Aerosol observation data assimilation is key to atmospheric environmental prediction. The original CMA chemistry–weather (CMA-CW) 3DVar data assimilation system uses simply fine and coarse particulate matter (PM_2.5 and PM_2.5–10) as control variables, with only concentrations of PM_2.5 and PM₁₀ being assimilated. In this study, a new 3DVar assimilation framework is developed, which considers seven aerosol species (black carbon, organic carbon, soil-dust, sea salt, sulfate, nitrate, and ammonium, instead of PM_2.5 and PM_2.5–10) in refined size segments as control variables, aiming to direct assimilate more aerosol related variables such as aerosol optical depth and lidar extinction and to provide accurate chemical initial fields for the CMA-CW model. Moreover, in the new assimilation framework, a novel equal-proportion distribution and compensation for background error increment is proposed and implemented, which produces physically more reasonable background error and thereby more reliable atmospheric–chemical analysis increments. The new assimilation framework is validated by idealized and real-case assimilation experiments, with reasonable performance. Based on the CMA-CW coupled model with the new assimilation framework and a rapid update cycle configuration, five-day CMA-CW simulation experiments for a widespread heavy fog–haze event in winter 2016 are carried out. The results demonstrate that assimilation of the surface aerosol observation data significantly improves the short-time forecast of atmospheric pollutants, due to refined and more precise information on aerosol compositions brought by the new assimilation framework. Meanwhile, the surface aerosol data assimilation also makes a positive contribution to visibility forecasting, significantly improving the visibility forecast in the heavy pollution areas.

The horizontal correlation function of background error in three-dimensional variational data assimilation (3DVar) determines the extent to which observational information propagates across grid points and influences the analysis at various spatial scales. This study explores the application of the Second-order auto-regressive (Soar), the Gaussian and the Supergauss function based on the CMA-MESO (China Meteorological Administration Mesoscale model). Results from the single-point test indicate that when using the u- and v-components as background error covariance, all the three correlation models can provide a reasonable representation of the wind field horizontal correlations, resulting in a coherent distribution of analysis increments. The Soar and Supergauss functions gain more information on meso- and micro-scales compared to the Gaussian components. Numerical simulations of the extreme rainstorm event reveal that the Soar and Supergauss function achieve a closer alignment with actual circulation and moisture fields compared to the Gauss function. Moreover, the Soar and Supergauss function can effectively increase the analysis information on meso- and micro-scales in the lower atmosphere, significantly improving precipitation forecast accuracy in central Henan province. The two options resolve the problems related to the unrealistically westward shift and underestimation of precipitation, leading to more consistency between observations and simulations. Compared to the Gauss correlation function, the Soar correlation function improves the equitable threat score (ETS) for 3 h accumulated precipitation forecast, particularly for heavy rainfall, which is meaningful for forecasting extreme precipitation events. For the 24 h precipitation forecast scores over 6 d period, the Supergauss function has a higher ETS but more false alarms compared to the Soar correlation function. Overall, the Soar correlation function shows certain advantages in meso- and micro-scales analyses, yet it still has limitations when compared to Supergauss models. Further research is needed to apply multiscale methods to enhance the performance of the Soar correlation function.

The CMA-CUACE-Haze atmospheric chemistry model developed in China is an important tool for aerosol process simulation and assessment, yet there is a lack of atmospheric chemistry variable analysis system. In this paper, an assimilation system for regional chemical weather coupling based on the CMA-MESO three-dimensional variational analysis is developed, which takes uncorrelated PM_2.5 and PM_2.5-10 variables as control variables and utilizes the modeled background error covariance to achieve assimilation analysis of aerosol observations of PM_2.5 and PM₁₀. The validity of the design of the coupled assimilation system is verified by aerosol single-station ideal tests, and assimilation experiment of aerosol PM_2.5 and PM₁₀ observations has been conducted for the heavy pollution process in December 2016. The analysis results indicate that the coupled atmospheric chemistry-weather assimilation system can perform simultaneous miniaturization analysis of aerosol observations and weather variable observations, and the analysis fields between atmospheric chemistry variables and weather variables do not affect each other. The assimilation of aerosol observations reasonably corrects the atmospheric chemical background field, and the analytical fields of PM_2.5 and PM₁₀ variables are closer to the observations. Aerosol assimilation has a significant effect on pollutant forecasts and the effect can last up to 72 h. The regional coupled chemistry-weather assimilation system developed in this study can provide more accurate chemical initial fields for the CMA-CUACE-Haze model.