Deep Learning Ensemble Emulation for NASA’s Goddard Earth Observing System (GEOS) Composition Forecast

As poor air quality continues to pose threats to humanity, better modeling of atmospheric composition is critical both for environmental and climate monitoring. The NASA Goddard Earth Observing System Composition Forecast (GEOS-CF) provides daily global air quality forecasts of atmospheric composition. Due to the full chemistry mechanisms and transport of hundreds of chemical tracers, GEOS-CF forecasts cannot extend beyond five days and probabilistic estimates of tracer concentrations are computationally infeasible to calculate. Probabilistic estimates are critical in quantifying the forecast uncertainty and achieved by generating an ensemble of models with slightly different initializations. We describe a two-year study to build a deep learning ensemble emulator. In this study, we describe why models such as NVIDIA’s FourCastNet are inadequate for learning atmospheric composition and can experience instabilities during training. We share competitive forecast skill for Carbon Monoxide, Nitric Oxide, Nitrogen Dioxide, and Ozone, using a novel air quality conditional generative adversarial network (AQcGAN) built for emulating ensembles. Using this model, we are able to forecast ten days into the future generating both ensemble mean and spread in seconds for a given ensemble, offering a low-cost option for extending the forecast window in GEOS-CF and for quantifying uncertainty. Results show that when compared to actual mean and spread of the ensembles, the emulator forecasts offer competitive skill in terms of low RMSE scores. We share results from an operational data assimilation study where the AQcGAN has been used as the ensemble. We describe future plans for integration with data assimilation methods and GEOS-CF.