Tropical Rainfall Measuring Mission (TRMM) precipitation radar measurements are
used to examine the variation in vertical structure of precipitation with
sea surface temperature (SST) over the Arabian Sea (AS) and Bay of Bengal
(BOB). The variation in reflectivity and precipitation echo top with SST is
remarkable over the AS but small over the BOB. The reflectivity increases
with SST (from 26 to 31 ∘C) by ∼1
and 4 dBZ above and below 6 km, respectively, over the AS, while its
variation is <0.5 dBZ over the BOB. The transition from shallow
storms at lower SSTs (≤27∘C) to deeper storms at higher
SSTs is strongly associated with the decrease in stability and
mid-tropospheric wind shear over the AS. In contrary, the storms are deeper at
all SSTs over the BOB due to weaker stability and mid-tropospheric wind
shear. At lower SSTs, the observed high aerosol optical depth (AOD) and low
total column water (TCW) over AS results in the small cloud effective radius
(CER) and weaker reflectivity. As SST increases, AOD decreases and TCW
increases, leading to a large CER and high reflectivity. The changes in these
parameters with SST are marginal over the BOB and hence the CER and
reflectivity. The predominance of collision–coalescence process below the
bright band is responsible for the observed negative slopes in the
reflectivity over both the seas. The observed variations in reflectivity originate at the cloud formation stage over both the seas, and these
variations are magnified during the descent of hydrometeors to the
ground.
Introduction
The Indian summer monsoon (ISM; June through September) is one of the most
complex weather phenomena, involving coupling between the atmosphere, land,
and ocean. At the boundary of the ocean and atmosphere, air–sea interactions
play a key role in the coupled Earth system (Wu and Kirtman, 2005; Feng et
al., 2018). The relations between sea surface temperature (SST) and precipitation are
the important measures for the air–sea interactions on different temporal
scales (Woolnough et al., 2000; Rajendran et al., 2012). Recent studies (Wang
et al., 2005; Rajeevan et al., 2012; Chaudhari et al., 2013, 2016; Weller et
al., 2016; Feng et al., 2018) have shown that the simulation of the ISM can be
improved with the exact representation of the SST–precipitation relationship.
SST modulates the meteorological factors that influence the formation and
evolution of different kinds of precipitating systems over tropical oceans
(Gadgil et al., 1984; Schumacher and Houze, 2003; Takayabu et al., 2010;
Oueslati and Bellon, 2015).
The studies dealing with SST and cloud and/or precipitation population considered
whole Indian Ocean to be a single entity (Gadgil et al., 1984; Woolnough et al.,
2000; Rajendran et al., 2012; Sabin et al., 2012; Meenu et al., 2012; Nair and
Rajeev, 2014; Roxy, 2014). But in reality the Bay of Bengal (BOB) and the
Arabian Sea (AS) of the Indian Ocean possess distinctly different features
(Kumar et al., 2014; Shige et al., 2017; Rajendran et al., 2018; Saikranthi et
al., 2019). The monsoon experiment (MONEX) and Bay of Bengal monsoon
experiment (BOBMEX) have shown how these two seas are different with respect
to each other, in terms of SST, background atmosphere and the occurrence of
precipitating systems (Krishnamurti, 1985; Houze and Churchill, 1987; Gadgil,
2000; Bhat et al., 2001). The SST in the AS cools between 10
and 20∘ N during the monsoon season, whereas warming is seen in
other global oceans between the same latitudes
(Krishnamurti, 1981). SST
variability is large over the AS than the BOB at seasonal and intraseasonal
scales (Sengupta et al., 2001; Roxy et al., 2013). The monsoonal winds (in
particular the low-level jet) are stronger over the AS than BOB (Findlater,
1969). Also, lower-tropospheric thermal inversions are more frequent and
stronger over the AS than BOB (Narayanan and Rao, 1981; Sathiyamoorthy et
al., 2013). Thus, the atmospheric and sea surface conditions, and in turn the
occurrence of different kinds of precipitating systems, are quite different
over the BOB and the AS during the ISM period. For instance, long-term
measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation
radar (PR) have shown that shallow systems are more prevalent over the AS,
while deeper systems occur frequently over the BOB (Liu et al., 2007;
Romatschke et al., 2010; Saikranthi et al., 2014, 2018; Houze et al., 2015).
