Articles | Volume 14, issue 8
Geosci. Model Dev., 14, 4977–4999, 2021
https://doi.org/10.5194/gmd-14-4977-2021
Geosci. Model Dev., 14, 4977–4999, 2021
https://doi.org/10.5194/gmd-14-4977-2021

Model description paper 12 Aug 2021

Model description paper | 12 Aug 2021

Latent Linear Adjustment Autoencoder v1.0: a novel method for estimating and emulating dynamic precipitation at high resolution

Christina Heinze-Deml et al.

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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
AR by Sebastian Sippel on behalf of the Authors (27 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (18 Jun 2021) by Gerd A. Folberth
ED: Referee Nomination & Report Request started (22 Jun 2021) by Gerd A. Folberth
RR by Anonymous Referee #2 (05 Jul 2021)
RR by Segolene Berthou (07 Jul 2021)
ED: Publish as is (07 Jul 2021) by Gerd A. Folberth
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Short summary
Quantifying dynamical and thermodynamical components of regional precipitation change is a key challenge in climate science. We introduce a novel statistical model (Latent Linear Adjustment Autoencoder) that combines the flexibility of deep neural networks with the robustness advantages of linear regression. The method enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner.