Spectral radiative-transfer modeling with minimized computation time by use of a neural-network technique

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.