Introduction of Water Vapor Dependent Coefficients to Multichannel Algorithms.

DOI

Bibliographic Information

Other Title
  • マルチチャネルアルゴリズムヘの水蒸気依存係数の導入

Abstract

The split-window algorithm produces a large error under humid conditions because of the dependence of each coefficient on the water vapor amount. The water vapor dependent (WVD) algorithm proposed by Francois et al. (1996) reduces such error by expressing each coefficient as a quadratic function of the water vapor amount. In the present article, the concept of the WVD algorithm is applied; to the multichannel (MC) algorithm which gives the surface temperature with the linear combination of the brightness temperatures measured at N channels; and to the extended multichannel (EMC) algorithm which gives the surface brightness temperature at each channel with the same combination. New algorithms named a MC/WVD and an EMC/WVD algorithms, as well as the WVD algorithm, express each coefficient as a quadratic function of the water vapor amount: in actual processing, a global data assimilation system, a sounder and the other data source provide the water vapor amount used in each coefficient. As the results of a simulation analysis with 964 atmospheric profiles, the rms errors of the new algorithms are showed to be 0.2-0.3 K smaller than those of the old algorithms under the conditions of sea observations. Particularly, the EMC/WVD algorithm is showed to be so robust against the emissivity uncertainty as to be applicable to land observations; the rms errors of the algorithm for AVHRR channel 4 and ASTER channel 12 are less than 1 K under the surface conditions including 97 terrestrial materials. And it is also showed that the land surface temperature (LST) offset (LST minus surface air temperature) is an important error factor for the new algorithms as well as the old algorithms.

Journal

Details 詳細情報について

  • CRID
    1390001204666966400
  • NII Article ID
    130003638608
  • DOI
    10.11440/rssj1981.20.137
  • ISSN
    18831184
    02897911
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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