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

Tang, Youhua; Campbell, Patrick C.; Lee, Pius; Saylor, Rick; Yang, Fanglin; Baker, Barry; Tong, Daniel; Stein, Ariel; Huang, Jianping; Huang, Ho-Chun; Pan, Li; McQueen, Jeff; Stajner, Ivanka; Tirado-Delgado, Jose; Jung, Youngsun; Yang, Melissa; Bourgeois, Ilann; Peischl, Jeff; Ryerson, Tom; Blake, Donald; Schwarz, Joshua; Jimenez, Jose-Luis; Crawford, James; Diskin, Glenn; Moore, Richard; Hair, Johnathan; Huey, Greg; Rollins, Andrew; Dibb, Jack; Zhang, Xiaoyang

doi:https://doi.org/10.5194/gmd-15-7977-2022

Articles | Volume 15, issue 21

Geosci. Model Dev., 15, 7977–7999, 2022

https://doi.org/10.5194/gmd-15-7977-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Geosci. Model Dev., 15, 7977–7999, 2022

https://doi.org/10.5194/gmd-15-7977-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 21

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

Youhua Tang et al.

Supplement

https://doi.org/10.5194/gmd-15-7977-2022-supplement

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.