A Neural Network Approach for a Fast and Accurate Computation of a Longwave Radiative Budget
Open Access
- 1 November 1998
- journal article
- Published by American Meteorological Society in Journal of Applied Meteorology and Climatology
- Vol. 37 (11) , 1385-1397
- https://doi.org/10.1175/1520-0450(1998)037<1385:annafa>2.0.co;2
Abstract
The authors have investigated the possibility of elaborating a new generation of radiative transfer models for climate studies based on the neural network technique. The authors show that their neural network–based model, NeuroFlux, can be used successfully for accurately deriving the longwave radiative budget from the top of the atmosphere to the surface. The reliable sampling of the earth’s atmospheric situations in the new version of the TIGR (Thermodynamic Initial Guess Retrieval) dataset, developed at the Laboratoire de Météorologie Dynamique, allows for an efficient learning of the neural networks. Two radiative transfer models are applied to the computation of the radiative part of the dataset: a line-by-line model and a band model. These results have been used to infer the parameters of two neural network–based radiative transfer codes. Both of them achieve an accuracy comparable to, if not better than, the current general circulation model radiative transfer codes, and they are much fast... Abstract The authors have investigated the possibility of elaborating a new generation of radiative transfer models for climate studies based on the neural network technique. The authors show that their neural network–based model, NeuroFlux, can be used successfully for accurately deriving the longwave radiative budget from the top of the atmosphere to the surface. The reliable sampling of the earth’s atmospheric situations in the new version of the TIGR (Thermodynamic Initial Guess Retrieval) dataset, developed at the Laboratoire de Météorologie Dynamique, allows for an efficient learning of the neural networks. Two radiative transfer models are applied to the computation of the radiative part of the dataset: a line-by-line model and a band model. These results have been used to infer the parameters of two neural network–based radiative transfer codes. Both of them achieve an accuracy comparable to, if not better than, the current general circulation model radiative transfer codes, and they are much fast...Keywords
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