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J. Earth Syst. Sci. (2021) 130:71 Ó Indian Academy of

https://doi.org/10.1007/s12040-021-01561-x (0123456789().,-volV)(0123456789().,-volV)

Statistical and dynamical based prediction over southeast India

1 1, 3 NUMAKANTH ,GCH SATYANARAYANA *, N NAVEENA,DSRINIVAS 2 and D V BHASKAR RAO 1Department of ECE, Center for Atmospheric , Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram 522 502, Andhra Pradesh, India. 2Department of and Oceanography, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India. 3National Centre for Medium Range Forecasting, Ministry of Earth Sciences, Sector-62, A-50 Noida, Uttar Pradesh, India. *Corresponding author. e-mail: [email protected]

MS received 12 June 2020; revised 10 December 2020; accepted 21 December 2020

Thunderstorms, associated with and heavy , are a weather hazard causing human deaths, urban Coods and damage to crops. Current work attempted to study the over Andhra Pradesh, coastal state in southeast India, using multiple satellite datasets, gridded rainfall, Doppler Images and Advanced Research Weather Research and Forecasting (ARW) model simulations during the pre- of 2017 and 2018. Thermodynamic stability indices computed using INSAT-3D/3DR satellite data were used to identify precursors and lead time of prediction. India Meteorological Department (IMD) daily gridded rainfall data were used to identify the thunderstorm occurrence days, and Doppler Radar Images and INSAT imagery were conjointly used to Bx the location. Eight severe thunderstorm cases were analyzed to assess the precursors and the predictability. Further, ARW model predictions for two thunderstorm cases were performed and stability indices computed using model output were compared with satellite-based indices for evaluation. Statistical metrics had shown good agreement of ARW model-based stability indices with satellite-based stability indices. Model had simulated rainfall and properties associated with thunderstorm activity. The results illustrated the predictability of the location and intensity of thunderstorms with 3–4 hrs lead time, which would Bnd usefulness in the real-time prediction of thunderstorms. Keywords. Thunderstorms; Andhra Pradesh; stability indices; ARW model.

