Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset

Abstract

Accurate and timely prediction of lightning occurrences plays a crucial role in safeguarding human well-being and the global environment. Machine-learning-based models have been previously employed for nowcasting lightning occurrence, offering advantages in computation efficiency. However, these models have been hindered by limited accuracy due to inadequate representation of the intricate mechanisms driving lightning and a restricted training dataset. To address these limitations, we present a machine learning approach that integrates aerosol features to more effectively capture lightning mechanisms, complemented by enriched satellite observations from the Geostationary Lightning Mapper (GLM). Through training a well-optimized LightGBM model, we successfully generate spatially continuous (0.25° by 0.25°) and hourly lightning nowcasts over the Contiguous United States (CONUS) during the summer season, surpassing the performance of competitive baselines. Model performance is evaluated using various metrics, including accuracy (94.3%), probability of detection (POD, 75.0%), false alarm ratio (FAR, 38.1%), area under curve of precision–recall curve (PRC-AUC, 0.727). In addition to the enriched dataset, the improved performance can be attributed to the inclusion of aerosol features, which has significantly enhanced the model. This crucial aspect has been overlooked in previous studies. Moreover, our model unravels the influence of aerosol composition and loading on lightning formation, indicating that high aerosol loading consisting of sulfates and organic compounds tends to enhance lightning activity, while black carbon inhibits it. These findings align with current scientific knowledge and demonstrate the immense potential for elucidating the complex mechanisms underlying aerosol-associated lightning phenomena.