气候变化领域集成服务门户(CCPortal): Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia

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DOI	10.1016/j.atmosenv.2021.118921
	Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia
	Lee S.; Song C.H.; Han K.M.; Henze D.K.; Lee K.; Yu J.; Woo J.-H.; Jung J.; Choi Y.; Saide P.E.; Carmichael G.R.
发表日期	2022
ISSN	1352-2310
卷号	271
英文摘要	To improve PM2.5 predictions in Northeast Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations. In contrast to the conventional method of BEC estimation that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. By taking the emission uncertainty into account, we found that the standard deviations in the BEC were significantly increased. The standard deviations became around three times larger than those in the conventional method at the surface. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over Northeast Asia using the Community Multiscale Air Quality (CMAQ) model initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1535 sites in China were assimilated every 6 h during the campaign period of the Korea-United States Air Quality Study (KORUS-AQ) (1 May–14 June 2016). The data assimilation with the new BEC showed better agreement with the surface PM2.5 observations than with the BEC from the conventional method. Our method was also more consistent with the observations in 24-h PM2.5 predictions than the conventional method (specifically, with a ∼44% reduction of negative biases). We concluded that increased standard deviations, together with updated horizontal and vertical length scales in the new BEC, improved the data assimilation and short-term predictions of the surface PM2.5. This paper also suggests several research efforts to further improve the BEC for better short-term PM2.5 predictions in Northeast Asia. © 2021
关键词	3DVAR Background error CMAQ Covariance Emission uncertainty PM2.5 predictions
语种	英语
scopus关键词	Air quality; Covariance matrix; Forecasting; Meteorology; Statistics; 3DVAR; Background errors; Background-error covariances; Community multi-scale air qualities; Covariance; Data assimilation; Emission uncertainties; Error covariance matrix; PM 2.5; Pm2.5 prediction; Aerosols; aerosol; air quality; data assimilation; emission inventory; particulate matter; prediction; three-dimensional modeling; aerosol; air quality; article; China; covariance; particulate matter 2.5; prediction; South Korea; uncertainty; United States; Northeast Asia
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248085
作者单位	School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea; Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, South Korea; The Seoul Institute, Seoul, South Korea; Department of Mechanical Engineering, University of Colorado, Boulder, CO, United States; Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research (NIER), Incheon, South Korea; Department of Advanced Technology Fusion, Konkuk University, Seoul, 143-701, South Korea; Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, United States; Department of Atmospheric and Oceanic Sciences, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, United States; Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, United States
推荐引用方式 GB/T 7714	Lee S.,Song C.H.,Han K.M.,et al. Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia[J],2022,271.
APA	Lee S..,Song C.H..,Han K.M..,Henze D.K..,Lee K..,...&Carmichael G.R..(2022).Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia.ATMOSPHERIC ENVIRONMENT,271.
MLA	Lee S.,et al."Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia".ATMOSPHERIC ENVIRONMENT 271(2022).