1. Introduction convective cumulonimbus , associated with lightning, gusty and high rate of rainfall. A Characterization and prediction of thunderstorms single cell thunderstorm will have an average life are critically important due to their socio-economic span of 1 hr and area extent of 10 km2. Although impacts in terms of human and livestock deaths short-lived, they cause extensive damage, and in from lightning, damage to property from winds and India they are noted to be the most hazardous of all , and Cash Coods due to intense rainfall with events, as 40% of the human high fall rate. Thunderstorms epitomize deep deaths are due to lightning; followed by extreme 71 Page 2 of 18 J. Earth Syst. Sci. (2021) 130:71 rainfall (24%) and heat waves (20%) and cold , yields high rainfall which is referred to as waves (15%) (lliyas et al. 2014). According to cloud burst (CritchBeld 1983). On the other hand, National Oceanic and Atmospheric Administration multicell thunderstorms are a cluster of single cells, (NOAA), about 45,000 thunderstorms occur on the new cells emerge from the restrained gust front earth annually (8 /sec) (Allaby 2003; of the single cell promoting rise of warm and moist Yashvant Das 2015); and in contrast, the approx- air. These multicell thunderstorms, Courish with imate number of lightning strikes over India are moderate to strong vertical shear, have a more than 9.2 million during 2019 (SW Monsoon longer duration of few hours and cause more 2019 Lightning Report, https://lightningcouncil. devastation over a larger area and for a longer blogspot.com/2019/12/sw-monsoon-2019-lightning- duration (Doswell and Brooks 1993). report-unique.html). The human deaths in India, Earlier studies denote that high moisture, due to the lightning , are about 2000/year, , vertical and a which are significantly higher than the developed lifting mechanism are the necessary parameters for countries like USA (27/yr) (Srinivas Vaddadi et al. the development of thunderstorms. The - 2015). develop on a spatial scale varying from a Thunderstorms can occur at any time of the day few kilometers to few hundred kilometers, and with and during all seasons under favourable atmo- a temporal scale ranging from few minutes to few spheric conditions, but are noted to occur mostly in hours. The height of the cloud base starts from 450 the afternoon and evening times of late and to 600 m above the earth’s surface, and the cloud . Thunderstorms form when warm and tops are observed to reach 12–15 km height in the moist air rises from the Earth’s surface due to tropical regions (Lutgens and Tarbuck 1982). The strong heating at the ground, which leads to cool- primary lifting arises from the thermodynamically ing of rising air from adiabatic expansion and and/or dynamical forcing. Strong surface heating consequently and formation of along with high moisture indicates higher thermo- clouds. If the warming is strong, the clouds can dynamic instability, whereas orographic lifting or reach very high up to the top of the mass convergence at lower levels are the favourable (*18 km in the ) evolving as cumulonimbus dynamical factors and a combined eAect of these clouds, in which electrical charges are generated. It two factors abet thunderstorm occurrence (Hak- is observed that positive (negative) charges build lander and Van Delden 2003; Manzato 2005). After up at the top (bottom) of the cloud, and lightning the formation of cloud, release due to occurs as the electrical discharges inside the cloud condensation process provides to the air and from the cloud to ground. Lightning causes parcel that support deep (Wallance and disaster in terms of human and livestock deaths as Hobbs 1977). As the in the it releases heat at a of *28,000°Cat plays a crucial role in the thunder- the strike location on the ground. Thunderstorms, development, the deep convection in a due to their deep convection, often have the for- thundercloud produces electrical charges differ- mation of hail which causes extensive damage to ently, positive (negative) at the top (bottom) of the property and crops. clouds that lead to lightning strikes, heavy rainfall, Due to the enormous socio-economic impacts, and winds (Doswell 1987; Houze 1993). several studies of thunderstorms have been The thunderstorm activity in India is highest attempted to understand their life cycle, charac- during the pre-monsoon and mostly occurs teristics and physical processes of formation and over northeast India, Kerala, Orissa, Bihar and development. Thunderstorms are formed as a sin- Andhra Pradesh. Even though no part of the gle cell or multiple cells. As thunderstorm denotes country is free from thunderstorm activity during strong upward rising of warm and moist air this season, maximum activity of 30 days/year is favourable for cloud formation, compensating over northeast India and south Kerala, whereas the downdrafts occur arising from cooling due to minimum activity is seen in Gujarat State, where evaporation of falling . The cool air thunderstorm days hardly exceeds Bve (Das et al. reaching the ground spread horizontally is referred 2014). Pant and Rupa Kumar (1997) revealed that to as gust front. Fairbridge (1967) revealed that a 60 thunderstorm occurrence days were observed single cell thunderstorm would have a shorter over northeast India and Bangladesh, and 80 days duration and aAects a small region with less dam- over Assam region. Kandalgaonkar et al. (2005) age. The single-cell thunderstorm, at the mature divided the Indian region into six sub-regions such J. Earth Syst. Sci. (2021) 130:71 Page 3 of 18 71 as north-west India (NWI), north-central India globally. Apart from India, some of the recent (NCI), north-east India (NEI), west-peninsular studies using stability indices are by Haklander India (WPI), south-peninsular India (SPI), and and Delden (2003) over ; Kunz (2007) east-peninsular India (EPI) by examining 260 sta- over Germany; and Sanchez et al. (2008) over tions data all over the Indian region, and reported Argentina. Considering the recent studies for that NEI region receives highest (25%) of total India, Ravi et al. (1999) developed two objective annual thunderstorm days, whereas WPI receives methods based on stability indices to predict lowest (8%). Tyagi (2007) studied the thunderstorms over Delhi; Mukhopadhyay et al. of thunderstorms over India considering data from (2003) attempted to predict thunderstorm and 390 IMD (India Meteorological Department) non-thunderstorm days using stability indices over , 50 Indian Air Force observatories, 6 NE India; Dhawan et al. (2008) explored the use- Bangladesh observatories, 2 Pakistan observatories fulness of statistical techniques for forecasting pre- and one each from Nepal and Sri Lanka and monsoon thunderstorms over NW India; Litta and reported that nearly 100–120 annual thunderstorm Mohanty (2008) have used the few thermodynamic days were observed over Assam and Sub-Hi- indices values to identify the occurrence of thun- malayan West Bengal in the east and Jammu derstorm activity in numerical model outputs. region in the north, which are slightly higher than Tyagi et al. (2011) studied thunderstorm occur- of earlier studies. rence in relation to several stability indices and Andhra Pradesh (AP), a southern state of India, reported that some of the indices (KI, DCI, experiences severe thunderstorms during the pre- SWEAT, DEW, HI, RH, LI, TTI: elaborated in monsoon season (March–May) with a maximum section 2) are better indicators as compared to during May both in terms of a number of activity CAPE, BRN, and BI indices for predicting deep days as well as the spatial distribution (Bhaskar convection over Kolkata. Rao et al. 2008; Dodla et al. 2017a, b; Murphy Till the advent of satellite era, all the studies Martin and Demetriades Nicholas 2005; Satya- pertaining to thunderstorms had the limitation of narayana et al. 2018, 2019; Umakanth et al. using data from IMD observatories. 2019, 2020a, b; Naveena et al. 2020; Satyanarayana Currently, IMD collects radiosonde observations and Dodla 2020). Thunderstorms caused extensive from 56 observatories only (https://pib.gov.in/ damage in AP and neighbourhood regions with PressReleaseIframePage.aspx?PRID=1599388), high voltage lightning strikes, e.g., on April 24, which are considered sparse to cover the vast 2018, a 36,749 volts killed 14 peo- extent of Indian subcontinent. All of the thunder- ple; lightning killed Bve persons in Anantpur dis- storm predictions were qualitative as the stability trict on 14 May 2017; 2 persons and 70 sheep in conditions at one location had to be representative Kurnool district, and a woman in west Godavari of a large area. Since 1970s, as the satellites started district, were killed by lightning on 27 May. providing atmospheric data, improvements in the Besides the fatalities, banana plantations and thermodynamic prediction as well as application of drum stick trees were extensively damaged in numerical models had become possible. Kurnool district on 27 May, 2017 due to hail (http:// During the last two decades, nwp.imd.gov.in/fdp˙now/2017/FDP˙STORM˙ techniques became more prominent as high-reso- Bulletin˙77˙210517˙M.pdf). This emphasizes lution spatial imageries are available. Both the the need to predict the occurrence of thunderstorms polar-orbiting (MODIS) and geostationary ahead of at least 3–6 hrs so as to provide a warning to (INSAT) satellites are now providing imageries in local people to facilitate precautionary measures to the visible and ranges along with deduc- save human lives. tible vertical proBles of temperature and Since thunderstorm formation is dependent on at high spatial and temporal resolutions. Polar thermodynamic (in)stability of the atmosphere, all orbiting satellites provide data at 50 km resolution the prediction methods were based on the inter- twice a day for any nearby region, whereas INSAT pretation of the stability in terms of indices till the provides data at 15-min interval and 10-km reso- advent of the application of high resolution non- lution. The utilization of satellite data products hydrostatic mesoscale models since 2000. Several gave a considerable advantage towards forecasting stability indices were developed considering the thunderstorm events. Das et al. (2014) and Yadava vertical distributions of temperature and humidity et al. (2020) studied the importance of TRMM-LIS in the lower atmosphere, which had application data in the analysis of thunderstorms and lightning 71 Page 4 of 18 J. Earth Syst. Sci. (2021) 130:71 activity over India. Jayakrishnan and Babu (2014) research works by Jayakrishnan and Babu (2014), used the MODIS satellite data for calculating three and Dinesh Kumar et al. (2020) are the major stability indices, namely K-index (KI), LIFTED motivation works for our current research study. INDEX (LI), TOTAL TOTALS INDEX (TTI), Apart from conventional thermodynamic sta- and identiBed their thresholds as\–4 for LI, 35–40 bility methodology, rapid developments in data for KI, 50–55 for TTI for the formation of thun- assimilation had made it possible to use high-res- derstorms over south peninsular India. The use of olution mesoscale weather prediction models for MODIS data has the limitation that it takes about the simulation and prediction of high-impact sev- 1–2 days to view the same point on the earth, ere local convective storms. A few studies have which hinders its application for real-time detec- been carried out for the simulation of thunder- tion of convection. storms over the Indian region (Litta and Mohanty In contrast, geostationary satellites provide 2008; Litta et al. 2012; Leena et al. 2019). Leena atmospheric data as imageries and Sounder at et al. (2019) simulated a pre-monsoon thunder- frequent time intervals and high spatial resolution, storm occurred over Pune with the ARW model and their continuous monitoring of the three-di- and reported that the model could simulate the mensional atmosphere helps to detect the genera- storm development, structure and evolution satis- tion of convective activity. The Indian National factorily as veriBed with observations. Litta and Satellite System INSAT-3D, INSAT-3DR geosta- Mohanty (2008) simulated a thunderstorm over tionary satellites, which were launched on 26th the Kolkata region using ARW-NMM (Non-hy- July 2013 and 8th September 2016, respectively, drostatic Mesoscale Model core), and reported that provide data as imageries and Sounder at every the model could broadly reproduce several features 30-min interval, and at 10 km spatial resolution of the thunderstorm event, such as spatial pattern covering the Indian subcontinent. Recent and temporal variability. Litta et al. (2012) studied studies by Kalsi (2002), Purdom (2003), Ivanova four thunderstorms of the pre-monsoon season over (2019), Lee et al. (2019), Umakanth et al. Kolkata with ARW-NMM model, and their results (2019, 2020a, b, c) revealed the importance of show that the model could predict the thunder- satellites in thunderstorm identiBcation and pre- storm occurrence with 6–12 hr lead time and with diction. Lee et al. (2019) have shown the usage of good agreement in spatial patterns, but with the geostationary Second Generation underestimation of precipitation intensity. San- (MSG) satellite in the detection and tracking of the deep et al. (2020) simulated the lightning Cashes rapidly developing thunderstorms over southern using National Centre for Medium Range Weather Africa. Purdom (2003) showed the usage of satel- Forecasting regional uniBed model (NCUM-R), lite data in the severe storm occur- and reported that increase of water path rence, and their intensity over South , USA. value as 200 g/m3 and with inclusion of -rain Kalsi (2002) demonstrated the use of satellite data collision process the model yielded escalation of in nowcasting the severe storms in the identiBca- lightning counts by 50% that were closer to the tion of large scale convective precipitation systems observations. Choudhury et al. (2020) simulated a such as monsoon depressions and tropical thunderstorm event of the pre-monsoon season over Indian region. The INSAT-3D satellite has over northeast India using ARW model and become useful to nowcasting the mesoscale con- reported that the number of lightning Cashes yiel- vective systems with the help of Sounder and ded with the Morrison microphysical and cloud top Imager products. Dineshkumar et al. (2020) stud- height based dynamical lightning parameterization ied the impact of assimilation of high-resolution scheme was in better agreement with correspond- atmospheric motion vectors from INSAT-3DR ing observations from TRMM- Lightning Imaging satellite data and reported significant improve- Sensor. ment in short-range . Jayakr- Prediction of thunderstorms using weather pre- ishnan and Babu (2014) used the MODIS satellite diction models is a complex task due to the non- data for calculating the stability indices like K-in- linearity of dynamics and microphysics of the dex (KI), (LI), total totals index (TTI), system. There are a few studies using the ARW which are the indicators of detection of the genesis model to simulate severe thunderstorms and to of thunderstorms, and identiBed their thresholds as understand the cloud microphysics and dynamics \–4 for LI, 35–40 for KI, 50–55 for TTI for con- over the Indian subcontinent and elsewhere (Litta vection to form over south peninsular India. The and Mohanty 2008; Litta et al. 2012; Das et al. J. Earth Syst. Sci. (2021) 130:71 Page 5 of 18 71

