title: Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011.
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.
  
  
  CONCLUSIONS
  This paper described an application of using optimal interpolation method to improve the model's ozone and PM2.5 estimation using surface measurement and satellite AOD. It highlights the usage of the operational AIRNow data set, which is available in near real time, and the MODIS AOD. With a similar method, we can also use other satellite products, such as the latest VIIRS products, to improve PM2.5 prediction.
authors.affiliation: a NOAA Air Resources Laboratory, College Park , MD , USA.|b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA.
authors.forename: Youhua
authors.initials: Y
authors.lastname: Tang
authors.affiliation: a NOAA Air Resources Laboratory, College Park , MD , USA.|b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA.
authors.forename: Tianfeng
authors.initials: T
authors.lastname: Chai
authors.affiliation: a NOAA Air Resources Laboratory, College Park , MD , USA.|b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA.
authors.forename: Li
authors.initials: L
authors.lastname: Pan
authors.affiliation: a NOAA Air Resources Laboratory, College Park , MD , USA.
authors.forename: Pius
authors.initials: P
authors.lastname: Lee
authors.affiliation: a NOAA Air Resources Laboratory, College Park , MD , USA.|b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA.|c Center for Spatial Information Science and Systems , George Mason University , Fairfax , VA , USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Tong
authors.affiliation: a NOAA Air Resources Laboratory, College Park , MD , USA.|b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA.
authors.forename: Hyun-Cheol
authors.initials: HC
authors.lastname: Kim
authors.affiliation: a NOAA Air Resources Laboratory, College Park , MD , USA.|d Northeast Institute of Geography and Agroecology , Chinese Academy of Sciences , Changchun , People's Republic of China.
authors.forename: Weiwei
authors.initials: W
authors.lastname: Chen
mesh_terms: D000336:Aerosols / Q000032:analysis*; D000393:Air Pollutants / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D010126:Ozone / Q000032:analysis*; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D058998:Remote Sensing Technology; D035501:Uncertainty; D014481:United States
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter; D010126:Ozone