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

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.