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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


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.

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