气候变化领域集成服务门户(CCPortal): Joint retrieval of ocean surface wind and current vectors from satellite SAR data using a Bayesian inversion method

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DOI	10.1016/j.rse.2021.112455
	Joint retrieval of ocean surface wind and current vectors from satellite SAR data using a Bayesian inversion method
	Elyouncha A.; Eriksson L.E.B.; Broström G.; Axell L.; Ulander L.H.M.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	This paper presents a method for joint retrieval of the ocean surface wind and current vectors using the backscatter and the Doppler frequency shift measured by spaceborne single-beam single-polarization synthetic aperture radar (SAR). The retrieval method is based on the Bayesian approach with the a priori information provided by atmospheric and oceanic models for surface wind and currents, respectively. The backscatter and Doppler frequency shift are estimated from the along-track interferometric SAR system TanDEM-X data. The retrieval results are compared against in-situ measurements along the Swedish west coast. It is found that the wind retrieval reduces the atmospheric model bias compared to in-situ measurements by about 1 m/s for wind speed, while the bias reduction in the wind direction is minor as the wind direction provided by the model was accurate in the studied cases. The ocean model bias compared to in-situ measurements is reduced by about 0.04 m/s and 12∘ for current speed and direction, respectively. It is shown that blending SAR data with model data is particularly useful in complex situations such as atmospheric and oceanic fronts. This is demonstrated through two case studies in the Skagerrak Sea along the Swedish west coast. It is shown that the retrieval successfully introduces small scale circulation features detected by SAR that are unresolved by the models and preserves the large scale circulation imposed by the models. © 2021 The Author(s)
英文关键词	Along-track InSAR; Bayesian inversion; Doppler frequency shift; Ocean surface currents; Ocean surface winds; Synthetic aperture radar
语种	英语
scopus关键词	Backscattering; Bayesian networks; Blending; Doppler effect; Maximum likelihood estimation; Ocean currents; Radar measurement; Space-based radar; Vector spaces; Wind; Along-track InSAR; Atmospheric model; Bayesian inversion; Current vectors; Doppler frequency shift; In-situ measurement; Ocean surface currents; Ocean surface winds; Radar data; Surface wind vector; Synthetic aperture radar
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178823
作者单位	Chalmers University of Technology, Gothenburg, Sweden; University of Gothenburg, Gothenburg, Sweden; Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
推荐引用方式 GB/T 7714	Elyouncha A.,Eriksson L.E.B.,Broström G.,et al. Joint retrieval of ocean surface wind and current vectors from satellite SAR data using a Bayesian inversion method[J],2021,260.
APA	Elyouncha A.,Eriksson L.E.B.,Broström G.,Axell L.,&Ulander L.H.M..(2021).Joint retrieval of ocean surface wind and current vectors from satellite SAR data using a Bayesian inversion method.Remote Sensing of Environment,260.
MLA	Elyouncha A.,et al."Joint retrieval of ocean surface wind and current vectors from satellite SAR data using a Bayesian inversion method".Remote Sensing of Environment 260(2021).