Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0
Christoph A. Keller, K. Emma Knowland, B. N. Duncan, Junhua Liu, Daniel C. Anderson, Sampa Das, Robert A. Lucchesi, Elizabeth W. Lundgren, Julie M. Nicely, J. E. Nielsen, Lesley Ott, Emily Saunders, Sarah A. Strode, Pamela Wales, Daniel J. Jacob, Steven Pawson
Abstract
The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25 degree) 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 and to support flight campaign planning, support of satellite observations, and air quality research. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide analyses and 5-day forecasts of atmospheric constituents including ozone (O 3 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), 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 show realistic simulated concentrations of O 3 , NO 2 , and CO, with normalized mean biases of -0.1 to -0.3, normalized root mean square errors (NRMSE) between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants under a variety of meteorological conditions, yet also highlight current limitations, such as an overprediction of summertime ozone 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-day hourly forecasts have skill scores comparable to the analysis. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.