Articles | Volume 19, issue 21
https://doi.org/10.5194/gmd-19-10651-2026
https://doi.org/10.5194/gmd-19-10651-2026
Methods for assessment of models
 | 
10 Nov 2026
Methods for assessment of models |  | 10 Nov 2026

Evaluating extreme precipitation forecasts: a threshold-weighted, spatial verification approach for comparing an AI weather prediction model against a high-resolution NWP model

Nicholas Loveday and Tracy Hertneky

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,346 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,318 7 21 1,346 1 1
  • HTML: 1,318
  • PDF: 7
  • XML: 21
  • Total: 1,346
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 04 Feb 2026)
Cumulative views and downloads (calculated since 04 Feb 2026)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,346 (including HTML, PDF, and XML) Thereof 1,337 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Nov 2026
Download
Short summary
This study introduces a verification method that accounts for differences in grid resolution when evaluating extreme event forecasts. We apply it to an artificial intelligence-based weather prediction model and a high-resolution numerical weather prediction model. Results show that, when assessed on equivalent neighborhood scales, the high resolution numerical weather prediction model only outperforms the AI system for short lead times in predicting extreme precipitation.
Share
Altmetrics
Final-revised paper
Preprint