Introduction
Clouds are vital in driving the climate system as they play an important role
in radiation budget, general circulation, and the hydrological cycle (Ramanathan
et al., 1989; Rossow and Lacis, 1990; Wielicki et al., 1995; Li et al.,
1995; Stephens, 2005; Yang et al., 2010; Huang, 2013). By interacting with both
shortwave and longwave radiation, clouds play a crucial role in the radiative
budget at the surface, within, and at the top of the atmosphere (Li et al.,
2011; Ravi Kiran et al., 2015; George et al., 2018). Clouds result from
water vapour transport and cooling by atmospheric motions. The forcing for
atmospheric circulation is significantly modified by vertical and
horizontal gradients in the radiative and latent heat fluxes induced by
clouds (Chahine et al., 2006; Li et al., 2005). The complexity of the
processes involved, the vast amount of information needed, including
vertical and spatial distribution, and the uncertainty associated with the
available data all add difficulties to determining how clouds contribute to
climate change (e.g. Heintzenberg and Charlson, 2009). In particular,
knowledge about cloud type is very important because the overall impact of
clouds on the Earth's energy budget is difficult to estimate, as it involves
two opposite effects depending on cloud type (Naud et al., 2003). Low,
highly reflective clouds tend to cool the surface, whereas high,
semi-transparent clouds tend to warm it because they let much of the
shortwave radiation through but are opaque to the longwave radiation.
Deep convective clouds (DCCs) neither warm nor cool the surface
because their cloud greenhouse and albedo forcings nearly balance. However,
DCCs produce fast vertical transport, redistribute water vapour and chemical
constituents, and influence the thermal structure of the upper troposphere
and lower stratosphere (UTLS) (Biondi et al., 2012; Uma et al., 2012).
Changes in the cloud vertical structure (locations of cloud top and base,
number and thickness of cloud layers) affect atmospheric circulations by
modifying the distribution of radiative and latent heating rates within the
atmosphere (e.g. Slingo and Slingo, 1988, 1991; Randall et al., 1989; Wang
and Rossow, 1998; Li et al., 2005; Chahine et al., 2006; Cesana and Chepfer,
2012; Rossow and Zhang, 2010; Rossow et al., 2005; Wang et al., 2014b). The
effects of cloud vertical structure (CVS) on atmospheric circulation have
been described using atmospheric models (e.g. Rind and Rossow, 1984; Crewell
et al., 2004). Crewell et al. (2004) underlined the importance of clouds in multiple scattering and
absorption of sunlight, processes that have a significant impact on
diabatic heating in the atmosphere. The vertical gradients of diabatic
heating in cloud distribution were more important to the circulation
strength than horizontal gradients (Rind and Rossow, 1984). These complex
phenomena are not yet fully understood and are subject to large
uncertainties. In fact, the assumed or computed vertical structure of cloud
occurrence in general circulation models (GCMs) is one of the main reasons
for the differences in modelled projections of future climate. For example,
most GCMs underestimate cloud cover, while only a few overestimate it (Xi
et al., 2010). Therefore, to improve the understanding of cloud-related
processes and then to increase the predictive capabilities of large-scale
models (including global circulation models), better and more accurate
observations of CVS are needed. The present work reports the diurnal and
seasonal variations in CVS over Gadanki using long-term high-vertical-resolution radiosonde observations.
Ground-based instruments (e.g. Warren et al., 1988; Hahn et al., 2001),
active sensor satellites (e.g. Stephens et al., 2008; Winker et al., 2007),
and upper-air measurements from radiosondes (Wang et al., 2000) are usually
applied to observe the CVS. Ground-based instruments such as lidar, cloud
radar, and ceilometers provide cloud measurements with continuous temporal
coverage. Lidars and ceilometers are very efficient in detecting clouds and
can locate the bottom of cloud layer precisely, but cannot usually detect
the cloud top due to attenuation of the beam within the cloud. The
vertically pointing cloud radar is able to detect the cloud top, although
signal artefacts can cause difficulties during precipitation (Nowak et al.,
2008). On the other hand, passive-sensor satellite data, such as from ISCCP
(the International Satellite Cloud Climatology Project) and MODIS (the
Moderate Resolution Imaging Spectroradiometer), have some limitations in
using the analyses presented in this study. For example, thin clouds are
indistinguishable from aerosols in ISCCP when optical thickness is less than
0.3–0.5) (Rossow and Garder, 1993); both ISCCP and MODIS underestimate
low-level clouds and overestimate middle-level cloud (Li et al., 2006; Naud
and Chen, 2010). Hence, conventional passive-sensor satellite measurements
largely miss comprehensive information on the vertical distribution of
cloud layers. The precipitation radar and TRMM Microwave Imager on-board the
Tropical Rainfall Measuring Mission (TRMM) satellite are helpless in
observing small-size particles despite its capability to penetrate
rainy cloud and obtain internal three-dimensional information, and
only larger rainfall particles can be observed due to the limitations of its
working broadband. On the other hand, active sensors such as the cloud
profiling radar (CPR) on CloudSat and the Cloud–Aerosol Lidar with Orthogonal
Polarization (CALIOP) aboard CALIPSO (Cloud Aerosol Lidar and Infrared
Pathfinder Satellite Observation) are achieving notable results by
including a vertical dimension to traditional satellite data. CPR is a 94 GHz nadir-looking radar that is able to penetrate optically thick
clouds, while CALIOP is able to detect tenuous cloud layers that are below
the detection threshold of radar. In other words, it has the ability to
detect shallow clouds. Therefore, an accurate location of the cloud top and
complete vertical structure information on the cloud can be obtained by the
combined use of CPR and CALIOP, because of their unique complementary
skills. Previous studies have shown that CloudSat–CALIPSO data have better
accuracy compared with ISCCP and ground observation data (Sassen and Wang,
2008; Naud and Chen, 2010; Kim et al., 2011; Noh et al., 2011; Jiang et al.,
2011). However, because the repeat time of these polar-orbiting satellites
for any particular location is very large, the time resolution of such
observations is low (L'Ecuyer and Jiang, 2010; Qian et al., 2012). Both
ground-based and space-based measurements have the problem of overlapping
cloud layers that hide each other.
