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Proceedings Paper

Cloud properties retrieval using neural networks
Author(s): Abidan Cerdena; Juan Carlos Perez; Albano Gonzalez
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Paper Abstract

In this work a method for determining the micro- and macro-physical properties of oceanic stratocumulus clouds at night-time (when only infrared data are available) is presented. It is based on the inversion of a radiative transfer model that computes the brightness temperatures in NOAA-AVHRR channels 3, 4 and 5. The inversion is performed using an artificial neural network (ANN), which is trained to fit the theoretical computations. A detailed study of the ANN parameters and training algorithms demonstrates the convenience of using the "back propagation with momentum" method. The proposed retrieval, using both uniform and adiabatic models for clouds, was validated using ground data collected in Tenerife (Canary Islands), and a good agreement was obtained in those pixels near the sample site. The convenience of using the adiabatic approximation is discussed.

Paper Details

Date Published: 30 November 2004
PDF: 9 pages
Proc. SPIE 5571, Remote Sensing of Clouds and the Atmosphere IX, (30 November 2004); doi: 10.1117/12.565228
Show Author Affiliations
Abidan Cerdena, Univ. of La Laguna (Spain)
Juan Carlos Perez, Univ. of La Laguna (Spain)
Albano Gonzalez, Univ. of La Laguna (Spain)


Published in SPIE Proceedings Vol. 5571:
Remote Sensing of Clouds and the Atmosphere IX
Adolfo Comeron; Michel R. Carleer; Richard H. Picard; Nicolaos I. Sifakis, Editor(s)

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