Remote sensing from the infrared atmospheric sounding interferometer instrument -: 1.: Compression, denoising, and first-guess retrieval algorithms -: art. no. 4619

被引:54
作者
Aires, F
Rossow, WB
Scott, NA
Chédin, A
机构
[1] NASA, Goddard Inst Space Studies, New York, NY 10025 USA
[2] Ecole Polytech, Meteorol Dynam Lab, F-91128 Palaiseau, France
关键词
infrared interferometer; principal component analysis; channel selection;
D O I
10.1029/2001JD000955
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
A principal component analysis (PCA) scheme is developed for treatment of observations from the high spectral resolution Infrared Atmospheric Interferometer (IASI) spaceborne instrument. Compression and denoising of IASI observations are performed using this PCA. This preprocessing methodology also allows for a fast pattern recognition to obtain a first guess from a climatological data set. The performance of the compression, denoising, and multivariate first-guess retrieval are evaluated with a large diversified data set of radiosondes atmospheres including rare events. Overall, the instrumental noise in the overall observed IASI spectrum goes from 0.9 to 0.2 K after denoising. This analysis procedure will be used by Aires et al. [2002c] to retrieve simultaneously temperature, water vapor and ozone atmospheric profiles.
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页数:16
相关论文
共 32 条
[1]  
ACHARD V, 1991, THESIS U P M CURIE P
[2]  
Aires F, 2002, J APPL METEOROL, V41, P144, DOI 10.1175/1520-0450(2002)041<0144:ARNNAF>2.0.CO
[3]  
2
[4]  
Aires F, 2002, J ATMOS SCI, V59, P111, DOI 10.1175/1520-0469(2002)059<0111:ROEBTI>2.0.CO
[5]  
2
[6]   Independent component analysis of multivariate time series:: Application to the tropical SST variability [J].
Aires, F ;
Chédin, A ;
Nadal, JP .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2000, 105 (D13) :17437-17455
[7]  
AIRES F, 1998, P AM METEOROL SOC, V98, P181
[8]  
AIRES F, 1999, THESIS U PARIS 9 DAU
[9]  
Aires F, 2002, J GEOPHYS RES, V107, DOI [10.1029/2001JD0001591, DOI 10.1029/2001JD0001591]
[10]  
Bishop C.M., 1999, Neural Networks for Pattern Recognition