Deep Generative Modeling of Air Quality Tracers

Accurate and timely forecasts of the physical and chemical processes of atmospheric compounds are critical functions for environmental and climate monitoring. Current methods rely on a series of numerical simulations that model the chemical reactions and physical transport of chemical and aerosol species throughout the atmosphere; however, they are computationally expensive to be operationally viable for finer-resolution forecasts. We are exploring the usage of generative deep learning models to emulate the full series of numerical simulations. We are training a conditional generative adversarial network (cGAN) to emulate the NOAA Community Multiscale Air Quality (CMAQ) Modeling System forecasts of species related to ozone and fine particulate matter (PM2.5). We are evaluating the trained cGAN on its prediction accuracy, its long-term forecasting capability, and its inference speed.