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\begin{document}

\title{Structure of pre-monsoon convective systems over a tropical coastal
region in southwest India using C-band polarimetric doppler weather
radar observations}



\author[1]{Dharmadas Jash}%
\author[2]{E A Resmi}%
\author[3]{Unnikrishnan C.K}%
\author[3]{Sumesh RK}%
\author[3]{Nita Sukumar}%
\author[3]{Sumit Kumar}%
\affil[1]{National centre for Earth science studies}%
\affil[2]{National centre for earth science studies}%
\affil[3]{National Centre for Earth Science Studies}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
The structure of pre-monsoon convective systems over southern peninsular
India using polarimetric doppler weather radar (DWR) observations has
been analyzed. Convective-stratiform separation has been done for eleven
convective events during Mar-May, 2018. The mean vertical profile of
reflectivity shows peak reflectivity of 32 dBZ near 3 km height for
convective regions and the bright band signature over stratiform regions
was observed. The frequency distributions of reflectivity at 3 km height
over convective and stratiform regions are of bell-shaped nature with
peaks at 32 dBZ and 18 dBZ respectively. A comprehensive analysis has
been done on two prominent convective cases on 13\textsuperscript{th}
and 25\textsuperscript{th} May 2018. Strong convective regions
represented by high reflectivity (\textgreater{} 45 dBZ) were noticed in
the PPI diagrams. Specific differential phase (K\textsubscript{dp}) has
been calculated from the slope of the filtered \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp}.
Heavy precipitation near surface is reflected in the high value of
K\textsubscript{dp} (\textgreater{} 5\selectlanguage{ngerman}° km\textsuperscript{-1}). High
values of Z\textsubscript{dr} (\textgreater{} 3 dB) were measured at
lower levels due to the oblate bigger raindrops. A fuzzy logic-based
hydrometeor identification algorithm has been applied with five
variables (Z\textsubscript{h}, Z\textsubscript{dr}, \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv},
K\textsubscript{dp,} and T) to understand the bulk microphysical
processes at different heights within convective regions. The presence
of bigger graupel particles near the melting layer indicates strong
updrafts within the convective core regions. The vertical ice
hydrometeor might signify the existence of a strong electric field
causing them to align vertically and this could be linked to lightning
occurrence associated with such systems.%
\end{abstract}%



\sloppy


\textbf{Structure of pre-monsoon convective systems over a tropical
coastal region in southwest India using C-band polarimetric doppler
weather radar observations}

Dharmadas Jash\textsuperscript{1,2}, Resmi E.A\textsuperscript{1},
Unnikrishnan C.K\textsuperscript{1}, Sumesh R.K\textsuperscript{1}, Nita
Sukumar\textsuperscript{1}, Sumit Kumar\textsuperscript{1,2}

\textsuperscript{1}National Centre for Earth Science Studies (NCESS),
P.B. No. 7250 Akkulam,

Thiruvananthapuram, Kerala, India - 695011.

\textsuperscript{2}Department of Atmospheric Sciences, Cochin University
of Science and Technology (CUSAT), Ernakulam, Kerala, India - 682022

Key points:

\begin{itemize}
\tightlist
\item
  Structure of pre-monsoon convective systems has been revealed using
  polarimetric radar and other supporting instruments.
\item
  Reflectivity values greater than 30 dBZ reaching up to 10 km height
  has been observed in the rapid development stage of thunderstorms.
\item
  Graupels along the high reflectivity columns inside the storms suggest
  presence of strong updraft.
\item
  Existence of vertical ice particles indicate strong electric field
  inside thunderstorms.
\end{itemize}

\textsuperscript{*}\emph{Corresponding author address} : E. A. Resmi,
National Centre for Earth Science Studies (NCESS), Thiruvananthapuram,
India; E-mail: resmi.ea@ncess.gov.in

\textbf{Abstract:} The structure of pre-monsoon convective systems over
southern peninsular India using polarimetric doppler weather radar (DWR)
observations has been analyzed. Convective-stratiform separation has
been done for eleven convective events during Mar-May, 2018. The mean
vertical profile of reflectivity shows peak reflectivity of 32 dBZ near
3 km height for convective regions and the bright band signature over
stratiform regions was observed. The frequency distributions of
reflectivity at 3 km height over convective and stratiform regions are
of bell-shaped nature with peaks at 32 dBZ and 18 dBZ respectively. A
comprehensive analysis has been done on two prominent convective cases
on 13\textsuperscript{th}~and 25\textsuperscript{th}~May 2018. Strong
convective regions represented by high reflectivity (\textgreater{} 45
dBZ) were noticed in the PPI diagrams. Specific differential phase
(K\textsubscript{dp}) has been calculated from the slope of the filtered
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp}. Heavy precipitation near surface is reflected in
the high value of K\textsubscript{dp}~(\textgreater{} 5\selectlanguage{ngerman}°
km\textsuperscript{-1}). High values of
Z\textsubscript{dr~}(\textgreater{} 3 dB) were measured at lower levels
due to the oblate bigger raindrops. A fuzzy logic-based hydrometeor
identification algorithm has been applied with five variables
(Z\textsubscript{h}, Z\textsubscript{dr}, \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv},
K\textsubscript{dp,}~and T) to understand the bulk microphysical
processes at different heights within convective regions. The presence
of bigger graupel particles near the melting layer indicates strong
updrafts within the convective core regions. The vertical ice
hydrometeor might signify the existence of a strong electric field
causing them to align vertically and this could be linked to lightning
occurrence associated with such systems.

\textbf{Keywords: Pre-monsoon, Convective systems, Doppler weather
radar, Hydrometeor identification}

\textbf{1. Introduction}

Thunderstorms are severe mesoscale weather phenomena that develop mainly
due to intense convection over the heated landmass and are accompanied
by heavy rainfall, lightning, and sometimes hail. They have a spatial
extent of a few kilometres to few hundred kilometres and a life span of
less than an hour to several hours (Tyagi et al., 2012; Saha et al.,
2014; Thakur et al 2019). Numerous thunderstorms occur daily across the
globe (Christian et al., 2003), a major fraction of which is over the
tropical belt. In the case of Indian subcontinent, most of the
thunderstorms occur during the pre-monsoon (March-April-May) season
(Singh \& Bhardwaj, 2019). They are locally known as
\emph{Kalbaisakhi}in West Bengal, \emph{Bordoichila} in Assam and
\emph{Andhi} in north-west India. A large amount of precipitation
particularly during the pre-monsoon season occur due to thunderstorm
events (Saha et al., 2014; Bhardwaj \& Singh, 2018). Using satellite
data, Cecil et al. (2014) has prepared lightning climatology across the
globe, which clearly shows different hotspots, especially over the
tropical region. Halder and Mukhopadhyay (2016) have identified five
lightning hotpots during pre-monsoon and one among them is over the
southern peninsular India. Using data from different observatories
across India, Tyagi (2007) has shown that, the highest annual
thunderstorm frequency is observed over Assam and sub-Himalayan West
Bengal in the east, Jammu region in the north and over Kerala, where the
frequency of thunderstorm is higher, in the southern peninsula. Manohar
and Kesarkar (2004) have shown that thunderstorm frequency peaks in the
month of May over southern India. Study by Unnikrishnan et al. (2021) on
lightning activity using TRMM-LIS data and ground-based lightning
detection network shows strong lightning activity over south India
particularly over the Kerala region. Effect of orography, along with
abundant supply of moisture from the sea and presence of land-sea breeze
are some of the important factors that favour the occurrence of
thunderstorms over the southwest peninsular region (Rao \& Srinivasan,
1969; Romatschke et al., 2011).

