[A-CG45] AsiaPEX field campaign strategies for changing Asian monsoon precipitation

[ACG45-02] Evaluating the Efficiency of Lightning Parameterization in the WRF Model for Simulating Lightning Events Over India

*Unashish Mondal¹, Subrat Kumar Panda¹, Toru Terao², Devesh Sharma¹ (1.Central University of Rajasthan, India, 2.Kagawa University, Takamatsu, Japan)

Keywords:Thunderstorm Indices, Lightning Potential Index, Model Skill Score, Lightning Parameterization

This study examines extreme weather events, particularly thunderstorms and lightning, over India, by evaluating the Weather Research and Forecasting (WRF) model’s performance using microphysical and lightning parameterization schemes. The spatiotemporal variability of lightning was analyzed, and a diurnal, monthly, and seasonal lightning climatology was developed for India using NASA TRMM-LIS satellite data (1998–2013). The WRF model was integrated for 30 hours with the NSSL-17 double-moment microphysics scheme, accurately reproducing meteorological structures. Simulations were conducted for two extreme events: Jaipur, Rajasthan (July 11, 2021) and Hooghly, West Bengal (June 7, 2021). The Lightning Potential Index (LPI) identified high-risk zones, and model-simulated lightning variables were validated against IITM, Pune ground observations. Thunderstorm indices derived from the model were compared with ERA5 reanalysis data, emphasizing the need for multiple indices rather than a single parameter for severe thunderstorm prediction. Advanced indices, including the Energy Helicity Index (EHI), Supercell Composite Parameter (SCP), and Significant Tornado Parameter (STP), effectively predicted severe thunderstorms. During the Hooghly event, EHI (>1), SCP (>3.5), STP (>1.2), and low Storm-Relative Helicity (SRH) at 3 km (100 m²/s²) indicated no helicity or tornado activity. In contrast, simpler indices like CAPE, K Index, and VT Index reliably predicted non-severe thunderstorms. Model efficiency was assessed using skill scores such as the Heidke Skill Score (HSS) and True Statistics Score (TSS), ranging between 60 precent and 80 percent, demonstrating model reliability. These findings highlight the potential of advanced WRF configurations in enhancing extreme weather predictions, contributing to improved forecasting and disaster preparedness.