Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7353-2022
https://doi.org/10.5194/gmd-15-7353-2022
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

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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Cited articles

Alahmari, S. S., Goldgof, D. B., Mouton, P. R., and Hall, L. O.: Challenges for the Repeatability of Deep Learning Models, IEEE Access, 8, 211860–211868, https://doi.org/10.1109/ACCESS.2020.3039833, 2020. a, b, c, d
Allaire, J. J., Ushey, K., Tang, Y., and Eddelbuettel, D.: Reticulate: R Interface to Python, GitHub [code], https://github.com/rstudio/reticulate (last access: 12 December 2021​​​​​​​), 2017. a
Association for Computing Machinery (ACM): Artifact Review and Badging Version 2.0, ACM, https://www.acm.org/publications/policies/artifact-review-badging, 2021. a
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l
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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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