The aforementioned studies mainly focused on the morphology of vertical
structure of precipitation, but none of them studied the variation in
vertical structure of precipitation (in terms of occurrence and intensity)
with SST and the differences in the vertical structure over AS and BOB. On
the other hand, information on the vertical structure of precipitation is
essential for improving the accuracy of rainfall estimation (Fu and Liu,
2001; Sunilkumar et al., 2015), understanding the dynamical and microphysical
processes of hydrometeor growth–decay mechanisms (Houze, 2004;
Geets and Dejene, 2005; Saikranthi et al., 2014; Rao et al., 2016), and improving the
latent heating retrievals (Tao et al., 2006, 2016). SST being the main
driving force to trigger precipitating systems through air–sea interactions
(Sabin et al., 2012; Nuijens et al., 2017) can alter the vertical structure
of precipitation (Oueslati and Bellon, 2015). Therefore, the present study
aims to understand the variation in vertical structure of precipitation (in
terms of precipitation top height and intensity) with SST over the AS and
BOB. Besides the SST, vertical structure can be modified by aerosols (or
CCN, mostly at the cloud formation stage) and thermodynamics of the ambient
atmosphere. For instance, recent studies have shown the impact of surface
PM10 aerosols in altering the vertical structure of precipitation (Guo
et al., 2018). All these parameters, therefore, are considered in the
present study to explain the differences in the vertical structure.
Data
The present study utilizes 16 years (1998–2013) of TRMM PR's 2A25 (version
7) dataset, being comprised of vertical profiles of attenuation corrected
reflectivity (Iguchi et al., 2009), during the ISM. The range resolution of
TRMM PR reflectivity profiles is 250 m with a horizontal footprint size of
∼4.3 and 5 km before and after the boosting of its orbit from
350 to 403 km, respectively. It scans ±17∘ from the nadir
with a beam width of 0.71 ∘, covering a swath of 215 km (245 km
after the boost). The uniqueness of TRMM PR data is their ability to
pigeonhole the precipitating systems into convective, stratiform and
shallow rain. This classification is based on two methods, namely the
horizontal method (H-method) and the vertical method (V-method; Awaka
et al., 2009). The original TRMM PR 2A25 vertical profiles of attenuation
corrected reflectivity are gridded to a three-dimensional Cartesian
coordinate system with a spatial resolution of 0.05∘× 0.05∘.
The detailed methodology of interpolating the TRMM PR
reflectivity data into the 3-D Cartesian grid is discussed in Houze et
al. (2007). This dataset is available at the University of Washington website
(http://trmm.atmos.washington.edu/, last access: 4 May 2017). Profiles are classified as deep
if their storm top reflectivity ≥17 dBZ lies above the 0 ∘C isotherm and shallow if it lies 1 km
below the 0 ∘C isotherm.
To understand the observed variations in the vertical structure of
precipitation in the light of microphysics of clouds, Moderate Resolution
Imaging Spectroradiometer (MODIS) AQUA satellite level 3 data (MYD08) are
considered. In particular, the daily atmospheric products of aerosol optical
depth (AOD; Hubanks et al., 2008) and cloud effective radius (CER) liquid
(Platnick et al., 2017) during the period 2003 and 2013 have been used. The MODIS
AOD dataset is a collection of aerosol optical properties at the 550 nm
wavelength as well as particle size information. Level 2 MODIS AOD is
derived from radiances using one of the three different algorithms,
i.e., the algorithm from Remer et al. (2005) for over the ocean, the Dark
Target algorithm for over land
(Levy et al., 2007) and the Deep Blue algorithm for brighter land surfaces
(Hsu et al., 2004). The CER is nothing but the weighted mean of the
size distribution of cloud drops, i.e., the ratio of the third moment to the second
moment of the drop size distribution. In the level 3 MODIS daily dataset,
aerosol and cloud products of level 2 data pixels with valid retrievals
within a calendar day are first aggregated and gridded to a daily average
with a spatial resolution of 1∘× 1∘. For CER
grid box values, CER values are weighted by the respective ice and/or liquid water
cloud pixel counts for the spatiotemporal aggregation and averaging
processes.
