Articles | Volume 18
https://doi.org/10.5194/asr-18-145-2021
https://doi.org/10.5194/asr-18-145-2021
17 Sep 2021
 | 17 Sep 2021

Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools

Tiziana Comito, Colm Clancy, Conor Daly, and Alan Hally

Viewed

Total article views: 3,118 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,861 213 44 3,118 34 37
  • HTML: 2,861
  • PDF: 213
  • XML: 44
  • Total: 3,118
  • BibTeX: 34
  • EndNote: 37
Views and downloads (calculated since 17 Sep 2021)
Cumulative views and downloads (calculated since 17 Sep 2021)

Viewed (geographical distribution)

Total article views: 2,949 (including HTML, PDF, and XML) Thereof 2,949 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 02 Jul 2024
Download
Short summary
Convection-permitting models allow for prediction of rainfall events with increasing levels of detail. However, this increased resolution can create problems such as the so-called double penalty problem when attempting to verify model forecast accuracy. This problem is amplified when trying to maximise the value of a convection-permitting ensemble prediction system (EPS). Post-processing of the EPS can help to overcome these issues. In this spirit, two new up-scaling algorithms based on Machine