EGU23-5914, updated on 22 Feb 2023
https://doi.org/10.5194/egusphere-egu23-5914
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Deep-S2SWind: A data-driven approach for improving sub-seasonal predictions of wind droughts 

Noelia Otero1,2 and Pascal Horton1
Noelia Otero and Pascal Horton
  • 1Institute of Geography, University of Bern, Bern, Switzerland (noelia.otero@giub.unibe.ch)
  • 2Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

How to cite: Otero, N. and Horton, P.: Deep-S2SWind: A data-driven approach for improving sub-seasonal predictions of wind droughts , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5914, https://doi.org/10.5194/egusphere-egu23-5914, 2023.

This abstract has been withdrawn on 30 Mar 2023.