normalized contribution of each habit to the total number (red) and to the total projected area (blue) of measured ice crystals during the three different time periods and integrated over the entire time period. For all time periods, single frozen droplets represented the dominant habit by number, whereas FDAs were dominant by projected area (see also Table 1). The fraction (by number) of single frozen droplets was 73.0 % (84.1 %; 70.6 %; 71.2 %) for all periods (period 1; period 2; period 3), whereas the area fraction of FDAs was 46.3 % (27.8 %; 49.6 %; 47.5 %). The fraction of well-defined pristine ice crystals, such as plates and columns, was less than 0.04 % by number and 0.12 % by area for all time periods, whereas unclassified crystals represented 6.1 % (3.9 %; 5.3 %; 7.5 %) by number for all periods (period 1; period 2; period 3) and 13.5 % (6.5 %; 10.9 %; 16.9 %) by area. These fractions of unclassified crystals were lower than those obtained from anvil cloud in the tropics (Um and McFarquhar, 2009) that showed more than 22 % and 37 % contributions by number and area, respectively. The presence of small crystals with relatively simple habit distributions shown in this study indicates that the anvil clouds were sampled in an early stage of development, which was verified using radar observations (Stith et al., 2014, 2016).
The average
In summary, single frozen droplets and their aggregates dominated the upper anvil clouds sampled in situ, with the relative frequency of occurrence of single frozen droplets and FDAs dependent on temperature and position within the anvil, consistent with the conceptual model proposed by Stith et al. (2014, Fig. 12) and further detailed in Stith et al. (2016, Fig. 9).
Among the 4667 CPI images of FDAs, the CHT technique succeeded in identifying
element frozen droplets for 4356 FDAs, whereas it failed for 311 FDAs
(6.66 %). The number, size, and 2-D position of the element frozen
droplets within the FDAs were thus determined automatically. Figure 6a
shows the frequency distribution of the number of element frozen
droplets within FDAs. The average number of frozen droplets within FDAs is
To determine microphysical (e.g., fall velocity) and scattering (e.g., asymmetry parameter) properties of cloud particles required for models, idealized 3-D models of the crystals are needed. However, cloud particle images recorded by cloud probes are silhouettes (i.e., 2-D images) of 3-D cloud particles (Nousiainen and McFarquhar, 2004). Retrieving the 3-D shapes of cloud particles based on the recorded silhouettes is difficult, especially for non-spherical ice crystals that have non-pristine shapes. It is easier to reconstruct 3-D shapes of well-defined pristine crystals, such as columns and plates. For example, an iterative approach to retrieve the 3-D shapes of bullet rosette crystals was developed (Um and McFarquhar, 2007). Assuming that the element crystals all had the same shape (e.g., plates), the 3-D shapes of more complex crystal aggregates (e.g., aggregates of plates) have also been reconstructed from crystal silhouettes (Um and McFarquhar, 2009).
FDAs consist of at least two element frozen droplets whose shapes are assumed
to be spheres even though the elements are in fact quasi-spherical, meaning
they have some departures from a spherical shape. The number, size, and 2-D
position of the element frozen droplets within the FDAs were determined from
the CPI images as explained in Sect. 3.2. Using this information, the 3-D
shapes of FDAs are reconstructed for the given 2-D silhouette (i.e., CPI
image) with the following assumptions:
element frozen droplets of FDAs are spheres; there is no overlap between the elements of the frozen droplets; and the maximum number of contacting points of an element frozen droplet with
other frozen droplets is two.
Since the relative positions (i.e.,
As the number of element frozen droplets increases, the number of possible
3-D realizations also increases. For FDAs with 20 element frozen droplets, a
maximum number of 262 144 different 3-D realizations is possible.
Considering all 262 144 3-D realizations of FDAs is impractical for
calculations of single-scattering properties. Thus, parameters that
characterize the 3-D shapes of particles and link the 3-D shapes to
scattering properties are required. Um and McFarquhar (2009) used several
parameters, such as the aggregation index (AI), the area ratio, and the
normalized projected area, to characterize the 3-D shape and to link to the
scattering properties of aggregates of plate crystals. The motivation for the
use of the AI is that the asymmetry parameter (
Six different examples of 3-D representations of FDA. Each image has the same projected area (gray) as the CPI image shown in Fig. 4 (top-left image).
The calculated maximum (blue), minimum (green), and average (red) aggregation index (AI) of FDAs (circles) as a function of the number of element frozen droplets. Best-fit lines are shown using solid lines.
A fractal dimension (
The average aggregation index (AI, red circles in Fig. 8) as a function of temperature (blue circles). The mean and standard deviation of AI for six temperature ranges are indicated by the red circles and vertical bars, respectively.