Some other methods have also been developed to detect cloud top heights from
passive sensors. The CO2-slicing method uses CO2 differential
absorption in the thermal infrared spectral range (Rossow and Schiffer,
1991; King and Vaughan, 2012; Platnick et al., 2003).
Ultraviolet radiances can
also be used as rotational Raman scattering causes depletion or filling of
solar Fraunhofer lines in the UV spectrum, depending on the Rayleigh
scattering above the cloud (Joiner and Bhartia, 1995; de Beek et al., 2001).
Similarly, the polarization of reflected light, at visible shorter
wavelength due to Rayleigh scattering, carries information on cloud top
height (Goloub et al., 1994; Knibbe et al., 2000). Finally, cloud top height
can also be retrieved by applying geometrical methods to stereo observations
(Moroney et al., 2002; Seiz et al., 2007; Wu et al., 2009). Global
Navigation Satellite System (GNSS) radio occultation (RO) profiles were used
to detect convective cloud top heights (Biondi et al., 2013). Recently,
Biondi et al. (2017) used GNSS RO profiles to detect the top altitude of
volcanic clouds and analysed their impact on the thermal structure of UTLS.
Multi-angle and bi-spectral measurements in the O2 A band were used to
derive the cloud top altitude and cloud geometrical thickness (Merlin et
al., 2016 and references therein). However, this method is restricted to
homogeneous plane-parallel clouds. For heterogeneous clouds or when aerosols
lay above the clouds the spectra of reflected sunlight in the O2 A band
become modified.
An indirect way to perform estimations of CVS is by using atmospheric
thermodynamic profiles measured by radiosondes. Radiosondes can penetrate
atmospheric (and cloud) layers to provide in situ data. The profiles of
temperature, relative humidity, and pressure measured by radiosondes provide
information about the CVS by identifying saturated levels in the atmosphere
(Zhang et al., 2010). In fact, radiosonde measurements are probably the best
measurements for deriving CVS from the ground (Wang et al., 2000; Eresmaa et
al., 2006; Zhang et al., 2010). Very recently, George et al. (2018) provided
CVS over India during depression and non-depression events during the south-west monsoon season
(July 2016) using 1 month of campaign data. However, detailed CVS in all
the seasons including diurnal variation over the Indian region has not been made so far
to the best of our knowledge.
The objective of this study is to examine the temperature structure of the UTLS
region during the occurrence of single-layer and multilayer clouds over
the Gadanki location (13.5∘ N, 79.2∘ E). In the first, we focus
on reporting the CVS using long-term (11 years) high-vertical-resolution
radiosonde observations. The paper is organized as follows: data and
methodology are described in Sect. 2. In Sect. 3, background weather
conditions during the period of analysis are described. Results and
discussion are given in Sect. 4. Finally, the summary and major conclusions
drawn from the present study are provided in Sect. 5.
Monthly percentage of radiosonde data available during April 2006–May 2017 at Gadanki. The percentage of discarded profiles in each month is
also shown in red.
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Data and methodology
Data
In this study, long-term (11 years) observations of high-vertical-resolution
radiosonde (Vaisälä RS-80, RS-92; Meisei RS-01GII, RS-6G, RS-11G,
IMS-100) data are used to analyse CVS over a tropical station, Gadanki. There
is no significant change in the accuracies of the meteorological parameters
from these different radiosondes. Most of these radiosondes were
launched around 17:30 local time (LT = UT + 05:30 h). In general, the
balloons are not launched during moderate to heavy rain conditions. However,
we have done a visual inspection of each radiosonde profile. The RH profiles
which show continuous saturation with height were discarded. Figure 1 shows
the monthly percentage of radiosonde data available from April 2006 to May 2017. In total, 3313 launches were made, out of which 98.9 % and 86.6 %
reached altitudes greater than 12.5 and 20 km, respectively. The data
which have a balloon-burst altitude less than 12.5 km (1.1 %) are
discarded. Also, we have put a condition on the number of profiles in a
month, which
should be more than seven to represent that month. After applying these two
conditions the total number of profiles was 3251. In addition, to study the
diurnal variations in CVS over Gadanki, we made use of radiosonde
observations taken from Tropical Tropopause Dynamics (TTD) campaigns (Venkat
Ratnam et al., 2014b) conducted during the Climate and Weather of Sun Earth
Systems (CAWSES) India Phase II programme (Pallamraju et al., 2014). During
these campaigns, radiosondes were launched every 3 h for
three continuous days in each month from December 2010 to March 2014 except in
December 2012 and January, February, and April 2013.
Methodology
Several methods are employed to determine the CVS from the profiles of
radiosonde data (Poore et al., 1995; Wang and Rossow, 1995; Chernykh and
Eskridge, 1996; Minnis et al., 2005; Zhang et al., 2010). Poore et al. (1995)
estimated the cloud base and cloud top using temperature-dependent dew-point
depression thresholds. First, the dew-point depression must be calculated at
every radiosonde level. According to Poore et al. (1995), a given atmospheric
level has a cloud if ΔTd<1.7 ∘C at T>0 ∘C, ΔTd<3.4 ∘C at 0>T>-20 ∘C, and ΔTd<5.2 ∘C at T<-20 ∘C.
Wang and Rossow (1995) used temperature, pressure, and RH profiles and
computed RH with respect to ice instead of liquid water for the levels with
temperatures lower than 0 ∘C. To this new RH profile they have
applied two RH thresholds (min RH = 84 % and max RH = 87 %). In
addition, if RH at the base (top) of the moist layer is lower than 84 %,
an RH jump exceeding 3 % must exist from the underlying (above) level.
According to the Chernykh and Eskridge (1996) method, the necessary condition
for the existence of clouds in a given atmospheric level is that the second
derivatives with respect to height (z) of temperature and RH are positive
and negative, respectively, i.e. T′′(z)≥0 and RH′′(z)≤0. Minnis et al. (2005)
provided an empirical parameterization that
calculates the probability of the occurrence of a cloud layer using RH and air
temperature from radiosondes. First, RH values must be converted to RH with
respect to ice when temperature is less than -20 ∘C. Second, the
profile has to be interpolated every 25 hPa up to the height of 100 hPa. An
expression to estimate the cloud probability (Pcld) as a function of
temperature and RH is then applied. In this expression, RH is given the
maximum influence as it is the most important factor in cloud formation.
