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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Oct 2022
Development and technical paper |
| 05 Oct 2022
| 05 Oct 2022
Repeatable high-resolution statistical downscaling through deep learning
Dánnell Quesada-Chacón
CORRESPONDING AUTHOR
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Klemens Barfus
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Christian Bernhofer
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
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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.
We improved the performance of past perfect prognosis statistical downscaling methods while...