The background atmospheric structure (winds and total column water) and SST
information are taken from the European Centre for Medium-Range Weather
Forecasting (ECMWF) Interim Reanalysis (ERA-Interim; Dee et al., 2011). ERA-Interim
runs 4D-VAR assimilation twice daily (00:00 and 12:00 UTC) to determine the most
likely state of the atmosphere at a given time (analysis). The consistency
across variables in space and time (during 12 h intervals) is thus
ensured by the atmospheric model and its error characteristics as specified
in the assimilation. ERA-Interim is produced at the T255 spectral resolution
(about 0.75∘; ∼83 km), with a temporal resolution
of 6 h for upper air fields and 3 h for surface fields. The original
0.75∘× 0.75∘ spatial resolution gridded
dataset is rescaled to a resolution of 0.125∘× 0.125∘.
The temporal resolution of the dataset used in the
present study is 6 h (00:00, 06:00, 12:00 and 18:00 UTC). The equivalent potential
temperature (θe) is estimated from the ERA-Interim datasets
using the following formula (Wallace and Hobbs, 2006):
θe=θexpLVwsCpT,
where θ is the potential temperature, Lv is the latent heat of
vaporization, ws is the saturation mixing ratio, Cp is the specific
heat at constant pressure and T is the absolute temperature.
The variation in the vertical structure of precipitation with SST is studied by
considering the dataset between 8–20∘ N and 63–72∘ E over
the AS and 8–21∘ N and 83–92∘ E over the BOB. These
regions of interest, along with the ISM seasonal mean SST over the two seas,
are depicted in Fig. 1. These regions are selected in such a way that the
costal influence on SST is eluded from the analysis. As the rainfall is
scanty over the western AS (west of 63∘ E latitude) during the ISM
(Saikranthi et al., 2018), this region is also not considered in the present
analysis. The seasonal mean SST is higher over the BOB than in the AS by
more than 1 ∘C during the ISM season, in agreement with Shenoi et
al. (2002). The nearest space- and time-matched SST data from ERA-Interim are
assigned to the TRMM PR and MODIS observations for further analysis.
Spatial distribution of ISM mean SST (in ∘C) obtained
from ERA-Interim reanalysis data over the AS (8–20∘ N, 63–72∘ E) and the BOB (8–21∘ N, 83–92∘ E). The regions
considered in this analysis over these two seas are shown with the boxes.
Variation in vertical structure of precipitation with SST
The occurrence (in terms of %) of conditional precipitation echoes
(Z≥17 dBZ) at different altitudes as a function of SST over the AS and
the BOB is shown in Fig. 2. The variation in precipitation echo occurrence
frequency with SST is quite different over both the seas. The top of the
precipitation echoes extends to higher altitudes with increasing SST over
the AS, while such variation is not quite evident over the BOB.
Precipitation echoes are confined to <8 km at lower SST (<28∘C)
over the AS but exhibit a gradual rise in height with
an increase in SST. Large population density of precipitation echoes at lower
altitudes is mainly due to the abundant occurrence of shallow storms over
the AS (Saikranthi et al., 2014, 2019; Rao et al., 2016). Interestingly, the
occurrence of precipitation echoes is seen at higher altitudes even at lower
SSTs over the BOB, indicating the presence of deeper storms. Such systems
exist at all SSTs over the BOB.
(a) and (b) represent the altitudinal distribution of occurrence
of conditional reflectivity (≥17 dBZ) as a function of SST with
respect to precipitation occurrence at that particular SST interval over the
AS and the BOB, respectively.