Finally, a cloud layer is set wherever Pcld ≥ 67 %. The Zhang et al. (2010) method is an improvement on the Wang and Rossow (1995) method. Instead
of a single RH threshold, Zhang et al. (2010) applied altitude-dependent
thresholds without the requirement of the 3 % RH jump at the cloud base
and top.
Costa-Suros et al. (2014) compared the CVS derived from these five methods
described above by using 193 radiosonde profiles acquired at the Atmospheric
Radiation Measurement (ARM) Southern Great Plains site during all seasons of
the year 2009. The performance of the five methods has been assessed by
comparing with Active Remote Sensing of Clouds (ARSCL) data taken as a
reference. Costa-Suros et al. (2014) concluded that three of the methods
(Poore et al., 1995; Wang and Rossow, 1995; Zhang et al., 2010) perform
reasonably well, giving perfect agreement for 50 % of the cases and
approximate agreements for 30 % of the cases. The other methods gave poor
results (lower perfect and/or approximate agreement and higher false
positive, false negative, or not coincident detections). Among the three
methods, the Zhang et al. (2010) method is the most recent version of the
treatment initially proposed in Poore et al. (1995) and Wang and Rossow (1995), and it provides good results (a perfect agreement of 53.9 % and an
approximate agreement of 29.5 %). Thus, the algorithm of Zhang et al. (2010) is used for detecting cloud layers in our analysis.
Cloud layers are associated with high RH values above some threshold as the
radiosonde penetrates through them. The cloud detection algorithm of Zhang et al. (2010) employs three height-resolving RH thresholds to determine cloud
layers: minimum and maximum RH thresholds in cloud layers (min-RH and
max-RH) and minimum RH thresholds within the distance of two adjacent layers
(inter-RH). The height-resolving thresholds of max-RH, min-RH, and inter-RH
values are specified in Table 1. The algorithm begins by converting RH with
respect to liquid water to RH with respect to ice at temperatures below
0 ∘C (see example in Fig. 2). The accuracy of RH measurement is
less than 5 % up to the altitude 12.5 km and hence the RH profile is
examined from the surface to 12.5 km (∼200 hPa) of altitude to
find cloud layers in seven steps: (1) the base of the lowest moist layer is
determined as the level at which RH exceeds the min-RH corresponding to this
level; (2) above the base of the moist layer, contiguous levels with RH over
the corresponding min-RH are treated as the same layer; (3) the top of the
moist layer is identified when RH decreases to below the corresponding
min-RH or RH is over the corresponding min-RH but the top of the profile is
reached; (4) moist layers with bases lower than 500 m a.g.l. (above ground
level) and thickness less than 400 m are discarded; (5) the moist layer is
classified as a cloud layer if the maximum RH within this layer is greater
than the corresponding max-RH at the base of this moist layer; (6) two
contiguous layers are considered as a one-layer cloud if the distance between
these two layers is less than 300 m or the minimum RH within this distance is
more than the maximum inter-RH value within this distance; and (7) clouds are
discarded if their thicknesses are less than 100 m.
Results from a flight of RS-11G radiosonde and cloud particle sensor
(CPS) sonde on the same balloon launched at 02:00 IST on 4 August 2017 at
Gadanki, India. Profiles of RH estimated with respect to water (solid black
line), ice (when temperatures are less than 0 ∘C, solid red line),
and number concentration (filled blue circles) from CPS sonde profile are
shown. Detected cloud-layer boundaries are shown by the filled gray
rectangular
boxes. The increase in the number concentration within the detected cloud layers
indicates that the cloud-layer boundaries detected in the present study are
accurate.
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Summary of height-resolving RH thresholds.
Height-resolving RH thresholds
Altitude range
min-RH
max-RH
inter-RH
0–2 km
92 %
95 %
84 %
2–6 km
90 %
93 %
82 %
6–12 km
88 %
90 %
78 %
>12 km
75 %
80 %
70 %
At the measurement location, we have boundary-layer lidar (BLL) and Mie lidar. When
there is an occurrence of multilayer configuration, BLL does not give an accurate
cloud base altitude for higher layers. Mie lidar gives the vertical
structure of cirrus clouds (usually occurring at higher altitude). Here, CVS
is examined only up to 12.5 km of altitude as the accuracy of RH measurements
is poor at higher altitudes. Also, Mie lidar is operated mostly during cloud-free conditions (only during
cirrus cloud or clear sky conditions). Further,
the timings of radiosonde and lidar measurements are different. Therefore, we
did not compare with the ground-based lidar measurements. On the other hand,
CloudSat–CALIPSO overpasses of the experiment location are around 02:00 and 14:00 LT, whereas regular
radiosonde launches are around 17:30 LT. Therefore, we did
not compare the CVS derived from regular radiosonde and CloudSat–CALIPSO
measurements. However, we have 3-hourly radiosonde observations for
three continuous days in every month during TTD campaigns. We did not get
collocated (space and time) measurements from CloudSat–CALIPSO and radiosonde
during these campaigns.
Seasonal mean distribution of OLR around the Gadanki location observed
during (a) pre-monsoon, (b) monsoon, (c) post-monsoon, and (d) winter seasons
averaged during 2006–2017. The symbol “X” indicates the location of
Gadanki.
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Before proceeding further, it is desired to verify whether the identified layers of
clouds are correct or not with independent observations. For that we have
launched cloud particle sensor (CPS) sonde (Fujiwara et al., 2016) at
Gadanki, which provides a profile of cloud number concentration. Results from
a flight of RS-11G radiosonde and cloud particle sensor (CPS) sonde on the
same balloon launched at 02:00 LT on 4 August 2017 at Gadanki, India, are shown in
Fig. 2. A sudden increase in the cloud number concentration within the
detected cloud layers indicates that the cloud-layer boundaries detected in the
present study are in good agreement.
The drawback of using the radiosonde data for detecting the CVS at a given
location is the radiosonde horizontal displacement due to the drift produced
by the wind. However, irrespective of the season, the maximum horizontal
drift of radiosonde when it reaches the 12.5 km of altitude is always less than
20 km (Venkat Ratnam et al., 2014a). One may expect different background
features within this 20 km, particularly localized convection, that may
influence the CVS. In order to assess this aspect, we used outgoing longwave
radiation (OLR) as a proxy for tropical convection. Figure 3a–d show the
seasonal mean distribution of OLR (from KALPANA-1 satellite) around the Gadanki
location obtained during the pre-monsoon, monsoon, post-monsoon, and winter
seasons averaged during 2006–2017. It can be noted that irrespective of
the season, homogeneous cloudiness prevailed for more than a 50 km radius
around the Gadanki location. Hence, the CVS detected from the radiosonde can be
treated as representative of the Gadanki location.
