Haitao Zheng1,2,3, Jianguo Liu 1, Xiao Tang3, Zifa Wang 3, Huangjian Wu3, Pingzhong Yan3, Wei Wang4 1 Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
2 University of Science and Technology of China, Hefei 230026, China
3 The State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
4 China National Environmental Monitoring Centre, Beijing 100012, China
Received:
December 18, 2017
Revised:
March 19, 2018
Accepted:
March 26, 2018
Download Citation:
||https://doi.org/10.4209/aaqr.2017.11.0522
Cite this article:
Zheng, H., Liu, J., Tang, X., Wang, Z., Wu, H., Yan, P. and Wang, W. (2018). Improvement of the Real-time PM2.5 Forecast over the Beijing-Tianjin-Hebei Region using an Optimal Interpolation Data Assimilation Method.
Aerosol Air Qual. Res.
18: 1305-1316. https://doi.org/10.4209/aaqr.2017.11.0522
HIGHLIGHTS
ABSTRACT
A routine air quality data assimilation (DA) system was established at the China National Environmental Monitoring Center (CNEMC) based on the optimal interpolation (OI) method. The surface observations from more than 1,400 stations across China were assimilated into a real-time air quality forecast system with three nested domains. The initial conditions of NO2, SO2 and PM2.5 in the three domains were optimized by the data assimilation system. The impact of the data assimilation on the real-time PM2.5 forecast over the Beijing-Tianjin-Hebei (BTH) Region during the heavy haze season of 2015 was evaluated. The results show that the DA can significantly improve real-time PM2.5 forecasts, reducing the root mean square error (RMSE) by 23%, 8.2% and 4.8% in the forecasts of the first, second and third day, respectively. The mean fractional bias and the mean fractional error of the forecast were reduced from 50.9% and 70.67% to 40% and 62.3%, respectively, and the performance changed from “criteria” to approaching “goal” (as defined by Boylan and Russell, 2006). Additionally, increasing the assimilation frequency can improve the DA system performance for real-time forecasts. As can be seen from the various cases studied here, the improvement in data assimilation is more significant when the bias of the model is higher and there is still much room for correction. The results also show a rapid decay of the DA effects on the PM2.5 forecast, which highlights the limitations of the current routine data assimilation system in which only initial conditions are optimized. Further improvements in the data assimilation system with meteorological data assimilation and chemical parameter optimization are needed.
Keywords:
Real-time PM2.5 forecast; Data assimilation; Optimal interpolation; Beijing-Tianjin-Hebei Region.
