Statistics – Computation
Scientific paper
Jul 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005georl..3213807c&link_type=abstract
Geophysical Research Letters, Volume 32, Issue 13, CiteID L13807
Statistics
Computation
11
Atmospheric Composition And Structure: Cloud/Radiation Interaction, Computational Geophysics: Neural Networks, Fuzzy Logic, Machine Learning, Atmospheric Processes: Remote Sensing
Scientific paper
Cloud parameter retrieval of inhomogeneous and fractional clouds is performed for a stratocumulus scene observed by MODIS at a solar zenith angle near 60°. The method is based on the use of neural network technique with multispectral and multiscale information. It allows to retrieve six cloud parameters, i.e. pixel means and standard deviations of optical thickness and effective radius, fractional cloud cover, and cloud top temperature. Retrieved cloud optical thickness and effective radius are compared to those retrieved with a classical method based on the homogeneous cloud assumption. Subpixel fractional cloud cover and optical thickness inhomogeneity are compared with their estimates obtained from 250m pixel observations; this comparison shows a fairly good agreement. The cloud top temperature appears also retrieved quite suitably.
Buriez Jean-Claude
Cornet Céline
Guillemet Bernard
Isaka Harumi
Riedi Jérôme
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