Evaluating Microphysics, Cumulus, and Lightning Parameterization Schemes in WRF Model for Thunderstorm Simulation Over East India

   Lightning originates from electrical discharges driven by the non-inductive charging mechanism within thunderstorms. The charge separation in these regions is governed by the surrounding convective environment, storm dynamics, and microphysical processes, including updraft velocity and ice content, which intensify the storm's electric field. In recent decades, advances in understanding cloud microphysics, charge separation mechanisms, and thundercloud electrical structure have significantly improved lightning forecasting. The selection and tuning of parameterization schemes, particularly for microphysics (MP), cumulus (Cu), and lightning (LP) processes, play a critical role in enhancing model performance and accuracy.

   This study uses various parameterization schemes to evaluate the performance of the Weather Research and Forecasting (WRF) model in simulating lightning and thunderstorm events. A severe thunderstorm event on May 14, 2022, over eastern India (West Bengal and Jharkhand), which recorded a peak 30-minute flash count of ~8000 flashes observed by the Indian Lightning Location Network (ILLN) was simulated in the WRF model. A total of 57 combinations of MP, Cu, and LP schemes were tested, using three nested domains (27 km, 9 km, 3 km) and analyzed the output of the inner domain (3 km). Seven MP schemes (WSM-6, Goddard, Thompson, Milbrandt, Morrison, WDM-5, WDM-6), two Cu schemes (Kain-Fritsch, Multi Kain-Fritsch), and two LP schemes (LP1: vertical velocity-based; LP2: 20 dBZ reflectivity-based) were assessed. 

   Results show better performance of LP2 over LP1 with higher correlation and lower standard deviation with the observed flash counts. For cumulus parameterization, Kain-Fritsch (KF) turned off for the inner domain, and achieved strong performance (correlation: 0.75–0.95) with lower RMSE and standard deviation. Among MP schemes, Morrison, Goddard, and WDM-6 consistently performed well across different combinations. The best-performing simulations included Goddard (LP2, KF on), Morrison (LP2, KF on), WDM-6 (LP2, KF off), and WDM-5 (LP2, KF on), achieving correlations of 0.94, 0.93, 0.91, and 0.91 with observed flash counts, respectively. This study underscores the WRF model's capability in simulating lightning activity with optimal parameterization combinations, particularly LP2 and KF schemes. These findings provide promising results for real-time lightning forecasting, aiding in mitigating lightning-related hazards.