Thunderstorms cause damage to crops, properties and even human lives
every year. It is estimated that between 1500 and 2800 deaths occurred
annually due to thunderstorms/lightning during 2001-2017 (Roy et al.,
2019). Heavy rainfall and high winds from these weather systems cause an
interruption in connectivity among different places and infrastructure
in general. Hence, there is an increasing demand for better nowcasting
of such weather systems. Several attempts have been made to predict such
systems using statistical approach (Ravi et al., 1999; Dhawan et al.,
2008; Rajeevan et al., 2012), satellite-based nowcasting (Purdom 2003;
Umakanth et al., 2021), numerical simulations (Abhilash et al., 2007;
Litta \& Mohanty 2008; Rajeevan et al., 2010; Litta et al., 2012;
Madhulatha \& rajeevan 2018; Leena et al 2019; Sad et al., 2021) and
even artificial intelligence (Elio et al., 1987; Litta et al., 2013;
Zhou et al., 2019). But because of their small-scale nature and innate
underlying nonlinearity, prediction of such systems is far from
desirable accuracy. More observations are required to understand the
features and internal structures of these systems which in turn will
help their forecasting. Most of the thunderstorm related studies in
India were on pre-monsoon thunderstorms (Nor'westers) occurring over
east and north-east parts of India (Litta \& Mohanty, 2008; Mukhopadhyay
et al., 2009; Tyagi et al., 2012; Thakur et al., 2019). A few studies
(Rajeevan et al., 2010; Suresh 2012; Agnihotri et al., 2020) have been
conducted on the thunderstorm occurrences over the southern peninsular
India, particularly over Kerala which is one of the potential lightning
hotspots in the southern peninsular India. Proximity of the Arabian Sea
backed by the towering Western Ghats orography influences the formation
and development of clouds and thunderstorms in the region.

Doppler weather radar (DWR) is one of the most relevant and reliable
instrument to monitor these weather events in 3-dimension, starting from
their genesis to dissipating stage. Radars have been used in numerous
studies (Mukhopadhyay et al., 2009; Rajeevan et al., 2010; Srivastava et
al., 2010; Litta et al., 2012; Suresh 2012) to understand the structure
and evolution of thunderstorms. But most of these studies mainly use
radar reflectivity and sometimes radial velocity also. However, studies
using polarimetric radars are rare particularly over the Indian region
mainly because of less availability of such data. Radars with
polarimetric capabilities could provide much more information about the
precipitating systems e.g., about size and shape of the hydrometeors
within the system.

Polarimetry has two major advantages viz. polarimetric measurements
improve the retrieval of microphysical parameters such as mean drop
size, rainfall estimation (Chandrasekar et al., 1990; Bringi et al.,
2006; Bringi et al., 2009; Cifelli et al., 2011) and polarimetric
clutter-detection techniques help in the removal of non-meteorological
echoes (Zrnic\selectlanguage{ngerman}´ \& Ryzhkov, 1999; Unal, 2009; Islam et al., 2012;
Lakshmanan et al., 2014). Since polarimetric measurements contain
information on the shape and size of the hydrometeors, they can be used
for better retrieval of hydrometeor types. Fuzzy-logic based hydrometeor
identification (HID) is a very efficient and popular method for
identifying hydrometeors within the radar scan volume (Vivekanandan et
al., 1999; Liu \& Chandrasekar, 2000; Keenan, 2003; Marzano et al.,
2006; Dolan \& Rutledge, 2009; Dolan et al., 2013). Such studies give
valuable information about different ice hydrometeors present at
different heights within a precipitating system. Unlike raindrops, it is
not easy to obtain information about ice particles using remote sensing
techniques, mainly because of their irregular shapes and varying
densities. Hydrometeor identification algorithms provide an indirect way
to obtain information on ice particles. Such information can help us
understand the charge separation and subsequent lightning in
thunderstorms as detailed in different laboratory studies (Takahashi,
1978; Jayaratne et al., 1983; Saunders et al., 1991). These studies
suggest that the non-inductive charge separation due to rebounding
collision between graupel and ice crystals in the presence of
super-cooled water droplets is the main mechanism of thunderstorm
charging. Hence hydrometeor identification is particularly important
during thunderstorm events. Subrahmanyam and Baby (2020) studied the
spatial structure of the Ockhi cyclone and implemented HID algorithm
using polarimetric doppler weather radar observations at the west coast
of southern peninsular India and provided information about polarimetric
signatures of rain-bearing clouds. However, the hydrometeor
classification studies are rare over the Indian region, mainly because
of the lack of radars with polarimetric capabilities.

C-band polarimetric doppler weather radar data and several other
observation data are used in this study to understand the features of
pre-monsoon thunderstorms over southern peninsular India. A hydrometeor
classification algorithm has been applied to obtain information on
hydrometeors. The paper is organized as follows, apart from the
introduction (Section 1), the data from different instruments and
methodology are described in Section 2. Results and discussions are
presented in Section 3. Section 4 summarizes the major
findings/conclusions drawn from the study.

\textbf{2. Data and Methodology}

We have identified eleven convective events over the southern peninsular
India during the pre-monsoon period (i.e., Mar to May) of 2018 using
C-band radar reflectivity field. For these events convective-stratiform
separation has been done to obtain statistics on radar reflectivity over
convective and stratiform regions. Two prominent convective events on
13\textsuperscript{th} May and 25\textsuperscript{th} May, 2018 have
been selected as representative cases for further analysis. Besides the
DWR data, we have used rain drop size distribution (DSD) data from
disdrometer, cloud base height (CBH; m) data from ceilometer, brightness
temperature data from INSAT satellite, ERA5 reanalysis data and also
radiosonde measurements. Disdrometer and ceilometer were installed over
the rooftop of the National Centre for Earth Science Studies (NCESS;
8.5228N, 76.9097E). Locations of the DWR and NCESS along with the
topography of the surrounding area are shown in Figure 1. A brief
description of the instruments and data is summarized in Table 1.

Optical disdrometer (model: OTT Parsivel, manufactured by OTT Hydromet,
Germany) is a laser-based system that detects all types of precipitation
at the surface (Löffler-Mang and Joss, 2000; Friedrich et al., 2013). It
measures rain DSD and fall velocity distribution in 32 size and velocity
classes as well as it provides rain rates (R; mm h\textsuperscript{-1})
and radar reflectivity (dBZ). The size of measurable liquid
precipitation particles ranges from 0.2 to 8 mm and it varies from 0.2
to 25 mm for solid precipitation particles. It can measure the particles
fall velocity from 0.2 to 20 ms\textsuperscript{-1}. The temporal
resolution of this data is 1 minute. The disdrometer used in this study
was installed over rooftop of NCESS.

