Downscaling of surface wind forecasts using convolutional neural networks

Dupuy, Florian; Durand, Pierre; Hedde, Thierry

doi:https://doi.org/10.5194/npg-30-553-2023

Articles | Volume 30, issue 4

https://doi.org/10.5194/npg-30-553-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-30-553-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 30, issue 4

Research article

|

29 Nov 2023

Research article |

| 29 Nov 2023

Downscaling of surface wind forecasts using convolutional neural networks

Florian Dupuy, Pierre Durand, and Thierry Hedde

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Cited articles

Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a

de Bode, M., Hedde, T., Roubin, P., and Durand, P.: Fine-Resolution WRF Simulation of Stably Stratified Flows in Shallow Pre-Alpine Valleys: A Case Study of the KASCADE-2017 Campaign, Atmosphere, 12, 1063, https://doi.org/10.3390/atmos12081063, 2021. a

de Bode, M., Hedde, T., Roubin, P., and Durand, P.: A Method to Improve Land Use Representation for Weather Simulations Based on High-Resolution Data Sets-Application to Corine Land Cover Data in the WRF Model, Earth Space Sci., 10, e2021EA002123, https://doi.org/10.1029/2021EA002123, 2023. a

Dujardin, J. and Lehning, M.: Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning, Q. J. Roy. Meteor. Soc., 148, 1368–1388, https://doi.org/10.1002/qj.4265, 2022. a, b, c, d, e, f, g, h, i, j, k, l

Dupuy, F., Duine, G.-J., Durand, P., Hedde, T., Roubin, P., and Pardyjak, E.: Local-Scale Valley Wind Retrieval Using an Artificial Neural Network Applied to Routine Weather Observations, J. Appl. Meteorol. Clim., 58, 1007–1022, https://doi.org/10.1175/JAMC-D-18-0175.1, 2019. a