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.