Ceilometer (model: CHM15k-Nimbus manufactured by Lufft Mess-und
Regeltechnik GmbH) is a ground-based remote sensing device that uses
standard lidar method to determine the cloud base height (CBH) from the
altitude profile of backscattered signals. It can provide cloud
thickness where the cloud layers do not totally attenuate the laser
beam. But the signals get attenuated in a rainy situation depending on
the number concentration and size of raindrops and hence signal to noise
ratio of the ceilometer decreases with increasing rain rate (Clothiaux
et al., 2000). Technical details of CHM15k can be obtained from the
previous studies by Heese et al. (2010) and Sumesh et al. (2019). The
CHM15k is operated with a vertical resolution of 15 m and the CBH is
measured with a temporal resolution of 15 s.

Brightness temperature data (Infrared Brightness Temperature, IRBT) has
been used as a proxy for the cloud top height. This data is obtained
from INSAT-3DR which is a multi-purpose geosynchronous spacecraft and
provides data with spatial resolution of 4x4 km and temporal resolution
of 30 minutes, of mesoscale phenomena in the visible and infrared (IR)
spectral bands (0.55-12.5 \selectlanguage{greek}μ\selectlanguage{english}m) over the Indian region. This data is
freely available through the https://www.mosdac.gov.in/ server.

The synoptic circulations over the study region were analysed using the
geopotential (m\textsuperscript{2} s\textsuperscript{-2}), u-wind (m
s\textsuperscript{-1}) and v-wind (m s\textsuperscript{-1}) variables
from ERA5 reanalysis hourly data having spatial resolution of
0.25\selectlanguage{ngerman}°x0.25°. Radiosonde measurements from India Meteorological
Department (IMD), Thiruvananthapuram have been utilized to analyse the
Convective available potential energy (CAPE; J kg\textsuperscript{-1}),
vertical profiles of temperature (K), mixing ratio (g
kg\textsuperscript{-1}), wind speed (m s\textsuperscript{-1}) and wind
direction (deg).

\textbf{2.1. DWR data and quality control}

C-band polarimetric Doppler Weather Radar (DWR), installed at VSSC,
Thiruvananthapuram (8.5374N, 76.8657E, 27 m above mean sea level)
operates at a frequency of 5.625 GHz and have a peak transmitting power
of 250 kW. The radar performs a volumetric scan of the surrounding
atmosphere within a radius of 240 km at 11 elevation angles (0.5°, 1°,
2°, 3°, 4°, 7°, 9°, 12°, 15°, 18° and 21°) with an azimuthal and radial
resolutions of 1° and 150 m respectively. One full volume scan takes
around 15 minutes. The radar provides base products such as reflectivity
at horizontal polarization (Z\textsubscript{h}), differential
reflectivity (Z\textsubscript{dr}), differential propagation phase
(\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp}), cross-correlation(\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv}), radial
velocity (V\textsubscript{r}) and Spectral width(\selectlanguage{greek}σ\selectlanguage{english}).
Z\textsubscript{dr}is the difference between reflectivities (in decibel)
at horizontal and vertical polarization, \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} is the
phase difference between the horizontally and vertically polarized
pulses. More information about these variables can be found in Doviak
and Zrnic (1993) and also in Bringi and Chandrasekar (2001). A
comprehensive detail about the radar is given in Mishra et al. (2020).
The validation of the radar data with other instruments showed that the
DWR reflectivity agrees quite well with GPM satellite measurements and
also the radar retrieved precipitation have a good correlation (0.89)
with ground based \emph{in-situ} measurements (Kumar et al., 2020).

Received signal by radar is often contaminated by signals reflected from
non-meteorological objects such as hills, birds etc., anomalous
propagation and also attenuation of the electromagnetic wave by
different types of hydrometeors (Ryzhkov \& Zrnic, 1998; Friedrich et
al., 2006; Unal, 2009; Lakshmanan et al., 2014). Even though the radar
signal processor takes into account many factors to give reasonably
accurate base products from the return signal, still the data needs
certain quality control measures. The use of simple thresholds for
different variables can be quite useful in removing unwanted echoes
(Ryzhkov \& Zrnic, 1998; Lakshmanan et al., 2014). The following quality
control measures are considered for this study- (i) pixels with
Z\textsubscript{h} \textgreater{} 70 dBZ or
\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv}\textless{}0.7 are ignored, (ii) topography data from
Shuttle Radar Topography Mission (SRTM) (Farr et al., 2007) to remove
ground clutter from hills present towards 40 km east of the radar
(Figure 1) using the method proposed by Friedrich et al., (2006). Figure
2 shows the radar reflectivity during an event on
13\textsuperscript{th}May, 2018 before quality control (Figure 2a) and
after quality control (Figure 2b). The clutter due to hills is present
on the reflectivity field before quality control, which is removed
nicely after applying the above-mentioned quality control measures.
Other variables (Z\textsubscript{dr}, \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} and
\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv}) were processed similarly.

\textbf{Table 1} . Overview of the instruments and the data used in this
study

\textbf{Data source}

\textbf{Parameters}

used in the study

\textbf{Spatial}

resolution

\textbf{Temporal resolution}

C-band polarimetric Doppler weather radar

Reflectivity at horizontal polarization (dBZ), differential reflectivity
(dB), differential propagation phase (deg.), cross-correlation

150 m along radial and 1\selectlanguage{ngerman}° along azimuth

\textasciitilde{} 15 min

Disdrometer (OTT parsivel)

Rain rate (mm h\textsuperscript{-1}), concentration of precipitation
particles in diameter classes 0.2-25 mm (m\textsuperscript{-3}
mm\textsuperscript{-1}).

In-situ

1 min

Ceilometer (CHM15k)

Cloud base height (m), cloud cover (oktas), cloud penetration depth (m)

-

15 sec

INSAT-3DR

Brightness temperature (K)

4 x 4 km

30 min

ERA-5

u-wind (m s\textsuperscript{-1}), v-wind (m s\textsuperscript{-1}),
geopotential (m\textsuperscript{2} s\textsuperscript{-2})

0.25° x 0.25°

1 hour

Radiosonde

Temperature (K), mixing ratio (g kg\textsuperscript{-1}), wind speed (m
s\textsuperscript{-1}), wind direction (deg.), CAPE (J
kg\textsuperscript{-1}) etc.

