A parametric model of estimating sea state bias based on JASON-1 altimetry

Shu Guang Li, Yun Hai Wang, Hong Li Miao, Xiao Guang Zhou, Hao Ran Ren, Gui Zhong Wang, Jie Zhang

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4 Citations (Scopus)


Based on the data of JASON-1 altimetry, the parametric model of estimating the sea state bias (SSB) was studied. Non-SSB signals within the altimeter data were eliminated by means of forming differences between measurements taken at crossover points. According to Taylor expansion, 32 parametric models of SSB were developed as the function of both the significant wave height and wave speed. The estimation values of each parametric model were derived from the linear regression, and then the optimal model was obtained via the process of evaluation and selection. Finally, the effectiveness of the parametric model was validated through comparing the SSB estimation of the model with the geophysical data records (GDR) of JASON-1. The results show that the parametric model is effective, and can be used for JASON-1 SSB correction.

Original languageEnglish
Pages (from-to)181-185+196
Number of pages6
JournalZhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)
Issue number2
Publication statusPublished - Apr 2013
Externally publishedYes


  • Difference at crossover points
  • JASON-1 altimetry
  • Linear regression
  • Parametric model
  • Sea state bias

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