A comparison of global scale FV3 versus regional scale NAM meteorological drivers for regional air quality forecasting

Meteorological inputs are critical to air quality numerical predictions. In this study, two different weather prediction models are used to drive a regional Community Multiscale Air Quality (CMAQ) model developed by USEPA. The first "met driver" is the Non-hydrostatic Multiscale Model with Arakawa B grid-staggering (NMMB) whereas the second one is the upgraded Global Forecasting System (GFS) with the Finite Volume Cubed-Sphere (FV3) core. Two one-month simulations (i.e., January and July 2019) with CMAQ grid-spacing of 12-km are completed to assess the impact of two met drivers, which have different forecast domains, dynamic cores, and physical parameterizations , on CMAQ predictions of surface O ₃ and PM _2.5. The National Emission Inventory (NEI) with base year 2014 V2 (NEI2014) and the blended global biomass burning emissions product (GBBEPx) are used to provide anthropogenic and fire emissions to drive the CMAQ predictions. The FV3GFS-Aerosol predictions provide dynamic aerosol lateral boundary layer conditions with full aerosols included for improving the FV3GFS/CMAQ PM _2.5 predictions. The predictions with both modeling systems will be evaluated against the AirNow observational data. Evaluations for different seasons (i.e., winter vs. summer) and over different regions (e.g., Eastern US vs. Western US) will be performed. Preliminary results indicate that the FV3GFS-driven CMAQ improves the nighttime over-predictions of surface O ₃ especially over the western US with overall better performance than the NMMB-driven CMAQ predictions. The FV3GFS-driven CMAQ shows large over-predictions of PM _2.5 during the early morning as the boundary-layer develops but under-predictions during daytime in summer. Detailed analyses will be presented to illustrate the causes for these FV3GFS/CMAQ prediction biases.