气候变化领域集成服务门户(CCPortal): Synergy of Satellite- and Ground-Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach

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DOI	10.1029/2019JD031884
	Synergy of Satellite- and Ground-Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach
	Li J.; Kahn R.A.; Wei J.; Carlson B.E.; Lacis A.A.; Li Z.; Li X.; Dubovik O.; Nakajima T.
发表日期	2020
ISSN	2169897X
卷号	125 期号:5
英文摘要	Satellite- and ground-based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474-member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground-based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root-mean-square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root-mean-square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space-time variability. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	aerosol remote sensing; data synergy; EnKF
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186133
作者单位	Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China; Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD, United States; State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States; NASA Goddard Institute for Space Studies, New York, NY, United States; International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; Laboratoire d'Optique Atmosphérique, CNRS/Universite Lille, Villeneuve d'Ascq, France; Japan Aerospace Exploration Agency, Tsukuba Space Center, Tsukuba, Japan
推荐引用方式 GB/T 7714	Li J.,Kahn R.A.,Wei J.,et al. Synergy of Satellite- and Ground-Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach[J],2020,125(5).
APA	Li J..,Kahn R.A..,Wei J..,Carlson B.E..,Lacis A.A..,...&Nakajima T..(2020).Synergy of Satellite- and Ground-Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach.Journal of Geophysical Research: Atmospheres,125(5).
MLA	Li J.,et al."Synergy of Satellite- and Ground-Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach".Journal of Geophysical Research: Atmospheres 125.5(2020).