Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 4
Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-2109-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-2109-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 4
Model experiment description paper
 | 
28 Apr 2020
Model experiment description paper |  | 28 Apr 2020

Configuration and intercomparison of deep learning neural models for statistical downscaling

Jorge Baño-Medina, Rodrigo Manzanas, and José Manuel Gutiérrez

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Short summary
In this study we intercompare different deep learning topologies for statistical downscaling purposes. As compared to the top-ranked methods in the largest-to-date downscaling intercomparison study, our results better predict the local climate variability. Moreover, deep learning approaches can be suitably applied to large regions (e.g., continents), which can therefore foster the use of statistical downscaling in flagship initiatives such as CORDEX.
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