Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign

Y. Tang; Y. Tang; P. C. Campbell; P. C. Campbell; P. Lee; R. Saylor; F. Yang; B. Baker; D. Tong; D. Tong; A. Stein; J. Huang; J. Huang; H.-C. Huang; H.-C. Huang; L. Pan; L. Pan; J. McQueen; I. Stajner; J. Tirado-Delgado; J. Tirado-Delgado; Y. Jung; M. Yang; I. Bourgeois; I. Bourgeois; J. Peischl; J. Peischl; T. Ryerson; D. Blake; J. Schwarz; J.-L. Jimenez; J. Crawford; G. Diskin; R. Moore; J. Hair; G. Huey; A. Rollins; J. Dibb; X. Zhang

doi:10.5194/gmd-15-7977-2022

Geoscientific Model Development (Nov 2022)

Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign

Y. Tang,
Y. Tang,
P. C. Campbell,
P. C. Campbell,
P. Lee,
R. Saylor,
F. Yang,
B. Baker,
D. Tong,
D. Tong,
A. Stein,
J. Huang,
J. Huang,
H.-C. Huang,
H.-C. Huang,
L. Pan,
L. Pan,
J. McQueen,
I. Stajner,
J. Tirado-Delgado,
J. Tirado-Delgado,
Y. Jung,
M. Yang,
I. Bourgeois,
I. Bourgeois,
J. Peischl,
J. Peischl,
T. Ryerson,
D. Blake,
J. Schwarz,
J.-L. Jimenez,
J. Crawford,
G. Diskin,
R. Moore,
J. Hair,
G. Huey,
A. Rollins,
J. Dibb,
X. Zhang

Affiliations

Y. Tang: NOAA Air Resources Laboratory, College Park, MD, USA
Y. Tang: Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA
P. C. Campbell: NOAA Air Resources Laboratory, College Park, MD, USA
P. C. Campbell: Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA
P. Lee: NOAA Air Resources Laboratory, College Park, MD, USA
R. Saylor: NOAA Air Resources Laboratory, College Park, MD, USA
F. Yang: NOAA National Centers for Environmental Prediction, College Park, MD, USA
B. Baker: NOAA Air Resources Laboratory, College Park, MD, USA
D. Tong: NOAA Air Resources Laboratory, College Park, MD, USA
D. Tong: Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA
A. Stein: NOAA Air Resources Laboratory, College Park, MD, USA
J. Huang: NOAA National Centers for Environmental Prediction, College Park, MD, USA
J. Huang: I.M. Systems Group Inc., Rockville, MD, USA
H.-C. Huang: NOAA National Centers for Environmental Prediction, College Park, MD, USA
H.-C. Huang: I.M. Systems Group Inc., Rockville, MD, USA
L. Pan: NOAA National Centers for Environmental Prediction, College Park, MD, USA
L. Pan: I.M. Systems Group Inc., Rockville, MD, USA
J. McQueen: NOAA National Centers for Environmental Prediction, College Park, MD, USA
I. Stajner: NOAA National Centers for Environmental Prediction, College Park, MD, USA
J. Tirado-Delgado: Office of Science and Technology Integration, NOAA National Weather Service, Silver Spring, MD, USA
J. Tirado-Delgado: Science & Technology Corporation, Hampton, VA, USA
Y. Jung: Office of Science and Technology Integration, NOAA National Weather Service, Silver Spring, MD, USA
M. Yang: NASA Langley Research Center, Hampton, VA, USA
I. Bourgeois: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
I. Bourgeois: NOAA Chemical Sciences Laboratory, Boulder, CO, USA
J. Peischl: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
J. Peischl: NOAA Chemical Sciences Laboratory, Boulder, CO, USA
T. Ryerson: NOAA Chemical Sciences Laboratory, Boulder, CO, USA
D. Blake: Department of Chemistry, University of California at Irvine, Irvine, CA, USA
J. Schwarz: NOAA Chemical Sciences Laboratory, Boulder, CO, USA
J.-L. Jimenez: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
J. Crawford: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
G. Diskin: NASA Langley Research Center, Hampton, VA, USA
R. Moore: NASA Langley Research Center, Hampton, VA, USA
J. Hair: NASA Langley Research Center, Hampton, VA, USA
G. Huey: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
A. Rollins: NOAA Chemical Sciences Laboratory, Boulder, CO, USA
J. Dibb: Earth Systems Research Center, University of New Hampshire, Durham, NH, USA
X. Zhang: Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD, USA

DOI: https://doi.org/10.5194/gmd-15-7977-2022
Journal volume & issue: Vol. 15
pp. 7977 – 7999

Abstract

Read online

The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.

Published in Geoscientific Model Development

ISSN: 1991-959X (Print); 1991-9603 (Online)
Publisher: Copernicus Publications
Country of publisher: Germany
LCC subjects: Science: Geology
Website: https://www.geoscientific-model-development.net/

About the journal