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This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var\udassimilation tool originally developed by the National Centers for\udEnvironmental Prediction (NCEP), to improve surface PM2.5\udpredictions over the contiguous United States (CONUS) by assimilating aerosol\udoptical depth (AOD) and surface PM2.5 in version 5.1 of the\udCommunity Multi-scale Air Quality (CMAQ) modeling system. An optimal\udinterpolation (OI) method implemented earlier (Tang et al., 2015) for the\udCMAQ modeling system is also tested for the same period (July 2011) over the\udsame CONUS. Both GSI and OI methods assimilate\udsurface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC,\udand MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI\udand OI generally help reduce the prediction biases and improve correlation\udbetween model predictions and observations. In the GSI experiments,\udassimilation of surface PM2.5 (particle matter with\uddiameter < 2.5 µm) leads to stronger increments in\udsurface PM2.5 compared to its MODIS AOD assimilation at the\ud550 nm wavelength. In contrast, we find a stronger OI impact of the\udMODIS AOD on surface aerosols at 18:00 UTC compared to the surface\udPM2.5 OI method. GSI produces smoother result and yields overall\udbetter correlation coefficient and root mean squared error (RMSE). It should\udbe noted that the 3D-Var and OI methods used here have several big\uddifferences besides the data assimilation schemes. For instance, the OI uses\udrelatively big model uncertainties, which helps yield smaller mean biases,\udbut sometimes causes the RMSE to increase. We also examine and discuss the\udsensitivity of the assimilation experiments' results to the AOD forward\udoperators

geoscientific model development

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