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

Authors :
Christoph Andrea Keller
K Emma Knowland
Bryan N Duncan
Junhua Liu
Daniel C Anderson
Sampa Das
Robert A Lucchesi
Elizabeth W Lundgren
Julie Megan Nicely
Eric Nielsen
Lesley E Ott
Emily Saunders
Sarah A Strode
Pamela A Wales
Daniel J. Jacob
Steven Pawson
Source :
Journal of Advances in Modeling Earth Systems. 13(4)
Publication Year :
2021
Publisher :
United States: NASA Center for Aerospace Information (CASI), 2021.

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 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.

Subjects

Subjects :
Meteorology And Climatology

Details

Language :
English
ISSN :
19422466
Volume :
13
Issue :
4
Database :
NASA Technical Reports
Journal :
Journal of Advances in Modeling Earth Systems
Notes :
802678.02.17.01.33
Publication Type :
Report
Accession number :
edsnas.20210011082
Document Type :
Report
Full Text :
https://doi.org/10.1029/2020MS002413