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A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods
Youhua Tang 1 , Mariusz Pagowski, 2 Tianfeng Chai, 1 Li Pan, 1 Pius Lee, 1 Barry Baker, 1 Rajesh Kumar, 3 Luca Delle Monache, 3 Daniel Tong, 1 Hyun-Cheol Kim 1
1  NOAA Air Resources Laboratory, College Park, MD, USA
2  NOAA Earth System Research Laboratory, Boulder, CO, USA
3  National Center for Atmospheric Research, Boulder, CO, USA

Published: 22 December 2017 by Copernicus GmbH in Geoscientific Model Development
Copernicus GmbH, Volume 10; 10.5194/gmd-10-4743-2017
Abstract: This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00UTC, and MODIS AOD at 18:00UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter< 2.5µm) leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.
Keywords: 3d Var, aerosol, air quality, Data assimilation, Environmental Prediction, GSI, interpolation, MODIS, OI, PM2.5
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