气候变化领域集成服务门户(CCPortal): Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system

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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 Y.; Sheng L.; Li Y.; Zhang W.; Zhou Y.; Wang W.; Zhao Y.
发表日期	2021
ISSN	0169-8095
卷号	249
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. © 2020 Elsevier B.V.
英文关键词	Air pollution; Data assimilation; LETKF; PM2.5 forecast; WRF-Chem
语种	英语
scopus关键词	Data acquisition; Mean square error; Chem systems; Correlation coefficient; Data assimilation systems; Forecast accuracy; PM2.5 concentration; PM2.5 mass; Root mean square errors; Forecasting; accuracy assessment; atmospheric pollution; concentration (composition); data assimilation; haze; particulate matter; spatial distribution; weather forecasting; China
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162121
作者单位	State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Department of Marine Meteorology, College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China; Ocean-Atmosphere Interaction and Climate Laboratory, Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, 266100, China; School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
推荐引用方式 GB/T 7714	Kong Y.,Sheng L.,Li Y.,et al. Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system[J],2021,249.
APA	Kong Y..,Sheng L..,Li Y..,Zhang W..,Zhou Y..,...&Zhao Y..(2021).Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system.Atmospheric Research,249.
MLA	Kong Y.,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).