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19 August 1998 Determination of integrated cloud liquid water and total precipitable water using a neural network algorithm
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Emmanuel Moreau, Cecile Mallet, Luc Casagrande, and Claude Klapisz "Determination of integrated cloud liquid water and total precipitable water using a neural network algorithm", Proc. SPIE 3503, Microwave Remote Sensing of the Atmosphere and Environment, (19 August 1998); https://doi.org/10.1117/12.319507
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