To provide an overview of the shapes of the FDAs, the
Relationships between the ratio of the radius of gyration
(
Relationship between aggregation index (AI) and the ratio
of radius of gyration to the average radius of elements
(
The
There is a fundamental difference in the nature of the variables AI and
During the 2012 Deep Convective Clouds and Chemistry (DC3) experiment the
National Science Foundation/National Center for Atmospheric Research
Gulfstream V (GV) aircraft sampled the upper anvils of two storms that
developed in eastern Colorado on 6 June 2012. A cloud particle imager (CPI)
mounted on the GV aircraft recorded images of ice crystals at altitudes of
12.0 to 12.4 km and temperatures (
The most important findings from this study are summarized as follows:
For all time periods, single frozen droplets represented the dominant
habit by number, whereas FDAs were dominant by projected area. The fraction
(by number) of single frozen droplets was 73.0 % (84.1 %; 70.6 %;
71.2 %) for all time periods (period 1; period 2; period 3), whereas the
area fraction of FDAs was 46.3 % (27.8 %; 49.6 %; 47.5 %). The fraction of well-defined pristine ice crystals (i.e., plates and
columns) was less than 0.04 % by number and 0.12 % by area for all
time periods, whereas unclassified crystals represented 6.1 % (3.9 %;
5.3 %; 7.5 %) by number for all periods (period 1; period 2; period
3) and 13.5 % (6.5 %; 10.9 %; 16.9 %) by area. The high concentrations of small crystals (i.e., single frozen
droplets) with relatively simple habit distributions shown in this study
indicates that the anvil clouds were sampled in an early stage of development as
also verified using radar data. The relative frequency of occurrence of single frozen droplets and
FDAs was dependent on temperature and position within the anvil, consistent
with the conceptual model proposed by Stith et al. (2014, 2016). The fraction
of single frozen droplets, in general, decreased as the GV penetrated into
the center of the anvil cloud, and then increased as it approached the cloud
edge for all three periods. The average number of element frozen droplets within FDAs is
The average diameter of the element frozen droplets ( The AI of FDAs decreases with an increase in the number of
element frozen droplets, which indicates that larger FDAs with more element
frozen droplets have more compact shapes. The AI of FDAs decreases with
increasing temperature, which agrees with the frequent occurrence of FDAs in
the lower regions (i.e., higher temperatures) of the upper anvil (Stith et
al., 2014). The calculated fractal dimensions of FDAs (1.20–1.43) in this study
are smaller than those of BC aggregates (1.53–1.85), which indicates that
FDAs have more linear branched shapes compared against the compact shapes of
BC aggregates. A strong positive relationship (
The results of this study have important implications for the improvement of
the calculations of the microphysical (e.g., fall velocity) and radiative
(e.g., asymmetry parameter) properties of ice crystals in upper anvil clouds,
especially continental convective clouds. To implement the results of this
study for numerical models and satellite-retrieval algorithms, a role of
electric fields within convective clouds should be identified and quantified
systemically. A recent laboratory experiment (Pedernera and Ávila, 2018)
showed that the collision and adhesion process was highly related to
electrical forces that stimulated the aggregation process of frozen droplet
aggregates. A subsequent study will calculate the single-scattering
properties and fall velocities of FDAs using the morphological features and
models of FDAs proposed here, which will have high impacts on clouds formed
over the US Great Plains and east Andes where strongest convection and electric
field exist.
The CPI imagery is available from UCAR/NCAR (2013) and low-rate GV data are available from UCAR/NCAR (2017).
JU, GMM, and JLS conceived the study, and JU wrote the paper with help from GMM and JLS. JLS collected the CPI, CDP, and 2DC data from aircraft. JU, YS, YGL, and YIY carried out the CPI image analysis. JU, CHJ, and JYL performed the fractal dimension analysis. JU, SSL, SSY, BGK, JWC, and ARK performed the cloud data analysis. All authors were involved in the scientific interpretation and discussion and commented on the paper.
The authors declare that they have no conflict of interest.
This work was supported by funding from the National Science Foundation under grant no. AGS 12-13311 and from the Advanced Study Program (ASP) at the National Center for Atmospheric Research. The National Center for Atmospheric Research is sponsored by the National Science Foundation. Part of this work was completed while Greg M. McFarquhar was on sabbatical at NCAR. This research was supported by the National Strategic Project – Fine particle of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of Health and Welfare (MOHW) (project no. NRF-2017M3D8A1092022). This study was also funded by the Korea Meteorological Administration Research and Development Program “Research and Development for KMA Weather, Climate, and Earth system Services Development of Application Technology on Atmospheric Research Aircraft” under (grant no. KMA2018-00222). We would like to acknowledge operational, technical, and scientific support provided by NCAR's Earth Observing Laboratory, sponsored by the National Science Foundation. Edited by: Ottmar Möhler Reviewed by: two anonymous referees