Preliminary validation of the GLI cryosphere algorithms with MODIS daytime data

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Abstract

Preliminary validation of the two GLI cryosphere algorithms is conducted by applying them to MODIS daytime data on June 18,2000. One of the algorithms has been developed for the discrimination of cloud/clear and snow/sea-ice, and the other for the retrievals of snow grain size and mass fraction of impurities mixed in snow. Analysis results show that cloudy areas around the northern polar region are successfully discriminated from areas under clear condition with various surface types such as snow, open ocean and bare land. Reasonable spatial distributions of snow grain size and mass fraction of impurities around the North Pole are also successfully retrieved in the range from nearly 0.0 to over 5000μm for snow grain size and 0.0-0.2 ppmw for snow impurities. Finally, possible sources of error in the retrieved parameters and remaining development points on the present version of the GLI algorithms to be improved for future advances are discussed.

Preliminary validation of the two GLI cryosphere algorithms is conducted by applying them to MODIS daytime data on June 18,2000. One of the algorithms has been developed for the discrimination of cloud/clear and snow/sea-ice, and the other for the retrievals of snow grain size and mass fraction of impurities mixed in snow. Analysis results show that cloudy areas around the northern polar region are successfully discriminated from areas under clear condition with various surface types such as snow, open ocean and bare land. Reasonable spatial distributions of snow grain size and mass fraction of impurities around the North Pole are also successfully retrieved in the range from nearly 0.0 to over 5000μm for snow grain size and 0.0-0.2 ppmw for snow impurities. Finally, possible sources of error in the retrieved parameters and remaining development points on the present version of the GLI algorithms to be improved for future advances are discussed.

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Codes

  • NII Article ID (NAID)
    110001068173
  • NII NACSIS-CAT ID (NCID)
    AA1129795X
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    13443437
  • Data Source
    CJPref  NII-ELS  IR 
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