Exacerbation of lightning activity during Indian summer monsoon season over India: Estimation of cloud‐to‐ground lightning activity in wet and dry convective regimes

Skillful prediction of thunderstorms/lightning is crucial as it causes severe damage to infrastructure and loss of human life. However, the simulation of lightning flash counts during the summer monsoon season is a major challenge in the numerical weather prediction model due to overestimating and underestimating flashes. A proper understanding of lightning events during active and break days of the Indian summer monsoon (ISM) from observations and the numerical model can help to diagnose the model biases. It is noted that active and break days of the ISM also correspond with wet and dry convective regimes. The various observational datasets identify the processes responsible for less and more lightning activity during ISM active and break periods respectively. The Weather Research and Forecasting model is used to gain an in‐depth understanding of lightning events. We hypothesize that the realistic population density of hydrometeors at the vertical level is crucial for the lifetime and growth of droplets facilitated by charge separation and generation of cloud electric fields. The relatively weak convection but localized strong updrafts are often favorable for intensifying lightning flashes during the break period. Plenty of mixed‐phase cloud hydrometeors are available in the middle level for a longer time, enhancing lightning flash counts. In contrast, strong convection in the active period is characterized by a steady ascent spread over a large area, which produces bigger hydrometeors, rainout through wet scavenging, and produce less lightning. This study demonstrates the performance of model simulation for the two contrasting monsoon events. It provides a pathway through the intracloud/cloud‐to‐ground method and aerosol–cloud–electrification interaction to improve the model biases for enhancing hazard prediction associated with and without heavy rainfall events in numerical weather prediction models.