-

-

\textbf{2.2. Convective-Stratiform separation}

Several studies have been done for the classification of precipitation
into convective and stratiform parts using \emph{in-situ} measurements
(Tokay \& Short, 1996; Testud et al., 2001; Bringi et al., 2003) and
weather radars (Steiner et al., 1995; Williams et al., 1995; Biggerstaff
\& Listemaa, 2000; Ulbrich \& Atlas, 2002; Thurai et al., 2010).
Convective and stratiform parts of the cloud systems exhibit
significantly different behaviours in terms of dynamics as well as
microphysics (Houze, 1997). Vertical air motions within these two
portions of a cloud system differ significantly; convective parts are
mainly driven by large narrow updrafts (5--10 m s\textsuperscript{-1} or
more), while stratiform portions are governed by gentler mesoscale
ascents (\textless{} 3 m s\textsuperscript{-1}). Thus, microphysical
processes responsible for particle growth within the convective and
stratiform parts are very different. Particles within convective cores
regions mainly grow by riming or accretion (collection of supercooled
liquid water droplets onto the ice particle surface), which leads to
large/dense hydrometeors, whereas in the stratiform region vapour
deposition and aggregation are dominating processes that lead to smaller
and less dense ice hydrometeors (though large aggregates may exist).

The convective-stratiform separation method of Steiner et al. (1995),
which is based on the texture of the radar reflectivity field is adopted
for the present study and is widely used by the radar community. This
method basically checks for two criteria viz. \emph{intensity}
or\emph{peakedness} criteria on the horizontal reflectivity field at 3
km height, to identify a grid point (pixel) as a convective center. Any
grid point with reflectivity at least 40 dBZ (intensity criteria) or
greater than a fluctuating threshold (peakedness criterion) depending on
the area-averaged background reflectivity (Z\textsubscript{bg}calculated
within a radius of 11km around the grid point), is considered as a
convective center. For each pixel identified as a convective center, all
surrounding pixels within a certain radius of influence are also
included as convective pixels. This radius of influence is dependent on
Z\textsubscript{bg}. Once all the convective pixels are identified, the
rest of the pixels with non-zero reflectivity values are assigned as
stratiform pixels.

\textbf{2.3.} \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} \textbf{data processing and
K\textsubscript{dp} calculation}

The differential propagation phase (\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp}) is the phase
difference between the horizontal and vertical polarized pulses on
traversing through the atmosphere. The differential propagation phase is
proportional to the water content along a rain path. Since, most of the
hydrometeors in the atmosphere are aligned with their major axis in the
horizontal plane and it's a range cumulative parameter, the value of
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} increases with propagation path. Now, the
unambiguous range of \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} usually is 180\selectlanguage{ngerman}° in the
alternate H/V transmission mode and 360° in the simultaneous H/V
transmission mode. Hence, for a long propagation path in rain,
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} values can easily exceed the unambiguous range and
then the \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} will be wrapped/folded which is usually
manifested as a sudden jump in the range profiles of
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp}. This issue with \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} is known as
phase wrapping/folding (Wang \& Chandrasekar, 2009; You et al., 2014).
The unfolding of these phases has been done by adding appropriate phase
offset (You et al., 2014). So, even after the quality control steps
mentioned in the previous section, \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} needs this extra
processing before it can be used in further analysis. In Figure 3a, such
a situation of phase wrapping is observed towards 15 km west of the
radar during a convective event. Then the phase are unfolded nicely and
the unfolded \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} is shown in Figure 3b.

Specific differential phase (K\textsubscript{dp}) is defined as the
slope of range profiles of \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} (Seliga \& Bringi, 1978;
Jameson, 1985; Bringi \& Chandrasekar, 2001) and is defined as follows.

\begin{equation}
\text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ K}_{\text{dp}}\left(r\right)=\left[\frac{\Phi_{\text{dp}}\left(r+\frac{\Delta r}{2}\right)-\Phi_{\text{dp}}\left(r-\frac{\Delta r}{2}\right)}{2r}\right]\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (1)\nonumber \\
 \end{equation}

K\textsubscript{dp} is an important parameter for meteorological
applications as it is closely related to rain intensity. More
importantly it's insensitive to signal attenuation during propagation,
radar calibration, partial beam blockage and the presence of hail (Aydin
et al., 1995; Zrnic \& Ryzhkov, 1996). This makes specific differential
phase very useful for precipitation estimation at heavy rain intensity
or during partial beam blockage. Though the estimation of
K\textsubscript{dp} seems quite simple, it requires further processing
of \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} range profiles before calculating the slope.
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} is known to be a very noisy parameter particularly
in regions with low rain rates and the process of differentiation
increases this noise even further. To tackle this, we have applied a
low-pass butterworth filter (Parks \& Burrus, 1987; Proakis \&
Manolakis, 1988) of order 10 with a cut-off scale of 2 km to reduce the
statistical fluctuation but keeping the overall features intact. Similar
filters with similar cut-off scales have been used in previous studies
(Hubbert et al., 1993; Wang \& Chandrasekar, 2009). Figure 4a shows the
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} Plan Position Indicator (PPI) at 2\selectlanguage{ngerman}° elevation angle
during the convective event on 13\textsuperscript{th} May after quality
control and unfolding. Then the previously mentioned filter has been
applied on this \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} and obtained a smoothed
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} (Figure 4b). Small scale fluctuations in the
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} field are nicely removed in the filtered
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp}. With this smoothed \selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} field
K\textsubscript{dp} has been estimated using Equation 1 and is shown in
Figure 4c. Another K\textsubscript{dp} estimate using slope of the
linear regression line (Balakrishnan \& Zrnic, 1990) has also been
calculated. Both the methods gave similar K\textsubscript{dp} values.
K\textsubscript{dp} field shows high values close to 9\selectlanguage{ngerman}°
km\textsuperscript{-1} at a distance of 5 to 15 km westward from the
radar, indicating presence of heavy precipitation. The blue line in this
plot represents the 281° azimuth. Along this direction original
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} (dot-dashed blue curve), filtered
\selectlanguage{greek}Φ\selectlanguage{english}\textsubscript{dp} (solid blue curve), estimated
K\textsubscript{dp}(red curves) are shown in Figure 4d. The ranges of
K\textsubscript{dp}values obtained here, agrees quite well with previous
studies on convective cases (Wang \& Chandrasekar, 2009; Dolan et al.,
2013).

\textbf{2.4. Hydrometeor identification}

A hydrometeor identification (HID) algorithm by Dolan et al. (2013) is
used to identify types of hydrometeors present at different heights
within a convective system. This is a fuzzy logic-based algorithm in
which a fuzzy logic score (\selectlanguage{greek}μ\selectlanguage{english}) is calculated (Equation 2) for each
hydrometeor type and the hydrometeor with the highest fuzzy logic score
is the most probable hydrometeor type at that grid point within the
radar scan volume.

\begin{equation}
\mu_{i}=\left[\frac{W_{Z_{\text{dr}}}\beta_{Z_{\text{dr}},i}+\ W_{K_{\text{dp}}}\beta_{K_{\text{dp}},i}+\ W_{\rho_{\text{hv}}}\beta_{\rho_{\text{hv}},i}}{W_{Z_{\text{dr}}}+\ W_{K_{\text{dp}}}+\text{\ W}_{\rho_{\text{hv}}}}\right]\beta_{T,i}\selectlanguage{greek}\text{\ β}\selectlanguage{english}_{Z_{h},i}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (2)\nonumber \\
 \end{equation}\begin{equation}
\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \beta=\ \frac{1}{1+\left[\left(\frac{x-m}{a}\right)^{2}\right]^{b}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (3)\nonumber \\
 \end{equation}