2015; Murthy et al. 2018; Sarkar et al. 2019; years 2017 and 2018 are considered. The study area Choudhury et al. 2020). is limited to Andhra Pradesh state in India, with The present study is motivated to assess the pre- the considered domain region as 12.5°–20°N and dictability of thunderstorm occurrence at a lead time 76°–85°E(Bgure 1). The following datasets were of few hours, using two methodologies. Firstly, using used for analysis. the INSAT 3D & 3DR satellite data of the vertical (a) The Doppler (DWR) images were proBles of temperature and humidity to compute the collected from the India Meteorological Depart- different stability indices and identify the suitability ment (IMD) website https://mausam.imd.gov. of each of the indices in the prediction of thunder- in/imd˙latest/contents/index˙radar.Thesewere storms. Secondly, ARW-ARW model is used to used to identify locations of thunderstorm occur- assess the predictability of thunderstorms. The study rences. These images were used to Bnd the char- region is chosen to be Andhra Pradesh, as this is one acteristics of the thunderstorm convection. of the 13 states in India with high frequency of (b) The real-time INSAT-3D satellite images were thunderstorms and also because of several reported collected from http://www.mosdac.gov.in/. human deaths during the last 5 years. Eight case These images are used to collocate the con- studies of thunderstorms, occurred during 2017 and vection (cloud image) with the thunderstorm 2018, are undertaken for assessment of the two occurrence location. methodologies in the prediction of thunderstorms (c) The vertical temperature and humidity proBles over Andhra Pradesh. This study could be consid- from INSAT-3D & INSAT-3DR satellites ered as a pilot study, and if the results are promising, (INSAT-3D ATBD document, 2015) were the methodology could easily be tested and adopted collected from the website http://www. for the entire Indian subcontinent. The study region mosdac.gov.in/. These data are used to com- and the dates of thunderstorm events are shown in pute atmospheric stability indices for the Bgure 1. This paper is organized as: section 2 brieCy periods of the occurrence of thunderstorms. describes about the datasets and methodology (d) The IMD daily gridded rainfall data at 0.25° adopted in this study, section 3 deals with the results, resolution over the Indian subcontinent (Pai and section 4 summarizes the results. et al. 2014) was used for the identiBcation of rainy days. Specifically isolated rainfall was 2. Data and methodology assumed to be coincident with thunderstorm occurrence. 2.1 Data (e) National Centers of Environmental Prediction (NCEP) (GFS) Opera- For this study, only the pre-monsoon season, i.e., tional Global Analysis and forecast Belds data the period from 1 March to 31 May for the two (http://www.ftp.ncep.noaa.gov/data/nccf/com/

