Difference Learning for Air Quality Forecasting Transport Emulation
This work addresses the need for finer-resolution air quality forecasting to protect human health, though it is incremental as it emulates an existing transport model.
The paper tackled the prohibitive computational cost of high-resolution air quality forecasting by developing a deep learning transport emulator that reduces computations while maintaining skill comparable to existing numerical models, including during extreme events.
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 locally 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 Continental United States. Their air quality forecasting model is based on a 15 km spatial resolution; however, the goal is to reach a three km spatial resolution. This is currently not feasible due in part to prohibitive computational requirements for modeling the transport of chemical species. In this work, we describe a deep learning transport emulator that is able to reduce computations while maintaining skill comparable with the existing numerical model. We show how this method maintains skill in the presence of extreme air quality events, making it a potential candidate for operational use. We also explore evaluating how well this model maintains the physical properties of the modeled transport for a given set of species.