To examine the variation in reflectivity profiles with SST, median profiles
of reflectivity in each SST bin are computed over the AS and the BOB
separately for deep and shallow systems and are depicted in Figs. 3 and 4,
respectively. The space- and time-matched conditional reflectivity profiles
are grouped into 1 ∘C SST bins, and then the median is estimated at
each height, only if the number of conditional reflectivity pixels (Fig. 3c,
f; Fig. 4c, f) is greater than 500. The median reflectivity profiles
corresponding to the deep systems are distinctly different over the AS and
the BOB (Fig. 3a, d), even at the same SST. Over the AS, reflectivity
of deep systems at different SSTs shows small variations (≤1 dBZ)
above the melting region (>5 km) but varies significantly
(∼4.5 dBZ) below the melting level (<5 km). These
variations in reflectivity profiles with SST are negligible (<0.5 dBZ)
over the BOB both above and below the melting region. The reflectivity
increases from ∼26.5 to ∼31 dBZ, with
an increase in SST from 26 to 30 ∘C over the AS, but
it is almost the same (∼30 dBZ) at all SSTs over the BOB
below the melting layer. The standard deviation of reflectivity,
representing the variability in reflectivity within the SST bin, is similar
at all SSTs over both the seas except for the 26 ∘C SST over AS.
At this SST, the standard deviation is less than
that of other SSTs by ∼1 dBZ.
(a) and (d) and (b) and (e) represent vertical profiles of median
reflectivity corresponding to deep systems and their standard deviation (in
dBZ) with SST over the AS and the BOB, respectively, during the ISM season.
(c) and (f) show the number of conditional reflectivity pixels at each
altitude used for the estimation of the median and standard deviation.
Same as Fig. 3 but for shallow precipitating systems.
The median reflectivity profiles of shallow storms depicted in Fig. 4a and d
also show a gradual increase in reflectivity from 20 dBZ to
∼22 dBZ as SST changes from 26 to 31 ∘C at the precipitation top altitude over the AS and do not show
any variation with SST over the BOB. However at 1 km altitude, except at
the 26 ∘C SST over the AS, the reflectivity variation with SST is not
substantial over both the seas. The standard deviations of reflectivity
profiles show ∼1 dBZ variation with SST (from 26 to 31 ∘C)
at all altitudes over the AS and do not show any
variation over the BOB. The standard deviation of reflectivity for shallow
storms varies from 3 to 4 dBZ at the precipitation top altitude and from 4.5 to
5.3 dBZ at 1 km altitude over the AS, while it is ∼4 dBZ at
precipitation top and ∼5.5 dBZ at 1 km altitude over the BOB.
Factors affecting the vertical structure of precipitation and their
variability with SST
The formation and evolution of precipitating systems over oceans depend on
dynamical, thermodynamical and microphysical factors, like SST, wind shear,
vertical wind velocity, stability, the CER, etc., and need to be considered for
understanding the vertical structure of precipitation (Li and Min, 2010;
Creamean et al., 2013; Chen et al., 2015; Shige and Kummerow, 2016; Guo et
al., 2018).
Dynamical and thermodynamical factors
Takahashi and Dado (2018) have shown that zonal wind variations can also
explain some variability of rain. To examine the impact of zonal wind on
rainfall over the Arabian Sea and Bay of Bengal, the data are segregated
into three wind regimes as weak (monsoon westerlies lie between 0 and 6 m s-1),
moderate (monsoon westerlies lie between 6 to 12 m s-1) and
strong (monsoon westerlies >12 m s-1) winds. The median
vertical profiles of reflectivity are computed for each SST bin,
corresponding to deep and shallow systems (not shown here). Two important
observations are noted from these figures. (1) Vertical profiles of
reflectivity show considerable variation (2–5 dBZ) in all wind categories
over the Arabian Sea, but such variations are absent over the Bay of Bengal.
