气候变化领域集成服务门户(CCPortal): Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors

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DOI	10.1016/j.rse.2021.112802
	Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors
	Tian, Jiaxin; Qin, Jun; Yang, Kun; Zhao, Long; Chen, Yingying; Lu, Hui; Li, Xin; Shi, Jiancheng
通讯作者	Qin, J (通讯作者)
发表日期	2022
ISSN	0034-4257
EISSN	1879-0704
卷号	269
英文摘要	Soil moisture controls the land surface water and energy budget and plays a crucial role in land surface processes. Based on certain mathematical rules, data assimilation can merge satellite observations and land surface models, and produce spatiotemporally continuous profile soil moisture. The two mainstream assimilation algorithms (variational-based and sequential-based) both need model error and observation error estimates, which greatly impact the assimilation results. Moreover, the performance of land data assimilation relies heavily on the specification of model parameters. However, it is always challenging to specify these errors and model parameters. In this study, a dual-cycle assimilation algorithm was proposed for addressing the above issue. In the inner cycle, the Ensemble Kalman Filter (EnKF) is run with parameters of both model and observation operators and their errors, which are provided by the outer cycle. Both the analyzed state variable and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of parameters of both the model and observation operators and their errors through an optimization algorithm. A series of assimilation experiments were first performed based on the Lorenz-63 model. The results illustrate that the performance of the dual-cycle algorithm substantially surpasses those of both the classical parameter calibration and the standard EnKF. Subsequently, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures were assimilated into the simple biosphere model scheme version 2 (SiB2) with a radiative transfer model as the observation operator in two experimental areas, namely Naqu on the Tibetan Plateau and a Coordinate Enhanced Observing (CEOP) reference site in Mongolia. The results indicate that the dual-cycle assimilation algorithm can simultaneously estimate model parameters, observation operator parameters, model error, and observation error, thus improving surface soil moisture estimation in comparison with other assimilation algorithms. Since the dualcycle assimilation algorithm can estimate the observation errors, it provides the potential for assimilating multi-source remote sensing data to generate physically consistent land surface state and flux estimates.
关键词	ENSEMBLE KALMAN FILTER SYSTEM COVARIANCES PARAMETERS SATELLITE PRODUCTS STATE SMOS SMAP
英文关键词	Land data assimilation; Land surface model; Soil moisture; Model error; Observation error; Parameter optimization
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000759691900003
来源期刊	REMOTE SENSING OF ENVIRONMENT
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260737
推荐引用方式 GB/T 7714	Tian, Jiaxin,Qin, Jun,Yang, Kun,et al. Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors[J]. 中国科学院青藏高原研究所,2022,269.
APA	Tian, Jiaxin.,Qin, Jun.,Yang, Kun.,Zhao, Long.,Chen, Yingying.,...&Shi, Jiancheng.(2022).Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors.REMOTE SENSING OF ENVIRONMENT,269.
MLA	Tian, Jiaxin,et al."Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors".REMOTE SENSING OF ENVIRONMENT 269(2022).