The methodology described in Sect. 2.2 to detect CVS is applied on high-vertical-resolution radiosonde data acquired during April 2006 to May 2017
from Gadanki, as well as special radiosondes launches during TTD campaigns
from December 2010 to March 2014. Results are presented in Sect. 4. Before
going further, it is desirable to examine the background meteorological
conditions prevailing over Gadanki during different seasons.
Background meteorological conditions
Time–altitude cross sections of monthly mean (a) temperature
anomaly, (b) relative humidity, (c) zonal wind, and (d) meridional wind
observed over Gadanki using radiosonde observations during April 2006 to May 2017. (e) Monthly mean outgoing longwave radiation (OLR) over Gadanki
obtained using KALPANA-1 data during April 2006 to May 2017 along with
standard deviation (vertical bars).
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The National Atmospheric Research Laboratory (NARL) at Gadanki is located about
120 km north-west of Chennai (Madras) on the east coast of the southern
Indian peninsula. This station is surrounded by hills with a maximum altitude
of 350–400 m above the station, and the station is at an altitude of 375 m a.m.s.l. (hereinafter all altitudes are mentioned above mean sea level). The
local topography is complex with a number of small hillocks around and a high
hill of ∼1 km about 30 km from the balloon launching site in the
north-east direction. The detailed topography of Gadanki is shown in Basha and
Ratnam (2009). Gadanki receives about 53 % of the annual rainfall during
the south-west monsoon (June to September) and 33 % of the annual rainfall
during the north-east monsoon (October to December) (Rao et al., 2008a). The rainfall
during the south-west monsoon occurs predominantly from the evening to
midnight period. About 66 % of total rainfall is convective in nature,
while the remaining rain is widespread stratiform in character (Rao et al.,
2008a).
Background meteorological conditions prevailing over the observational site
are briefly described based on the radiosonde data collected during April 2006
to May 2017. The seasons are classified as winter (December–January–February), pre-monsoon (March–April–May),
monsoon
(June–July–August–September), and post-monsoon (October–November). The
climatological monthly mean contours of the temperature anomalies, relative
humidity, and zonal and meridional winds are shown in Fig. 4a–d,
respectively. From the surface to 1 km of altitude, temperature anomalies show
seasonal variability with warmer temperatures during pre-monsoon months and
relatively lower temperatures during the winter season (Fig. 4a). Temperature
anomalies do not show significant seasonal variation from 1 km of altitude to
the middle troposphere, but significant seasonal differences are observed in
the lower stratosphere. There are significant seasonal variations in the RH
(Fig. 4b). During winter, RH is small (40–50 %) from the surface to
∼3 km of altitude and is almost negligible above. However,
during the other seasons, particularly in the peak monsoon months (July and
August), large RH values (60–70 %) are noticed up to 10 km of altitude.
During winter, easterlies are observed up to 4–6 km of altitude and westerlies
above (Fig. 4c). There seem to be weak easterlies between 14 and 20 km
of altitude during the pre-monsoon. During the monsoon season low-level
westerlies exist below 7–8 km and easterlies above. The Tropical Easterly
Jet (TEJ) is prevalent over this region in the SW monsoon season, with peak
velocity sometimes reaching more than 40 m s-1 (Roja Raman et al., 2009).
There are large vertical shears during the monsoon in the zonal wind.
Easterlies exist up to 20 km of altitude during the post-monsoon season. In
general, meridional velocities are very small and northerlies are
observed up to 8 km and southerlies above in all the seasons, except during
the monsoon (Fig. 4d). During the winter and monsoon, relatively stronger
southerlies and northerlies prevailed, respectively, between 12 and 15 km
of altitude. A clear annual oscillation can be noticed in both zonal and
meridional velocities. Similar variations are also observed by the MST radar
located at the same site between 4 and 20 km (Venkat Ratnam et al., 2008; Basha
and Ratnam, 2013; Nath et al., 2009). Monthly mean OLR around
Gadanki at 17:30 LT is shown in Fig. 4e. Low values of OLR (<220 W m-2) around the Gadanki location indicate that the occurrence of very deep
convection during the monsoon season, consistent with the occurrence of high
RH values up to 10 km of altitude during the monsoon season (Fig. 4b).
Results
By adopting the methodology described in Sect. 2.2 we have detected a
total of 4309 cloud layers from 3251 radiosonde launches at the Gadanki location
during the period of data analysis. For each season, cloud layers during
April 2006–May 2017 are averaged to obtain the composite picture of CVS.
Seasonal variability in cloud layers is discussed in Sect. 4.2.
Diurnal variation of single-layer and multilayer clouds
There are studies on the diurnal variation of cloud layers outside the Indian
region, for example over Porto Santo Island during the Atlantic
Stratocumulus Transition Experiment (ASTEX) by Wang et al. (1999), over San
Nicolas Island during the First ISCCP Regional Experiment (FIRE) by Blaskovic et al. (1990),
and over Shouxian (32.56∘ N, 116.78∘ E) by
Zhang et al. (2010). To the authors' knowledge there are no studies on the diurnal
variability of cloud layers over the Indian region. For the first time over
the Indian region, the diurnal variability of cloud layers is studied by
using radiosonde observations taken from TTD campaigns. Figure 5a–d
describe the diurnal variations of single-layer and multilayer clouds
during the pre-monsoon, monsoon, post-monsoon, and winter seasons over the Gadanki
region. As mentioned in Sect. 2.1, from December 2010 to March 2014, we have
launched radiosondes every 3 h for three continuous days in every
month except during December 2012 and January, February, April, 2013. The total number of
profiles taken during the pre-monsoon, monsoon, post-monsoon, and winter seasons
are 160, 254, 101, and 199, respectively. Among these the number of cloudy
profiles are 93 in the pre-monsoon, 241 in the monsoon, 63 in the post-monsoon, and 96 in
the winter season.
