Comparison of CAMS and CMAQ analyses of surface-level PM2.5 and O3 over the conterminous United States (CONUS)

Abstract

To reduce economic and health impacts from poor air quality (AQ) in the U.S., the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of surface-level ozone (O₃), fine particulate matter (PM_2.5), and other pollutants so that advance notice and warning can be issued to help individuals and communities limit their exposure. The NAQFC uses the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model for operational forecasts. This study is a first step in proposing a potential upgrade to the current operational NAQFC bias-correction system, by examining potential candidates for a gridded analysis (“truth”) dataset.

In this paper, we compare the performance of the “analysis” time series over the period of August 2020–December 2021 at EPA AirNow stations for both PM_2.5 and O₃ from raw Copernicus Atmosphere Monitoring Service (CAMS) reanalyses, raw CAMS near real-time forecasts, raw near real-time CMAQ forecasts, bias-corrected CAMS forecasts, and bias-corrected CMAQ forecasts (CMAQ FC BC). This 17-month period spans two wildfire seasons, to assess model “analysis” performance in high-end AQ events. In addition to determining the best-performing gridded product, this process allows us to benchmark the performance of CMAQ forecasts against other global datasets (CAMS reanalysis and forecasts). For both PM_2.5 and O₃, the bias correction algorithm employed here greatly improved upon the raw model time series, and CMAQ FC BC was the best-performing model “analysis” time series, having the lowest RMSE, smallest bias error, and largest critical success index at multiple thresholds.