Articles | Volume 13, issue 4
Geosci. Model Dev., 13, 2109–2124, 2020
Geosci. Model Dev., 13, 2109–2124, 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.

Related authors

Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment
Joaquín Bedia, Jorge Baño-Medina, Mikel N. Legasa, Maialen Iturbide, Rodrigo Manzanas, Sixto Herrera, Ana Casanueva, Daniel San-Martín, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 13, 1711–1735,,, 2020
Short summary

Related subject area

Atmospheric sciences
Downscaling of air pollutants in Europe using uEMEP_v6
Qing Mu, Bruce Rolstad Denby, Eivind Grøtting Wærsted, and Hilde Fagerli
Geosci. Model Dev., 15, 449–465,,, 2022
Short summary
WRF v.3.9 sensitivity to land surface model and horizontal resolution changes over North America
Almudena García-García, Francisco José Cuesta-Valero, Hugo Beltrami, J. Fidel González-Rouco, and Elena García-Bustamante
Geosci. Model Dev., 15, 413–428,,, 2022
Short summary
Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators
Prabhakar Shrestha, Jana Mendrok, Velibor Pejcic, Silke Trömel, Ulrich Blahak, and Jacob T. Carlin
Geosci. Model Dev., 15, 291–313,,, 2022
Short summary
Development of aerosol optical properties for improving the MESSy photolysis module in the GEM-MACH v2.4 air quality model and application for calculating photolysis rates in a biomass burning plume
Mahtab Majdzadeh, Craig A. Stroud, Christopher Sioris, Paul A. Makar, Ayodeji Akingunola, Chris McLinden, Xiaoyi Zhao, Michael D. Moran, Ihab Abboud, and Jack Chen
Geosci. Model Dev., 15, 219–249,,, 2022
Short summary
The sensitivity of simulated aerosol climatic impact to domain size using regional model (WRF-Chem v3.6)
Xiaodong Wang, Chun Zhao, Mingyue Xu, Qiuyan Du, Jianqiu Zheng, Yun Bi, Shengfu Lin, and Yali Luo
Geosci. Model Dev., 15, 199–218,,, 2022
Short summary

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,, 2018. a
Baño Medina, J., Manzanas, R., and Gutiérrez, J. M.: SantanderMetGroup/DeepDownscaling: GMD paper accepted for publication (Version v1.2), Zenodo,, 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,, 2020. a, b, c, d
Cannon, A. J.: Probabilistic Multisite Precipitation Downscaling by an Expanded Bernoulli-Gamma Density Network, J. Hydrometeorol., 9, 1284–1300,, 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,, 2019. a
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.