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Dryad

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

Cite this dataset

Wang, Hong (2022). Chemistry-weather interacted model system GRAPES_Meso5.1/CUACE CW V1.0 [Dataset]. Dryad. https://doi.org/10.5061/dryad.m63xsj45f

Abstract

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.

Funding

Key Program of NSFC-Tongyong Union Foundation, Award: 42090031

Ministry of Science and Technology of the People's Republic of China, Award: 2019YFC0214601