Thunderstorms are usually associated with sudden rainfall, blustery winds along with lightning and thunder. They develop on a spatial and temporal scale in the order of 10-100 kms and 1-12 h. They result in huge loss of lives and crops. Andhra Pradesh region is vulnerable to severe thunderstorms during the hot period of pre-monsoon season. The Prediction of thunderstorms over Andhra Pradesh (India) has been attempted for every 15minutes time interval using INSAT-3D& INSAT-3DR satellite data along with WRF model data for the time period 2017 and 2018.
In two years, seven severe thunderstorm cases were identified using Real-time satellite images, Doppler Radar Images and IMD daily gridded rainfall data. Statistically based Atmospheric stability indices such as K Index (KI), Lifted Index (LI), Total Totals Index (TTI), Total Precipitable water(TPW), Humidity Index(HI) and Wind Index(WI) associated with severe convection system over Andhra Pradesh during pre-monsoon season were identified to provide guidance to convection and thunderstorm activity.
These indices give us a clear indication of development of Convective system before 3 -4 hours. INSAT-3D and INSAT-3DR satellite, WRF model have shown the prediction of thunderstorm with accuracy over the region. Results of this study indicate the predictability of thunderstorm activity by using INSAT-3D and INSAT-3DR satellite data plays an important role in disaster management.
According to a rough guess by National Oceanic and Atmospheric Administration (NOAA), 45000 thunderstorms may be seen yearly on the earth, almost eight lightning may be seen per second around the world (Yashvant Das, 2015; Allaby, 2003).
Though taken very light, the magnitude of lightning disaster is very massive as it causes more than 2000 deaths every year in India (Srinivas Vaddadi et.al, 2015). India’s death toll from lightning strikes is far higher than that in developed countries such as USA . On an average 27 people die due to lightning strikes every year. The lack of a effective thunderstorm warning system is often cited as a major reason for the more number of deaths over the country.
Another reason for the lightning deaths is most of the people work outdoors in India when compared to other populated parts of the world (Earth Networks organisation). In southern parts of India, Andhra Pradesh (AP) experiences severe thunderstorms during the pre-monsoon season and more during May. These thunderstorms are known to have caused extensive damage in the region of AP and neighbourhood. On April 24, 2018 a 36,749 volts lightening strike was reported by the Disaster Management Department. However, 14 people lost their lives. During the year of 2017, lightning killed five persons in Anantpur district on 14 May, 2017; 2 persons and 70 sheep in Kurnool district, and a woman in West Godavari district, all on 27 May, 2017. Besides the fatalities, banana plantations and drum stick trees were extensively damaged in Kurnool district on 27 May, 2017 due to hail. (sources from and
Thunderstorm is usually classified as a ‘mesoscale’ phenomenon with its space scale between a few kilometers and a few hundred kilometres and its timescale from a few minutes to a few hours. They result in human loss and crop damages. Thunderstorm occur due to convective instability in the atmosphere. Strong heating of the landmass during midday initiates convection. Availability of moisture in lower trosphere and cool dry air in upper troposphere leading to low level convergence and upper level divergence. During Pre-Monsoon, thunderstorm activity starts in March and reaches its maximum phase in May, both in terms of number of days of activity as well as spatial distribution (Bhaskar Rao et.al, 2008). However, there are differences in the activity from sub division to sub-division.
The main region’s of high thunderstorm activity in our country during the pre-monsoon season are Northeast India, Kerala, Orissa, Bihar and Andhra Pradesh. They are as much as 30-40 days in Northeast India and in south Kerala. Although no part of the country is free from thunderstorm activity during this season, the minimum activity is in Gujarat State – where the number of thunderstorm days hardly exceeds five for the whole season. (Srinivasan et al. 1973). Recent studies showed that Andhra Pradesh alone got 194 thunderstorms in 2014 and 406 Thunderstorms in 2015 (Ray et al, 2014).This shows that there is growing demand for thunderstorm forecast.
Major contributions to the study of thunderstorms in India between 1940 and 1975 came from Mull and Rao (1948) , Desai and Rao (1954), Ramaswamy (1956) , Das et al.(1957), Koteswaram and Srinivasan (1958), De and Sen (1961), Mull et al. (1963).All of the studies used conventional and radar data. The convective systems are very diffuse and difficult to detect on synoptic charts, radar weather observations, and statistical and numerical models. Hence, the forecasting of thunderstorms becomes very difficult. Adequate objective prediction of thunderstorms would lead to better operational planning and precautionary measures by the user agencies (Ravi et al, 1999). However, conventional methods have some constrains in accurate forecasting of severe thunderstorms. The main difficulty is mainly due to insufficient weather observational networks over the country. (Chauduri et al, 2013).
The advent of satellite era and remote sensing data from satellites has provided an opportunity to use satellite derived indices for the prediction of thunderstorms. Research by Kandalgaonkar et, al (2003) and Chauduri et al, (2013) on lightening showed the usage of TRMM-LIS data for studying the lightenings and thunderstorms. Data from MODIS TERRA and AQUA satellites have become available since 1999 and 2002 respectively, and a few studies on thunderstorms using MODIS data have been attempted (Jayakrishnan. P. R et.al, 2014). Jayakrishnan and Babu (2014) used the MODIS satellite derived stability indices such as K Index (KI) , Lifted Index (LI) , Total Totals Index (TTI) to identify their thresholds for convective formation over south peninsular India. The main constraint on the use of MODIS data is that its passes take 1-2 days and so any point on the earth could be explored at best once in a day. In contrast, INSAT-3D & INSAT-3DR satellites were launched on 26th July, 2013 and 8th September 2016 respectively. Both provides data through imagery and sounder every 30 minutes and at 10 km resolution covering the Indian subcontinent which enables continuous monitoring of the atmosphere for the generation of convective activity. Recent studies by de Conining E et al 2015, purdom 2003, Kalsi S. R 2002, Goyal et al 2017 reveals the importance of satellites in thunderstorm identification and prediction.
De Conining E et al 2015 have shown the usage of geostationary Meteosat Second Generation satellite to identify and track the rapidly developing thunderstorms over South Africa and southern Africa.
Purdom 2003 has shown the usage of satellite data in nowcasting the severe storms for identifying their occurrence and intensity prior itself.
Kalsi 2002 has shown the usage of satellite data in nowcasting the severe storms for identifying their occurrence and intensity prior itself.
Goyal et al 2017 have tried to nowcast the mesoscale convective systems with the help of INSAT-3D satellite. They used the convective cloud cluster technique for improving the nowcasting of convective systems.
Ali Et al 2011 have used the artificial neural network for prediction of thuderstorm. This neural network was evaluated by using cross validation technique .
In this paper, the Prediction of thunderstorms over Andhra Pradesh (India) has been attempted for every 15minutes time interval using INSAT-3D& 3DR satellite data for the time period 2017 and 2018. In two years, seven severe thunderstorm cases were identified using IMD daily gridded rainfall data , Doppler Radar Images and Thunderstorm Reports. Statistically based Thermodynamic Atmospheric stability indices have been identified to provide guidance to convection and thunderstorm activity. The study region and the dates of Thunderstorm event was shown in Figure 1.
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