Open AccessNeural network analysis of the radiative interaction between neighboring pixels in inhomogeneous clouds
Author(s) -
Faure Thierry,
Isaka Harumi,
Guillemet Bernard
Publication year - 2001
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2000jd900686
Subject(s) - pixel , radiative transfer , zenith , physics , wavelength , radiant flux , transmittance , radiative flux , solar zenith angle , optics , artificial neural network , radiance , remote sensing , computational physics , computer science , geology , artificial intelligence , atmospheric sciences
In this study, we analyzed the effect of the radiative interaction between neighboring pixels on the high‐resolution radiant flux of bounded cascade inhomogeneous clouds by using a one‐layer mapping neural network as generalized regression analysis. The analysis was done for reflectance, transmittance, and absorptance at different wavelengths under different conditions of illumination. The sign and magnitude of output coefficients indicate how neighboring pixels contribute to the radiant flux of a target pixel. We found that the variation of output coefficients with the distance from the target pixel changes significantly in its shape and horizontal extent not only with the type of radiant flux we consider but also with the wavelength and solar zenith angle. The mapping neural network clearly reveals the asymmetric feature of radiative interaction between neighboring pixels under oblique illumination, which illustrates the shadowing and enhancing effects of local cloud inhomogeneity. The present analysis shows that the mapping neural network is a flexible method of analysis when used as a generalized regression analysis.