Prediction of lightning activity over Bangladesh using diagnostic and explicit lightning parameterizations of WRF model

Lightning discharge from thunderstorms is a major weather hazard that leads to substantial loss of lives and properties in Bangladesh, particularly in the pre-monsoon season (March–May) due to frequent lightning activity. In this study, numerical simulations in predicting the lightning flashes using diagnostic and explicit lightning parameterization options in WRF (Weather Research and Forecasting) model are performed for three selected pre-monsoon lightning events (01 April 2019, 26 May 2020 and 20 May 2021) over Bangladesh. Additionally, this study investigates the WRF model sensitivity to five microphysics and three planetary boundary layer schemes. The combination of Morrison and YSU (Yonsei University scheme) is found to be the best configuration by comparing the RMSE (root mean square error) of hourly area averaged rainfall. The lightning flash counts are estimated by using four diagnostic methods based on (1) maximum updraft intensity ( \({{\text{w}}}_{{\text{max}}}\) ${\text{w}}_{\text{max}}$ ), (2) 20 dBZ cloud top, (3) level of neutral buoyancy, (4) Lightning Potential Index (LPI) conditioned to cloud hydrometeors and updraft, and (5) an explicit physics-based method from cloud electrification referred to as WRF-Elec. The WWLLN (World Wide Lightning Location Network) and NASA LIS (Lightning Imaging Sensor) observations are used to compare the simulated lightning flashes for the selected events. The study also analyzes 24-h (hour) accumulated rainfall that shows a good consistency with the observations from NASA GPM datasets. An assessment based on Fraction Skill Score (FSS) and performance diagrams is carried out to achieve deeper insights into the performance of model simulation in predicting rainfall. In a qualitative assessment framework, the spatial patterns of lightning flashes derived from WRF-Elec simulations, used for predicting the primary regions of lightning events, exhibit good agreement with observations.