Diagnosing Summertime PM2.5 Biases of the Community Multiscale Air Quality Model

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 are compared and evaluated using AirNow observational data. A case study focused on large PM_2.5 biases and discrepancies over the southeastern United States compares aircraft-derived PBL height and surface weather observation station data against the corresponding predicted meteorology. The results show that, during a cold front passage, the NAM-CMAQ overpredicted PM_2.5, while the FV3GFS-CMAQ underpredicted PM_2.5. This divergence is temporally consistent with the meteorological biases, which suggest that the FV3GFS-CMAQ underprediction is largely due to enhanced vertical mixing. Additional case studies over different regions and time periods with cold front passages are recommended to validate the results of this study.