Figure 1. Study region of Andhra Pradesh. Numbers indicate district names, which are given on the right side. Red star indicates the location of the occurrence of the selected thunderstorms. 71 Page 6 of 18 J. Earth Syst. Sci. (2021) 130:71

gfs/prod/) available at 0.25° spatial resolution at the values of the dewpoint and temperature at 850 hPa 3-hr time intervals. These data were used to pro- level and lower the values of temperature at 500 hPa, duce the initial and the time varying boundary TTI value will be higher and indicate higher atmo- conditions necessary for integrating the ARW spheric instability. TTI value higher than 44 K indi- model to perform numerical experiments. cates thunderstorm occurrence. This index is actually a combination of the vertical totals and cross totals, which are deBned as follows: 2.2 Methodology Cross totals, Stability indices, namely K index (KI), lifted index CT ¼ Td850 ÀT500; (LI), humidity index (HI), wind index (WI) and total totals index (TTI) are calculated using ver- Vertical totals, tical proBles of temperature and humidity from VT ¼ T850 ÀT500; using INSAT 3D/3DR Sounder data. Indices are computed at each of the identiBed location of a Total totals, thunderstorm. These computed indices up to sev- eral hours before the time formation of thunder- TT ¼ CT þ VT ¼ T850 þ Td850À2T500: ð2Þ storm are analyzed to identify possible precursors The total totals index (TTI) takes into account for detecting the thunderstorm formation which both the static stability conditions () and could subsequently be used for forecasting and to the moisture content in the lower levels of the ascertain the lead time. The statistical-based atmosphere. thermodynamic atmospheric indices that are con- sidered for the identiBcation of convection from the INSAT-3D & 3DR data are brieCy presented here. 2.2.3 Lifted index (LI) Lifted index (LI) is dependent on the stability of 2.2.1 K index (KI) the lower half of the troposphere. LI values lower The K index is determined by using the air and dew than –2 are indicative of thunderstorm occurrence point at different levels of the (Galway 1956). atmosphere using the formula (George 1960): LI ¼ T500ÀTparcel 500 ð3Þ KI ¼ ðÞþT850 ÀT500 Td850 À ðÞT700 ÀTd700 where T500 is the environment temperature and ð1Þ Tparcel500 is temperature of the air parcel at 500 hPa. In the computation, the lifted index assumes that where T is the air temperature and Td is the dew the air near the surface will be lifted to 500 mb, and point temperature. the possibility of surface air to rise to 500 hPa The values of KI represent the stages of con- depends on the stability of atmosphere below 500 vection, higher the value, higher is the probability hPa. A negative LI indicates that the atmosphere is of thunderstorm occurrence. The KI is computed unstable and conducive for development of thunder- using a variety of environmental observations. storm, whereas positive value means the atmosphere During thunderstorm season, the large KI indicates is stable, not favourable for convection. For appli- conditions favourable for air-mass thunderstorms. cability, the lifted index is more indicative of the However, KI values can decrease significantly for severity of thunderstorms rather than the probability thunderstorm development associated with a syn- of thunderstorm occurrence. The formula indicates optic scale low system (non-air-mass that LI values change rapidly and would be less thunderstorms). The KI values can change signif- (more) positive, i.e., more unstable (stable) during icantly over a short time period due to temperature daytime (night time) because of heating (cooling). and moisture .

2.2.2 Total totals index (TTI) 2.2.4 Wind index (WI)

The total totals index (TTI) is derived from the tem- The wind index (WINDEX) is deBned as the perature lapse rate between 850 and 500 hPa levels and maximum possible convective wind gusts that the moisture content at 850 hPa (Miller 1967). Higher could occur in thunderstorms (McCann 1994). J. Earth Syst. Sci. (2021) 130:71 Page 7 of 18 71 ÂÃÀÁ 0:5 2.3 Statistical evaluation WI ¼ 5 HM Â RQ G2À30 þ QLÀ2QM ; ð4Þ where HM is the height of the melting level in km Statistical metrics, comprising of correlation coef- above the ground; G is the temperature lapse rate Bcient (CC), root mean-square error (RMSE), in °CkmÀ1 from the surface to the melting level; mean absolute error (MAE), index of agreement QL is the mixing ratio in the lowest 1 km above the (IOA) and were computed to evaluate the surface; QM is the mixing ratio at the melting ARW model performance in comparison with level; and RQ = QL/12, but not [ 1. INSAT-3D & INSAT-3DR satellite data in the Strong wind gusts associated with isolated deep prediction of thunderstorms during the study convection in a weakly-sheared environment are period. mostly due to , especially microbursts The statistical formulae (Wilks 2006) are given (Fujita 1990; Atkins and Wakimoto 1991; Proctor as follows: P and Bowles 1992). Based on the ideas of Proctor n i¼1ðfi À f ÞðÞoi À o (1989) and Wolfson (1990), McCann (1994) COR ¼ qPffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiq ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiP ; ð6Þ n 2 n 2 developed the WINDEX to represent a microburst- i¼1ðfi À f Þ i¼1ðoi À oÞ gust as WINDEX represents the environmental conditions that lead to dry or wet microbursts, and 1 Xn MAE ¼ jjf À o ; ð7Þ it does not either estimate extreme surface winds in n i i thunderstorms or indicate storm probability. i¼1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi WINDEX is suitable to estimate gust strength in P severe local storms such as thunderstorms, but not n ðÞf À o 2 RMSE ¼ i¼1 i i ; ð8Þ the organized storms such as cyclones (Kessinger n et al. 1988). P n ðf À o Þ2 P i¼1 i i IOA ¼ 1:0 À n 2 ; ð9Þ 2.2.5 Humidity index (HI) i¼1 jfi À ojþjoi À ojÞ

