Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 31, issue 2
Articles | Volume 31, issue 2
https://doi.org/10.5194/npg-31-247-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-247-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 31, issue 2
Research article
 | Highlight paper
 | 
25 Jun 2024
Research article | Highlight paper |  | 25 Jun 2024

Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations

John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad

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Executive editor
This is a timely paper given the recent rise in data-driven and AI-based weather forecasting. It offers two key contributions. First, the paper provides (potentially the first, but at least one of the first) comparisons of AI-based and physics-based weather forecasting models based on station data (rather than the commonly used comparisons based on gridded ERA5 data). And second, the paper assesses and quantifies the effect of statistical post-processing on forecasts from AI-based weather models, which may also be the first of its kind.
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Short summary
During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.
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