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3D-Var versus Optimal Interpolation for Aerosol Assimilation: a Case Study over the Contiguous United States
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: 11 July 2017 by Copernicus GmbH in Geoscientific Model Development Discussions
Copernicus GmbH, Volume 10; 10.5194/gmd-2017-147
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. GSI results are compared with those obtained using the optimal interpolation (OI) method (Tang et al., 2015) for July, 2011 over CONUS. Both GSI and OI assimilate surface PM2.5 observations at 00, 06, 12, and 18 UTC, and MODIS AOD at 18 UTC. In the GSI experiments, assimilation of surface PM2.5 leads to stronger increments in surface PM2.5 compared to the MODIS AOD assimilation. In contrast, we find a stronger impact of MODIS AOD on surface aerosols at 18 UTC compared to the surface PM2.5 OI assimilation. The increments resulting from the OI assimilation are spread in 11×11 horizontal grid cells (12 km horizontal resolution) while the spatial distribution of GSI increments is controlled by its background error covariances, and the horizontal/vertical length scales. The assimilations of observations using both GSI and OI generally help reduce the prediction biases, and improve correlation between model predictions and observations. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). In this study, OI uses the relatively big model uncertainties, which helps yield better mean biases, but sometimes causes the RMSE increase. We also examine and discuss the sensitivity of the assimilation experiments results to the AOD forward operators
Keywords: MODIS, interpolation, aerosol, Environmental Prediction, statistical, GSI, OI, 3d Var, Surface Pm2.5, ºÎ, GsÎ, OÎ
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