ATMOSPHERIC SCIENCE LETTERS
Atmos. Sci. Let. 17: 286–293 (2016)
Published online 22 March 2016 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/asl.657
Entrainment and droplet spectral characteristics
in convective clouds during transition to monsoon
Sudarsan Bera,* G. Pandithurai and Thara V. Prabha
Indian Institute of Tropical Meteorology, Pune, India
*Correspondence to:
S. Bera, Indian Institute of
Tropical Meteorology, Dr. Homi
Bhabha Road, Pashan, Pune
411008, India.
E-mail: [email protected]
Received: 15 June 2015
Revised: 13 October 2015
Accepted: 18 February 2016
Abstract
In situ observations in growing deep cumulus in polluted and clean environments during the
transition to monsoon over Indian peninsula are used to investigate entrainment effects on
droplet size distribution and the width of possible cloud cores. Pre-monsoon clouds indicate
reduction in spectral width in the diluted cloud volumes due to lateral entrainment whereas
monsoon cloud has higher spectral width. Relative dispersion is observed to increase with the
distance from cloud core of monsoon cloud while for pre-monsoon clouds it remained almost
constant. Enhanced entrainment in polluted pre-monsoon clouds is responsible for narrow
cloud core compared to monsoon cloud.
Keywords:
entrainment; spectral characteristics; CAIPEEX
1. Introduction
Cumulus clouds are widely prevalent over tropical
region and contribute significantly to the global radiative forcing and hydrological cycle (Morrison and
Grabowski, 2007). Studying their characteristics is
important to understand the cloud development and
warm rain process (Devenish et al., 2012). Entrainment of dry air in cumulus clouds has been a subject
of numerous studies due to its importance in several
dynamical and microphysical processes (Paluch and
Baumgardner, 1989; Grabowski, 2007). Entrainment in
cumulus clouds impacts microphysical parameters significantly and aerosol pollution can modify the entrainment response on cloud properties (Jiang et al., 2006).
One of the fundamental problems in cloud physics
is to understand the rapid onset of warm rain process (Tölle and Krueger, 2014), which can be either
linked to droplet size distribution (DSD) broadening by
entrainment-mixing mechanisms (Lasher-Trapp et al.,
2005) or to the microphysics of less diluted cloud core
(Blyth et al., 2005). Broadening of DSD produced by
entrainment-mixing is responsible for accelerating coalescence growth (Berry and Reinhardt, 1974) and is
also important for radiative properties of the clouds
(Lasher-Trapp et al., 2005). Representation of entrainment and impact on microphysics accurately in climate
models is needed (Tölle and Krueger, 2014) and in
situ observations has great value in this respect, especially over south Asia where these processes in monsoon clouds are less understood.
The width of cloud core decreases in polluted environment due to enhanced evaporation rate (Seigel,
2014). Possibility of evaporation-entrainment feedback
in polluted cumulus clouds is also suggested (Jiang
et al., 2006; Small et al., 2009) and lateral entrainment may impact width of cloud core where adiabatic
cloud properties are preserved. It could be hypothesized
that lateral entrainment dilutes the cloud edges but an
inner core remained with maximum liquid water content (LWC) and droplet number concentration, which is
quite different for a pre-monsoon and a monsoon cloud.
So, the cloud volume can be separated in two parts: an
inner core (less diluted) and a shell (highly diluted) surrounding the core. The strength of entrainment dictates
the possible width of the inner core.
