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DOI	10.1016/j.atmosenv.2021.118896
	Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method
	Zhang, Shan; Tian, Xiangjun; Han, Xiao; Zhang, Meigen; Zhang, Hongqin; Mao, Huiqin
通讯作者	Tian, XJ (通讯作者)
发表日期	2022
ISSN	1352-2310
EISSN	1873-2844
卷号	271
英文摘要	Particulate pollution is a serious environmental problem that affects regional air quality and the global climate. We developed an advanced joint chemical data assimilation system to improve atmospheric aerosol forecasting based on the nonlinear least squares four-dimensional variational (NLS-4DVar) method. The chemical initial conditions (ICs) and emission fluxes were optimized jointly every 24 h in the system by assimilating multi-time observations. The NLS-4DVar approach allows us to perform joint assimilation with a large state vector benefited from its high computational efficiency. Observed hourly surface fine particulate matter (PM2.5) concentrations were assimilated into the Weather Research and Forecasting model coupled with the Community Multiscale Air Quality (WRF-CMAQ) model with 32-km spatial resolution. According to the results of sensitivity experiments, simultaneous adjustment of ICs and emissions brings more significant improvement of PM2.5 48-h forecasting than optimizing only the ICs. The impact of joint assimilation on PM2.5 mass concentration forecasting over China from 10 to November 24, 2018 was evaluated. In the Yangtze River Delta region, joint assimilation reduced the root mean square error by 48.4% for estimations of the initial concentration fields, 21.9% for 24-h forecasts, and 13.4% for 48-h forecasts. We also assessed the differences between optimized and prior emissions. These results indicate that the joint data assimilation system can effectively reduce the uncertainty in PM2.5 predictions during pollution episodes by simultaneously optimizing ICs mass concentrations and emissions.
关键词	VARIATIONAL DATA ASSIMILATION ENSEMBLE KALMAN FILTER QUALITY MODELING CMAQ AEROSOL EMISSIONS WEATHER RESEARCH SYSTEM PM10 IMPROVEMENT INVENTORY OZONE
英文关键词	Data assimilation; Air quality forecast; NLS-4DVar method; WRF-CMAQ model
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000791993800004
来源期刊	ATMOSPHERIC ENVIRONMENT
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260689
推荐引用方式 GB/T 7714	Zhang, Shan,Tian, Xiangjun,Han, Xiao,et al. Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method [J]. 中国科学院青藏高原研究所,2022,271.
APA	Zhang, Shan,Tian, Xiangjun,Han, Xiao,Zhang, Meigen,Zhang, Hongqin,&Mao, Huiqin.(2022). Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method .ATMOSPHERIC ENVIRONMENT,271.
MLA	Zhang, Shan,et al." Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method ".ATMOSPHERIC ENVIRONMENT 271(2022).