Difference Learning for Air Quality Forecasting Transport Emulation

Human health is negatively impacted by poor air quality including increased risk for respiratory and cardiovascular disease. Due to a recent increase in extreme air quality events, both globally and specifically in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events. The National Oceanic and Atmospheric Administration provides air quality forecasting guidance for the United States. Their air quality forecasting model is currently forecasting at a 15 km resolution, however for improved forecast skill, the goal is to reach a 3 km resolution. This is currently not feasible due prohibitive computational needs for the transport of chemical species. In this work we describe a deep learning transport emulator that is able to reduce computations and maintain skill comparable with the existing model. We show how this method performs well in the presence of extreme air quality events, making it a potential candidate for operational use in the near-term future. We also evaluate how well this model maintains the physical properties of the modeled transport.