Humidity index (HI) assesses the degree of satu- where oi and f i correspond to observations and ration at the mandatory levels 850, 700, and 500 forecast values, respectively. hPa (Jacovides 1990). It explains the importance of deep layer of high relative humidity in the gener- 2.4 ARW model ation of thunderstorms. The Advanced Research Weather Research and HI ¼ ðÞTÀTd 850þðÞTÀTd 700þðÞTÀTd 500; ð5Þ Forecasting (ARW) modelling system is one of two where T is temperature and Td is cores of the Weather Research and Forecasting temperature. (WRF) modelling system developed by National Center for Atmospheric Research (NCAR). The ARW model version 3.6.1 is used in the present 2.2.6 Total precipitable water (TPW) study. The model uses non-hydrostatic system of equations for atmospheric motion and is noted to TPW indicates the amount of moisture available in be applicable for simulation and prediction studies a column of air above a Bxed point. It does not of different scales of motion ranging from few indicate how much it will rain but only the amount meters to thousands of kilometers. The model of moisture, that is present in the air. For example, system has versatility to choose the grid domain, a TPW value of 25.4 mm does not indicate it will horizontal resolution and parameterization rain 1 inch, rather indicates all the moisture above schemes of physical processes. Skamarock et al. a location if condensed would be 1 inch. The TPW (2008) provide a complete description of the ARW value is also an instantaneous value of the amount modelling system. of moisture in the air above a location. It can The ARW model is designed to have two-way precipitate more than the precipitable value nested two domains with horizontal resolutions as amount since moisture convergence can occur and 9 and 3 km and with 42 vertical levels (Bgure 2). precipitation falls over a span of time and is not The details of the model conBguration are given in instantaneous. table 1. The initial and time varying boundary 71 Page 8 of 18 J. Earth Syst. Sci. (2021) 130:71 conditions are taken from the NCEP GFS data. total number of thunderstorm occurrences, eight The ARW model is integrated continuously for 36 cases were identiBed with their locations distributed hrs and the predicted atmospheric variables for the and representative of the AP region (Bgure 3). For later 24-hr period (Brst 12-hr output is discarded as all the eight cases, imagery from the nearest DWR of model spin-up) at 15 min interval were stored. (Doppler Weather Radar) and INSAT 3D/3DR are These data were used for model evaluation. The scrutinized to ascertain the spatial characteristics of model predicted temperatures, dew point temper- the thunderstorm. Further vertical proBles of tem- atures, wind and relative humidity were used for perature and humidity from INSAT 3D/3DR at the computation of the thermodynamic stability 15-min intervals are used to compute the stability indices (LI, KI, TTI, WI, HI, TPW) so as to indices from 5 hrs before to identify the Cuctuations understand the thermodynamic conditions for the indicative of the formation of deep convection. The occurrence of thunderstorms over the Andhra Pra- values of brightness temperature (BT), six types of desh region during 2017 and 2018 cases (table 2). stability indices and rainfall are presented in table 2. It is seen that the BT values range from 180 to 200 K (lower values indicate higher level) evidential of 3. Results and discussion deep convection; the LI values are negative ranging from 6.9 to –11.3 (negative values favour convec- The occurrence of thunderstorms during April and À tion and higher the negative value means higher May of 2017 and 2018 over Andhra Pradesh (AP), atmospheric instability favouring deeper convec- which is the study region, are identiBed through tion); KI values range from 322.6 to 332.6 (values examination of spatial distributions of daily rainfall. higher than 293 K indicate convection initiation and The days associated with any low-pressure system higher values denote higher instability); TPW val- (which rarely manifest) are discarded. Locations of ues range from 40.3 to 59.2 (the values are indica- rainfall higher than 2 cm are considered as due to tive of only the moisture and are used along with thunderstorm over the area. Since the gridded other indices); TTI values range from 50.1 to 62.0 rainfall is available at 0.25° resolution, the occur- ([44 indicate formation of thunderstorms, [50 rence of thunderstorm is taken to occur at the indicate severe thunderstorms); HI values range nearest grid point. The results are to be viewed and from 39.0 to 65.9 (higher values indicate moist layer interpreted subject to this constraint. For this rea- below 500 hPa which favour convection); and WI son, the rainfall for the month at all the values range from 41 to 84.6 (higher values indicate four grid points around the rainfall observation and larger vertical wind shear which favour deep the thunderstorm location is conBrmed considering convection). the rainfall intensity. The examination of spatial Although eight thunderstorms have been ana- distributions of daily rainfall yielded the occurrence lyzed, as reviewed in the preceding, two cases of of thunderstorms to be 39 and 50 cases during thunderstorm occurrences over AP region (one April–May of 2017 and 2018, respectively. Of the each from 2017 and 2018) are analyzed in detail. For each case, the INSAT 3D/3DR imager data are analyzed to identify the location and sounder data are used to compute the time variation of stability indices. Further ARW model was used to make 24-hr prediction and the model output at different times was used to compute model-based stability indices. The satellite-based and model output- based stability indices are compared to evaluate the performance of ARW model.

3.1 Case-1: May 21, 2017

A thunderstorm was noted to occur over Sathanapalle village in Guntur district, AP, on May 21, 2017. According to NDMA, at least 10 Figure 2. Model domain for numerical experiments. people were dead, gusty winds and severe rainfall J. Earth Syst. Sci. (2021) 130:71 Page 9 of 18 71

Table 1. Details of the model.

Model WRF (ARW core) Version 3.6.1 Dynamics Primitive equation, non-hydrostatic Vertical resolution 42 levels Domains Domain1: 8°–23°N; 73.5°–89°E Domain2: 12.5°–20°N; 76.5°–85°E Horizontal resolution 9 km 3 km Radiation Dudhia scheme for RRTM scheme for long wave Initial and boundary conditions NCEP GFS Global Forecast Cumulus convection Grell–Freitas old simpliBed Planetary boundary layer Mellor–Yamada–Janjic TKE scheme Explicit moisture Lin et al. scheme Surface layer physics Monin–Obukhov (Janjic) scheme Land surface Noah LSM

Table 2. INSAT-3D & 3DR satellite-derived stability indices at peak stage of thunderstorm.

