Simulation of a severe thunderstorm event during the field experiment of STORM programme 2006, using WRF-NMM model

Abstract

In the tropics, most of the extreme weather events are convective in nature. Many parts over the Indian region experience thunderstorms at higher frequency during the pre-monsoon months (March-May), when the atmosphere is highly unstable because of high temperatures prevailing at lower levels. During April and May, the eastern and northeastern parts of the country, i.e. Gangetic West Bengal, Jharkhand, Orissa, Bihar, Assam and parts of other northeastern states are affected by higher frequency of severe thunderstorms, locally named as 'Kal-baishakhi' or 'Nor'westers'. Realizing the importance of better understanding and prediction of these severe local storms over east and northeast India and their socio-economic impact, the Department of Science and Technology, Government of India organized a national coordinated programme on 'Severe Thunderstorm Observation and Regional Modelling (STORM)' to be carried out in the premonsoon season of 2006-10. Mesoscale models are essential for the accurate prediction of such high-impact weather events. In the present study, an attempt has been made to simulate one thunderstorm event that occurred on 20 May 2006 at Kolkata (22.52°N, 88.37°E) during the field experiment of STORM 2006, using Non-hydrostatic Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system with different initial conditions. This model has been developed by the National Oceanic and Atmospheric Administration/National Centers for Environment Prediction. The model results are validated with STORM field experiment data. The model performed well in capturing stability indices, which act as indicators of severe convective activity along with the thunderstorm-affected parameters as in the observations. The results of these analyses show that the 3 km WRF-NMM has better capability when it comes to thunderstorm simulation. This suggests that high-resolution models have the potential to provide unique and valuable information for severe thunderstorm forecasters.