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.
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.
the Creative Commons Attribution 4.0 License.
Configuration and intercomparison of deep learning neural models for statistical downscaling
Santander Meteorology Group, Institute of Physics of Cantabria, CSIC-University of Cantabria, Santander, Spain
Rodrigo Manzanas
Santander Meteorology Group, Dpto. de Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander, Spain
José Manuel Gutiérrez
Santander Meteorology Group, Institute of Physics of Cantabria, CSIC-University of Cantabria, Santander, Spain
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Latest update: 10 Dec 2024
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.
In this study we intercompare different deep learning topologies for statistical downscaling...