气候变化领域集成服务门户(CCPortal): Quantifying the Representativeness Errors Caused by Scale Transformation of Remote Sensing Data in Stochastic Ensemble Data Assimilation

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DOI	10.1109/JSTARS.2022.3149957
	Quantifying the Representativeness Errors Caused by Scale Transformation of Remote Sensing Data in Stochastic Ensemble Data Assimilation
	Liu, Feng; Zhao, Zebin; Li, Xin
通讯作者	Liu, F (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China. ; Li, X (通讯作者)，Chinese Acad Sci, Inst Tibetan Plateau Res, State Key Lab Tibetan Plateau Earth Syst Resource, Natl Tibetan Plateau Data Ctr, Beijing 100101, Peoples R China.
发表日期	2022
ISSN	1939-1404
EISSN	2151-1535
起始页码	1968
结束页码	1980
卷号	15
英文摘要	Representativeness error caused by scale transformation (REST) is an intrinsic property of data assimilation, as assimilating new observations likely involves the fusion of multisource and multiscale data. Earlier studies focused on specific cases and failed to obtain a general concept. This study attempts to achieve a further understanding of REST in both theory and practice. Based on scale-related definitions and formulations, the statistical RESTs of observation errors and analysis errors are deduced in stochastic ensemble data assimilation. Experiments based on ensemble Kalman filter are conducted to validate the interpretability of the proposed formulations. A synthetic experiment uses the stochastic Lorenz model as the forecasting operator, and a real-world experiment employs a simple biosphere model as the forecasting operator and uses a series of mixed ground-based and remote sensing soil moisture observations. The results confirm that REST should be proportional to the scale difference when assimilating direct observations and both system states and observations are homogeneous processes. Due to the nonlinearity in modeling, assimilation, and scale transformation, increasing RESTs are found if the scale of the observation is much larger than that of the state space, or multiscale observations are added into the assimilation system. Quantifying REST improves the understanding of uncertainty in data assimilation, but further studies on REST are required in both theory and practice, for example, REST correlates with other errors in forcing, parameters, and models, and introduces an observation operator to assimilate indirect observations.
关键词	MODEL WATER ECOSYSTEM ENERGY FLUX FLOW SIB2 CO2
英文关键词	Stochastic processes; Data assimilation; Remote sensing; Biological system modeling; Data models; Forecasting; Earth; Heihe watershed allied telemetry experimental research (HiWATER); scaling; lorenz model; remote sensing; SiB2 model; soil moisture; stochastic process; uncertainty; wireless sensor networks
语种	英语
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000764795200004
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254726
作者单位	[Liu, Feng; Zhao, Zebin] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Li, Xin] Chinese Acad Sci, Inst Tibetan Plateau Res, State Key Lab Tibetan Plateau Earth Syst Resource, Natl Tibetan Plateau Data Ctr, Beijing 100101, Peoples R China; [Li, Xin] Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Liu, Feng,Zhao, Zebin,Li, Xin. Quantifying the Representativeness Errors Caused by Scale Transformation of Remote Sensing Data in Stochastic Ensemble Data Assimilation[J]. 中国科学院西北生态环境资源研究院,2022,15.
APA	Liu, Feng,Zhao, Zebin,&Li, Xin.(2022).Quantifying the Representativeness Errors Caused by Scale Transformation of Remote Sensing Data in Stochastic Ensemble Data Assimilation.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,15.
MLA	Liu, Feng,et al."Quantifying the Representativeness Errors Caused by Scale Transformation of Remote Sensing Data in Stochastic Ensemble Data Assimilation".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15(2022).