Development of four-dimensional variational assimilation system based on the GRAPES–CUACE adjoint model (GRAPES–CUACE-4D-Var V1.0) and its application in emission inversion

Abstract

Abstract. In this study, a four-dimensional variational (4D-Var) data assimilation system was developed based on the GRAPES–CUACE (Global/Regional Assimilation and PrEdiction System – CMA Unified Atmospheric Chemistry Environmental Forecasting System) atmospheric chemistry model, GRAPES–CUACE adjoint model and L-BFGS-B (extended limited-memory Broyden–Fletcher–Goldfarb–Shanno) algorithm (GRAPES–CUACE-4D-Var) and was applied to optimize black carbon (BC) daily emissions in northern China on 4 July 2016, when a pollution event occurred in Beijing. The results show that the newly constructed GRAPES–CUACE-4D-Var assimilation system is feasible and can be applied to perform BC emission inversion in northern China. The BC concentrations simulated with optimized emissions show improved agreement with the observations over northern China with lower root-mean-square errors and higher correlation coefficients. The model biases are reduced by 20 %–46 %. The validation with observations that were not utilized in the assimilation shows that assimilation makes notable improvements, with values of the model biases reduced by 1 %–36 %. Compared with the prior BC emissions, which are based on statistical data of anthropogenic emissions for 2007, the optimized emissions are considerably reduced. Especially for Beijing, Tianjin, Hebei, Shandong, Shanxi and Henan, the ratios of the optimized emissions to prior emissions are 0.4–0.8, indicating that the BC emissions in these highly industrialized regions have greatly reduced from 2007 to 2016. In the future, further studies on improving the performance of the GRAPES–CUACE-4D-Var assimilation system are still needed and are important for air pollution research in China.