Where, \selectlanguage{greek}μ\selectlanguage{english}\textsubscript{i} is the fuzzy logic score for the
i\textsuperscript{th} hydrometeor type. \selectlanguage{greek}β\selectlanguage{english}\textsubscript{j,i} is the
membership function for i\textsuperscript{th} hydrometeor types and
j\textsuperscript{th} variable (Equation 3). W\textsubscript{j} is the
weight factor for the j\textsuperscript{th} variable. The values of
these membership function parameters and the weights are taken as in
Dolan et al. (2013), which are obtained from simulation at C-band. Five
variables viz. Z\textsubscript{h}, Z\textsubscript{dr},
K\textsubscript{dp}, \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv} and temperature (T) are used to
calculate the fuzzy logic score. Seven types of hydrometeors have been
considered viz. drizzle (DZ), rain (RN), ice crystals (CR), aggregates
(AG), low-density graupel (LDG), high-density graupel (HDG), and
vertically oriented ice (VI). Graupels are ice particles with diameter
of 2-5 mm, which grow mainly due to riming process i.e., collection of
supercooled water droplets onto the surface of ice crystals and
subsequent freezing. Temperature for the HID scheme has been obtained
from radiosonde measurements by IMD Thiruvananthapuram at 5:30 IST
(Indian Standard Time). Radar data interpolated on a 0.5x0.5x0.5 km grid
have been used for the HID analysis.

\textbf{3. Results and discussions}

\textbf{3.1. Reflectivity statistics over convective and stratiform
regions}

An implementation of the convective-stratiform separation algorithm is
depicted in Figure 5 during the convective event on
13\textsuperscript{th} May, 2018. Figure 5a shows the reflectivity field
averaged between 2.5 and 3.5 km height. Convective-stratiform separation
algorithm is then applied on this horizontal reflectivity field and the
results are shown in Figure 5b. The red and blue pixels are identified
as convective and stratiform precipitation respectively. Not only high
reflectivity regions, but also other regions with strong gradient have
been identified as convective regions. The convective-stratiform
separation has been implemented for all the volume scans available for
all the eleven convective events during Mar-May, 2018 and the
corresponding reflectivity statistics are shown in Figure 6. Figure 6(a,
b) shows the contour frequency by altitude diagram (CFAD) of the
reflectivity over the convective and stratiform regions. The convective
core is visible near 3 km height though such feature is not visible in
case of stratiform. Figure 6c shows the mean vertical profile of
reflectivity over the convective and stratiform regions. For the
convective case (red), mean reflectivity gradually increases with height
from ground level and reaches a maxima near 3 km height and then
gradually decreases with height. Similar features in the reflectivity
profile were found over the tropical region by Zipser and Lutz (1994).
The peak value of the reflectivity is about 32 dBZ. On the other hand,
in stratiform case mean reflectivity remains almost uniform up to 4 km
height and it peaks near 5 km height and then gradually decreases with
height. This peak in the reflectivity signifies the bright band (caused
by enhanced reflectivity from melting ice particles near 0 \selectlanguage{ngerman}°C level)
over stratiform regions. Figure 6d shows the frequency distribution of
reflectivity at 3 km height. The peak of the distributions over the
convective (red) and stratiform (blue) regions are well separated though
there is an overlap between the two distributions. The dashed vertical
line represents the reflectivity corresponding to the rain rate of 10 mm
h\textsuperscript{-1}. Here we have used
Z=168R\textsuperscript{1.4}relation, which was obtained from another
study by Jash et al. (2019) over this region using micro rain radar
data. This result clearly shows that use of a rain rate threshold (e.g.,
10 mm h\textsuperscript{-1}) to separate convective and stratiform rain
is questionable, though such simple classification method is often
useful in many studies (Testud et al. 2001; Bringi et al. 2003; Sisodiya
et al. 2020).

\textbf{3.2. Evolution of convective systems}

An in-depth analysis is performed on two prominent convective events on
13\textsuperscript{th} May and 25\textsuperscript{th} May, 2018 for the
understanding of the evolution and structure of pre-monsoon convective
systems over southern peninsula.

\textbf{3.2.1 An overview of the synoptic conditions}

Favourable synoptic and thermodynamic conditions help in the
organization of convective storms to develop into severe ones
(Mukhopadhyay et al., 2009). High moisture, atmospheric instability,
vertical wind shear and a lifting mechanism are the different necessary
conditions for the development of thunderstorms. Hence, an overview of
the synoptic conditions before and during the events will give more
insights into their development. Geopotential height anomaly and wind
data from ECMWF Reanalysis v5 (ERA5) at 12 UTC and vertical profile of
equivalent potential temperature (\selectlanguage{greek}θ\selectlanguage{english}\textsubscript{e}), mixing ratio,
wind speed, wind direction from radiosonde measurements by IMD,
Thiruvananthapuram at 00 UTC and 12 UTC were used to look into the
environmental conditions for the events.

A low-pressure area formed in the south west Arabian Sea (Figure 7a) on
13\textsuperscript{th} May, 2018 which was evident from the minimum
geopotential height anomaly at 700 hPa levels between 55-65E and 4-10N,
was far from the study region. Under this influence, the mean wind was
from Bay of Bengal to the Arabian Sea in the easterly direction (figure
7a). The strong negative gradient of the \selectlanguage{greek}θ\selectlanguage{english}\textsubscript{e} profile up
to 3 km height shows the instability in the lower atmosphere (Figure
7c). The mixing ratio profiles indicate the presence of moist layers
between 2-6 km levels, also suggest the existence of favourable
atmospheric conditions for the formation of thunderstorm. Wind direction
changed abruptly along the vertical which is due to the turbulence
associated with the unstable lower atmosphere. Heavy rainfall in
isolated places were reported over Kerala and Tamil Nadu by IMD. These
conditions lead to the formation of convective system over inland region
on 13 May 2018 in the afternoon hours between 16:00-22:30 IST.

The convective event occurred over southern peninsula on
25\textsuperscript{th} May 2018, between 13:00-19:00 IST. The lower
geopotential height anomaly at 700 hPa level clearly demonstrates the
convection is active and strong (Figure 7b) evident with scattered low
and medium clouds favouring the intense to very intense convection. The
sounding analysis over the study region indicates that,
\selectlanguage{greek}θ\selectlanguage{english}\textsubscript{e} and mixing profiles in the morning and evening hours
shows unstable and moist layers in the near surface levels. Also, the
near surface wind was more than 10 ms\textsuperscript{-1} from the south
westerly directions (near to monsoon onset). Further to the west,
cyclonic vortex Meknu (T5.0) was formed over west central adjoining
south west Arabian sea with lay centred over 15.2\selectlanguage{ngerman}°N and 54.3°E.

