Implications of AI for Atmospheric Predictability of Convection and Winter Storms

Recent advances in artificial intelligence (AI), specifically with applications of deep learning, have brought paradigm-shifting changes to Numerical Weather Prediction.  Recent AI-based NWP systems have rivaled traditional physics-based global NWP systems according to some verification metrics.  However, the performance of these systems for extreme events, and their implications for atmospheric predictability, has not yet been fully explored.    In this study, the practical predictability for winter storms in eastern North America will be compared using forecasts generated by several traditional NWP and AI-NWP systems.   In addition to domain-wide verification statistics, the realism of cyclone structure and evolution will be evaluated at different forecast lead times.  We plan to discuss the ensemble predictability of events, evaluating the sensitivity of the AI-NWP systems to initial condition perturbations, with implications for data assimilation.  Finally, at the mesoscale, we will demonstrate a convection initiation nowcasting system that utilizes deep learning to generate probabilities of new convection forming at lead times under one hour, which we interpret using explainable AI and uncertainty quantification.