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| A parametric model of estimating sea state bias based on JASON-1 altimetry |
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LI Shu-guang1, WANG Yun-hai1,2, MIAO Hong-li3, ZHOU Xiao-guang3, REN Hao-ran3, WANG Gui-zhong3, ZHANG Jie2
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(1.College of Science in China University of Petroleum, Qingdao 266580, China;2.The First Institute of Oceanography, SOA, Qingdao 266061, China;3.College of Information Science and Engineering in Ocean University of China, Qingdao 266100, China)
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| Abstract: |
| 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. |
| Key words: JASON-1 altimetry sea state bias parametric model linear regression difference at crossover points |
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