\textbf{3.2.2 Convective event on 13\textsuperscript{th} May, 2018}

Development of the convective system on 13\textsuperscript{th} May 2018
(16:00-22:30 IST) is captured in the plan position indicator (PPI)
diagrams of radar reflectivity field at consecutive times during the
event (Figure 8). The convective clouds started developing over the land
around 25 km east of the radar location at 16:00 IST and then gradually
it started moving westward. This movement of the system was due to the
prevailing easterly wind (Figure 7a). The cloud system passed over NCESS
location around 18:00 IST (Figure 8d). As soon as it reached over the
NCESS location extremely heavy rainfall started, which was observed in
the rain rate measured by disdrometer (Figure 9a). The rain rate crossed
100 mm h\textsuperscript{-1} and sustained in that range for over an
hour. Gradually the rain intensity declined to a range of 0.1 - 1 mm
h\textsuperscript{-1}, which was basically the stratiform precipitation
following the main convective activity. The rain DSD obtained by the
disdrometer shows an abundance of bigger raindrops (diameter
\textgreater{} 3 mm) during this intense convective spell followed by
smaller drops at the later stage of the event. The deep convective cloud
system eventually moved over the Arabian Sea around 30 km westward from
the radar location and meanwhile it turned into a stratiform system
(Figure 8g-8i). The IMD weather report also mentioned about the rainfall
during these hours. This event was associated with rapid development of
deep convective clouds as observed in the evolution of the cloud top
infrared brightness temperature (IRBT) measured from satellite
(INSAT-3DR). A lower brightness temperature signifies a higher cloud top
height. Figure 10a-10e shows the spatial and temporal evolution of the
brightness temperature during this event. Around 18:00 IST much of the
region was having brightness temperature below 200 K revealing the
occurrence of deep clouds over most of the region. Figure 11 (red curve)
shows the temporal evolution of the brightness temperature over the
NCESS location (averaged over a 12x12 km area centered at NCESS). A
rapid decrease in the brightness temperature started at 15:45 IST and
reached a minimum value of 185 K at 17:45 IST, which demonstrates how
fast such a deep system can develop within such a short span of time.
Also, cloud base height measured by ceilometer shows (Figure 12a) the
presence of multilevel clouds. Before 17:00 IST mostly high-level clouds
are detected (CBH \textasciitilde{} 7 km) and then just before the
precipitation starts all three cloud layers are having cloud base below
2.5 km. Such a low cloud base height and high cloud top height (inferred
from low IRBT values) measures the depth of the cloud system. Once the
rain rate reduced it detected multilevel clouds. The CAPE value of 1713
J kg\textsuperscript{-1} was observed from the nearest radiosonde
measurements in the mooring hour (05:30 IST) which was indicative of
already existing moderate instability in the atmosphere which built up
further and eventually led to strong updraft during evening hours.

The vertical structure of the storm in terms of DWR polarimetric
measurements and associated hydrometeor identification is shown in
Figure 13. Averaged reflectivity between 2.5 and 3.5 km height during
rapid initial development stage of the storm shows active convective
regions (Figure 13a). Then a vertical cross section along the convection
line AB has been considered to analyse the vertical structure of the
storm. Figure 13b shows the vertical cross section of reflectivity at
horizontal polarization (Z\textsubscript{h}) along the convection line
AB. The x-axis represents the distance from point A towards point B.
Reflectivity values greater than 30 dBZ reaching up to 10 km height
signifies the existence of strong updraft within the convective core
region. This strong updraft can keep the larger hydrometeors (bigger
raindrops, graupels etc.) float aloft for longer period giving them more
time to grow further by the collision-coalescence process for raindrops
and by riming process for ice particles (Schuur et al. 2001). Since,
reflectivity is proportional to the 6\textsuperscript{th} power of the
particle diameter (Bringi \& Chandrasekar 2001), these larger particles
produce such strong reflectivity values even at higher altitudes.

Figure 13c shows the vertical cross section of differential reflectivity
(Z\textsubscript{dr}) along the convection line.
Z\textsubscript{dr}value gives a measure of the oblateness of
precipitation particles and hence could be useful in distinguishing
between larger raindrops, hail, and graupel due to differences in shape
and orientation. Since raindrops (diameter\textgreater{} 1 mm) are
deformed into oblate spheroid shape due to aerodynamic forces
(Pruppacher \& Beard, 1970) with a preferred orientation of their major
axes in the horizontal direction (and therefore
Z\textsubscript{h}\textgreater{} Z\textsubscript{v}),
Z\textsubscript{dr} is positive and increases with raindrop size. This
increase in the value of Z\textsubscript{dr} with raindrop size is shown
quantitatively in Bringi et al. (2009) in terms of a polynomial fit
between observed Z\textsubscript{dr} and mean drop diameter measured by
disdrometer. Z\textsubscript{dr} values greater than 2 dB were observed
which indicate presence of bigger raindrops or melting bigger ice
particles (Anderson et al. 2011) below 4 km height. Bigger raindrops are
also observed in the disdrometer measurements of rain DSD (Figure 9a).

Z\textsubscript{dr} values are much smaller at higher altitudes (above
0° isotherm \textasciitilde{}5 km height) as the ice particles such as
aggregate, graupel, hail, tend to be spherically symmetric or tumble
while falling, causing low values of Z\textsubscript{dr}. The lower
value of dielectric constant for ice compared to water is another factor
behind lower Z\textsubscript{dr} for ice particles. Within the strong
convective region at heights above the melting layer, a higher value of
Z\textsubscript{dr} along with high value of
K\textsubscript{dp}indicates supercooled liquid drops above freezing
level (Hubbert et al., 1998). The \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv} shows high values
(\textgreater{}0.95) throughout the entire cross section (Figure 13d)
and \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv}depends on several factors such as eccentricity,
distribution of canting angle, irregular shape and mixture of different
types of hydrometeors. Relatively lower values of \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv} at
the central region and at higher altitudes within the cross-section,
could be attributed to mixture of ice particles with rain.

The estimated K\textsubscript{dp} (Figure 13e) shows that the spatial
pattern of K\textsubscript{dp} is in tandem with that of reflectivity
though there are differences. High values (greater than 5\selectlanguage{ngerman}°
km\textsuperscript{-1}) of K\textsubscript{dp} below melting level
suggest the presence of intense convective precipitation with bigger
raindrops formed due to the coalescence process or due to melting
graupel. As drop eccentricity increases with diameter, the differential
propagation phase increases causing higher values of K\textsubscript{dp}
within regions of intense convective precipitation. A similar structure
of K\textsubscript{dp}within convective regions is reported by Ryzhkov
et al. (2002). Higher values of K\textsubscript{dp} above freezing level
suggests prevalence of supercooled droplets which can help in formation
of graupel particles via the riming process.

Identified hydrometeor types are shown in Figure 13f. Below the melting
level, it is mainly dominated by rain (RN) and above melting level, ice
aggregates (AG) are the dominating hydrometeors. At heights between 4.5
to 8 km, within the convective core regions graupel (HDG) particles are
abundant. Similar findings are obtained in Dolan et al. (2013), in which
HDG was found close to the melting level and LDG at higher heights.
Within such convective cores reaching up to 10 km height, liquid
droplets are pushed to heights much above the freezing level and they
stay there as unstable supercooled droplets. Upon contact with
ice-aggregates they immediately freeze onto the surface forming bigger
ice particles viz. graupel. The strong updraft can sustain these
graupels in air for longer helping them grow even further. The presence
of vertical ice indicates the existence of electric field which forces
these particles to orient vertically and this could be due to the
charging via the collisions between graupels and smaller ice particles,
as confirmed by different laboratory experiments (Takahashi, 1978;
Jayaratne et al., 1983; Saunders et al., 1991).