Brightness temperature Rainfall LI KI TPW TTI HI WI Cases Date of event (k) (mm) (K) (K) (mm) (K) (K) (m/s) 1 May 21, 2017 185.2 27.1 –7.8 328.3 53.1 62.0 65.9 74.4 2 May 10, 2017 192.3 38.7 –11.3 332.0 59.2 57.6 36.1 71.1 3 May 8, 2017 189.4 48.1 –10 327.2 54.3 53.9 39.0 79.2 4 Apr 29, 2017 185.7 35.1 –9.4 330.6 55.0 53.9 48.1 72.2 5 Apr 24, 2018 187.8 35.6 –8.5 332.6 47.1 55.9 58.3 84.6 6 May 5, 2018 198.2 35.2 –7.5 322.6 40.3 50.1 55.1 52.0 7 May 2, 2018 180.1 44.1 –6.9 328.2 57.6 56.0 54.1 41 8 May 14, 2017 192.3 39.4 –10.2 322.0 56.2 54.6 38.1 74.1 was identiBed (https://www.deccanchronicle.com/ are shown in Bgure 5. The simulation clearly nation/current-affairs/210517/possibility-of-thunder- depicted isolated rainfall of 10–12 mm, strong storms-in-interior-districts-met-dept.html) reCectivity and larger cloud fraction, all in agree- ment with the observations, specifically the loca- Date 21 May 2017 tion and intensity of the thunderstorm. The time Area of event Sathanapalle region series of brightness temperature and rainfall, Latitude and longitude 16.4°N; 80°E observed from 00 (5:30 IST) to 24 UTC at 16.4°N Brightness temperature 190 K latitude and 80°E longitude, are shown in (peak stage) Bgure 6(a). The BT was 270 K from 00 to 08 UTC, Peak time 1200 UTC and then the BT values rapidly decreased up to 10 The distributions of 24-hr rainfall for 21 May UTC, the difference being 70 K, which is indicative 2017, along with INSAT 3D imagery, brightness of the formation of a thunderstorm. The BT was temperature (BT) and DWR Radar image corre- 190 K at 12 UTC (17:30 IST), and this value sponding to 1200 UTC on 21 May, 2017 are shown indicates a mature severe thunderstorm over the in Bgure 4. Isolated rainfall of 10–20 mm over selected location. Thereafter the BT values Sathanapalle was clearly noted (Bgure 4a), and increased up to 16 UTC which correspond to dis- concentric cloud was seen in INSAT image sipation of the thunderstorm. The dotted line (Bgure 4b); BT values of 180–200 K indicative of indicates rainfall in mm, no rainfall was noted up to deep convection (Bgure 4c); and DWR image 10 UTC, thereafter the rainfall increased till 12 showing deep convection (Bgure 3d) are clearly UTC yielding 30 mm of rainfall. These features elucidated. ARW simulated spatial distributions of conBrm the occurrence of a thunderstorm with 24-hr rainfall, cloud reCectivity and cloud fraction peak intensity around 12 UTC. The changes in BT 71 Page 10 of 18 J. Earth Syst. Sci. (2021) 130:71

Figure 3. Time series of the daily rainfall for the 2-month period from 1 April to 31 May of 2017 and 2018 and dates denote the selected for case studies.

Figure 4. Spatial distributions of (a) IMD 24-hr accumulated rainfall at 00 UTC of May 22, 2017; (b) INSAT-3D cloud image, (c) INSAT-3D BT (K), and (d) DW Radar image at 12 UTC of May 21, 2017. indicative of deep convection were evident before occurrence of a severe thunderstorm (Bgure 6b). 3 hrs. The time series plot of K index and humidity index At peak stage, LI value reached –8 K, whereas are shown in Bgure 6(c). It is clearly observed that TPW value was 58 mm both of which favour the there was a clear increase in the values of humidity J. Earth Syst. Sci. (2021) 130:71 Page 11 of 18 71

Figure 5. Spatial distributions of ARW model derived (a) 24-hr accumulated rainfall (mm) at 00 UTC of May 22, 2017; (b) reCectivity (), and (c) cloud fraction at 12 UTC of May 21, 2017 (black round circle indicates the location of the occurrence of thunderstorm).

Figure 6. Hourly time series plots of (a) brightness temperature (K) and rainfall (mm), (b) lifted index and total precipitable water (mm), (c) K index and humidity index, and (d) total totals index and wind index plotted using INSAT 3D & INSAT 3DR satellite data for the period from 00 UTC of 21 May to 00 UTC of 22 May, 2017. index. This is a good precursor for a convective sudden increase of 14 mm, 4 K, 11 K, 7 K and 11.4 system to occur. The total totals index and wind m/s, respectively. An examination of the develop- index are shown in Bgure 6(d), wherein the increase ment of the convective system every 15 min, the in wind-index parameter provides a clear indica- BT value had a sudden drop of 35 K before 2 hrs of tion of increased wind shear at the region of the peak stage and a rainfall of 10 mm. LI value convection. was –3K before 3 hrs. TPW, TTI, HI and WI At the initial stage of convection (08 UTC), values had an increase of 15 mm, 7 K, 17 K and 12 there were sudden changes in stability indices m/s, respectively, before 2 hrs of the peak stage of which are presented in Bgure 7. The BT and LI the convective system. KI values have an increase values had a sudden drop of 22 K and 5 K, and the of 4 K since 3 hrs before the storm. At peak stage, other indices like TPW, KI, HI, TTI, WI had all indices reached the threshold indicative of 90% 71 Page 12 of 18 J. Earth Syst. Sci. (2021) 130:71

Figure 7. 24-hr time series plots of (a) K index, (b) humidity index, (c) total precipitable water, (d) total totals index, and (e) wind index plotted using INSAT 3D & INSAT 3DR satellite data and ARW model output for the period from 00 UTC of 21 May to 00 UTC of 22 May, 2017.

