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

机译：利用神经网络技术以最少的计算时间进行光谱辐射传递建模

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摘要

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.
机译：提出了一种基于神经网络技术减少辐射传递模型计算时间的新方法。这种方法可提供高光谱分辨率，而不会显着降低精度。严格的辐射传输模型用于计算几个选定波长的辐射值，并且神经网络算法将它们补充为具有高光谱分辨率的辐射值的完整光谱。此方法用于紫外线和可见光谱范围。结果证明了神经网络学习这一特定任务的能力。仅通过严格的辐射传递模型和结合神经网络算法的模型，就可以计算出超过20,000种针对各种大气的紫外线指数值。两种方法之间的一致性通常约为±1％;计算时间减少了20倍以上。新算法可用于各种高质量的辐射传递模型，从而加快了计算时间。

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