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YANG Tingting,CHEN Zhongbiao,HE Yijun. 2015. A new method to retrieve salinity profiles from sea surface salinity observed by SMOS satellite. Acta Oceanologica Sinica, 34(9):85-93
A new method to retrieve salinity profiles from sea surface salinity observed by SMOS satellite
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
Received:September 23, 2014  Revised:February 28, 2015
DOI:10.1007/s13131-015-0735-3
Key words:salinity profile  Soil Moisture and Ocean Salinity (SMOS) data  Argo data  sea surface salinity
中文关键词:  盐度廓线  SMOS卫星  Argo浮标  海表面盐度
基金项目:
Author NameAffiliationE-mail
YANG Tingting School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China  
CHEN Zhongbiao School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China  
HE Yijun School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China yjhe@nuist.edu.cn 
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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.
中文摘要:
      本文提出了一种利用SMOS卫星观测的海表面盐度反演海水盐度廓线的新方法。首先利用经验正交函数分析Argo浮标观测的盐度廓线来获得盐度廓线的主要模态,然后提出两种统计模型来估算各模态的时间系数。第一种是线性模型,建立了SMOS观测的表面盐度与Argo观测的表面盐度之间的关系,进而建立SMOS的观测值与时间系数之间的线性关系;第二种是非线性模型,利用神经网络建立时间系数与SMOS观测的盐度、观测时间、观测区域的经度和纬度之间的关系。与Argo测量的盐度廓线作比较,利用线性模型和非线性模型反演的上层400米盐度的均方根误差分别为0.08-0.16和0.08-0.14,分别比气候态平均值小0.01-0.07和0.01-0.09。最后分析了本方法的误差来源。
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