Articles | Volume 15, issue 21
https://doi.org/10.5194/gmd-15-7977-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7977-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
07 Nov 2022
Model evaluation paper |
| 07 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
NOAA Air Resources Laboratory, College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Patrick C. Campbell
NOAA Air Resources Laboratory, College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Pius Lee
NOAA Air Resources Laboratory, College Park, MD, USA
Rick Saylor
NOAA Air Resources Laboratory, College Park, MD, USA
Fanglin Yang
NOAA National Centers for Environmental Prediction, College Park, MD, USA
Barry Baker
NOAA Air Resources Laboratory, College Park, MD, USA
Daniel Tong
NOAA Air Resources Laboratory, College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Ariel Stein
NOAA Air Resources Laboratory, College Park, MD, USA
Jianping Huang
NOAA National Centers for Environmental Prediction, College Park, MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Ho-Chun Huang
NOAA National Centers for Environmental Prediction, College Park, MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
NOAA National Centers for Environmental Prediction, College Park, MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Jeff McQueen
NOAA National Centers for Environmental Prediction, College Park, MD, USA
Ivanka Stajner
NOAA National Centers for Environmental Prediction, College Park, MD, USA
Jose Tirado-Delgado
Office of Science and Technology Integration, NOAA National Weather
Service, Silver Spring, MD, USA
Science & Technology Corporation, Hampton, VA, USA
Youngsun Jung
Office of Science and Technology Integration, NOAA National Weather
Service, Silver Spring, MD, USA
Melissa Yang
NASA Langley Research Center, Hampton, VA, USA
Ilann Bourgeois
Cooperative Institute for Research in Environmental Sciences, University
of Colorado Boulder, Boulder, CO, USA
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Jeff Peischl
Cooperative Institute for Research in Environmental Sciences, University
of Colorado Boulder, Boulder, CO, USA
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Tom Ryerson
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Donald Blake
Department of Chemistry, University of California at Irvine, Irvine, CA,
USA
Joshua Schwarz
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Jose-Luis Jimenez
Cooperative Institute for Research in Environmental Sciences, University
of Colorado Boulder, Boulder, CO, USA
James Crawford
School of Earth and Atmospheric Sciences, Georgia Institute of
Technology, Atlanta, GA, USA
Glenn Diskin
NASA Langley Research Center, Hampton, VA, USA
Richard Moore
NASA Langley Research Center, Hampton, VA, USA
Johnathan Hair
NASA Langley Research Center, Hampton, VA, USA
Greg Huey
School of Earth and Atmospheric Sciences, Georgia Institute of
Technology, Atlanta, GA, USA
Andrew Rollins
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Jack Dibb
Earth Systems Research Center, University of New Hampshire, Durham, NH,
USA
Xiaoyang Zhang
Department of Geography and Geospatial Sciences, South Dakota State
University, Brookings, SD, USA
Data sets
The NOAA-EPA Atmosphere-Chemistry Coupler (NACC) (v1.3.2) Patrick Campbell https://doi.org/10.5281/zenodo.5507489
Model code and software
The Advanced National Air Quality Forecast Capability (NAQFC) (v1.1.0) Patrick Campbell https://doi.org/10.5281/zenodo.5507511
Short summary
This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
This paper compares two meteorological datasets for driving a regional air quality model: a...
