Chemistry-weather interacted model system GRAPES_Meso5.1/CUACE CW V1.0

The Chinese Meteorology Administration chemistry model CUACE is online
integrated into the mesoscale operational weather prediction (NWP) model
(GRAPES_Meso5.1) and aerosol-cloud-radiation interaction is achieved to
establish the first version (V1) of chemistry-weather (CW) interacted
model GRAPES-Meso5.1/CUACE CW V1. The most polluted winter 2016-2017 is
selected to study the meteorology impacts on haze/fog prediction, the
impact of aerosol-radiation, aerosol-cloud, and CW interaction (ARI, ACI,
CWI) on haze/fog prediction, and NWP. Single way model without CWI
displays reasonable PM2.5 and visibility prediction in general. However,
modeled PM2.5 peaks are underestimated and visibility valleys are
overestimated during haze/fog pollution, the underestimation of relative
humidity (RH) contributes major to this misestimation; CWI model cut the
negative errors of PM2.5 peaks and the positive errors of visibility
valleys. The improvement of 5km and 3km low visibility by CWI during
severe haze/fog period is more obvious than that of 10 km, which just
compensates for the largest deficiency in low visibility prediction
related to severe haze/fog by single way model; The NWP including sea
level pressures, relative humidity(RH), temperature, wind speed are also
improved by CWI from surface to upper troposphere; ARI contributes larger
to the predicted PM2.5, visibility and NWP improvement than that of ACI,
their relative contributions varies with model vertical height and the
overlapping condition of cloud and aerosols. Due to the joint contribution
of RH and PM2.5, CWI’s improvement in visibility is larger than PM2.5.
This study illustrates the importance of including CWI in the air quality
prediction model.