| 一种利用SMOS卫星观测的海表面盐度反演海水盐度廓线的方法 |
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| 引用本文： | 杨婷婷,陈忠彪,何宜军.一种利用SMOS卫星观测的海表面盐度反演海水盐度廓线的方法[J].海洋学报(英文版),2015,34(9):85-93. |
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| 作者姓名： | 杨婷婷 陈忠彪 何宜军 |
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| 作者单位： | 南京信息工程大学海洋科学学院, 南京 210044, 江苏,南京信息工程大学海洋科学学院, 南京 210044, 江苏,南京信息工程大学海洋科学学院, 南京 210044, 江苏 |
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| 摘 要： | This paper proposes a new method to retrieve salinity profiles from the sea surface salinity(SSS) observed by the Soil Moisture and Ocean Salinity(SMOS) satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS(SMOS_SSS) with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error(RMSE) of the linear and nonlinear model are 0.08–0.16 and 0.08–0.14 for the upper 400 m, which are 0.01–0.07 and 0.01–0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.
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| 关 键 词： | 盐度廓线 SMOS卫星 Argo浮标 海表面盐度 |
| 收稿时间： | 2014/9/23 0:00:00 |
| 修稿时间： | 2015/2/28 0:00:00 |
A new method to retrieve salinity profiles from sea surface salinity observed by SMOS satellite |
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| YANG Tingting,CHEN Zhongbiao and HE Yijun.A new method to retrieve salinity profiles from sea surface salinity observed by SMOS satellite[J].Acta Oceanologica Sinica,2015,34(9):85-93. |
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| Authors: | YANG Tingting CHEN Zhongbiao and HE Yijun |
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| Institution: | School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China |
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| Abstract: | This paper proposes a new method to retrieve salinity profiles from the sea surface salinity (SSS) observed by the Soil Moisture and Ocean Salinity (SMOS) satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS (SMOS_SSS) with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error (RMSE) of the linear and nonlinear model are 0.08-0.16 and 0.08-0.14 for the upper 400 m, which are 0.01-0.07 and 0.01-0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed. |
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| Keywords: | salinity profile Soil Moisture and Ocean Salinity (SMOS) data Argo data sea surface salinity |
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