It implies that the reported differences in reflectivity profiles over the
Arabian Sea and Bay of Bengal exist in all wind regimes. (2) The variation in
reflectivity with SST increases with a weak to strong wind regime over the
Arabian Sea, indicating some influence of wind on reflectivity (rainfall)
variation.
To understand the role of stability and instability, θe values
computed (1) using the ERA-Interim datasets during the ISM period over
the AS and the BOB are averaged for a season and are depicted in Fig. 5a
and b, respectively. The surface θe (at 1000 hPa) values
are larger over the BOB than those over the AS for the same SST, indicating that
the instability and convective available potential energy (CAPE) could be
higher over the BOB. Indeed, higher CAPE is seen over the BOB (Fig. S1 in the
Supplement;
calculated following Emanuel, 1994) than the AS at all SSTs by a magnitude
>300 J kg-1. The θe increases with SST from
358 to 368 K from 27 to 31 ∘C and from 350 to 363 K
from 26 to 31 ∘C over the BOB and the AS, respectively. The
CAPE also increases with a rise in SST over both the seas. To know the
stability of the atmosphere, θe gradients are considered.
Irrespective of SST, positive gradients in θe are observed
between 900 and 800 hPa levels over the AS, indicating the presence of strong
stable layers. The strength of these stable layers decreases with increasing
SST. These stable layers are formed mainly due to the flow of continental
dry warm air from the Arabian Desert and Africa above the maritime air, causing
temperature inversions below 750 hPa level over the AS during the ISM period
(Narayanan and Rao, 1981). However, over the BOB, such temperature inversions
are not seen in the lower troposphere.
(a) and (b), respectively, represent the vertical profiles of mean
θe (in K) with SST over the AS and the BOB during the ISM
season. (c) and (d) and (e) and (f) are same as (a) and (b) but for mean
vertical velocity (in Pa s-1) and wind gradient with reference to 950 hPa
level (in m s-1).
To understand the effect of the wind field on the vertical structure of
precipitation, profiles of ISM seasonal mean vertical wind velocity and
vertical shear in horizontal wind at various SSTs over the AS and the BOB
are shown in Fig. 5c, d, e and f. The updrafts are
prevalent at all SSTs throughout the troposphere over the BOB, whereas
downdrafts are seen in the mid-troposphere (between 200 and 600 hPa levels)
up to 27 ∘C, and updrafts are seen in the entire troposphere at higher SSTs
over the AS. Also, the magnitude of the vertical wind velocity varies
significantly with SST in the mid-troposphere over the AS. Over the BOB, the
magnitude of updrafts increases with altitude in the lower and mid-troposphere but does not vary much with SST. In the mid-troposphere,
updrafts are stronger by >0.02 Pa s-1 over the BOB than
over the AS. The profiles shown in Fig. 5e and f are the mean vertical
shear in horizontal wind estimated following Chen et al. (2015) at different
levels with reference to the 950 hPa level. The wind shear increases with
increasing altitude at all the SSTs up to 400 hPa, but the rate of increase
is distinctly different between the AS and the BOB at SSTs less than
28 ∘C and nearly the same at higher SSTs. The wind shear decreases
systematically with SST (∼1.5 m s-1 for a 1∘
increase in SST) in the mid-troposphere over the AS, while the change is
minimal over the BOB (∼2 m s-1 for 27 and 31 ∘C).
Chen et al. (2015) highlighted the importance of the mid-tropospheric wind shear
in generating mesoscale local circulations, like low-level cyclonic and
upper-level anticyclonic circulations. This feature is apparent over the AS,
where downdrafts are prevalent in mid-troposphere to upper troposphere and updrafts in the
lower troposphere at lower SSTs. As SST increases, the wind shear decreases
and the updraft increases in the mid-troposphere to upper troposphere. However, over the BOB the
wind shear is relatively week when compared to the AS, and hence the updrafts
are seen up to 200 hPa level at all SSTs. The weaker CAPE and stable
mid-troposphere coupled with upper- to mid-tropospheric downdrafts at lower
SSTs over the AS inhibit the growth of precipitating systems to higher
altitudes and in turn precipitate in the form of shallow rain. This result
is in accordance with the findings of Shige and Kummerow (2016) that showed
that the static stability at lower levels inhibits the growth of clouds and
promotes the detrainment of clouds over the Asian monsoon region and is
considered to be an important parameter in determining the precipitation top
height. As SST increases, large CAPE and updrafts in the mid-troposphere
collectively support the precipitating systems to grow to higher altitudes,
as evidenced in Fig. 2a. On the other hand, large CAPE and updrafts in the
mid-troposphere prevalent over the BOB at all SSTs are conducive to the
precipitating systems growing to higher altitudes, as seen in Fig. 2b.
