Articles | Volume 13, issue 9
Geosci. Model Dev., 13, 4399–4412, 2020
https://doi.org/10.5194/gmd-13-4399-2020
Geosci. Model Dev., 13, 4399–4412, 2020
https://doi.org/10.5194/gmd-13-4399-2020

Model description paper 21 Sep 2020

Model description paper | 21 Sep 2020

RadNet 1.0: exploring deep learning architectures for longwave radiative transfer

Ying Liu et al.

Model code and software

RadNet release for GMD Y. Liu, J. M. Monteiro, and R. Caballero https://doi.org/10.5281/zenodo.3884964

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Short summary
The calculation of atmospheric radiative transfer is the most computationally expensive part of climate models. Reducing this computational burden could potentially improve our ability to simulate the earth's climate at finer scales. We propose using a statistical model – a deep neural network – to compute approximate radiative transfer in the earth's atmosphere. We demonstrate a significant reduction in computational burden as compared to a traditional scheme, especially when using GPUs.