Entrainment broadens the DSD and produces dispersion in droplet size spectra (Lu et al., 2008). Spectral
width (𝜎) is the standard deviation of DSD and relative dispersion (𝜀) is the ratio of spectral width to
mean radius (<r>) (Lu et al., 2008). Dispersion effect
can offset the Twomey cooling (Twomey, 1974) in polluted clouds (Liu and Daum, 2002) and sometimes up
to 39% (Pandithurai et al., 2012) using in situ observations. Relative dispersion (varies within a narrow range
of 0.25–0.35 for warm convective clouds observed over
Istanbul region) is not correlated with the droplet number concentration (N d ) or LWC of the clouds but the
variance of relative dispersion changes with N d and
LWC (Tas et al., 2015). Higher variance was associated
with low N d and LWC typically at cloud edges, due to
lateral entrainment. Most of the studies discussed lateral entrainment in shallow cumulus or stratocumulus
and very less attention is given to the deep cumulus with
a few (3–4) kilometer depth. Shallow cumulus is dominated by cloud top entrainment (Paluch, 1979) whereas
lateral entrainment is more significant in deep cumulus (Böing et al., 2014). Deep cumulus that forms a
significant component of the monsoon cloud systems
might exhibit dilution much inside of the clouds due
to larger vertical extend unlike the shallow cumulus
where dilution is limited to outer cloud shell driven by
buoyancy gradient (Heus and Jonker, 2008; Small et al.,
2009). The dynamics of deep cumulus is also greatly
© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in
any medium, provided the original work is properly cited.
Entrainment and droplet spectral characteristics
287
Table 1. Summary of the flights (15, 16, 21 and 22 June) which are been used in the article. Number of clouds and number
of samples correspond to warm cloud region only and the same have been presented in the article. Sub-cloud aerosol number
concentration (Na ) is measured by PCASP. CDP observed mean (warm cloud) droplet number concentration (Nd ) and droplet
effective diameter (De ) are presented here. Environmental RH corresponds to out cloud samples. Sub-cloud updraft is observed at
100 m below cloud base by AIMMS probe.
Flight
date
No. of
clouds
No. of
samples
Cloud
base (m)
Na
(cm−3 )
Nd
(cm−3 )
De
(𝛍m)
RH (%) in
boundary layer
RH (%) in
cloudy layer
Sub-cloud
updraft (m s−1 )
15 June
16 June
21 June
22 June
14
18
14
15
186
192
156
180
2813
2709
2452
2062
936.9 ± 117.3
1178.4 ± 226.8
967.5 ± 195.4
330.5 ± 69.3
332.7 ± 118.2
501.1 ± 242.6
417.3 ± 169.5
186.5 ± 35.2
12.14 ± 1.64
12.26 ± 1.48
13.06 ± 1.67
16.88 ± 2.83
59.8 ± 12.6
57.2 ± 15.4
63.5 ± 12.3
71.2 ± 11.9
74.0 ± 4.1
73.4 ± 3.3
78.4 ± 7.8
77.8 ± 6.4
1.77 ± 1.12
1.68 ± 1.32
1.57 ± 1.21
1.82 ± 1.35
influenced by environmental thermodynamics (Prabha
et al., 2011). It was also noted that rain drop formation process in these clouds is more favorable in the
cloud core region (Khain et al., 2013). This study is
aimed to understand the cloud edge entrainment and
core dispersion characteristics while transition to monsoon happened over the Indian peninsula. The effect of
entrainment on DSD and spectral dispersion in deep
cumulus clouds is discussed as a function of distance
from the cloud core.
2. Experiment and methodology
The study used the in situ cloud observations of growing cumulus taken during the Cloud Aerosol Interaction
and Precipitation Enhancement Experiment phase-I
(CAIPEEX-I), conducted over peninsular India during
June 2009. Each flight corresponds to about 2 h of
observation at 1 Hz (100 m) but only cloud penetration
data are mainly used for detailed microphysical analysis. Number of clouds and number of samples used in
the study are indicated in Table 1. A detailed description of the instruments and overview of the experiment
is provided by Kulkarni et al. (2012). The case studies
(15, 16, 21 and 22 June) considered here are deep
cumulus-congestus clouds observed over Hyderabad
(17.45∘ N, 78.46∘ E) during transition to summer monsoon period. The same observations are been used
as representative of pre-monsoon (15, 16 June) and
monsoon (22 June) clouds (Prabha et al., 2011, 2012;
Khain et al., 2013; Bera et al., 2016). Cloud droplet
probe (CDP) and a hot-wire sensor are used to measure
droplet size and LWC, respectively. Passive cavity
aerosol spectrometer probe (PCASP) measured aerosol
number concentration (range 0.1–3.0 μm). Temperature and humidity are measured by AIMMS-20 probe.