(a) Mean and standard error of AOD and (b) TCW (in kg m-2)
with SST over the AS and the BOB during ISM.
Microphysical factors
The observed differences in reflectivity profiles of precipitation with SST
could originate at the cloud formation stage itself or be manifested during
the evolution stage or due to both. Information on AOD and the CER would be
ideal for inferring microphysical processes at the cloud formation stage. CER
values are mainly controlled by the ambient aerosol concentration and the
available moisture (Twomey, 1977; Albrecht, 1989; Tao et al., 2012;
Rosenfeld et al., 2014). For a fixed liquid water content, as the concentration
of aerosols increases, the number of cloud drops increases and the CER decreases
(Twomey, 1977). To understand the variation in AOD and total column water
(TCW) and the resultant CER with SST, the mean AOD and TCW for different SST
bins are plotted in Fig. 6a and b. The mean and standard error are
calculated only when the number of data points is more than 100 in each SST
bin. AOD decreases from 0.62 to 0.31 with a rise in SST from 26
to 31 ∘C over the AS, but only from 0.42 to 0.36, as SST varies
from 27 to 30 ∘C and then increases at higher SSTs
over the BOB. The variation in TCW with SST (Fig. 6b) shows a gradual
increase with SST over the AS, while it decreases initially from
27 to 28 ∘C and then increases over the BOB. At a
given SST the TCW is more in the BOB (>8 mm) than in the AS.
The decrease in AOD and an increase in TCW with SST result in an increase in
the CER (14.7 to 20.8 µm from 26 to 31 ∘C)
over the AS (Fig. 7). On the other hand, the CER does not show much variation
with SST (18.5 to 19.5 µm from 27 to 31 ∘C)
over BOB due to smaller variations in AOD and TCW. This also shows that
the cloud droplets are smaller in size at lower SSTs over the AS than BOB,
while they are bigger and nearly equal in size at higher SSTs. Since
reflectivity is more sensitive to the precipitating particle size (Z∝D6),
the smaller-sized hydrometeors at lower SSTs over the
AS yield weaker reflectivity than over the BOB (both for deep and shallow
systems). As the SST increases, the CER, as well as the reflectivity, increases
over the AS. At higher SSTs, the CER values are approximately equal over
both the seas and in turn the observed reflectivities (Figs. 3a, 4a).
This suggests that the variations seen in the reflectivity originate in
the cloud formation stage itself.
Variation in mean and standard error of CER liquid (in µm)
with SST over the AS and the BOB during the ISM season.
The hydrometeors also evolve during their descent to the ground due to
several microphysical processes. These processes can be inferred from the
vertical structure of precipitation or vertical profiles of reflectivity.
