Articles | Volume 13, issue 4
Geosci. Model Dev., 13, 2109–2124, 2020
https://doi.org/10.5194/gmd-13-2109-2020
Geosci. Model Dev., 13, 2109–2124, 2020
https://doi.org/10.5194/gmd-13-2109-2020

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 et al.

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Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment
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Cited articles

Ba, W., Du, P., Liu, T., Bao, A., Luo, M., Hassan, M., and Qin, C.: Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin, Xinjiang, China, J. Arid Land, 10, 905–920, https://doi.org/10.1007/s40333-018-0068-0, 2018. a
Baño Medina, J., Manzanas, R., and Gutiérrez, J. M.: SantanderMetGroup/DeepDownscaling: GMD paper accepted for publication (Version v1.2), Zenodo, https://doi.org/10.5281/zenodo.3731351, 2020. a, b
Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., Casanueva, A., San-Martín, D., Cofiño, A. S., and Gutiérrez, J. M.: Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment, Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, 2020. a, b, c, d
Cannon, A. J.: Probabilistic Multisite Precipitation Downscaling by an Expanded Bernoulli-Gamma Density Network, J. Hydrometeorol., 9, 1284–1300, https://doi.org/10.1175/2008JHM960.1, 2008. a
Chapman, W. E., Subramanian, A. C., Monache, L. D., Xie, S. P., and Ralph, F. M.: Improving Atmospheric River Forecasts With Machine Learning, Geophys. Res. Lett., 46, 10627–10635, https://doi.org/10.1029/2019GL083662, 2019. a
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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.