DEVELOPMENT OF A REGIONAL DATA ASSIMILATION SYSTEM AND ITS APPLICATION IN TWO DISTINCT AREAS OF CHINA FOR ESTIMATING CO SURFACE FLUX

Traditional methods are time-consuming and labor-intensive for CO flux estimations, there are generally significant uncertainties concerning the results. Data assimilation method has been adopted in recent years for flux optimization in many studies and has proven to be an effective way to improve the accuracy of the CO flux. In this study, a regional data assimilation system, i.e., TracersTracker, was developed based on the POD4DVAR (Proper Orthogonal Decomposition Four-dimensional Variational) method, the CMAQ (Community Multi-scale Air Quality) model and the WRF (Weather Research and Forecasting) model. The system was then applied in two distinct representative areas, i.e., Shangdianzi and Waliguan, for estimating the surface anthropogenic CO flux in 2016. Results show that the CO posteriors in the two study areas were generally higher than the CO priories, but the variations of the posteriors in Shangdianzi and Waliguan are quite different. The overall increase rates of the posteriors are 29.1% and 61.2% in Shangdianzi and Waliguan, respectively. Posteriors optimized by the TracersTracker system significantly improve the accuracy and the correlation of CO simulations in both study areas, which proves that the TracersTracker system is an effective tool for improving the accuracy of the CO emission flux.