Diurnal variations of one-layer, two-layer, three-layer, and four- or more-layer clouds observed during (a) pre-monsoon, (b) monsoon, (c)
post-monsoon, and (d) winter seasons.
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Diurnal variations of mean vertical locations (base and top) and
thicknesses of one-layer clouds observed during (a) pre-monsoon, (b)
monsoon, (c) post-monsoon, and (d) winter seasons.
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Diurnal variations of mean vertical locations (base and top) and
thicknesses of two-layer clouds observed during (a) pre-monsoon, (b)
monsoon, (c) post-monsoon, and (d) winter seasons.
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From Fig. 5a–d for four seasons, diurnal variations of cloud occurrence
show a maximum between 23:00 and 05:00 LT and a minimum at 14:00 LT, except during
the monsoon season. During the monsoon season, a minimum in cloud occurrence occurred
at 11:00 LT. Using infrared brightness temperature data over the Indian region
Gambheer and Bhat (2001), Zuidema (2003), and Reddy and Rao (2018) observed the
maximum frequency of occurrence of clouds during late night to early morning
hours. The percentage of occurrence of one-layer and multilayer clouds shows
noticeable diurnal variations in all seasons except in the monsoon season.
The maximum percentage of occurrence in one-layer clouds is at 08:00 LT in the pre-monsoon
season and it is at 17:00 LT during the post-monsoon and winter seasons. For all
the seasons, the maximum percentage of occurrence in multilayer clouds is
between 20:00 and 05:00 LT. Figure 6a–d describe the mean vertical locations
(base and top) and cloud thicknesses of one-layer clouds during the pre-monsoon,
monsoon, post-monsoon, and winter seasons, respectively. During the monsoon
season, the maximum in cloud top altitude is at 05:00 LT and the minimum is at
14:00 LT (Fig. 6b). In general, the cloud base of one-layer cloud occurs at higher
altitude between 11:00 and 14:00 LT and it occurs at relatively low altitudes between
20:00 and 08:00 LT. Except during the post-monsoon season, single-layer clouds are
high-level clouds with a base at greater than 5 km most of the time. During
the post-monsoon season, single-layer clouds are low-level at 05:00 LT
(cloud base altitude of 1.4 km) and middle-level clouds between 14:00 and 02:00 LT
(Fig. 6c). During the pre-monsoon and monsoon seasons, the thickness of single-layer
clouds reaches a maximum at 23:00 LT and a minimum at 14:00 LT (Fig. 6a, b).
The minimum in one-layer cloud thickness at 14:00 LT is due to the increase
in
cloud base altitude and simultaneous decrease in cloud top altitude. There is
not much variability in the thickness of one-layer clouds during the post-monsoon and
winter seasons (Fig. 6c, d). Figure 7a–d and Fig. S1a–d in the Supplement are the same as
Fig. 6a–d but for two-layer and three-layer clouds. Similar to one-layer
cloud, the cloud base of the bottom layer of two-layer clouds shows a maximum
between 11:00 and 14:00 LT and a minimum between 20:00 and 08:00 LT. The thickness of the top
layer and bottom layer of two-layer clouds reaches a minimum value between
11:00 and 14:00 LT. The upper layer of two-layer clouds shows a maximum in thickness at
23:00 LT and minimum at 11:00 LT during the monsoon season (Fig. 7b).
Cloud maintenance and development are strongly modulated by diabatic
processes, namely solar heating and longwave (LW) radiative cooling (Zhang
et al., 2010). Near noontime (11:00–14:00 LT), solar heating is so strong that
(1) evaporation of cloud drops may occur and (2) atmospheric stability may
increase, thus suppressing cloud development. So near noontime, the vertical
development of single-layer clouds and the vertical development of the
uppermost layer of multiple layers of cloud are suppressed due to solar
heating. This effect is predominant during the monsoon season for one-layer and
two-layer clouds (Figs. 6b and 7b) and during the pre-monsoon and
post-monsoon seasons for three-layer clouds (Fig. S1a, c). However,
for lower layers of cloud in a multiple-layer cloud configuration, solar
heating is greatly reduced because of the absorption and scattering
processes of the upper layers of cloud. In general the maximum in surface
temperature occurs around 15:20 LT (Reddy and Rao, 2018). The ground surface
is warmer than any cloud layer so through the exchange of LW radiation, the
cloud base gains more energy. This facilitates cloud development and leads
to a maximum in cloud altitude and thickness between 14:00 and 17:00 LT (Figs. 7a, b, d, and S1a). This effect is
predominant during the winter season for two-layer clouds (Fig. 7d) and during the pre-monsoon season for three-layer
clouds (Fig. S1a). As the sun sets, LW radiative cooling starts to
dominate over shortwave (SW) radiative warming. Cloud top temperatures begin
to lower, which increases atmospheric instability and fuels the development
of single-layer clouds and the uppermost layer of cloud in multiple-layer
cloud configurations. At sunset, solar heating diminishes and LW cooling
strengthens, which may explain why there is a peak between 20:00 and 23:00 LT in
the thickness of one-layer clouds and the uppermost layer of two-layer
cloud. This effect is clearly observed in the monsoon season (Figs. 6b, 7b, S1b). We conclude that
diurnal variability in the base, top, and thickness
for single-layer, two-layer, and three-layer clouds is significant. Hence
there can be a bias in cloud vertical structure when we are studying the
composite over a season by using polar satellites.
Percentage of occurrence of the (a) cloud base altitude, (b) cloud
top altitude, and (c) cloud thickness observed during different seasons over
Gadanki. Altitude bin size is 500 m.
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In the next section, we show the seasonal variability in cloud layers using
long-term (11 years) observations of high-vertical-resolution radiosonde over
Gadanki. Note that most of these radiosondes were launched around 17:30 LT and hence there will be bias in
the results due to the diurnal variability of cloud
layers, which we have discussed above. Hence the results related to the seasonal
variability of cloud layers are only representative of 17:30 LT.
