Preprints
https://doi.org/10.5194/egusphere-2022-356
https://doi.org/10.5194/egusphere-2022-356
 
09 Jun 2022
09 Jun 2022

Evaluation of the NAQFC Driven by the NOAA Global Forecast System Version 16: Comparison with the WRF-CMAQ Downscaling Method During the Summer 2019 FIREX-AQ Campaign

Youhua Tang1,2, Patrick Campbell1,2, Pius Lee1, Rick Saylor1, Fanglin Yang3, Barry Baker1, Daniel Tong1,2, Ariel Stein1, Jianping Huang3,4, Ho-Chun Huang3,4, Li Pan3,4, Jeff McQueen3, Ivanka Stajner3, Jose Tirado-Delgado5,6, Youngsun Jung5, Melissa Yang7, Ilann Bourgeois8,9, Jeff Peischl8,9, Tom Ryerson9, Donald Blake10, Joshua Schwarz9, Jose-Luis Jimenez8, James Crawford11, Glenn Diskin7, Richard Moore7, Johnathan Hair7, Greg Huey11, Andrew Rollins9, Jack Dibb12, and Xiaoyang Zhang13 Youhua Tang et al.
  • 1NOAA Air Resources Laboratory, College Park, MD, USA
  • 2Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA
  • 3NOAA National Centers for Environmental Prediction, College Park, MD, USA
  • 4I.M. Systems Group Inc., Rockville, MD, USA
  • 5Office of Science and Technology Integration, NOAA National Weather Service, Silver Spring, MD, USA
  • 6Eastern Research Group Inc, USA
  • 7NASA Langley Research Center, Hampton, VA, USA
  • 8Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
  • 9NOAA Chemical Sciences Laboratory, Boulder, CO, USA
  • 10Department of Chemistry, University of California at Irvine, Irvine, CA, USA
  • 11School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
  • 12Earth Systems Research Center, University of New Hampshire, Durham, NH, USA
  • 13Department of Geography & Geospatial Sciences, South Dakota State University, Brookings, SD, USA

Abstract. The latest operational National Air Quality Forecasting Capability (NAQFC) has been advanced to use the Community Multi-scale Air Quality (CMAQ) model version 5.3.1 with CB6 (carbon bond version 6)-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 has been accomplished by development of the meteorological preprocessor, NOAA-EPA Atmosphere-Chemistry Coupler (NACC), which is 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 output to drive CMAQ (e.g., FV3-GFSv16) even though they are in different grids. Here we compare and evaluate GFSv16-CMAQ vs. WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019. 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 mainly from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems have overall similar performance with some differences for certain events, species and regions. The GFSv16 meteorology tends to have stronger planetary boundary layer height diurnal variability (higher during daytime, and lower at night) than WRF over the U.S. Pacific coast, and it also predicted lower nighttime 10-m winds. In summer 2019, GFS-CMAQ system showed better surface O3 than WRF-CMAQ at night over the CONUS domain; however, their PM2.5 predictions showed mixed verification results: GFS-CMAQ yielded better mean bias but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via the interpolation is feasible and yields reasonable results compared to the commonly-used WRF downscaling approach.

Journal article(s) based on this preprint

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
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022,https://doi.org/10.5194/gmd-15-7977-2022, 2022
Short summary

Youhua Tang et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-356', Anonymous Referee #1, 22 Jul 2022
    • AC1: 'Reply on RC1', Y. Tang, 01 Sep 2022
  • RC2: 'Comment on egusphere-2022-356', Anonymous Referee #2, 25 Jul 2022
    • AC2: 'Reply on RC2', Y. Tang, 01 Sep 2022
  • EC1: 'Comment on egusphere-2022-356', Jason Williams, 12 Aug 2022
    • AC3: 'Reply on EC1', Y. Tang, 01 Sep 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-356', Anonymous Referee #1, 22 Jul 2022
    • AC1: 'Reply on RC1', Y. Tang, 01 Sep 2022
  • RC2: 'Comment on egusphere-2022-356', Anonymous Referee #2, 25 Jul 2022
    • AC2: 'Reply on RC2', Y. Tang, 01 Sep 2022
  • EC1: 'Comment on egusphere-2022-356', Jason Williams, 12 Aug 2022
    • AC3: 'Reply on EC1', Y. Tang, 01 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Y. Tang on behalf of the Authors (01 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Sep 2022) by Jason Williams
RR by Anonymous Referee #3 (19 Sep 2022)
RR by Anonymous Referee #1 (20 Sep 2022)
ED: Publish subject to minor revisions (review by editor) (20 Sep 2022) by Jason Williams
AR by Y. Tang on behalf of the Authors (28 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (10 Oct 2022) by Jason Williams

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Y. Tang on behalf of the Authors (26 Oct 2022)   Author's adjustment   Manuscript
EA: Adjustments approved (02 Nov 2022) by Jason Williams

Journal article(s) based on this preprint

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
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022,https://doi.org/10.5194/gmd-15-7977-2022, 2022
Short summary

Youhua Tang et al.

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Short summary
This paper compared two meteorological data for driving the regional air quality model: a regional meteorological modelling using WRF (WRF-CMAQ), and the direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods have mixed performance depending on the corresponding meteorological settings and performances. The direct interpolation is a viable method to drive air quality models.