A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
Updated 1 h ago · first seen 11 Sept 2026
paper_01M294FPMNFQ55YRVYVFFEATEN
- Published
- 11 Sept 2026
- T1 · 1 h ago
- arXiv
- 2609.10564
- T1 · 1 h ago
- Category
- physics.ao-ph
- T1 · 1 h ago
Abstract
Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains unclear in challenging environments such as Northern Norway, where narrow fjords and rapidly changing weather result in highly variable local wind conditions. In this case study, we evaluate FourCastNet3 (FCN3), GraphCast, and ECMWF High Resolution Forecast (HRES) for wind speed forecasting using multi-year station observations from Northern Norway, focusing on their relative performance, generalization beyond the training period, and performance under high-wind conditions. Our results show that HRES slightly outperforms FCN3 and GraphCast, with an overall RMSE of 2.89 $\mathrm{m\,s^{-1}}$, compared to 2.96 $\mathrm{m\,s^{-1}}$ for FCN3 and 2.94 $\mathrm{m\,s^{-1}}$ for GraphCast. Notably, the MLWP models maintain comparable performance beyond their respective training periods, with no clear evidence of noticeable degradation. FCN3 performs best under high-wind conditions, although all models substantially underestimate strong winds. Our findings suggest that MLWP has become competitive with NWP for local wind, but further refinements are still needed to capture complex terrain better.
Authors 8
Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen, Georgios Leontidis, Arnt-B{\o}rre Salberg, S\'ebastien Lef\`evre, Robert Jenssen, Kristoffer Wickstr{\o}m
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
- Arxiv announce type
- cross
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
- arXiv id
- 2609.10564
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
- Categories
- physics.ao-ph, cs.LG
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
- Primary category
- physics.ao-ph
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
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Attributed facts
9
Source tiers
T19
Freshest observation
1 h ago
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None
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- Authors
- Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains unclear in challenging environments such as Northern Norway, where narrow fjords and rapidly changing weather result in highly variable local wind conditions. In this case study, we evaluate FourCastNet3 (FCN3), GraphCast, and ECMWF High Resolution Forecast (HRES) for wind speed forecasting using multi-year station observations from Northern Norway, focusing on their relative performance, generalization beyond the training period, and performance under high-wind conditions. Our results show that HRES slightly outperforms FCN3 and GraphCast, with an overall RMSE of 2.89 $\mathrm{m\,s^{-1}}$, compared to 2.96 $\mathrm{m\,s^{-1}}$ for FCN3 and 2.94 $\mathrm{m\,s^{-1}}$ for GraphCast. Notably, the MLWP models maintain comparable performance beyond their respective training periods, with no clear evidence of noticeable degradation. FCN3 performs best under high-wind conditions, although all models substantially underestimate strong winds. Our findings suggest that MLWP has become competitive with NWP for local wind, but further refinements are still needed to capture complex terrain better. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
- New paperPaperA Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
New paper: A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 1 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.