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Long term analysis of thunderstorm related parameters over Visakhapatnam and Machilipatnam, India

Umakanth N., Satyanarayana G. Ch., Simon B., Rao M. C., Babu N. Ranga

Acta Geophysica

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2020

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Vol. 68, no. 3

921--932

EN

In this present work, an attempt has been made to analyze various thunderstorm-related parameters and their infuence over the two stations Visakhapatnam (VSK) and Machilipatnam (MTM). The thunderstorm-related parameters used in the present study are convective available potential energy (CAPE), lifted index, K-index, total totals index (TTI), humidity index, convective inhibition, thunderstorm prediction index (TPI), deep convective index (DCI) and updraft vertical velocity. This analysis was carried out using NCEP NCAR reanalysis monthly data for the time period from 1948 to 2012. These parameters have given good guidance for studying the thunderstorm event. We also analyzed IMD thunderstorm occurrence days reported at two stations, i.e., VSK and MTM with NCEP NCAR (daily data) calculated CAPE, TTI, TPI and DCI parameter threshold days in pre-monsoon season for every year during the time period 2010 to 2019. Out of those four parameters, TTI has shown good correlation with the IMD recorded days. So we have attempted the prediction of thunderstorms using artifcial neural network (ANN) and auto-regressive moving average (ARMA) techniques for TTI parameter. While using these techniques, we have experimented in three training sets, i.e., 90%, 80% and 70%. Another attempt has been made to assess the skill of ARMA and ANN techniques in forecasting the occurrence of thunderstorm activity at VSK and MTM stations. The present study suggests that ANN has high skill than ARMA. From this study, we can understand that VSK has more chances for thunderstorms than MTM.

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Impact of convection and stability parameters on lightning activity over Andhra Pradesh, India

Umakanth N., Satyanarayana G. Ch., Simon B., Kumar Prashant, Rao M. C.

Acta Geophysica

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2020

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Vol. 68, no. 6

1845--1866

EN

This paper brings out the interconnection of fash rate density (FRD) with convection and stability parameters over Andhra Pradesh (AP), India. The convection parameters include rainfall, relative humidity, specifc humidity, surface air temperature (SAT) and air temperature (at 850 mb). The stability parameters include convective available potential energy (CAPE), lifted index, K-index, total totals index (TTI), humidity index and total precipitable water. Both convective and stability parameters indicate good correlation with lightning activity. SAT and AT 850 mb have shown good correlations with lightning, which is a clear indication of interaction between warm air and dry air. CAPE and TTI have shown strong positive correlation with lightning activity. The correlation coefcient between FRD and CAPE is 0.81. We have also studied the infuence of convective and stability parameters during lightning and no lightning activity. Later, we also attempted the estimation of lightning activity by using artifcial neural network model. By using convection and stability parameters, six learning algorithms were used for training the artifcial neural network. Scaled conjugate gradient backpropagation training algorithm has given the better estimation, whereas resilient backpropagation training algorithm has shown the poor estimation of FRD.

3

Analysis of various thermodynamic instability parameters and their association with the rainfall during thunderstorm events over Anakapalle (Visakhapatnam district), India

Umakanth N., Satyanarayana G. Ch., Simon B., Rao M. C., Kumar M. Tanooj, Babu N. Ranga

Acta Geophysica

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2020

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Vol. 68, no. 5

1549--1564

EN

Thunderstorm events usually take place in cumulonimbus clouds which are complemented with intense rainfall and highspeed winds. In general, rainfall parameter has massive signifcance when compared to other parameters. In this paper, a group of thunderstorm-related stability parameters were analyzed for pre-monsoon season only. Later, we also tried to study the association between thunderstorm-related stability parameters and rainfall parameter in pre-monsoon season over Anakapalle (Visakhapatnam district) during 2001–2010. We have utilized ERA-Interim ECMWF reanalysis daily datasets for this study. We also tried to compare IMD thunderstorm occurrence days with NOAA CPC-calculated rainfall days in pre-monsoon season over Anakapalle region for every year during 2001–2010. Out of those parameters, upward vertical velocity, convective available potential energy, K-index (KI), humidity index and total totals index parameters have shown good thresholds supporting the rainfall activity during pre-monsoon season. Later, we have also attempted the prediction of DCI and KI parameters over Anakapalle region using artifcial neural network (ANN) and auto-regressive moving average (ARMA) techniques. In comparison between the two techniques, ANN technique has shown good correlation with ERAInterim ECMWF reanalysis data.