Norwegian Meteorological Institute, P.O. Box 43, Blindern, 0313 Oslo, Norway
Thomas N. Nipen
Norwegian Meteorological Institute, P.O. Box 43, Blindern, 0313 Oslo, Norway
Ivar A. Seierstad
Norwegian Meteorological Institute, P.O. Box 43, Blindern, 0313 Oslo, Norway
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: 1,222 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,059
131
32
1,222
32
38
HTML: 1,059
PDF: 131
XML: 32
Total: 1,222
BibTeX: 32
EndNote: 38
Views and downloads (calculated since 08 Dec 2023)
Cumulative views and downloads
(calculated since 08 Dec 2023)
Total article views: 1,005 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
843
131
31
1,005
32
38
HTML: 843
PDF: 131
XML: 31
Total: 1,005
BibTeX: 32
EndNote: 38
Views and downloads (calculated since 25 Jun 2024)
Cumulative views and downloads
(calculated since 25 Jun 2024)
Total article views: 217 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
216
0
1
217
0
0
HTML: 216
PDF: 0
XML: 1
Total: 217
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: 1,222 (including HTML, PDF, and XML)
Thereof 1,180 with geography defined
and 42 with unknown origin.
Total article views: 1,005 (including HTML, PDF, and XML)
Thereof 965 with geography defined
and 40 with unknown origin.
Total article views: 217 (including HTML, PDF, and XML)
Thereof 215 with geography defined
and 2 with unknown origin.
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.
This is a timely paper given the recent rise in data-driven and AI-based weather forecasting. It...
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.
During the last 2 years, tremendous progress has been made in global data-driven weather models...