Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system

doi:10.1016/j.atmosres.2020.105366

	Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system
	Kong, Yawen 3; Sheng, Lifang 1,2; Li, Yanpeng 4; Zhang, Weihang 2; Zhou, Yang 2; Wang, Wencai 2; Zhao, Yuanhong 2
刊名	ATMOSPHERIC RESEARCH
	2021-02-01
卷号	249 页码:14
关键词	Data assimilation WRF-Chem PM2.5 forecast LETKF Air pollution
ISSN号	0169-8095
DOI	10.1016/j.atmosres.2020.105366
通讯作者	Kong, Yawen(kongyw.16b@igsnrr.ac.cn)
英文摘要	To improve the PM2.5 forecast during severe haze episodes, we developed a data assimilation system based on the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the WRF-Chem model to assimilate surface PM2.5 observations. The data assimilation system was successful in optimizing the initial PM2.5 mass concentrations. The root-mean-square error (RMSE) of the initial PM2.5 concentrations after assimilation decreased at 76.75% of the stations and the RMSE reduction exceeds 30% at 20.7% of the stations. The correlation coefficients for the PM2.5 analyses increased by more than 0.3 at 33% of the stations. The forecasts for the spatial distribution and evolution of the haze were improved remarkably after assimilation while the forecasts without assimilation usually significantly underestimated the PM2.5 mass concentrations during the severe haze episodes. The RMSE of the 24-h forecasts after assimilation can be reduced by 32.02% in the polluted regions. During haze episodes, the 48-h forecasts after assimilation can benefit from the assimilation to a similar extent with the 24-h forecasts. Both the forecast accuracy and the duration of assimilation benefits were improved remarkably which demonstrate the effectiveness of the 4D-LETKF-PM2.5 data assimilation system, and further experiments are to be conducted to improve its performance.
资助项目	National Natural Science Foundation of China[41675146]
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000596915000002
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/137696]
专题	中国科学院地理科学与资源研究所
通讯作者	Kong, Yawen
作者单位	1.Ocean Univ China, Ocean Atmosphere Interact & Climate Lab, Key Lab Phys Oceanog, Qingdao 266100, Peoples R China 2.Ocean Univ China, Coll Ocean & Atmospher Sci, Dept Marine Meteorol, Qingdao 266100, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Peoples R China
推荐引用方式 GB/T 7714	Kong, Yawen,Sheng, Lifang,Li, Yanpeng,et al. Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system[J]. ATMOSPHERIC RESEARCH,2021,249:14.
APA	Kong, Yawen.,Sheng, Lifang.,Li, Yanpeng.,Zhang, Weihang.,Zhou, Yang.,...&Zhao, Yuanhong.(2021).Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system.ATMOSPHERIC RESEARCH,249,14.
MLA	Kong, Yawen,et al."Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system".ATMOSPHERIC RESEARCH 249(2021):14.