气候变化领域集成服务门户(CCPortal): Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

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DOI	10.5194/acp-20-6015-2020
	Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period
	Ha S.; Liu Z.; Sun W.; Lee Y.; Chang L.
发表日期	2020
ISSN	1680-7316
起始页码	6015
结束页码	6036
卷号	20 期号:10
英文摘要	The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the threedimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2:5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2:5 observations. Consistent with previous studies, the assimilation of surface PM2:5 measurements alone still underestimates surface PM2:5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2:5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. © 2020 BMJ Publishing Group. All rights reserved.
语种	英语
scopus关键词	accuracy assessment; aerosol; air quality; atmospheric pollution; data assimilation; forecasting method; GOCI; MODIS; optical depth; particulate matter; spatiotemporal analysis; South Korea
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141330
作者单位	National Center for Atmospheric Research, Boulder, CO, United States; National Institute of Environmental Research, Incheon, South Korea
推荐引用方式 GB/T 7714	Ha S.,Liu Z.,Sun W.,et al. Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period[J],2020,20(10).
APA	Ha S.,Liu Z.,Sun W.,Lee Y.,&Chang L..(2020).Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period.Atmospheric Chemistry and Physics,20(10).
MLA	Ha S.,et al."Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period".Atmospheric Chemistry and Physics 20.10(2020).