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
2021
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.
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