Abstract. The authors developed a three-dimensional variational (3-DVAR) aerosol
extinction coefficient (AEC) and aerosol mass concentration (AMC) data
assimilation (DA) system for aerosol variables in the Weather Research and
Forecasting–Chemistry (WRF–Chem) model with the WRF–Chem using the Model
for Simulating Aerosol Interactions and Chemistry (MOSAIC) scheme. They
establish an AEC observation operator and its corresponding adjoint based on
the Interagency Monitoring of Protected Visual Environments (IMPROVE)
equation and investigate the use of lidar AEC and surface AMC DA to forecast
mass concentration (MC) profiles of PM2.5 (particulate matter with an
aerodynamic diameter of less than 2.5 µm) across China. Two sets of
data were assimilated: AEC profiles captured by five conventional Mie
scattering lidars (positioned in Beijing, Shijiazhuang, Taiyuan, Xuzhou, and
Wuhu) and PM2.5 and PM10 MC data obtained from over 1500 ground
environmental monitoring stations across China. Three DA experiments (i.e.,
a PM2.5 (PM10) DA experiment, a lidar AEC DA experiment, and a
simultaneous PM2.5 (PM10) and lidar AEC DA experiment) with a 12 h
assimilation period and a 24 h forecast period were conducted. The
PM2.5 (PM10) DA reduced the root mean square error (RMSE) of the
surface PM2.5 MC in the initial field of the model by 38.6 µg m−3 (64.8 %). When lidar AEC data were assimilated, this reduction was 10.5 µg m−3 (17.6 %), and a 38.4 µg m−3 (64.4 %)
reduction occurred when the two data sets were assimilated simultaneously,
although only five lidars were available within the simulation region
(approximately 2.33 million km2 in size). The RMSEs of the forecasted
surface PM2.5 MC 24 h after the DA period in the three DA experiments
were reduced by 6.1 µg m−3 (11.8 %), 1.5 µg m−3 (2.9 %),
and 6.5 µg m−3 (12.6 %), respectively, indicating that the
assimilation and hence the optimization of the initial field have a positive
effect on the PM2.5 MC forecast performance over a period of 24 h after
the DA period.