Impact of Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth and AirNow PM2.5 assimilation on Community Multi-scale Air Quality aerosol predictions over the contiguous United States

In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and AirNow PM2.5 measurements are assimilated into the Community Multi-scale Air Quality (CMAQ) model using an optimal interpolation (OI) method. Over a 30 day test period in July 2011, three assimilation configurations were used in which MODIS AOD and AirNow PM2.5 measurements were first assimilated separately before being assimilated simultaneously. The background error covariance is estimated using both the National Meteorological Center approach and the Hollingsworth-Lnnberg method. The AOD observations from Terra are assimilated at 17Z and the Aqua AOD observations are assimilated at 20Z each day. AirNow PM2.5 measurements are assimilated 4 times a day at 00Z, 06Z, 12Z, and 18Z. Model performances are measured by the daily averaged and domain-averaged biases and the root-mean-square errors (RMSEs) obtained by comparing the predictions with the AirNow PM2.5 observations that were not assimilated. Either assimilating the MODIS AOD or assimilating the AirNow PM2.5 alone helps PM2.5 predictions over the entire 30 days. The case that assimilates the observations from both sources has the best performance. While the CMAQ PM2.5 results exhibit exaggerated diurnal variations compared to the AirNow measurements, this is not as severe at rural sites as at urban or suburban sites. It was also found that assimilating the total AOD observations is more beneficial for correcting the PM2.5 underestimations than directly assimilating the AirNow PM2.5 measurements every 6 h. While the simple approach of applying the AOD scaling factors uniformly throughout the vertical columns proved effective, it is liable to produce substantial errors. This is demonstrated by a high-AOD event.