\textbf{3.2.3 Convective event on 25\textsuperscript{th} May, 2018}

An organized mesoscale convective system over southern peninsular India
prior to the onset of the southwest monsoon occurred on
25\textsuperscript{th} May, 2018 during 13:00-19:00 IST. The CAPE value
of 148 J kg\textsuperscript{-1} was observed from the nearest radiosonde
measurements in the morning hour (05:30 IST) and increased to 1092 J
kg\textsuperscript{-1} in the afternoon hours (17:30 IST) suggesting the
unstable layers favouring the formation of convective event. The
development of the convective system is presented in the PPI diagrams of
radar reflectivity field from 12:58 IST to 16:10 IST consecutive times
during the event (Figure 14). Unlike the convective system on
13\textsuperscript{th} May, this system started forming over the oceanic
region (westward from radar) as well as over the land (north-east ward
from radar) around 13:00 IST and gradually covered more and more region.
Compared to the previous system, this system had a much larger spatial
extent with a lower value of the peak reflectivity during the
development stage, suggesting lesser convective activity compared to the
13\textsuperscript{th} May event. Rainfall and the embedded rain DSD
during the event were recorded by the disdrometer at NCESS (Figure 9b).
The peak rain rate (\textasciitilde{}10 mm h\textsuperscript{-1}) was
significantly lower compared to that for the previous system (100 mm
h\textsuperscript{-1}). The drops of diameter greater than 3 mm are
absent on 25\textsuperscript{th} May, 2018 suggesting lesser updraft
speed and hence lesser time for the growth of raindrops. The
spatial-temporal evolution of the brightness temperature (INSAT-3DR
data) during the event (Figure 10f-10j \& 11) reveals a slow development
of the cloud system. The development is markedly different from the one
on 13\textsuperscript{th} May event, which was much more rapid. Around
15:30 IST the lowest brightness temperature of \textasciitilde{}220 K
was noted, which was significantly higher than the convective event on
13\textsuperscript{th} May 2018 (\textless{}185 K), indicating less deep
cloud systems. The ceilometer observation shows the presence of clouds
having the base height below 5 km level (Figure 12b) during the
initiation of the convective system. The dissipation phase of the event
was registered with high level clouds (5 \textless{} CBH \textless{} 10
km) over the region.

The vertical structure inside the system is shown through a vertical
cross along a convective region (Figure 15). The spatial distribution of
the reflectivity averaged between 2.5 and 3.5 km height during
developing stage is shown in Figure 15a. A vertical cross section along
the line AB through the convective region is taken and hydrometeor
identification has also been done. The overall features of the different
variables are very much similar to those in Figure 13. The reflectivity
core at a distance between 5 and 20 km from point A was observed and it
reached up to a height of 7 km (Figure 15b), which was 10 km on
13\textsuperscript{th} May event. The Z\textsubscript{dr} values (Figure
15c) along the convection line are much lower due to smaller drop size
as observed in the DSD from disdrometer. Values of
\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv}(Figure 15d) are high (\textgreater{}0.9) indicating
presence of rain with smaller drops at lower levels. The structure of
K\textsubscript{dp}(Figure 15e) follows the structure of
Z\textsubscript{h} but values are less (\textless{}4\selectlanguage{ngerman}°
km\textsuperscript{-1}) compared to that in 13\textsuperscript{th} May
case. This is mainly because of lower rain rate and relatively smaller
drops with less eccentricity, resulting in smaller difference between
the reflectivity at horizontal and vertical polarization. Identified
hydrometeor types shown in Figure 15f are quite similar to the ones in
13\textsuperscript{th} May case (Figure 13f). Rain (RN), aggregates (AG)
and graupels (HDG) are the main hydrometeor types identified. The
graupels are present mainly in the highest reflectivity column. In this
case the abundance of graupels is much less compared to the
13\textsuperscript{th} May case. This is because of lower updraft as
inferred from the brightness temperature data. The occurrence of drizzle
(DZ) types was also identified along the vertical cross section.

\textbf{4. Summary}

The present study is focused on the structure of pre-monsoon convective
systems over a tropical coastal region in southern peninsular India.
Observations from several instruments such as Doppler weather radar
(DWR), disdrometer, ceilometer, INSAT-3DR satellite data, radiosonde
measurements are used in the study. Using the quality controlled DWR
data, 11 convective events have been identified by inspecting the
reflectivity field from DWR. Out of which, the convective events
occurred on 13\textsuperscript{th} May and 25\textsuperscript{th} May
2018 has been analysed in detail to understand the development of
mesoscale cloud systems. Convective-stratiform separation has been done
for all the events. Following are the major conclusions of the study.

\begin{itemize}
\tightlist
\item
  Convective-stratiform separation clearly demarcates the distinct
  difference in reflectivity profiles over convective and stratiform
  regions. A peak in the mean reflectivity profile near 3 km height is
  registered for convective regions. Stratiform regions are
  characterized by a peak reflectivity near melting layer signifying the
  bright band and almost constant reflectivity profile between 1km and 4
  km levels. The distribution of the reflectivity values at a height of
  3 km shows bell shaped nature and there is an overlap between
  distributions for the convective and stratiform precipitations. It
  also shows that using a single threshold for reflectivity or rain rate
  may not be useful for convective-stratiform separation.
\item
  The reflectivity PPI captured the spatial and temporal evolution of
  the convective cases on 13\textsuperscript{th} and
  25\textsuperscript{th} May 2018 from the initiation to the dissipative
  stages of both the events. The development of the system on
  13\textsuperscript{th} May was much more rapid with cloud tops
  reaching much higher altitudes as clearly seen in the brightness
  temperature observed from satellite. Disdrometer measurements of rain
  rate and DSD during the two events show that the event on
  13\textsuperscript{th} May, was associated with high rain rate
  (\textgreater{}100 mm h\textsuperscript{-1}) having bigger raindrops
  (diameter \textgreater{} 3mm) during the first hour of the event. Even
  though the spatial extent of the system on 25\textsuperscript{th} May,
  was larger, much lower rain rate (\textless{}10 mm
  h\textsuperscript{-1}) with relatively smaller (diameter \textless{} 3
  mm) raindrops was observed.
\item
  Vertical structures inside the storms during rapid development stage
  have been obtained by taking vertical cross sections of reflectivity
  through major convective regions. The reflectivity values show
  convective cores reaching 10 km height on 13\textsuperscript{th}May
  and about 7 km height on 25\textsuperscript{th} May. High values of
  Z\textsubscript{dr} at lower levels were observed on
  13\textsuperscript{th} May, due to the oblate spheroid shape of the
  bigger raindrops. The structure of K\textsubscript{dp} field is quite
  similar to that of reflectivity in both the cases. High values of
  K\textsubscript{dp} reveals the presence of intense rainfall on
  13\textsuperscript{th} May, as K\textsubscript{dp} is mainly dominated
  by bigger raindrops.
\item
  Fuzzy-logic based hydrometeor identification (HID) has been done along
  the vertical cross sections over prominent convective regions. HID
  analysis shows presence of graupel at middle levels within the
  convective core regions revealing presence of strong updrafts. Ice
  aggregates and rain are the dominant hydrometeors above and below
  melting level respectively. Presence of vertical ice signifies the
  presence of electric field inside the storm. Such electric field may
  be generated due to non-inductive charging via collision between
  graupel and smaller ice crystals.
\end{itemize}

It would be worth studying the observed lightning activity (if any)
during these events as presence of vertical ice indicates toward
development of electric field. If major lightning activity occurred
during these events, then it would support the collision charging
mechanism as graupels are identified within the convective core regions.

