Chemistry‐Weather Interacted Model System GRAPES_Meso5.1/CUACE CW V1.0: Development, Evaluation and Application in Better Haze/Fog Prediction in China
Hong Wang, X.Y. Zhang, Ping Wang, Yue Peng, Wanchun Zhang, Zhaodong Liu, Changpeng Han, Shenglan Li, Yafei Wang, Huizheng Che, Liping Huang, Huating Liu, Lu Zhang, C. H. Zhou, Z. S., Fenlin Chen, Xiaoyan Ma, X. J. Wu, B. H. Zhang, Xiaojing Shen
Abstract
Abstract The Chinese Meteorology Administration chemistry model Chinese Unified Atmospheric Chemistry Environment (CUACE) is online integrated into the mesoscale operational numerical 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 PM 2.5 and visibility prediction in general. However, modeled PM 2.5 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 bias of PM 2.5 peaks and the positive bias of visibility valleys. The improvement of 5 and 3 km 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 with severe haze/fog by single way model; The NWP including sea level pressures, RH, temperature, wind speed are also improved by CWI from surface to upper troposphere; ARI contributes larger to the predicted PM 2.5 ,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 PM 2.5 , CWI's improving on visibility is larger than PM 2.5 . This study illustrates the importance of including CWI in air quality prediction model.