A climatology of trapped lee waves over Britain and Ireland, derived using machine learning

Jonathan Coney; Andrew Ross; Leif Denby; He Wang; Simon Vosper; Annelize van Niekerk; Tom Dunstan

doi:https://doi.org/10.5194/egusphere-egu23-7629

[Back] [Session AS1.19]

EGU23-7629, updated on 25 Feb 2023

https://doi.org/10.5194/egusphere-egu23-7629

EGU General Assembly 2023

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

A climatology of trapped lee waves over Britain and Ireland, derived using machine learning

Jonathan Coney¹, Andrew Ross¹, Leif Denby^1,3, He Wang², Simon Vosper⁴, Annelize van Niekerk⁵, and Tom Dunstan⁴

Jonathan Coney et al.

¹School of Earth and Environment, University of Leeds, Leeds, United Kingdom (mm16jdc@leeds.ac.uk)
²School of Computing, University of Leeds, Leeds, United Kingdom
³Danish Meteorological Institute, Copenhagen, Denmark
⁴Met Office, Exeter, United Kingdom
⁵European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

How to cite: Coney, J., Ross, A., Denby, L., Wang, H., Vosper, S., van Niekerk, A., and Dunstan, T.: A climatology of trapped lee waves over Britain and Ireland, derived using machine learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7629, https://doi.org/10.5194/egusphere-egu23-7629, 2023.

This abstract has been withdrawn on 20 Apr 2023.