• DocumentCode
    2488182
  • Title

    An inversion technique for estimating the wind-dependent short wave spectral density from the CMOD4 and composite surface models

  • Author

    Chen, Jiffeng ; Kasilingam, Dayalan

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Massachusetts Univ., North Dartmouth, MA, USA
  • Volume
    4
  • fYear
    1997
  • fDate
    3-8 Aug 1997
  • Firstpage
    1862
  • Abstract
    Calculations of the normalized radar cross-section (NRCS) from the CMOD4 model are inverted using the composite surface model (CSM) to estimate the short wave spectral density in the C-band region of the short wave spectrum. Simulations of the composite surface model are used to train an artificial neural network. Short wave spectral densities from a variety of short wave models are used with the CSM. The simulated NRCS from the CSM is used as the input parameter and the short wave spectral density is used as the output parameter. The properly trained neural network is then used with the CMOD4 model to generate the corresponding short wave spectra at different wind speeds. These wave spectra are compared with predictions of existing empirical wave spectral models and appear to agree well in shape. However, the actual spectral levels were quite different
  • Keywords
    atmospheric techniques; backscatter; geophysics computing; inverse problems; meteorological radar; neural nets; radar cross-sections; radar theory; remote sensing by radar; wind; C-band; CMOD4; SHF; atmosphere; backscatter; composite surface model; inversion method; measurement technique; meteorological radar; neural net; neural network; normalized radar cross-section; ocean wave; radar remote sensing; radar scattering; radar theory; sea surface; short wave spectral density; wind; wind speed; Backscatter; Electromagnetic scattering; Neural networks; Oceans; Radar scattering; Rough surfaces; Sea surface; Surface roughness; Surface waves; Wind speed;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International
  • Print_ISBN
    0-7803-3836-7
  • Type

    conf

  • DOI
    10.1109/IGARSS.1997.609110
  • Filename
    609110