Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-4977-2021
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, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen

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Cited articles

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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.