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. In this study, a four-dimensional variational (4D-Var) dataassimilation system was developed based on the GRAPES–CUACE (Global/RegionalAssimilation and PrEdiction System – CMA Unified AtmosphericChemistry Environmental Forecasting System) atmosphericchemistry 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) dailyemissions in northern China on 4 July 2016, when a pollution eventoccurred in Beijing. The results show that the newly constructedGRAPES–CUACE-4D-Var assimilation system is feasible and can be applied toperform BC emission inversion in northern China. The BC concentrationssimulated with optimized emissions show improved agreement with theobservations over northern China with lower root-mean-square errors andhigher correlation coefficients. The model biases are reduced by 20 %–46 %.The validation with observations that were not utilized in the assimilationshows that assimilation makes notable improvements, with values of the modelbiases reduced by 1 %–36 %. Compared with the prior BC emissions, which arebased on statistical data of anthropogenic emissions for 2007, the optimizedemissions are considerably reduced. Especially for Beijing, Tianjin, Hebei,Shandong, Shanxi and Henan, the ratios of the optimized emissions to prioremissions are 0.4–0.8, indicating that the BC emissions in these highlyindustrialized regions have greatly reduced from 2007 to 2016. In thefuture, further studies on improving the performance of the GRAPES–CUACE-4D-Varassimilation system are still needed and are important for air pollutionresearch in China.

geoscientific model development

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