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\begin{document}

\title{Description of the NASA GEOS Composition Forecast Modeling System
GEOS-CF v1.0}



\author[1]{Christoph A. Keller}%
\author[1]{K. Emma Knowland}%
\author[2]{Bryan N Duncan}%
\author[1]{Junhua Liu}%
\author[1]{Daniel C Anderson}%
\author[2]{Sampa Das}%
\author[3]{Robert A Lucchesi}%
\author[4]{Elizabeth W Lundgren}%
\author[2]{Julie M. Nicely}%
\author[5]{Jon Eric Nielsen}%
\author[2]{Lesley E. Ott}%
\author[3]{Emily Saunders}%
\author[6]{Sarah A. Strode}%
\author[7]{Pamela A Wales}%
\author[4]{Daniel J. Jacob}%
\author[2]{Steven Pawson}%
\affil[1]{Universities Space Research Association}%
\affil[2]{NASA Goddard Space Flight Center}%
\affil[3]{Science Systems and Applications, Inc}%
\affil[4]{Harvard University}%
\affil[5]{SSAI at NASA Goddard Space Flight Center}%
\affil[6]{Universities Space Research Assocaition}%
\affil[7]{University of Maryland, College Park}%


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  \date{June 16, 2024}


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\begin{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\textsubscript{3}), carbon monoxide (CO),
nitrogen dioxide (NO\textsubscript{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\textsubscript{3}, NO\textsubscript{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.%
\end{abstract}%



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