A new approach based on a neural-network technique for reduction in the computation time of radiative-transfer models is presented. This approach gives high spectral resolution without significant loss of accuracy. A rigorous radiative-transfer model is used to calculate radiation values at a few selected wavelengths, and a neural-network algorithm replenishes them to a complete spectrum with radiation values at a high spectral resolution. This method is used for the UV and visible spectral ranges. The results document the ability of a neural network to learn this specific task. More than 20,000 UV-index values for all kinds of atmosphere are calculated by both the rigorous radiative-transfer model alone and the model in combination with the neural-network algorithm. The agreement between both approaches is generally of the order of ∓1%; the computation time is reduced by a factor of more than 20. The new algorithm can be used for all kinds of high-quality radiative-transfer model to speed up computation time.
© 2001 Optical Society of America
(010.1320) Atmospheric and oceanic optics : Atmospheric transmittance
(030.5620) Coherence and statistical optics : Radiative transfer
(040.7190) Detectors : Ultraviolet
(200.4260) Optics in computing : Neural networks
Harry Schwander, Anton Kaifel, Ansgar Ruggaber, and Peter Koepke, "Spectral Radiative-Transfer Modeling with Minimized Computation Time by Use of a Neural-Network Technique," Appl. Opt. 40, 331-335 (2001)