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 Kalbaisakhiin West Bengal, Bordoichila in Assam and 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´ & 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.