Preprints
https://doi.org/10.48550/arXiv.2309.01247
https://doi.org/10.48550/arXiv.2309.01247
08 Dec 2023
 | 08 Dec 2023
Status: this preprint is open for discussion.

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

Abstract. During the last two years, tremendous progress in global data-driven weather models trained on numerical weather prediction (NWP) re-analysis data has been made. The most recent models trained on the ERA5 at 0.25° resolution demonstrate forecast quality on par with ECMWF's high-resolution model with respect to a wide selection of verification metrics. In this study, one of these models, the Pangu-Weather, is compared to several NWP models with and without probabilistic post-processing for 2-meter temperature and 10-meter wind speed forecasting at 183 Norwegian SYNOP stations up to +60 hours ahead. The NWP models included are the ECMWF HRES, ECMWF ENS and the Harmonie-AROME ensemble model MEPS with 2.5 km spatial resolution. Results show that the performances of the global models are on the same level with Pangu-Weather being slightly better than the ECMWF models for temperature and slightly worse for wind speed. The MEPS model clearly provided the best forecasts for both parameters. The post-processing improved the forecast quality considerably for all models, but to a larger extent for the coarse-resolution global models due to stronger systematic deficiencies in these. Apart from this, the main characteristics in the scores were more or less the same with and without post-processing. Our results thus confirm the conclusions from other studies that global data-driven models are promising for operational weather forecasting.

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

Status: open (until 07 Apr 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2838', Anonymous Referee #1, 02 Jan 2024 reply
    • AC1: 'Reply on RC1', John Bjørnar Bremnes, 31 Jan 2024 reply
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad

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: 127 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
126 0 1 127 0 0
  • HTML: 126
  • PDF: 0
  • XML: 1
  • Total: 127
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 08 Dec 2023)
Cumulative views and downloads (calculated since 08 Dec 2023)

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: 123 (including HTML, PDF, and XML) Thereof 123 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Mar 2024
Download
Short summary
During the last two years tremendous progress in global data-driven weather models trained on re-analysis data has been made. 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.