Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0

Keller, C. A., K. E. Knowland, B. Duncan, J. Liu, D. C. Anderson, S. Das, R. A. Lucchesi, E. W. Lundgren, J. Nicely, E. Nielsen, L. Ott, E. Saunders, S. Strode, P. A. Wales, D. J. Jacob, and S. Pawson (2021), Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0, J. Adv. Modeling Earth Syst..

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a highresolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space-based and in-situ observations. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide hindcasts and 5-days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations for years 2018–2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of −0.1 to 0.3, normalized root mean square errors between 0.1–0.4, and correlations between 0.3–0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20%–50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-days forecasts have skill scores comparable to the 1-day hindcast. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach. Plain Language Summary Accurate forecasting of the compostion of the atmosphere is important for a variety of applications, including air pollution mitigation, support of satellite and other remote-sensing observations, and research applications. Producing such forecasts is computationally expensive due to the complexity of atmospheric chemistry, which interacts with weather on all scales. Here we present the NASA Goddard Earth Observing System composition forecast (GEOS-CF) system, which produces global forecasts of major atmospheric constituents such as ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). On a daily basis, the model tracks the atmospheric concentrations of more than 250 chemical species in more than 55 million model grid cells, computing the interactions between those species using the state-of-the-science GEOS-Chem chemistry model. We present an in-depth evaluation of the GEOS-CF model through comparison against independent observations. We show how the model captures many observed features of atmospheric composition, such as spatio-temporal variations in air pollution due to changes in pollutant emissions, weather, and chemistry. We also highlight some of the model deficiencies, for example, with respect to the simulation of aerosol particles. Finally, we demonstrate how surface observations and model data can be combined using machine learning to provide improved local air quality forecasts.

Research Program: 
Modeling Analysis and Prediction Program (MAP)
Funding Sources: