Development and Evaluation of a Novel 3D Variational Assimilation Framework for Regional Chemistry–Weather Forecasting

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.