Abstract
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 18:00 UTC, and MODIS AOD at 18:00 UTC. 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 < 2.5 µm) leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC 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.
Highlights
The existing US National Air Quality Forecasting Capability (NAQFC) run by the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP) provides daily 48 h ozone and PM2.5 forecasts using the Community Multi-scale Air Quality (CMAQ) modeling system with 12 km horizontal grid resolution
Tang et al (2015) used both surface PM2.5 and MODIS aerosol optical depth (AOD) to adjust the initial condition of a chemical transport models (CTMs) with the optimal interpolation (OI) method, and the model biases were successfully reduced
The OI assimilation of PM2.5 leads to more localized increments compared to Gridpoint Statistical Interpolation (GSI) since the OI increment is spread over 11 × 11 grid cells, while the GSI increment spread depends on the horizontal length scales
Summary
The existing US National Air Quality Forecasting Capability (NAQFC) run by the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP) provides daily 48 h ozone and PM2.5 (particle matter with diameter < 2.5 μm) forecasts using the Community Multi-scale Air Quality (CMAQ) modeling system with 12 km horizontal grid resolution. Chemical data assimilation techniques have been developed to improve initial conditions of chemical transport models (CTMs) and yield better prediction (Elbern et al, 1997, 2000, 2007; Elbern and Schmidt, 1999, 2001; Bocquet et al, 2015) by blending the information from a model estimate. As most monitoring data are located near surface, this method was usually applied to the near-surface field Another method is indirect guessing, e.g., comparing satellite-retrieved aerosol optical depth (AOD) with modeled AOD to estimate the biases of modeled column mass concentrations and make the corresponding adjustment. Tang et al (2015) used both surface PM2.5 and MODIS AOD to adjust the initial condition of a CTM with the optimal interpolation (OI) method, and the model biases were successfully reduced. A comparison for their 1-month performances will be discussed
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