Seasonal variability in the cloud layers
Figure 8a–c describe the percentage of occurrence of the base, top, and thickness
of cloud layers observed during different seasons over Gadanki. The cloud
base altitude shows a bimodal distribution in all seasons except during
the pre-monsoon season (Fig. 8a). During the pre-monsoon season, the peak of cloud
base altitude distribution is observed at ∼6.2 km
(∼7.5 %). During the other three seasons (monsoon,
post-monsoon, and winter), the first peak in cloud base altitude is observed
between 2 and 3 km of altitude and the second peak is observed at
∼6.2 km. Using CloudSat observations over the Indian monsoon
region, Das et al. (2017) also reported that the cloud base altitude over
the Indian monsoon region shows a bimodal distribution. However, the first peak
in cloud base altitude is observed at ∼14 km, while the second
maximum is at 2 km.
The cloud top altitude increases above 12 km of altitude and has a maximum at
12.5 km in all seasons (Fig. 8b). Note that we restrict maximum altitude
to 12.5 km due to limitation in providing reliable water vapour above that
altitude from normal radiosondes. At lower altitudes, during the monsoon
season the peak in cloud top altitude is at 2.9 km and it increases to 3.3 km during the post-monsoon season. However,
we have also checked the cloud
vertical structure until 18 km. There is no significant difference in the
cloud base and cloud top altitude distribution (See Fig. S2). Das et al. (2017)
reported that there are two peaks in the cloud top altitude: one at
∼17 km and other at ∼3 km. The peaks in
cloud base and cloud top at higher altitudes as observed by Das et al. (2017)
could be due to the occurrence of cirrus clouds.
Mean vertical locations (base and top), cloud thicknesses, and
percentage of occurrence of (a) low-level clouds, (b) middle-level clouds, (c)
high-level clouds, and (d) deep convective clouds observed during different
seasons.
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The cloud base altitude values are subtracted from the cloud top altitude for
each cloud layer to extract the cloud thickness. Figure 8c describes the
percentage of occurrence of cloud thickness observed during different
seasons. The occurrence of thicker clouds decreases exponentially. The cloud
thickness has a maximum below 500 m for all seasons, which constituted about
34.7 %, 26.5 %, 31.2 %, and 36.6 % of the total observed cloud
layers during the pre-monsoon, monsoon, post-monsoon, and winter seasons,
respectively. In general, for all seasons, more than 65 % of cloud
layers have cloud thickness <2 km.
Different cloud types occurring at different height regions have a spectrum
of effects on the radiation budget (Behrangi et al., 2012). Therefore, the
clouds have been classified into four groups based on the cloud base altitude
and their thickness (Lazarus et al., 2000; Zhang et al., 2010): (1) low-level
clouds with bases lower than 2 km and thickness less than 6 km;
(2) middle-level clouds with bases ranging from 2 to 5 km; (3) high-level
clouds with bases greater than 5 km; and (4) deep convective cloud
(hereafter called DCC) with a base less than 2 km and thicknesses greater than
6 km. These four types of clouds account for 11.97 %, 26.71 %,
59.36 %, and 1.95 % of all cloudy cases, respectively. Figure 9a–d
describe the mean vertical locations (base and top), cloud thicknesses, and
percentage of occurrence of low-, middle-, and high-level clouds and DCC observed
during different seasons. At the Gadanki location, there is a distinct
persistence of high-level clouds over all the seasons. The occurrence of
high-level clouds is 69.05 %, 58.49 %, 55.5 %, and 58.6 %
during the pre-monsoon, monsoon, post-monsoon, and winter seasons,
respectively (Fig. 9c). In general, after the dissipation of deep convective
clouds they spread large anvils and persist as high-level clouds for
a longer duration. These high-level clouds could be due to in situ generated
convective systems or propagated from the surrounding oceans.
Zuidema (2003) reported that the deep convective systems generated over
the central and western Bay of Bengal (BoB) advect toward the inland region of
southern peninsular India and dissipate. In general, the high-level clouds
follow background winds at those levels. Especially during the monsoon season
due to the strong westerly winds in the upper levels, high-level clouds which
originate from MCS over BoB advect into the Indian region and
contribute to the high-level cloud occurrence. Hence the outflow caused by
deep convective systems could be responsible for the higher percentage
of occurrence of high-level clouds. Low-level (middle-level) clouds
contribute about 3.74 %, 10.45 %, 16.27 %, and 20.89 %
(27.04 %, 29.35 %, 24.28 %, and 18.67 %) of all cloudy cases
during the pre-monsoon, monsoon, post-monsoon, and winter seasons,
respectively (Fig. 9a, b).
The thicknesses of low-, middle-, and high-level clouds have minimum values during
the winter season and maximum values in the monsoon season (Fig. 9a–c). DCCs
have a minimum thickness in winter and a maximum in the pre-monsoon season (Fig. 9d).
The average cloud base (cloud top) altitudes for low-, middle-, and
high-level clouds and deep convective clouds are 1.74 km (3.16 km), 3.59 km (5.55 km),
8.79 km (10.49 km), and 1.22 km (11.45 km), respectively. Over the Indian summer
monsoon region, Das et al. (2017) reported that the percentage of occurrence of
high-level clouds is more than the other three cloud types. Over Shouxian
(32.56∘ N, 116.78∘ E), Zhang et al. (2010)
reported that the percentage of occurrence of low-, middle-, and
high-level clouds
and deep convective clouds is 20.1 %, 19.3 %, 59.5 %, and
1.1 %, respectively.
Percentage of occurrence of (a) one-layer, (b) two-layer, (c)
three-layer, and (d) four- or more-layer clouds observed during different
seasons.
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Single-layer and multilayer clouds
By interacting with both shortwave and longwave radiation, clouds play
a crucial role in the radiative budget at the surface, within, and at the top of
the atmosphere. Over the tropics, the zonal mean net cloud radiative effect
differences between multilayer clouds and single-layer clouds were positive
and dominated by shortwave cloud radiative effect differences (Li et al.,
2011). This is because multilayer clouds reflect less sunlight to the
top of the atmosphere and transmit more to the surface and within the
atmosphere than single-layer clouds as a whole. As a result, multilayer
clouds warm the Earth–atmosphere system when compared to single-layer clouds
(Li et al., 2011). In this study, we studied the occurrence of single-layer
and multilayer clouds obtained during different seasons at the Gadanki location.
