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Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0The 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 observation sand 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 (O3), carbon monoxide (CO), nitrogen dioxide (NO2), 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, ozone sonde and surface observations show realistic simulated concentrations of O3, NO2, 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 over prediction of summertime ozone over the Southeast United States. GEOS-CFv1.0 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.
Document ID
20210011082
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Christoph Andrea Keller
(Universities Space Research Association Columbia, Maryland, United States)
K Emma Knowland
(Universities Space Research Association Columbia, Maryland, United States)
Bryan N Duncan
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Junhua Liu
(Universities Space Research Association Columbia, Maryland, United States)
Daniel C Anderson
(Universities Space Research Association Columbia, Maryland, United States)
Sampa Das
(Universities Space Research Association Columbia, Maryland, United States)
Robert A Lucchesi
(Science Systems and Applications (United States) Lanham, Maryland, United States)
Elizabeth W Lundgren
(Harvard University Cambridge, Massachusetts, United States)
Julie Megan Nicely
(University System of Maryland Adelphi, Maryland, United States)
Eric Nielsen
(Science Systems & Applications, Inc. Hampton, VA, USA)
Lesley E Ott
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Emily Saunders
(Science Systems & Applications, Inc. Hampton, VA, USA)
Sarah A Strode
(Universities Space Research Association Columbia, Maryland, United States)
Pamela A Wales
(Universities Space Research Association Columbia, Maryland, United States)
Daniel J. Jacob
(Harvard University Cambridge, Massachusetts, United States)
Steven Pawson
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Date Acquired
March 8, 2021
Publication Date
April 21, 2021
Publication Information
Publication: Journal of Advances in Modeling Earth Systems
Publisher: Wiley Open Access
Volume: 13
Issue: 4
Issue Publication Date: April 1, 2021
e-ISSN: 1942-2466
URL: https://agupubs.onlinelibrary.wiley.com/journal/19422466
Subject Category
Meteorology And Climatology
Funding Number(s)
WBS: 802678.02.17.01.33
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
NASA Peer Committee
Keywords
GEOS-CF
Modeling
Global Forecast
Atmospheric Composition
Document Inquiry

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