References

• Serifi et al., 2021: Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks
  • paper: https://www.frontiersin.org/articles/10.3389/fclim.2021.656479/full
  • github/code: https://github.com/aserifi/convolutional-downscaling
• Höhlein et al., 2020: A Comparative Study of Convolutional Neural Network Models for Wind Field Downscaling
  • paper: https://doi.org/10.1002/met.1961
  • code: https://github.com/khoehlein/CNNs-for-Wind-Field-Downscaling
• Leinonen et al., 2020: Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network
  • paper: https://doi.org/10.1109/TGRS.2020.3032790
  • code: https://github.com/jleinonen/downscaling-rnn-gan
• Dujardin and Lehning, 2020: Multi-Resolution Convolutional Neural Network for High-Resolution Downscaling of Wind Fields from Operational Weather Prediction Models in Complex Terrain
  • AGU Presentation: https://agu.confex.com/agu/fm20/videogateway.cgi/id/8802?recordingid=8802
  • Master Thesis M. Schaer: https://infoscience.epfl.ch/record/282346
• Winstral et al., 2017: Statistical Downscaling of Gridded Wind Speed Data Using Local Topography
  • paper: https://doi.org/10.1175/JHM-D-16-0054.1
• Daniele Nerini, 2020: Probabilistic Deep Learning for Postprocessing Wind Forecasts in Complex Terrain
  • presentation: https://vimeo.com/465719202
• Amato et al., 2020: A novel framework for spatio-temporal prediction of environmental data using deep learning
  • paper: https://doi.org/10.1038/s41598-020-79148-7
  • code: https://github.com/federhub/ST_DeepLearning
• Robert er al., 2012: Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks
  • paper: https://doi.org/10.1002/joc.3550