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Proceedings Paper

An adjusted parameter scheme of land-surface emissivity for assimilation of microwave satellite data
Author(s): Peiming Dong; Jishan Xue
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Paper Abstract

Land surface emissivity, which varies widely with surface type, is important for the assimilation of microwave satellite data. The fast radiative transfer model RTTOV-7 developed by ECMWF had been introduced into the Global/regional assimilation and prediction system (Grapes)-3Dvar, a new three dimensional data assimilation system developed by the Research Center for Numerical Meteorological Prediction, Chinese Academy of Meteorological Sciences, to assimilate ATOVS microwave satellite radiance directly. To improve the accuracy of land surface emissivity, the NOAA/NESDIS microwave land surface emissivity model developed by F. Weng is merged into RTTOV-7 and an adjusted parameter scheme is designed to provide the surface parameters for the microwave land surface emissivity model. These surface parameters are produced from a global data assimilation system (GDAS) including a boundary layer model in NOAA/NESDIS. The result shows that the accuracy of land surface emissivity for a variety of land types is improved. It results in the improvement of the accuracy of simulated satellite radiance. Following the satellite microwave radiance operating near the window regions, which are affected strongly by land surface emissivity, could be utilized in the assimilation system to investigate their impact on numerical weather forecast.

Paper Details

Date Published: 29 August 2005
PDF: 9 pages
Proc. SPIE 5890, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: Numerical Atmospheric Prediction and Environmental Monitoring, 58900M (29 August 2005); doi: 10.1117/12.613600
Show Author Affiliations
Peiming Dong, Chinese Academy of Meteorological Sciences (China)
Jishan Xue, Chinese Academy of Meteorological Sciences (China)


Published in SPIE Proceedings Vol. 5890:
Atmospheric and Environmental Remote Sensing Data Processing and Utilization: Numerical Atmospheric Prediction and Environmental Monitoring
Hung-Lung Allen Huang; Hal J. Bloom; Xiaofeng Xu; Gerald J. Dittberner, Editor(s)

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