Several horizontal penetrations lasting few seconds are
conducted at different levels at and above cloud base.
The observations presented here are from growing
cumulus which is conditionally probed to fulfill the
objective of the experiment. The growth stage of cloud
is also validated by observing incloud vertical velocity
from cloud base to 1 km above and found more than
80% of the cloud penetration data having updraft, as
described by Gerber et al. (2008) to select growing
cumulus. Aircraft observations taken at a frequency of
1 Hz corresponds to about 100 m spatial resolution. The
cloud samples were selected from the warm region of
cloud, without any precipitation size drops in DSD as
also verified with the help of a cloud imaging probe (as
described by Prabha et al., 2011). A threshold droplet
number concentration (N d ) of 10 cm−3 as followed
by Rangno and Hobbs (2005) is used to identify the
incloud samples. Adiabatic liquid water is calculated
by using the linear relationship LWCad = Cw h, where
Cw is the condensation rate and h is the height above
cloud base (Brenguier, 1991). Adiabatic fraction (AF)
is the ratio of LWC and LWCad . Cloud core width is
determined by the cloudy region with highest LWC and
AF (>0.4). Distance from highest LWC point to cloud
edge (N d < 10 cm−3 ) is used to normalize the distance
(between 0 and 1).
Mixing fraction of cloudy mass (𝜒), i.e the ratio of
adiabatic cloudy mass at cloud base and total mass after
entrainment (adiabatic cloudy air + dry entrained air) at
height level z above cloud base, is used to estimate fractional entrainment rate (𝜆) by following the approach
of Lu et al. (2012a) and detailed description of this
methodology is provided in Appendix S1. Entrainment
rate (𝜆) is estimated as
ln 𝜒
,
(1)
h
where 𝜒 is the mixing fraction of the cloudy air at height
level h above the cloud base. We used integrated mixing fraction (𝜒) between cloud base and the observation level and averaged fractional entrainment rate (cf.
Appendix S1 for details). Mixing fraction approach for
estimating entrainment rate has advantage as measurement of incloud temperature and water vapor is not
required that may have errors due to wetting of sensors. This method can be applied in remote-sensing
technique to estimate entrainment rate. This approach
also connects the estimation of entrainment rate directly
to the microphysical effect of entrainment (Lu et al.,
2014), and could be intercompared with similar estimates from remote sensing.
𝜆=−
3. Results and discussion
The study discusses the effect of entrainment on microphysics and core width of cumulus clouds developing
© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.
Atmos. Sci. Let. 17: 286–293 (2016)
288
under varying aerosol and thermodynamic conditions
(transition to monsoon). The large-scale meteorological conditions are depicted in Figure S1, Supporting Information. The wind vectors at 850-mb pressure
level show that south-westerly wind dominated over
Hyderabad on 22 June (as an indication of monsoon
onset). Vertical variation of potential temperature (𝜃)
and water vapor mixing ratio (qv ) from radiosonde measurements are shown in Figure S2. Boundary layer
temperature decreases and moisture content increases
after the onset of monsoon. It may be noted that
depth of the atmospheric boundary layer (ABL) and
cloud base height (Table 1) decreases as monsoon sets,
compared to pre-monsoon days due to high moisture
transport and low incoming solar radiation reducing
surface heating. Thermodynamic and microphysics of
pre-monsoon and monsoon clouds are discussed by
Prabha et al. (2011) and Bera et al. (2016). The summary of the flights which is presented in Table 1 shows
highest sub-cloud aerosol number concentration (N a )
on 16 June (1178 cm−3 ) and after onset N a reduced
to 330 cm−3 on 22 June. Other two cases (15 and 21
June) are moderately polluted. RH in the boundary
layer increases progressively with the monsoon onset
although mid-layer RH remains closely same. Monsoon cloud (22 June) has the largest effective diameter and least droplet number concentration (Table 1).
