A statistical approach to fast nowcasting of lightning potential fields

Abstract

Thunderstorms and associated hazards like lightning can pose a serious threat to people outside and infrastructure. Thus, very short-term prediction capabilities (called nowcasting) have been developed to capture this threat and aid in decision-making on when to bring people inside for safety reasons. The atmospheric research and operational communities have been developing and using nowcasting methods for decades, but most methods do not rely on formal statistical approaches. A novel and fast statistical approach to nowcasting of lightning threats is presented here that builds upon an integro-difference modeling framework. Inspiration from the heat equation is used to define a redistribution kernel, and a simple linear advection scheme is shown to work well for the lightning prediction example. The model takes only seconds to estimate and nowcast and is competitive with a more complex image deformation approach that is computationally infeasible for very short-term nowcasts.