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
<p>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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-11-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/7977/2022/gmd-15-7977-2022.pdf |
| _version_ | 1811330491969699840 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Y. Tang |
| collection | DOAJ |
| description | <p>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 (O<span class="inline-formula"><sub>3</sub></span>) than WRF-CMAQ at night
over the CONUS domain; however, the models' fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) 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.</p> |
| first_indexed | 2024-04-13T16:02:34Z |
| format | Article |
| id | doaj.art-e76cf64781a04ab6818afaa3a1e4a643 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-04-13T16:02:34Z |
| publishDate | 2022-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-e76cf64781a04ab6818afaa3a1e4a6432022-12-22T02:40:30ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-11-01157977799910.5194/gmd-15-7977-2022Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaignY. Tang0Y. Tang1P. C. Campbell2P. C. Campbell3P. Lee4R. Saylor5F. Yang6B. Baker7D. Tong8D. Tong9A. Stein10J. Huang11J. Huang12H.-C. Huang13H.-C. Huang14L. Pan15L. Pan16J. McQueen17I. Stajner18J. Tirado-Delgado19J. Tirado-Delgado20Y. Jung21M. Yang22I. Bourgeois23I. Bourgeois24J. Peischl25J. Peischl26T. Ryerson27D. Blake28J. Schwarz29J.-L. Jimenez30J. Crawford31G. Diskin32R. Moore33J. Hair34G. Huey35A. Rollins36J. Dibb37X. Zhang38NOAA Air Resources Laboratory, College Park, MD, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USANOAA Air Resources Laboratory, College Park, MD, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USANOAA Air Resources Laboratory, College Park, MD, USANOAA Air Resources Laboratory, College Park, MD, USANOAA National Centers for Environmental Prediction, College Park, MD, USANOAA Air Resources Laboratory, College Park, MD, USANOAA Air Resources Laboratory, College Park, MD, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USANOAA Air Resources Laboratory, College Park, MD, USANOAA National Centers for Environmental Prediction, College Park, MD, USAI.M. Systems Group Inc., Rockville, MD, USANOAA National Centers for Environmental Prediction, College Park, MD, USAI.M. Systems Group Inc., Rockville, MD, USANOAA National Centers for Environmental Prediction, College Park, MD, USAI.M. Systems Group Inc., Rockville, MD, USANOAA National Centers for Environmental Prediction, College Park, MD, USANOAA National Centers for Environmental Prediction, College Park, MD, USAOffice of Science and Technology Integration, NOAA National Weather Service, Silver Spring, MD, USAScience & Technology Corporation, Hampton, VA, USAOffice of Science and Technology Integration, NOAA National Weather Service, Silver Spring, MD, USANASA Langley Research Center, Hampton, VA, USACooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USANOAA Chemical Sciences Laboratory, Boulder, CO, USACooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USANOAA Chemical Sciences Laboratory, Boulder, CO, USANOAA Chemical Sciences Laboratory, Boulder, CO, USADepartment of Chemistry, University of California at Irvine, Irvine, CA, USANOAA Chemical Sciences Laboratory, Boulder, CO, USACooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USASchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USANASA Langley Research Center, Hampton, VA, USANASA Langley Research Center, Hampton, VA, USANASA Langley Research Center, Hampton, VA, USASchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USANOAA Chemical Sciences Laboratory, Boulder, CO, USAEarth Systems Research Center, University of New Hampshire, Durham, NH, USADepartment of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD, USA<p>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 (O<span class="inline-formula"><sub>3</sub></span>) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM<span class="inline-formula"><sub>2.5</sub></span>) 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.</p>https://gmd.copernicus.org/articles/15/7977/2022/gmd-15-7977-2022.pdf |
| spellingShingle | 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 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 Geoscientific Model Development |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://gmd.copernicus.org/articles/15/7977/2022/gmd-15-7977-2022.pdf |
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