Chemistry-Weather Interacted Model System GRAPES_Meso5.1/CUACE CW V1.0: Development, Evaluation and Application in Better Haze/Fog Prediction in China

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 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 bias of PM2.5 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 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 improving on visibility is larger than PM2.5. This study illustrates the importance of including CWI in air quality prediction model. Double way atmospheric chemistry model considering complete aerosol-radiation-cloud interaction is the hot and difficult issue in climate, weather and air quality (AQ) modeling. Focusing on better prediction of haze/fog pollution in China, the first version (V1) of chemistry-weather (CW) interacted model GRAPES-Meso5.1/CUACE CW V1 is established by online coupling the Chinese Meteorology Administration CUACE chemistry model with the updated operational mesoscale weather model GRAPES_Meso5.1 and the further completion of aerosol-cloud-radiation interaction in it. The meteorology impacts on haze/fog prediction including PM2.5 and visibility is discussed. The impacts of aerosol-cloud interaction (ACI), aerosol radiation interaction (ARI) and the both (CWI) on haze/fog prediction and mesoscale NWP is further studied. The study results shows that the important impacts of relative humidity on high PM2.5 and low visibility and the improving of PM2.5, low visibility prediction and NWP by ARI, ACI and CWI during haze/fog episode, indicating the importance of CW model in better AQ prediction and NWP in polluted region.

Automated summary

The study integrates meteorology and chemistry models to improve haze/fog prediction, and finds that relative humidity has important impacts on visibility and PM2.5, and aerosol-radiation-cloud interaction has positive effects on visibility and PM2.5 prediction.