Sub-cloud updraft is similar (1.5–1.8 ± 1.2 ms−1 ) in all
clouds. The information of cloud samples and number
of clouds during each flight used in the study is presented in Table 1.
The DSD as a function of droplet diameter (D) during
the four flights is shown in Figure 1. DSD is shown for
entire flight (cloud base to cloud top) which is not used
for the rest of the analysis as we are focusing on warm
and non-precipitating part of the clouds. The altitude
of flight observation, corresponding effective diameter (De ), distance from cloud core and spectral width
of DSD are depicted on the top of each DSD at 1 Hz.
Relatively small droplets (D < 30 μm) are observed in
pre-monsoon clouds (15, 16 June) with narrow spectral width compared to the clouds on 21 and 22 June,
which produced larger droplets (D > 30 μm) and broadened DSDs. Precipitation size droplets (De > 24 μm) are
formed above 4 km altitude on 22 June (which is not
considered for later part) and characterized by broader
DSDs in contrast to other three clouds. The threshold
for rain drops to form is De > 24 μm is reported by Pinsky and Khain (2002) and Rosenfeld et al. (2002). The
dash line (black) on the top panel of Figure 1(a)–(d)
indicates the height level, below which incloud data are
used for the main analysis of the study. The figure provides valuable information of spectral width as a function of distance from cloud core at different altitudes.
This shows that spectral width and the distance from
cloud core are in negative correlation for pre-monsoon
clouds (higher spectral width at cloud core) but positively correlated for monsoon cloud (indicated vertical black line in the sub plot). Information of droplet
number concentration (N d ) averaged within the warm
S. Bera, G. Pandithurai and T. V. Prabha
region of cloud is provided in Table 1. N d is observed
to be higher in all clouds except for 22 June (monsoon
cloud). Entrainment of dry air and subsequent mixing
dilutes the cloudy mass. Due to dilution both LWC and
N d reduces. Figure 2(a) shows observed N d in highly
diluted (AF < 0.4) and slightly diluted (AF > 0.4) cloud
samples at different altitude levels. We see that N d significantly reduces in the highly diluted region compared to slightly diluted region in case of pre-monsoon
clouds but the changes are very less in monsoon cloud.
Another aspect is that the variance of N d (represented
by the error bars) at different altitude levels is much
higher in the highly diluted part of the pre-monsoon
clouds. These variances are associated with the evaporation of droplets in the highly diluted cloud samples.
Most of these cases show a continued increase in N d
above the cloud base (mainly in the slightly diluted part
of the warm cloud, cf. Figure 2(a)). This is attributed to
the incloud activation as already discussed in the previous study (Prabha et al., 2011). In spite of significant
reduction in droplet number concentration, effective
radius of droplets does not show any significant changes
between highly diluted and slightly diluted cloud volumes in both pre-monsoon or monsoon clouds (figure
not presented).
3.1. Impact of lateral entrainment on cloud core
width
For studying entrainment effect, we have selected only
long cloud passes with a minimum pass time of 6 s
(600 m length) as followed by Small et al. (2009). Probability distribution of AF (Figure 2(b)) shows that polluted clouds are more adiabatic (AF > 0.7 exists in
pre-monsoon clouds only) in cloud core region but
the probability of higher AF (>0.4) decreases sharply
which suggests a narrow convective core (samples number is less). In case of the monsoon cloud, the probability of higher AF (>0.4) remained almost constant at
higher value which is in contrast to the pre-monsoon
clouds where rapid decrease is seen. This result suggests that lateral entrainment impacts the core width
of pre-monsoon and monsoon clouds differently. A
new analysis is presented to investigate the entrainment effect on cloud core width based on maximum
LWC. AF is varied as a function of distance (normalized) from highest LWC region (Figure 3). Convective
core is retained with maximum LWC (or AF) and as
we move from convective core to cloud edges, LWC
(or AF) decreases gradually. The width of cloud core
is the distance over which AF remained high (>0.4).
