气候变化领域集成服务门户(CCPortal): Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method

CCPortal

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 S.; Tian X.; Han X.; Zhang M.; Zhang H.; Mao H.
发表日期	2022
ISSN	1352-2310
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. © 2021 Elsevier Ltd
关键词	Air quality forecast Data assimilation NLS-4DVar method WRF-CMAQ model
语种	英语
scopus关键词	Air quality; Computational efficiency; Factorization; Integrated circuits; Mean square error; Particles (particulate matter); Weather forecasting; Air quality forecasts; Data assimilation; Data assimilation systems; Environmental problems; Initial conditions; Mass concentration; NLS-4dvar method; Particulate pollution; PM 2.5; WRF-CMAQ model; Least squares approximations; aerosol; air quality; data assimilation; global climate; numerical model; particulate matter; aerosol; air quality; article; China; clinical article; forecasting; least square analysis; particulate matter 2.5; prediction; river; uncertainty; weather; China; Yangtze River
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/248092
作者单位	International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; Center for Excellence in Urban Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China; Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People's Republic o...
推荐引用方式 GB/T 7714	Zhang S.,Tian X.,Han X.,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 S.,Tian X.,Han X.,Zhang M.,Zhang H.,&Mao H..(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 S.,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).