Developing an optimized parameterization scheme for deriving a lightning threat product from a global model for all seasons over India.

This study marks the utilization of medium-range forecasts of cloud-to-ground (CG) lightning threats over India across all seasons from a global model. CG flashes are derived from two lightning parameterization schemes: Price and Rind (PR92) scheme and Lopez Scheme and is evaluated against earth network lightning sensor data. Both methods with existing storm detection criteria initially produced a lightning with overestimated counts and large spatial extent. Hence a Revised PR92-Lopez Blended (RPLB) scheme is developed by redefining the storm detection points in each scheme and combined them by giving separate weights for land and ocean through a regression-based approach. RPLB gives an improved skill in spatial and frequency distribution, and reduced false alarms with respect to the individual schemes up to a five-day lead time. The estimated CG flashes are then categorized into threat levels ranging up to extreme for effective use in the early warning and decision support systems.