The percentage of occurrence of single-layer, two-layer, three-layer, and four- or more-layer clouds during the
pre-monsoon, monsoon, post-monsoon, and winter
seasons is shown in Fig. 10a–d. Single-layer, two-layer, and three-layer
clouds account for 40.80 %, 30.71 %, and 19.68 % of all cloud
configurations, respectively. Despite the low frequency of occurrence of
one-layer clouds over Gadanki, they exhibit pronounced seasonal variation in
magnitude with very low frequency during the pre-monsoon season. This may be due
to the strong warm and dry atmospheric conditions from the surface to the boundary-layer top (Fig. 4a, b). The percentage
of occurrence of single-layer
(multilayer) clouds during the pre-monsoon, monsoon, post-monsoon, and winter
seasons is 7.7 %, 14.2 %, 8.48 %, and 10.42 % (7.93 %,
34.58 %, 10.83 %, and 5.86 %), respectively. There is a
significant occurrence of multilayer clouds during the monsoon season compared to the other
seasons, indicating that the development of multilayer clouds is favourable
under warm and moist atmospheric conditions (Fig. 4a, b). Among the different
cloud layers, two-layer clouds have a maximum percentage of occurrence
(16.6 %) during the monsoon season (Fig. 10b). Luo et al. (2009) reported the
occurrence of multilayer clouds over the Indian region during the summer
season and attributed it to the complex cloud structure associated with the
monsoon system. Zhang et al. (2010) reported that multilayer cloud
occurrence frequency is relatively higher during summer months (June, July,
and August) than autumn months (September, October, and November) over
Shouxian. Recently, Using the 4 years of combined observations of CloudSat
and CALIPSO, Subrahmanyam and Kumar (2017) reported the maximum frequency of
occurrence of two-layer clouds over the Indian subcontinent during June, July,
and August. This they attributed to the presence of Indian summer
monsoon circulation over this region, which is dominated by the formation of
various kinds of clouds such as cumulus, stratocumulus, and cirrus. Very
recently, George et al. (2018) reported CVS using radiosonde launches
during depression and non-depression events in the south-west monsoon
season using 1 month of field campaign data over Kanpur, India.
Mean base, top, and thicknesses of cloud layers of single-layer,
two-layer, and three-layer clouds.
Multilayer clouds
Cloud base
Cloud top
Cloud
altitude
altitude
thickness
(km)
(km)
(km)
Single-layer cloud
6.32
9.24
2.92
Upper layer
two-layer clouds
8.51
11.23
2.72
three-layer clouds
9.63
11.79
2.16
Middle layer
three-layer clouds
6.69
7.80
1.11
Lower layer
two-layer clouds
4.08
5.56
1.48
three-layer clouds
3.04
4.31
1.27
Mean vertical locations (base and top) and cloud thicknesses of (a)
one-layer clouds, (b) two-layer clouds, and (c) three-layer clouds observed
during different seasons.
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Figure 11a–c describe the mean vertical locations (base and top) and cloud
thicknesses of single-layer, two-layer, and three-layer clouds during
different seasons. Except during the winter season, single-layer clouds are
thicker than the layers forming multilayer clouds. Also, upper-layer clouds
are thicker than lower-layer clouds in multilayer clouds. This could be due
to the exchange of longwave radiation between the cloud base of the upper layer and
the cloud top of the lower layer. As a result, there is a strong reduction in longwave
radiation cooling at the top of the lower layer of cloud in the presence of
upper layers of cloud (Zhang et al., 2010; Wang et al., 1999; Chen and
Cotton, 1987).
Irrespective of the season, single-layer clouds are high-level clouds; i.e.
the
cloud base is >5 km (Fig. 11a). Maximum cloud top altitude and
cloud thickness occurred during the monsoon season for single-layer clouds
(Fig. 11a) and the uppermost layer of multilayer cloud configurations
(Fig. 11b, c). This is consistent with the low OLR values (<220 W m-2) observed during the monsoon season (Fig. 4e). Except during
the pre-monsoon season, the cloud base, cloud top, and cloud thickness values of the lower
layer of multilayer clouds are the same during the monsoon, post-monsoon, and winter
seasons. During the pre-monsoon season, the cloud base and cloud top of the lower
layer of multilayer clouds occurred at relatively higher altitudes
(Fig. 11b, c). Similarly, there are no significant variations in cloud
thickness in the middle layer of three-layer clouds between the seasons. However,
the cloud base and cloud top of the middle layer of three-layer clouds during
the pre-monsoon season occurred at relatively higher altitudes than the other
three seasons (Fig. 11c). Table 2 describes the mean base, top, and
thicknesses of cloud layers of single-layer, two-layer, and three-layer
clouds. In two-layer clouds, the thickness of the upper-level cloud layer
is about the same as that of single-layer clouds. In three-layer clouds,
the base and top heights of the lowest layer of cloud are similar to those of
the lowest layer of cloud in two-layer clouds.
Composite (2006–2016) percentage of occurrence of (a) clear and
cloud conditions, (b) low-level, middle-level, high-level, and deep
convective cloud, and (c) one-, two-, three-, and four- or more-layer clouds
observed with respect to the date of monsoon arrival over the Gadanki
location. Zero on the x axis indicates the date of monsoon arrival over the Gadanki
location.
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Variability in CVS with respect to SW monsoon arrival over
Gadanki
CVS plays an important role in the summer monsoon because it can
significantly affect the atmospheric heat balance through latent heating
caused by water phase changes and through the scattering of radiation. In this
section we discuss the variability in different clouds with respect to the
date of arrival of the south-west (SW) monsoon over Gadanki. SW monsoon onset
occurs over the Kerala coast (south-west coast of India) during the last week of
May or first week of June. In general, the climatological mean monsoon
onset over Kerala (MOK) is on 1 June with ±7 days. It is to be noted
that the climatological onset date is obtained from IMD long-term onset dates
and the arrival date over Gadanki is picked up manually from the yearly onset
date lines over the India map given by IMD.
