Advances in Earth Science ›› 2013, Vol. 28 ›› Issue (8): 890-896. doi: 10.11867/j.issn.1001-8166.2013.08.0890

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Research Progress of Retrieving Atmosphere Humidity Profiles from AIRS Data

Liu Yang 1, Cai Bo 2, Ban Xianxiu 1, Yuan Jian 1, Geng Shujiang 1, Zhao Shuhui 1, Li Shuaibin 1   

  1. 1.Liaoning Weather Modification Office,Shenyang 110016,China;
    2.Air Force Meteorological Centre of Shenyang Military Region, Shenyang 110015,China
  • Received:2013-06-06 Revised:2013-07-02 Online:2013-08-10 Published:2013-08-10

Liu Yang, Cai Bo, Ban Xianxiu, Yuan Jian, Geng Shujiang, Zhao Shuhui, Li Shuaibin. Research Progress of Retrieving Atmosphere Humidity Profiles from AIRS Data[J]. Advances in Earth Science, 2013, 28(8): 890-896.

With the breakthrough of the satellite remote sensing key technology, satellite spectral resolution has reached to the level of distinguish between each spectral line, the researchers begin to research atmosphere profile and various micro constituent inversion using huge number of channels at the same time. This paper make a comment of the advances in Atmospheric humidity profile inversion using AIRS data, Analyzed and discussed the research status of clear sky atmospheric profiles from four aspects: training data, information extraction and dimensionality reduction of channels, inversion algorithm and accuracy improvement of Inversion. CIMSS and brightness temperature simulated used SARTA are usually chose to retrieve water vapor profile using AIRS data. Summarized two kinds of methods to do information extraction and dimensionality reduction, the first is spectral information compression, compared with PCA found that ICA is more practical. The second is channel selection, that is keeping part of channel contained more atmospheric profile information to achieve dimensionality reduction. It is worth mentioning that we need to choose different channel combination under different regional climate types, underlying surface, season, and real-time weather condition. Introduces three kinds of inversion algorithm: eigenvector regression algorithms(ERA), the Newton method(NM), and aritificial neural network algorithms(ANN). Compare the three methods found that ERA is simple, but the precision is not ideal; NW has high precision but it is not suitable for business application due to its long computing time; ANN has high computing speed and its precision can meet the requirements, thus, ANN has excellent foreground. Analyzed several samples classification method and additional factors, and give some suggestions to improve inversion algorithm. Finally, provide a brief introduction of infrared cloudcleared and inversion atmospheric profile under cloud condition.

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