Table 3. Statistical metrics for the thunderstorm event of May computed for comparison of ARW with INSAT 21, 2017. showed KI had the highest 0.95 correlation coefB- Stability indices BIAS MAE RMSE CC IOA cient and the BIAS value as 0.92 (table 3), which is KI 0.92 1.27 1.46 0.95 0.94 a very good indication of the predictability of the HI 1.44 1.81 2.32 0.87 0.9 severe convective storm. The other indices also had TTI 4.47 4.84 3.46 0.62 0.66 high CC, lower RMSE supportive of the KI as WI 1.26 1.76 2.74 0.88 0.74 indicators for the prediction of thunderstorm. TPW 4.2 4.45 3.0 0.70 0.63 MAE: Mean absolute error; RMSE: root mean square error; 3.2 Case-2: April 24, 2018 CC: correlation coefBcient; IOA: index of agreement. On April 24, 2018 Disaster Management Depart- probability for the occurrence of a severe thun- ment reported that 14 people were dead and - derstorm. All stability indices were normalized at sonal crops, particularly mango and cashew, were the dissipating stage (16 UTC). The increased badly damaged due to heavy winds and rain asso- values of TPW, KI, HI, TTI, WI explain the ciated with thunderstorms across Vizianagaram development of convective system, whereas the district (https://time.com/5256964/india-lightning- decreased values of BT and LI explain the severity strikes-monsoon/). of convective system. Date 24 April 2018 For the prediction of thunderstorm event, ARW Area of event Vepada Region model derived indices were compared with those Latitude and longitude 18°N; 83.1°E derived from INSAT 3D & 3DR satellite data. The Brightness temperature 185 K stability indices such as KI, HI, TPW, TTI and WI (peak stage) computed from INSAT 3D & 3DR satellite were Peak time 0700 UTC compared with the ARW model computed values and are shown in Bgure 7 and statistical metrics are In this case, IMD gridded daily rainfall, INSAT- given in table 3. The development of convective 3D BT image and Doppler Radar images corre- system was studied from both the datas and the sponding to 07 UTC of 24 April, 2018 are shown in severity of convective system is noted to be pre- Bgure 8. IMD daily rainfall is shown isolated rain- dicted 3 hrs before. The statistical metrics fall; INSAT image shows concentric cloud; BT J. Earth Syst. Sci. (2021) 130:71 Page 13 of 18 71

Figure 8. Spatial distributions of (a) IMD 24-hr accumulated rainfall at 00 UTC of April 25, 2018; (b) INSAT-3D cloud Image, (c) INSAT-3D BT (K), and (d) DW Radar Image 07 UTC of 24 April, 2018.

Figure 9. Spatial distributions of ARW model (a) 24-hr accumulated rainfall (mm) at 00 UTC of April 25, 2018; (b) reCectivity (dbz); and (c) cloud fraction at 12 UTC of April 24, 2018 (black round circle indicates the location of the occurrence of thunderstorm). values to be 180–200 K; and DWR image showed reCectivity and cloud fraction typically reCect the deep convection, all over the Vepada region. These location of the thunderstorm occurrence. Thus, the Bgures clearly indicate that the thunderstorm ARW simulates both the location and intensity of occurred over Vepada region located in Viziana- the thunderstorm. The thunderstorm occurrence garam district. Corresponding ARW simulated was identiBed at 18°N and 83.1°E as shown in spatial distributions of 24-hr rainfall, cloud reCec- Bgure 10. The time series of brightness temperature tivity and cloud fraction are presented in Bgure 9. and rainfall from 00 (05:30 IST) to 23 UTC at The simulation shows scattered rain in all Andhra 18°N, 83.1°E are presented in Bgure 10(a). The BT Pradesh, but with higher rainfall of 10–12 mm (solid line) had a value of 270 K at 00 UTC, which over Vepada region. Contrastingly, the cloud decreased to 258.39 K at 04 UTC indicative of the 71 Page 14 of 18 J. Earth Syst. Sci. (2021) 130:71

Figure 10. Hourly time series plots of (a) brightness temperature (K) and rainfall (mm), (b) lifted index and total precipitable water (mm), (c) K index and humidity index, and (d) total totals index and wind index plotted using INSAT 3D & INSAT 3DR satellite data for the period from 00 UTC of 24 April to 00 UTC of 25 April, 2018. initial stage of the development of thunderstorm. values have a sudden drop of 20 K and 3 K, The BT value decreased to 185 K, a drop of 80 K respectively; while other indices like TPW, KI, HI, from 04 to 07 UTC, and this value indicates a TTI, WI, had sudden increase of 5 mm, 3 K, 10 K, severe thunderstorm (mature stage). Thereafter 5 K and 10.2 m/s, respectively. An assessment of the BT values increased till 16 UTC (dissipative the convective system for every 15 min, the BT stage). The rainfall time series (dotted line) indi- value had a sudden drop of 30 K, 3 hrs before the cates no rainfall up to 0430 UTC, thereafter peak and yielded a rainfall of 15 mm. At peak increased till 07 UTC yielding 40 mm. These stage, all indices reached the threshold of 90% Bgures show thunderstorm had occurred at 07 indicative of severe thunderstorm occurrence. All UTC, and changes in the precursors before three stability indices were normalized at dissipating hours. stage (16 UTC). The increased values of TPW, KI, As of stability indices, LI value reached –9 K and HI, TTI, WI explain rapid development of con- TPW value was 54 mm at the peak of thunder- vective system and the decreased values of BT and storm which indicates the occurrence of severe LI clearly indicate the severity of convective thunderstorm (Bgure 10b). The time series plot of system. KI and HI are shown in Bgure 10c, which clearly The ARW model outputs were used to compute denotes high values 3 hrs before the peak of thun- the stability indices, which were compared with derstorm. The HI is a good precursor for a con- those values derived from the INSAT 3D & 3DR vective system to occur. The total totals index and satellite data for validation. These are shown in wind index, shown in Bgure 10(d), illustrate the Bgure 11 and table 4. The development of convec- severity of convective system and the increase in tive system was studied from both data and the WI parameter gives a clear indication of increased severity of convective system is predicted 3 hrs wind shear at the region of convection. before. The statistical metrics had yielded corre- At the initial stage of convection, 04 UTC, there lation coefBcient of 0.86 and the BIAS value as 0.22 were sudden changes in stability indices which for KI. The TPW had the highest CC of 0.9 and were discussed below (Bgure 11 a–d). BT and LI low BIAS of 0.3 which denote moisture accretion. J. Earth Syst. Sci. (2021) 130:71 Page 15 of 18 71

Figure 11. 24-hr time series plots of (a) K index, (b) humidity index, (c) total precipitable water, (d) total totals index, and (e) wind index plotted from INSAT 3D & INSAT 3DR satellite data and ARW model output for the period from 00 UTC of 24 April to 00 UTC of 25 April, 2018.