Cloud penetrations at different altitude levels (color
code represents height above cloud base) are considered
with a threshold penetration time of 10 s which is about
1 km penetration length so that convective core exists
in between the cloud edges. Number of clouds used in
the analysis (Figure 3(a)–(d)) indicated by marker legend correspond to each cloud pass. One of the important
results is a sharp reduction of AF within first 30–40%
distance is observed in case of polluted pre-monsoon
© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.
Atmos. Sci. Let. 17: 286–293 (2016)
30
–1
10
5
4
3
2
1
2000
1000
0
40
2
0
–1
50
100
150
200
Number of Spectra
250
–2
De (μ m)
–1
10
7
6
5
4
3
100
150
200
Number of Spectra
250
5
4
3
2
1
0
40
–2
300
30
25
20
15
10
(d)
1000
20
10
0
20
2000
1
30
1
30
d - dc(m)
30
25
20
15
10
(c)
2
40
50
Altitude (km)
–2
300
De (μ m)
7
6
5
4
3
100
150
200
250
Number of Spectra
σ (μm)
Altitude (km)
0
20
σ (μm)
Altitude (km)
d - dc (m)
1
0
5
4
3
2
1
De (μ m)
40
50
Droplet diameter (μm) d - dc (m)
2
30
25
20
15
10
σ (μ m)
0
(b)
1000
Droplet diameter (μm)
1000
7
6
5
4
3
2000
Droplet diameter (μm)
5
4
3
2
1
De (μm)
30
25
20
15
10
(a)
289
σ (μm)
7
6
5
4
3
2000
Droplet diameter (μm)
d - dc (m)
Altitude (km)
Entrainment and droplet spectral characteristics
2
1
30
0
20
–1
10
50
100
150
Number of Spectra
200
–2
Figure 1. Droplet size distribution (DSD) of four research flight observations. (a)–(d) represents 15, 16, 21 and 22 June,
respectively. Droplet number concentration per unit size (dN/dD) is presented in the log scale by the color code shown in the
right panel. Altitude of observation (red line), effective diameter (De ; blue line), distance from cloud core (d − dc , magenta line) and
spectral width of DSD (green line) is plotted on the top sub panel of each plot.
clouds but monsoon cloud shows a slower decrease of
AF and indicates a wider core possibly due to weaker
entrainment. The linear fit (black line) also indicate similar results where AF decreases more rapidly (higher
negative slope) with distance from core in pre-monsoon
clouds but in case of monsoon cloud, AF decreases
slowly (lower negative slope) with distance from core.
As the distance is normalized by core to edge distance,
the actual width of cloud core may vary for different
cloud penetration. For a 2 km cloud penetration length
the width of cloud core will be around 600–800 m in
pre-monsoon cases. It may also be noted that the AF
in the core region is higher in the pre-monsoon clouds
compared to the monsoon cloud. The monsoon cloud is
also less adiabatic at lower elevation and more adiabatic
in the core region at 2 km height above cloud base (this
is observed in selected cloud penetration samples but
is not an overall feature), however, in a pre-monsoon
cloud, throughout the warm region, the high adiabatic
region (though narrow) may be noted.
Figure 3(e)–(h) shows the PDF for AF > 0.4 and
AF < 0.4 as a function of the distance from highest
LWC point. The probability of higher AF (>0.4) is
reduced drastically as the distance increases in case of
polluted pre-monsoon clouds. This essentially implies
that less-diluted region (or cloud core) exists in a very
narrow region close to the highest LWC point. So,
these cumuli are mostly diluted even several hundred
meters inside of the cloud due to lateral entrainment.
But monsoon cloud (Figure 3(h)) has relatively wider
core so that higher AF (>0.4) can exists even far away
from the highest LWC point.
3.2. Droplet spectral characteristics
Now, we will discuss the effect of entrainment on
droplet spectral width (𝜎) and relative dispersion (𝜀),
which are important parameters used in cloud models.
