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