The median reflectivity profiles of deep systems show a gradual increase
from ∼10 to 6 km, and an abrupt enhancement is seen just
below 6 km over both the seas (Fig. 3a, d). The sudden enhancement at
the freezing level (radar bright band) is primarily due to the aggregation
of hydrometeors, change in the dielectric factor from ice to water and change in
fall speed from ice hydrometers to raindrops (Fabry and Zawadzki, 1995; Rao
et al., 2008; Cao et al., 2013). Below the bright band, raindrops grow from
the
collision–coalescence process and reduce their size by either breakup and/or
evaporation processes. The collision–coalescence results in negative slope
in the reflectivity profile, whereas breakup and evaporation results in
positive slope (Liu and Zipser, 2013; Cao et al., 2013; Saikranthi et al.,
2014; Rao et al., 2016). The observed negative slope (∼-0.3 dBZ km-1)
in the median reflectivity profiles below the bright band
indicates dominance of low-level hydrometeor growth over both the seas. The
magnitude of the slope decreases with SST over the AS, while it is nearly
equal at all SSTs over the BOB. It indicates that the growth rate decreases with
SST over the AS and remains the same at all SSTs over the BOB. The median
reflectivity profiles of shallow systems also show negative slopes
(∼-1 dBZ km-1) at all SSTs, representing the predominance
of low-level hydrometeor growth by collision–coalescence processes over both
the seas.
The present analysis shows that the observed reflectivity changes with SST
over both the seas originate at the cloud formation stage and being magnified
further during the descent of hydrometeors to the ground.
Conclusions
Sixteen years of TRMM PR 2A25 reflectivity profiles and 11 years of MODIS
AOD and CER data are utilized to understand the differences in variation in
vertical structure of precipitation with SST over AS and BOB. Precipitation
top height increases with SST over the AS, indicating that systems grow to
higher altitudes with increase in SST, while it is almost same at all SSTs,
indicating that the systems are deeper over the BOB. The decrease in
stability and mid-tropospheric wind shear with SST over the AS favor the
formation of deeper system at higher systems. However the low stability and
small wind shear at all SSTs over the BOB help the formation of deeper
systems. The variation in reflectivity with SST is found to be remarkable
over the AS and marginal over the BOB. The reflectivity increases with a rise
in SST over the AS and remains the same at all SSTs over the BOB. This
change in reflectivity over the AS is more prominent below the freezing-level height (∼4 dBZ) than the above (∼1 dBZ).
Over the AS, the abundance of aerosols and less moisture at SSTs smaller than 27 ∘C
result in high concentration of smaller cloud droplets. As
SST increases, the aerosol concentration decreases and moisture increases,
leading to the formation of bigger cloud droplets. Thus, the reflectivity
increases with a rise in SST over the AS. On the other hand, AOD, TCW and the CER
do not show substantial variation with SST over the BOB, and hence the change
in reflectivity is small. Over the BOB, the mid-troposphere is wet, and
hydrometeor's size at the formation stage is nearly the same at all SSTs.
The evolution of hydrometeors during their descent is also similar at all
SSTs. The collision–coalescence process is predominant below the bright
band region over both the seas and is responsible for the observed negative
slope in the reflectivity profiles.
Data availability
The authors would like to thank Robert Houze and his team for the interpolated 3-D gridded TRMM PR dataset
(http://trmm.atmos.washington.edu,
last access: 4 May 2017; University of Washington, 2017),
the ECMWF (https://apps.ecmwf.int/datasets/,
last access: 4 May 2017; ECMWF, 2017) team for providing the ERA-Interim
dataset, and the MODIS (https://ladsweb.modaps.eosdis.nasa.gov/,
last access: 4 May 2017; LAADS DAAC, 2017) science team for
providing the AOD and CER dataset.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-10423-2019-supplement.
Author contributions
KS conceived the idea. KS and BR designed the analysis, plotted the figures and wrote the
paper. TNR and SKS
contributed in discussions as well as in improving the quality of the
paper.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Interactions between
aerosols and the South West Asian monsoon”. It is not associated
with a conference.
Acknowledgements
The authors express their gratitude to
J. Srinivasan for his fruitful discussions and valuable suggestions in
improving the quality of the paper. The corresponding author would like
to thank Department of Science & Technology (DST), India, for providing
the financial support through the grant number
DST/INSPIRE/04/2017/001185. We thank the anonymous referees for their
critical comments in improving the quality of the paper.
Financial support
This research has been supported by the Department of
Science & Technology, India (grant no. DST/INSPIRE/04/2017/001185).
Review statement
This paper was edited by Armin Sorooshian and reviewed by five anonymous referees.
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