Figure 4(a)–(d) shows joint probability distribution
of spectral width and distance from highest LWC
point. Spectral width in monsoon cloud is observed
higher than polluted pre-monsoon clouds which is
in agreement with previous studies (Prabha et al.,
2011, 2012; Bera et al., 2016). Higher values of spectral width are also observed in a region of highest
LWC (also reported by Prabha et al., 2012) and as we
© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.
Atmos. Sci. Let. 17: 286–293 (2016)
290
S. Bera, G. Pandithurai and T. V. Prabha
(a)
(b)
0.25
June 15
June 16
June 21
June 22
0.20
4
PDF
Altitude (km)
5
0.15
0.10
3
June 15
June 16
June 21
June 22
2
0
300
600
900
Nd (cm-3)
1200
0.05
0.00
0.0
1500
0.2
0.4
0.6
0.8
1.0
AF
Figure 2. (a) Droplet number concentration (Nd ) of four research flights (represented by different colors) for highly diluted region
(solid marker) and slightly diluted region (empty marker). Error bars represents standard deviations at different height levels. (b)
Probability distribution function (PDF) of AF.
1.0
June 15
(a)
Y = -0.42∗X + 0.48
0.8
AF
0.6
0.4
0.2
0.0
0.0 0.2 0.4 0.6 0.8 1.0
Distance from highest LWC point
0.14
AF < 0.4
AF > 0.4
(e)
0.1
0.5
0.9
1.3
1.7
2.1
2.5
2.8
June 16
(b)
Y = -0.44∗X + 0.54
10
11
14
18
18
18
21 0.0 0.2 0.4 0.6 0.8 1.0
25
Distance from highest LWC point
0.1
0.5
0.9
1.3
1.7
2.1
2.5
2.8
June 21
0.1
0.5
0.9
1.3
1.7
2.1
2.5
2.8
(c)
Y = -0.49∗X + 0.57
10
12
12
12
15
18
23 0.0 0.2 0.4 0.6 0.8 1.0
Distance from highest LWC point
(f)
June 22
(d)
Y = -0.35∗X + 0.44
0.1
0.5
0.9
1.3
1.7
2.1
2.5
2.8
11
11
13
14
16
17
22 0.0 0.2 0.4 0.6 0.8 1.0
10
12
11
18
18
19
Distance from highest LWC point
(g)
(h)
0.12
PDF
0.10
0.08
0.06
0.04
0.02
0.00
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
Distance from highest LWC point Distance from highest LWC point
0.0
0.2
0.4
0.6
0.8
1.0
Distance from highest LWC point
0.0
0.2
0.4
0.6
0.8
1.0
Distance from highest LWC point
Figure 3. (a)–(d) Variation of AF with distance from highest LWC point. Here the distance is normalized by cloud core to edge
distance. Color code represents height above cloud base in kilometer and the size of markers represents cloud penetration length
(in seconds) as shown in the right panel. Linear fit of scatter points is represented by black straight line and corresponding linear
fit equation is shown on the top. (e)–(h) PDF of AF > 0.4 (black bars) and AF < 0.4 (cyan bars) (respectively for 15, 16, 21 and 22
June) as a function of distance from highest LWC point.
move towards cloud edges spectral width decreases in
all clouds except the monsoon cloud (22 June). The
changes of spectral width at cloud edges (highly diluted
region) of pre-monsoon clouds are mainly associated
with lateral entrainment but collision-coalescence may
contribute to DSD change in monsoon cloud where
larger droplets are present. The result suggests opposite
response (narrowing of DSD) of deep cumulus clouds
in dry pre-monsoon condition compared to the shallow
cumulus where broadened DSDs are observed due to
entrainment mixing (Tölle and Krueger, 2014 and reference therein). The monsoon cloud, however, shows
contrasting behavior where higher spectral width is
seen for samples far away from highest LWC point.
The appearance of higher 𝜎 in the highly diluted cloud
edge region of monsoon cloud could be caused by
© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.