Figure 12 shows the composite (2006–2016) percentage of occurrence of clear
sky and cloud days (Fig. 12a), low-level, middle-level, high-level, and deep
convective clouds (Fig. 12b), and one-, two-, three-, and four- or more-layer clouds (Fig. 12c) with respect to monsoon arrival date. Figure 13a and b describe the mean vertical locations (base and top) and cloud
thicknesses of single-layer and two-layer clouds with respect to monsoon arrival
date. Day zero in Figs. 12a, b and 13a, b indicates the date of
monsoon arrival over the Gadanki location. The percentage of occurrence of clear
sky conditions prior to the monsoon arrival over the Gadanki location decreases
and reduces to zero on the date of monsoon arrival (Fig. 12a). This
indicates that the estimated dates of monsoon arrival over the Gadanki location are
correct. From day 4 onwards the cloudiness start increases and peaks on
day 18 (Fig. 12a). The percentage of occurrence of middle-level clouds
decreases until 5 days prior to the monsoon arrival (Fig. 12b). Subsequently,
the middle-level cloud percentage increases and does not show significant
variability after the monsoon arrival. There are no deep convective clouds
prior to and during the monsoon arrival over the Gadanki location (Fig. 12b). They
occurred on days 3, 9, 10, 17, and 20. During and after the arrival of the
monsoon, the percentage of occurrence of multilayer clouds is always greater
than the single-layer clouds except on days 3 and 4 (Fig. 12c). On day zero
it is noted that single-layer clouds are high-level clouds and they are
thicker with thickness ∼6.7 km (Fig. 13a). In two-layer
clouds the bottom layer is middle-layer cloud and the top layer is high-level
cloud (Fig. 13b). The bottom layer is thicker than the top layer. During
deep convective clouds and middle-level clouds, single-layer clouds prevailed. The
thickness of single-layer clouds shows large variability with thickness
ranging from 300 m to 5 km during the first week after the arrival of the
monsoon. In the second week, the thickness ranges from 2 to 5 km (Fig. 13a). After the arrival of the monsoon, the
thickness of the bottom layer in two-layer cloud is relatively higher than the top layer (Fig. 13b). Thicker
single-layer clouds and the bottom layer of two-layer clouds after the monsoon
arrival over Gadanki is due to the increase in tropospheric water vapour.
Composite (2006–2016) variations of mean vertical
locations (base and top) and thicknesses of one-layer clouds and two-layer
clouds observed with respect to the date of monsoon arrival over the Gadanki
location. Zero on the x axis indicates the date of monsoon arrival over the Gadanki
location.
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Summary
Cloud vertical structure (CVS) is studied for the first time over India
using long-term high-vertical-resolution radiosonde measurements at the Gadanki
location obtained during April 2006 to May 2017. In order to obtain diurnal
variation in CVS, we have used 3-hourly launched radiosondes for 3 days in
each month during December 2010 to March 2014. CVS is obtained following Zhang et al. (2010) whereby it relies on height-resolved relative humidity thresholds.
After obtaining the cloud layers they are segregated into low-, middle-, and high-level clouds depending upon their altitude of occurrence. Detected layers
are verified using independent measurements from cloud particle sensor (CPS)
sonde launched from the same location. A very good match between these two
independent measurements is noticed.
Composite (2006–2016) temperature profiles during clear sky,
one-layer, two-layer, three-layer, and four- or more-layer cloud occurrences.
The respective temperature difference profiles from clear sky conditions are
shown with dashed lines.
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First, the diurnal variations in CVS over Gadanki are studied using radiosonde
observations taken from TTD campaigns conducted during the CAWSES India Phase II
programme. During the pre-monsoon and monsoon seasons, the thickness of single-layer
clouds reaches a maximum at 23:00 LT and a minimum at 14:00 LT. The upper layer of
two-layer clouds shows a maximum in thickness at 23:00 LT and a minimum at 11:00 LT during the monsoon season.
Radiosonde measurements around 17:30 LT were used to
study the seasonal variability in CVS. After ascertaining the cloud layers
they are segregated into different seasons to obtain the seasonal variation of
CVS. High-level clouds account for 69.05 %, 58.49 %, 55.5 %, and
58.6 % of cloud layers identified during the pre-monsoon, monsoon,
post-monsoon, and winter seasons, respectively, indicating that high cloud layers
are most prevalent at the Gadanki location. Single-layer, two-layer, and
three-layer clouds account for 40.80 %, 30.71 %, and 19.68 % of
all cloud configurations, respectively. Multilayer clouds occurred more
frequently during the monsoon with 34.58 %. Maximum cloud top altitude
and cloud thickness occurred during the monsoon season for single-layer
clouds and the uppermost layer of multilayer cloud configurations.
Further, we have discussed the variability in different clouds with respect
to the date of arrival of the south-west (SW) monsoon over the Gadanki location.
Prior to, during, and after the SW monsoon arrival over the Gadanki location,
high-level cloud occurrence is more than the other cloud types. The
middle-level cloud occurrence decreases until 5 days prior to the monsoon
arrival and subsequently increases. There are no deep convective clouds
prior to and during the monsoon arrival over the Gadanki location. The thickness of
single-layer clouds shows large variability during the first week after
the arrival of the monsoon. But it increases significantly between 8 and 11 days after the monsoon arrival. After the
arrival of the monsoon,
the thickness of the bottom layer in two-layer cloud is relatively higher than the
top layer. Thicker single-layer clouds and the bottom layer of two-layer clouds
after the monsoon arrival over Gadanki is due to the increase in
tropospheric water vapour.
These cloud layers are expected to significantly affect the background
temperature in the troposphere and lower stratosphere. The composite
(2006–2016) temperature profiles during clear sky, one-layer, two-layer,
three-layer, and four- or more-layer cloud occurrences are shown in Fig. 14.
The temperature differences between the cloudy (single, two, three, four or more layers) and clear sky
conditions are shown with dashed lines in Fig. 14. The striking result here is the occurrence of peak cooling (peak
warming) below (above) the cold-point tropopause (CPT) altitude. The
magnitude of cooling (warming) increases from single-layer to four- or
more-layer cloud occurrence. The peak cooling and warming during four- or more-layer cloud occurrence are 0.9 K (at 15.7 km) and 3.6 K (at 18.1 km).
Both single-layer and multilayer clouds show warming between 5 and 14.5 km of altitude. Peak warming
of 0.8 K at 9.5 km for single-layer
cloud and 1.3 K at 10.2 km for multilayer clouds is observed and these
altitudes are close to the cloud top altitude of single-layer cloud and the top
layer of multilayer clouds (Table 2). A detailed study on the impact of
single-layer and multilayer clouds on UTLS dynamics and thermodynamics
will be the subject of our subsequent article, including their
radiative forcing.