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 Tang, Patrick 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

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 | EF: Editorial file upload
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
AR by Y. Tang on behalf of the Authors (10 Oct 2022)  Manuscript 

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.

Viewed

Total article views: 631 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
507 104 20 631 31 3 4
  • HTML: 507
  • PDF: 104
  • XML: 20
  • Total: 631
  • Supplement: 31
  • BibTeX: 3
  • EndNote: 4
Views and downloads (calculated since 09 Jun 2022)
Cumulative views and downloads (calculated since 09 Jun 2022)

Viewed (geographical distribution)

Total article views: 566 (including HTML, PDF, and XML) Thereof 566 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Jan 2024
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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.