Atmos. Sci. Let. 17: 286–293 (2016)
Spectral width (μm)
Entrainment and droplet spectral characteristics
2.6
(a) 2.4
2.4
2.2
2.2
2
2
1.8
1.8
(b)
(c)
2.4
1.6
0.4
0.6
0.8
(e)
0.42
2
2.6
0.04
0.03
0.02
2
0.38
0.2
0.4
0.6
1.6
0.8
Distance from highest LWC point
0.2
0.4
0.6
0.8
0.05
(h)
0.35
0.04
0.4
0.36
0.38
0.36
0.38
0.34
0.34
0.32
0.03
0.25
0.02
0.2
0.01
0.32
0.3
0.3
0.3
0.28
0.3
0.36
0.34
0.32
0.28
0.2
0.4
0.6
0.2
0.8
Distance from highest LWC point
0
Distance from highest LWC point
(g)
0.42
0.4
0.01
1.8
1.2
(f)
0.05
2.4
1.4
0.2 0.4 0.6 0.8
Distance from highest LWC point
Distance from highest LWC point
2.8
2.2
1.2
0.2
2.2
1.6
1.4
1.2
(d)
3.2
3
1.8
1.6
1.4
Relative dispersion
291
0.4
0.6
0.8
0.2
Distance from highest LWC point
0.4
0.6
0.8
0.2 0.4 0.6 0.8
0
Distance from highest LWC point Distance from highest LWC point
Figure 4. (a)–(d) Contour plot of joint probability distribution of spectral width (𝜎) and distance from highest LWC point. (e)–(h)
Joint probability distribution of relative dispersion (𝜀) and distance from highest LWC point. Here the distance is normalized by
cloud core to edge distance.
(a)
15 June
16 June
21 June
22 June
2000
1200
800
1200
800
15 June
16 June
21 June
22 June
400
400
0
0.0
2000
1600
h - hCB (m)
h - hCB (m)
1600
(b)
0.2
0.4
0.6
0.8
1.0
0
0.0
Mixing Fraction of Cloud parcel
0.5
1.0
1.5
2.0
2.5
3.0
-1
Fractional entrainment rate (km )
Figure 5. Vertical variation of mixing fraction of cloud parcel (a) and fractional entrainment rate (b). Error bars represent
measurement uncertainties.
collision-coalescence of the larger droplets (Devenish
et al., 2012) which are not present in pre-monsoon
clouds (Prabha et al., 2011). As the environment is
moist and aerosols near clouds are pre-moistened as
illustrated by (Konwar et al., 2015), there is partial
evaporation of droplets, which broaden the spectra in
downdrafts and may activate in cloud updrafts. The
presence of collision-coalescence in monsoon deep
convective cloud is reported by Prabha et al. (2011)
and Khain et al. (2013). It is also evident from Figure 4
that probability is higher only close to highest LWC
region in pre-monsoon clouds but there exists higher
probability even close to cloud edge region in monsoon
cloud. Relative dispersion (Figure 4(e)–(h)) in polluted
pre-monsoon clouds is higher than observed in monsoon cloud which is in agreement with the previous
study by Pandithurai et al. (2012). Inside the cloud
core relative dispersion has a range of 0.3–0.4 in
pre-monsoon clouds and 0.2–0.3 in monsoon clouds.
In all pre-monsoon cases, relative dispersion remained
almost constant with distance from highest LWC point
which is in agreement with Tas et al. (2015), who
reported that relative dispersion does not vary with
LWC or N d . It should be pointed out that the higher
variances noted are due to the cloud edge entrainment. However, monsoon cloud has distinct feature
© 2016 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd
on behalf of the Royal Meteorological Society.
Atmos. Sci. Let. 17: 286–293 (2016)
292
where relative dispersion increases significantly with
the distance from cloud core although mean relative
dispersion is less compared to pre-monsoon clouds.
Similar features are presented in Figure S3 where
the variation of averaged spectral width and relative
dispersion with the distance from cloud core is shown.
In addition to entrainment-mixing effect, cloud droplet
spectral dispersion can also be influenced by vertical
velocity and aerosol variations (Pawlowska et al., 2006;
Lu et al., 2012b).
