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Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011
Youhua Tang, Tianfeng Chai, Li Pan, Pius Lee, Daniel Tong, Hyun-Cheol Kim, Weiwei Chen
NOAA Air Resources Laboratory, College Park, MD, USA

Published: 19 June 2015 by Informa UK Limited in Journal of the Air & Waste Management Association
Informa UK Limited, Volume 65; 10.1080/10962247.2015.1062439
Abstract: We employed an optimal interpolation (OI) method to assimilate AIRNow ozone/PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) data into the Community Multi-scale Air Quality (CMAQ) model to improve the ozone and total aerosol concentration for the CMAQ simulation over the contiguous United States (CONUS). AIRNow data assimilation was applied to the boundary layer, and MODIS AOD data were used to adjust total column aerosol. Four OI cases were designed to examine the effects of uncertainty setting and assimilation time; two of these cases used uncertainties that varied in time and location, or "dynamic uncertainties." More frequent assimilation and higher model uncertainties pushed the modeled results closer to the observation. Our comparison over a 24-hr period showed that ozone and PM2.5 mean biases could be reduced from 2.54 ppbV to 1.06 ppbV and from -7.14 µg/m³ to -0.11 µg/m³, respectively, over CONUS, while their correlations were also improved. Comparison to DISCOVER-AQ 2011 aircraft measurement showed that surface ozone assimilation applied to the CMAQ simulation improves regional low-altitude (below 2 km) ozone simulation.
Keywords: ozone, MODIS, aerosol, uncertainties, measurements, Data assimilation, Optimal Interpolation, CMAQ, AOD
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