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Proceedings Paper

Determination of integrated cloud liquid water and total precipitable water using a neural network algorithm
Author(s): Emmanuel Moreau; Cecile Mallet; Luc Casagrande; Claude Klapisz
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Paper Abstract

A new algorithms is developed whereby the cloud liquid water path (LWP) and the total precipitable water (TPW) may be determined from microwave radiometric data. A large meteorological database obtained from the European Centre for Medium-Range Weather Forecasts forecast model is used to simulate, with a radiative transfer model, brightness temperatures (TB) at the top of the atmosphere for the special sensor microwave imagery frequencies. A single- hidden-layer ANN was used. An error backpropagation training algorithm was applied to train the ANN. A first comparison with a log-linear regression algorithm, shows that the ANN can represent more accurately the underlying relationship between TB and, TPW and LWP. The ANN seems to be able to give a better fit at large values of LWP. Furthermore in the case of TPW, a validation is made with radiosonde data, with another new algorithm.

Paper Details

Date Published: 19 August 1998
PDF: 9 pages
Proc. SPIE 3503, Microwave Remote Sensing of the Atmosphere and Environment, (19 August 1998); doi: 10.1117/12.319507
Show Author Affiliations
Emmanuel Moreau, Ctr. d'Etude de l'Environement Terrestre et Planetaire (France)
Cecile Mallet, Ctr. d'Etude de l'Environement Terrestre et Planetaire (France)
Luc Casagrande, Ctr. d'Etude de l'Environement Terrestre et Planetaire (France)
Claude Klapisz, Ctr. d'Etude de l'Environement Terrestre et Planetaire (France)


Published in SPIE Proceedings Vol. 3503:
Microwave Remote Sensing of the Atmosphere and Environment
Tadahiro Hayasaka; Dong Liang Wu; Yaqiu Jin; JingShang Jiang, Editor(s)

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