Diagnosing Summertime PM2.5 Biases of the CMAQ Model Driven by the FV3GFS

Particulate matter with a diameter of 2.5 micrometers or less (PM_2.5) is one of the most harmful ambient air pollutants to human health. To improve regional air quality forecasting, it is essential to upgrade numerical weather prediction models to more accurately predict planetary boundary layer (PBL) processes. The Environmental Protection Agency’s Community Multiscale Air Quality (CMAQ) model is currently driven by the regional-scale North American Mesoscale (NAM) weather model with 12-km horizontal resolution and a high-order local PBL mixing scheme. In favor of a unified forecast system, the NAM will soon be replaced by the Finite Volume Cubed-Sphere Global Forecasting System (FV3GFS) weather model with 13-km horizontal resolution and an eddy-diffusivity mass-flux PBL scheme. The ability of the operational NAM-CMAQ and experimental FV3GFS-CMAQ to predict PM_2.5 over the contiguous United States (CONUS) for June 2019 will be compared and evaluated using AirNow observational data. A case study focused on large PM_2.5 biases and discrepancies over Gulf Coast states will compare aircraft-derived PBL height and surface weather observation station data against the corresponding predicted meteorology. Preliminary results suggest that both the NAM-CMAQ and FV3GFS-CMAQ consistently underpredict daytime PM_2.5 and overpredict nighttime PM_2.5 during the summer. Model surface temperature bias appears to be relatively well correlated with CMAQ daytime PM_2.5 underprediction. Detailed spatial and temporal analyses will be presented to provide insight into the possible meteorological factors influencing these PM_2.5 biases.