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.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Referee Nomination & Report Request started (21 Apr 2020) by Juan Antonio Añel
RR by Anonymous Referee #1 (23 Apr 2020)
RR by Anonymous Referee #2 (02 May 2020)
AR by Anna Mirena Feist-Polner on behalf of the Authors (22 Apr 2020)  Author's response    Manuscript
ED: Reconsider after major revisions (05 May 2020) by Juan Antonio Añel
AR by Ying Liu on behalf of the Authors (08 Jun 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (20 Jun 2020) by Juan Antonio Añel
RR by Anonymous Referee #1 (01 Jul 2020)
ED: Publish subject to minor revisions (review by editor) (02 Jul 2020) by Juan Antonio Añel
AR by Ying Liu on behalf of the Authors (11 Jul 2020)  Author's response    Manuscript
ED: Publish as is (21 Jul 2020) by Juan Antonio Añel
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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.