Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 1
Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-251-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 1
Development and technical paper
 | 
10 Jan 2023
Development and technical paper |  | 10 Jan 2023

Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range

Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, and Claudia Plant

Viewed

Total article views: 2,284 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,808 426 50 2,284 46 37
  • HTML: 1,808
  • PDF: 426
  • XML: 50
  • Total: 2,284
  • BibTeX: 46
  • EndNote: 37
Views and downloads (calculated since 12 Jul 2022)
Cumulative views and downloads (calculated since 12 Jul 2022)

Viewed (geographical distribution)

Total article views: 2,284 (including HTML, PDF, and XML) Thereof 2,096 with geography defined and 188 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
The production of wind energy is increasing rapidly and relies heavily on atmospheric conditions. To ensure power grid stability, accurate predictions of wind speed are needed, especially in the short range and for extreme wind speed ranges. In this work, we demonstrate the forecasting skills of a data-driven deep learning model with model adaptations to suit higher wind speed ranges. The resulting model can be applied to other data and parameters, too, to improve nowcasting predictions.
Read more