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Article dans une revue

Mapping neural network computation of high-resolution radiant fluxes of inhomogeneous clouds

Abstract : In this study, we evaluated the ability of Mapping Neural Network (MNNs) to calculate the high-resolution radiant fluxes of one-dimensional and two-dimensional inhomogeneous clouds. The test were done for different types of clouds, radiand fluxes, and wavelenghts under different conditions of illumination. The MNNs were trained with training data sets composed of the Monte Carlo (MC) radiant fluxes of bounded cascade clouds and used to compute first the high-resolution MC radiant fluxes (reflectance, transmittance, and absorptance) of bounded cascade inhomogeneous clouds. The test showed their rather good performance, which was compared with that of the Nonlocal Independent Pixel Approximation when possible. Their generalization ability was, second, tested with inhomogeneous clouds of other types : white noise clouds and bounded cascade inhomogeneous clouds with fractional cloud cover. In these cases, the MNNs, trained with different fields, have still good performance. However, the results were found to be much better for the reflectance than for the transmittance and absorptance. In the case of the fractionnal cloud cover, debased responses appear as well in transition zones. We show that the generalization ability could be improved significantly by completing the training sets with new additional patterns when they are applied to types of inhomogeneous clouds different, than the clouds of the training set. These results reveal that the MNNs represent a flexible method of function approximation to compute the high-resolution radiant flux of bounded cascade and other inhomogeneous clouds.
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Article dans une revue
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https://hal.inrae.fr/hal-02580295
Déposant : Migration Irstea Publications <>
Soumis le : jeudi 14 mai 2020 - 20:19:14
Dernière modification le : vendredi 15 mai 2020 - 02:35:26

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  • HAL Id : hal-02580295, version 1
  • IRSTEA : PUB00009973

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Thierry Faure, H. Isaka, B. Guillemet. Mapping neural network computation of high-resolution radiant fluxes of inhomogeneous clouds. Journal of geophysical research-atmospheries, 2001, pp.47. ⟨hal-02580295⟩

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