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遥感技术与应用  1998, Vol. 13 Issue (1): 1-7    DOI: 10.11873/j.issn.1004-0323.1998.1.1
遥感应用     
从SSM/I亮温反演海洋上大气可降水量
陈洪滨 王普才 孙海冰 吕达仁
(中国科学院大气物理研究所 北京 100029)
Retrievals of Over-Ocean Precipitable Water from the SSM/I Measurements with Several Regression Algorithms
CHEN Hongbin WANG Pucai SUN Haibing LU Daren
(Institute of Atmospheric Physics,The Chinese Academy of Sciences,Beijing 100029)
 全文: PDF 
摘要:

利用日本NASDA提供的SSM/I和相应的海岛气象探空资料,对几种有代表性的SSM/I反演大气可降水量算式的反演结果进行了比较分析。结果表明,目前业务反演采用的算式过高地估计了低大气可降水量,而对于高大气可降水量(>4.5g/cm2)又过低地估计了;对反演值进行一个三次多项式订正,从总体上并不改善反演效果。针对以上问题,提出一个改进的混合分段反演算式。

关键词: 大气可降水量SSM/I反演算式    
Abstract:

A SSM/I and collocated radiosonde observation data set provided by the NASDA(Japan) was used to retrieve the total precipitable water (PW) over oceans. The retrieval results obtained with several regression algorithms were compared to the radiosonde measurements. It is shown that : (a) the routinely operational algorithm of Alishouse et al.(1990) yields significant underestimates in high PW regime and overestimates in low PW regime; (b) a cubic correction by Colton and Poe (1994) is not sufficient and globally improves very slightly the retrieval results; and (c) the regression algorithm with the form of brightness temperature (TB) function ln(280-TB) seems to give a little largely scattered retrievals but without noticeable over-and underestimates in low and high PW regimes. To improve the estimation of the oceanic precipitable water from the SSM/I TB measurements, a composite algorithm with different forms of TB function in low, medium and high PW regimes is proposed and tested.

Key words: Precipitable water    SSM/I    Retrieval algorithm
收稿日期: 1997-10-21 出版日期: 2012-02-06
:  TP722.6/P732  
基金资助:

本工作得到日本NASDA AMSR反演算式项目(A2-RA-A-0017)以及我国航天技术有关微波遥感项目资助。

作者简介: 陈洪滨,男,1960年4月出生,研究员,主要研究领域是大气辐射传输和大气微波主被动遥感。
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引用本文:

陈洪滨 王普才 孙海冰 吕达仁. 从SSM/I亮温反演海洋上大气可降水量[J]. 遥感技术与应用, 1998, 13(1): 1-7.

CHEN Hongbin WANG Pucai SUN Haibing LU Daren. Retrievals of Over-Ocean Precipitable Water from the SSM/I Measurements with Several Regression Algorithms. Remote Sensing Technology and Application, 1998, 13(1): 1-7.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.1998.1.1        http://www.rsta.ac.cn/CN/Y1998/V13/I1/1

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