Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 19
Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 19
Development and technical paper
 | 
05 Oct 2022
Development and technical paper |  | 05 Oct 2022

Repeatable high-resolution statistical downscaling through deep learning

Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer

Data sets

Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning" Dánnell Quesada-Chacón https://doi.org/10.5281/zenodo.5809553

Model code and software

dquesadacr/Rep_SDDL: Submission to GMD Dánnell Quesada-Chacón https://doi.org/10.5281/zenodo.5856118

Singularity container for "Repeatable high-resolution statistical downscaling through deep learning" Dánnell Quesada-Chacón https://doi.org/10.5281/zenodo.5809705

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Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
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