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.

Data sets

Original CRCM5-LE dataset description and data access provided by the ClimEx project Martin Leduc, Alain Mailhot, Anne Frigon, Jean-Luc Martel, Ralf Ludwig, Gilbert B. Brietzke, Michel Giguère, François Brissette, Richard Turcotte, Marco Braun, and John Scinocca https://doi.org/10.1175/JAMC-D-18-0021.1

Sample data of the CRCM5-LE for applications of the Latent Linear Adjustment autoencoder Christina Heinze-Deml, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen https://doi.org/10.5281/zenodo.3949748

Model code and software

Pretrained Models and Link to Github Code archive Christina Heinze-Deml, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen https://doi.org/10.5281/zenodo.3950045

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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.