Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0.
Christoph A Keller, K Emma Knowland, Bryan N Duncan, Junhua Liu, Daniel C Anderson, Sampa Das, Robert A Lucchesi, Elizabeth W Lundgren, Julie M Nicely, Eric Nielsen, Lesley E Ott, Emily Saunders, Sarah A Strode, Pamela A Wales, Daniel J Jacob, Steven Pawson
Author Information
Christoph A Keller: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
K Emma Knowland: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
Bryan N Duncan: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
Junhua Liu: NASA Goddard Space Flight Center Greenbelt MD USA.
Daniel C Anderson: NASA Goddard Space Flight Center Greenbelt MD USA.
Sampa Das: NASA Goddard Space Flight Center Greenbelt MD USA.
Robert A Lucchesi: NASA Goddard Space Flight Center Greenbelt MD USA.
Elizabeth W Lundgren: School of Engineering and Applied Sciences Harvard University Cambridge MA USA.
Julie M Nicely: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
Eric Nielsen: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
Lesley E Ott: NASA Goddard Space Flight Center Greenbelt MD USA.
Emily Saunders: NASA Goddard Space Flight Center Greenbelt MD USA.
Sarah A Strode: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
Pamela A Wales: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
Daniel J Jacob: School of Engineering and Applied Sciences Harvard University Cambridge MA USA.
Steven Pawson: NASA Goddard Space Flight Center Greenbelt MD USA. ORCID
The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (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 (O), carbon monoxide (CO), nitrogen dioxide (NO), sulfur dioxide (SO), and fine particulate matter (PM). 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 O, NO, 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 SO and an overprediction of summertime O 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.