Table 4. Statistical metrics for the thunderstorm event of April i.e., humidity or wind were not comparatively 24, 2018. better.

Stability indices BIAS MAE RMSE CC IOA KI 0.22 1.83 1.03 0.86 0.92 4. Summary and conclusions HI 3.69 5.63 6.31 0.5 0.67 TTI 8.2 8.55 8.88 0.69 0.66 The predictability of thunderstorms is studied, WI 6.4 7.65 8.62 0.56 0.51 using INSAT 3D & 3DR data and ARW model TPW 0.3 1.3 1.19 0.97 0.92 experiments, over Andhra Pradesh region in India. MAE: Mean absolute error; RMSE: root mean square error; Eight thunderstorm cases during the pre-monsoon CC: correlation coefBcient; IOA: index of agreement. season of 2017 and 2018 are studied. Analysis of INSAT 3D/3DR data and ARW model predictions were made to understand the predictability. The following conclusions are drawn from the results HI, TTI and WT had CC of 0.5–0.7 and BIAS of obtained. The present study would be beneBcial for 3.7–8.2. The RMSE values are well below with operational forecasters to predict the occurrence of respect to their magnitudes. These metrics thunderstorms with the available satellite data and emphasize the ARW model performance in the modelling methodologies. prediction of thunderstorms over the study region. An assessment of the magnitudes and time series of 1. The days of the occurrences of thunderstorms Bve stability indices derived using ARW simulated are identiBed through analysis of IMD gridded data had indicated that K index is the best pre- daily rainfall data at 0.25° resolution with the cursor index, and also superior due to the consid- threshold of 2 cm/day. eration of both the temperature and humidity 2. The location of the occurrence is identiBed variations in the 850–700 hPa layer. In contrast, through the collocation of BT (brightness tem- TT-index could not be better, although it considers perature) and cloudiness from INSAT data and temperature and humidity but because of the IMD gridded rainfall data. considered layer to be 850–500 hPa. The rest of the 3. Thermodynamic stability indices computed three indices, which are based on one parameter, from INSAT 3D/3DR vertical temperature 71 Page 16 of 18 J. Earth Syst. Sci. (2021) 130:71

and humidity proBles have shown rapid Results of this study pronounce the importance changes, at least from 3 hrs before, that favour of INSAT-3D and INSAT-3DR satellite data and deep convection. All of the indices (KI, LI, TTI, the usefulness of mesoscale weather prediction WI, HI, TPW) along with BT values have models in the prediction of thunderstorm. This been found to be useful in the prediction of study is one of the few attempts to predict the thunderstorms. thunderstorms over Andhra Pradesh region using INSAT-3D and INSAT-3DR satellites and weather (a) TA threshold BT of 185–192 K for peak prediction models in the thunderstorm prediction. convection is clearly identiBed. The spa- These results indicate improvements in real-time tial distribution of BT, using 10 km prediction of thunderstorms in general and Andhra resolution data, helped to delineate the Pradesh in particular. precise area of convection. A significant drop in BT value conBrms deep convec- tion and rainfall and its increase denote Acknowledgements dissipation of thunderstorm. (b) The LI values\–5 indicated the occurrence The authors acknowledge the data sources: of severe thunderstorm activity. INSAT-3D & 3DR satellite data from SAC, ISRO, (c) The KI values[313 showed 90% chance for India; rainfall data from India Meteorological the occurrence of severe thunderstorm Department and MODIS satellite data from activity. NASA, USA. Part of the research is funded by (d) The HI values \ 40 showed the threshold CSIR-SRF, Govt. of India under sanction No. for thunderstorms. 09/1068(0001)/2018-EMR-I and SERB, Govt. of (e) The TTI values[50 K showed a chance for India under Grant No. ECRA/2016/001295. This scattered thunderstorms. research is supported by CSIR-SRF, Govt. of India (f) The WI values [ 40 m/s showed the occur- under sanction No. 09/1068(0001)/2018-EMR-I. rence of severe and isolated thunderstorms. (g) The TPW values[45 mm showed a chance for severe convection activity. Author statement 4. Weather prediction experiments using ARW model had yielded vertical proBles of tempera- N Umakanth: Data collection, visualization, com- ture at 3-km resolution, and stability indices putation, software, visualization, data collection were computed using the model generated and validation. G Ch Satyanarayana: Conceptu- proBles of temperature and humidity. These alization, problem envision, methodology adoption indices were in good agreement with satellite- and writing. N Naveena: References collection and based indices, indicating rapid changes in the preparation. D Srinivas: Analysis and visualiza- stability indices at 3-hr lead time, emphasizing tion. D V Bhaskar Rao: Problem conceiving, draft the use of ARW model in the prediction of preparation, review and . thunderstorms. 5. ARW model simulations have conclusively shown that K index, representative of the References convective instability of 850–700 hPa layer, is the best of all the considered Bve indices in the Allaby M 2003 (Dangerous Weather Series); Facts on prediction of thunderstorm activity over the File Science Library, New York, 50p. study region. Atkins N and Wakimoto R 1991 Wet microburst activity over the southeastern : Implications for forecast- 6. ARW model had simulated the cloud amount ing; Wea. Forecast. 6 470–482. and rainfall conBrming the occurrence of the Bhaskar Rao D V, Someshwar D and Sanjeeva Rao P 2008 thunderstorm at the observed location. This Mesoscale modeling of atmospheric processes in India; emphasizes the advantage of model simulation Dept. of Science and Technology, Government of India, in the prediction of thunderstorm activity with 113p. Choudhury B A, Konwar M, Hazra A, Mohan G M, Pithani P, a good lead time. Ghude S D, Deshamukhya A and Barth M C 2020 A 7. The stability indices give us a clear indication of diagnostic study of and lightning Cash rates in the development of thunderstorm convection at a severe pre-monsoon thunderstorm over northeast India; a lead time of 3–4 hrs. Quart. J. Roy. Meteorol. Soc. 146(729). J. Earth Syst. Sci. (2021) 130:71 Page 17 of 18 71

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Corresponding editor: KAVIRAJAN RAJENDRAN