\textbf{Acknowledgments}

The authors sincerely thank the Director, National Centre for Earth
Science Studies (NCESS) for encouragement and support. The authors are
grateful to Dr. D. Padmalal, Head, Atmospheric Science Group, for the
insightful comments. The authors acknowledge the support of Mr. Vincent
Ferrer on the topography map of the study area. The authors would like
to acknowledge the Meteorological and Oceanographic Satellite Data
Archival Centre (MOSDAC) of the Space Application Centre (SAC), ISRO for
supplying the DWR and INSAT-3DR data.

\textbf{Data Availability Statement}

The Doppler weather radar data used in this study is freely available
through the https://www.mosdac.gov.in/ server. The in-situ data used in
this study will be shared on the acceptance of the manuscript.

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George, J. P., Banerjee, S. K., Thampi, S. B., \& Pradhan, D. (2007).
Assimilation of Doppler weather radar observations in a mesoscale model
for the prediction of rainfall associated with mesoscale convective
systems. \emph{Journal of Earth System Science} , \emph{116} (4),
275--304. https://doi.org/10.1007/s12040-007-0026-2

Agnihotri, G., Gouda, K. C., \& Das, S. (2021). Characteristics of
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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Terrain height (m) over the study area and the
locations of the C- band DWR and NCESS observatory are given. Concentric
circles represent distance from the radar for better reference.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} PPI diagrams of radar reflectivity at 2\selectlanguage{ngerman}° elevation
(a) before quality control and (b) after quality control at 18:54:12
IST, 13\textsuperscript{th} May, 2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} PPI diagrams at 2\selectlanguage{ngerman}° elevation of (a) folded
\selectlanguage{greek}φ\selectlanguage{english}\textsubscript{dp} and (b) unfolded \selectlanguage{greek}φ\selectlanguage{english}\textsubscript{dp} at 18:54:12
IST, 13\textsuperscript{th} May, 2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} . PPI at 2\selectlanguage{ngerman}° elevation of (a)unfolded
\selectlanguage{greek}φ\selectlanguage{english}\textsubscript{dp}, (b)filtered \selectlanguage{greek}φ\selectlanguage{english}\textsubscript{dp} and (c)estimated
K\textsubscript{dp}. Blue line in (b, c) represents 281\selectlanguage{ngerman}° azimuth. (d)
Variation of \selectlanguage{greek}φ\selectlanguage{english}\textsubscript{dp}(blue) and K\textsubscript{dp}(red)
along 281\selectlanguage{ngerman}° azimuth. DATA: 18:54:12 IST, 13-may-2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5} . (a) PPI diagrams of radar reflectivity at 2\selectlanguage{ngerman}°
elevation averaged between 2.5 - 3.5 km height at 18:54:12 IST, on
13\textsuperscript{th} May, 2018 and (b) the identified convective (red)
and stratiform (blue) regions.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6} . Contour frequency by altitude diagram (CFAD) of
radar reflectivity for (a) convective and (b) stratiform regions. (c)
Mean vertical profile of reflectivity over convective (red) and
stratiform (blue) regions, (d) frequency distribution of reflectivity at
3 km height. The dotted line represents rain rate of 10 mm
h\textsuperscript{-1}.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 7} . Horizontal wind vectors overlaid with geopotential
height anomaly on (a) 13\textsuperscript{th} May 2018 at 12Z, (b)
25\textsuperscript{th} May 2018 at 12Z using ERA5 dataset. Vertical
profiles of (c) \selectlanguage{greek}θ\selectlanguage{english}\textsubscript{e}, (d) mixing ratio, (e) wind speed and
(f) wind direction on 13\textsuperscript{th} May 2018 at 00Z (red),
25\textsuperscript{th} May 2018 at 00Z (green) and
25\textsuperscript{th} May 2018 at 12Z (blue) from radiosonde.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 8} . PPI diagrams of radar reflectivity at 2\selectlanguage{ngerman}° elevation
angle at 16:20:18 IST to 21:55:40 IST (a-i) during the event on
13\textsuperscript{th} May, 2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 9} . Time series of rain rate (red curve) overlaid with
rain DSD (colour bar) during the convective events on (a)
13\textsuperscript{th} May, 2018 and (b) 25\textsuperscript{th} May,
2018 using disdrometer data.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure 10} . Spatial-temporal evolution of infrared brightness
temperature during the convective events on 13\textsuperscript{th} May,
2018 (a-e) and 25\textsuperscript{th} May, 2018 (f-j) using INSAT-3DR
satellite data.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\textbf{Figure 11} . Time series of infrared brightness temperature (K)
during the convective events on 13\textsuperscript{th} May, 2018 (red)
and 25\textsuperscript{th} May, 2018 (blue) using INSAT-3DR satellite
data.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{Figure 12} . Time series of cloud base height (m) of layer 1
(pink), layer 2 (cyan) and layer 3 (blue) clouds during the convective
events on (a) 13\textsuperscript{th} May, 2018 and (b)
25\textsuperscript{th} May, 2018 using ceilometer measurements at NCESS.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

\textbf{Figure 13} . (a) Radar reflectivity averaged between 2.5 and 3.5
km height on 13\textsuperscript{th} May, 2018. Vertical cross section of
(b) reflectivity, (c) Z\textsubscript{dr} , (d) \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv} ,
(e) K\textsubscript{dp} and (f) identified hydrometeor types along AB
convection line at 17:07:59 IST.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}

\textbf{Figure 14} . PPI diagrams of radar reflectivity at 2\selectlanguage{ngerman}° elevation
angle at 12:58:39 IST to 16:10:26 IST (a-i) during the event on
25\textsuperscript{th} May, 2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image15/image15}

\end{center}
\end{figure}

\textbf{Figure 15} . (a) Radar reflectivity averaged between 2.5 and 3.5
km height on 25\textsuperscript{th} May, 2018. Vertical cross section of
(b) reflectivity, (c) Z\textsubscript{dr}, (d) \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{hv} , (e)
K\textsubscript{dp} and (f) hydrometeor types along AB convection line
at 14:15:13 IST.

\selectlanguage{english}
\FloatBarrier
\end{document}

