Diagnosing Summertime PM2.5 Biases of the Community Multiscale Air Quality Model
Open Access
- Author:
- Yang, Benjamin
- Area of Honors:
- Meteorology
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Kenneth James Davis, Thesis Supervisor
Johannes Verlinde, Thesis Honors Advisor - Keywords:
- PM2.5
CMAQ
Air Quality
Atmospheric Dispersion
Numerical Weather Prediction
Planetary Boundary Layer
NAM
FV3GFS
Nocturnal Cold Front
Enhanced Vertical Mixing - Abstract:
- Particulate matter with a diameter of 2.5 micrometers or less (PM2.5) 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. The Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model is driven by two National Oceanic and Atmospheric Administration (NOAA) numerical weather predictions: the operational North American Mesoscale (NAM) model and the experimental Finite Volume Cubed-Sphere Global Forecasting System (FV3GFS) model. PM2.5 predictions by both models were compared and evaluated over the contiguous United States (CONUS) from 1-19 June 2019, using AirNow observations. Aircraft-derived planetary boundary layer (PBL) height and surface weather station observations were compared against the corresponding predicted meteorology. The FV3GFS-CMAQ generally predicted less PM2.5 than the NAM-CMAQ in the eastern United States. Following a cold front passage over the Southeast, the NAM-CMAQ overpredicted PM2.5, while the FV3GFS-CMAQ underpredicted PM2.5. Similar divergences in PM2.5 predictions occurred on other cold front days. Enhanced vertical mixing due to wind shear in the FV3GFS-CMAQ weakened the temperature inversion in the nocturnal boundary layer, allowing for warmer and drier air from aloft to be mixed down. Due to this enhanced vertical mixing, the FV3GFS-CMAQ likely overpredicted PBL height and dry deposition, thereby reducing surface PM2.5 concentrations. The NAM-CMAQ probably has the preferred PBL scheme and resolution in our case study, as underprediction may cause greater PM2.5 exposure. Horizontal advection and wet deposition are other important PM2.5 removal mechanisms, which should be explored more extensively in future case studies over various regions and time periods.