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

Abstract

In this present work, an attempt has been made to analyze various thunderstorm-related parameters and their influence 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 artificial 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.