3.3. Entrainment rate
The above results suggest that entrainment impacts
the cloud core width and microphysical parameters distinctly in pre-monsoon and monsoon clouds.
Changes in entrainment rate are used to explain the
observed cloud features. Vertical distribution of fractional entrainment rate (𝜆) and mixing fraction (𝜒) is
investigated in Figure 5 by adapting the methodology
as suggested by Lu et al. (2012a). Cumulus clouds
entrain dry air during its development stage and as
a result mixing fraction of cloudy mass reduces as
it deepens. So, for higher entrainment rate, mixing
fraction becomes smaller. Mixing fraction at any specific level above cloud base indicates the degree of
mixing of cloudy volume with the environmental air
in comparison with the cloud base. It is almost same
at cloud base height (ideally equal to 1.0) but as depth
increases clouds are getting mixed with their close
environment and fraction of cloudy mass decreases.
At any height level, pre-monsoon polluted clouds have
lower fraction of cloudy mass. Thus the pre-monsoon
clouds are relatively more mixed (𝜒 is smaller) with the
environment than the monsoon cloud. Vertical distribution of fractional entrainment rate (𝜆) also indicates
similar results (Figure 5). Higher entrainment rate is
observed in polluted pre-monsoon clouds and lowest
entrainment rate is seen in less polluted monsoon cloud
(22 June) at any height levels except the cloud base.
The results discussed above support the argument that
polluted pre-monsoon clouds exhibit stronger entrainment which impacts cloud microphysical parameters
significantly. The entrainment rate observed here is
comparable with the previous studies (Gerber et al.,
2008; Lu et al., 2012a).
4. Conclusion
The entrainment and spectral characteristics of clouds
during the transition to monsoon period over southern peninsular region were investigated using in situ
airborne observations. The entrainment of unsaturated
environmental air decreases N d significantly in the
highly diluted regions compared to less diluted volume in pre-monsoon cases. Lateral entrainment produces higher variance of N d due to droplet evaporation in most diluted part. Analysis based on the highest LWC point indicates that less-diluted region of
S. Bera, G. Pandithurai and T. V. Prabha
the pre-monsoon clouds is much narrow compared
to the monsoon cloud where a wider cloud core is
found. Enhancement of entrainment rate is observed
in polluted pre-monsoon clouds where high aerosol
concentration in combination with environmental conditions favor cloud development. Spectral width of
DSD is found to decrease in the highly diluted region
of pre-monsoon clouds associated with droplet evaporation which is in contrast to the shallow clouds
where broadening of DSD due to entrainment-mixing
is reported in many previous studies. But after transition to monsoon, it is observed that spectral width
increases in the highly diluted part which can be due to
collision-coalescence process active in monsoon cloud
as well as the partial evaporation and activation (as
shown by Prabha et al., 2011). Relative dispersion (𝜀)
remained nearly constant with varying distance from
cloud core in case of polluted pre-monsoon clouds
but opposite effect is observed in less polluted monsoon cloud where relative dispersion increases significantly at cloud edges where higher spectral width is
observed.
Acknowledgements
IITM and the CAIPEEX projects are fully funded by the Ministry
of Earth Sciences (MoES), Government of India, New Delhi.
Authors acknowledge the dedication of many IITM scientists in
carrying out CAIPEEX experiment and data collection.
Supporting information
The following supporting information is available:
Appendix S1. Calculation of entrainment rate.
Figure S1. Contour plot of sea level pressure (SLP) and wind
vector at 850 mb pressure level. The location of experiment
(Hyderabad) is marked by black square.
Figure S2. Vertical profile of environmental potential temperature (𝜃) and water vapor mixing ratio (qv ) from radiosonde observation.
Figure S3. Variation of (a) averaged spectral width, (b) averaged
relative dispersion, (c) standard deviation of spectral width and
(d) standard deviation of relative dispersion as a function of
distance from highest LWC point. Four flights are represented
by different colors and markers as indicated in (a).
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