Machine Learning–Based Lightning Parameterizations for the CONUS

Abstract The diversity in the lightning parameterizations for numerical weather and climate models causes considerable uncertainty in lightning prediction. In this study, we take a data-driven approach to address the lightning parameterization problem, by combining machine learning (ML) techniques with the rich lightning observations from the World Wide Lightning Location Network. Three ML algorithms are trained over the contiguous United States (CONUS) to predict lightning stroke density in a 1° box based on the information about the atmospheric variables in the same grid (local) or over the entire CONUS (nonlocal). The performance of the ML-based lightning schemes is examined and compared with that of a simple, conventional lightning parameterization scheme of Romps et al. We find that all ML-based lightning schemes exhibit a performance that is superior to that of the conventional scheme in the regions and in the seasons with climatologically higher lightning stroke density. To the west of the Rocky Mountains, the nonlocal ML lightning scheme achieves the best overall performance, with lightning stroke density predictions being 70% more accurate than the conventional scheme. Our results suggest that the ML-based approaches have the potential to improve the representation of lightning and other types of extreme weather events in the weather and climate models.