A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway — Paper

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: arxiv.org/abs/2609.10564
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
PDF: arxiv.org/pdf/2609.10564
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

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

1 h ago

Conflicts

None

No models linked to this paper yet.

Authors: Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · Authors

1 claims · 1 propertiesShow all properties

Authorsauthors1

Claim history for Authors
Value	Valid from → to	Status	Source	Confidence	Extractor
Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen, Georgios Leontidis, Arnt-B{\o}rre Salberg, S\'ebastien Lef\`evre, Robert Jenssen, Kristoffer Wickstr{\o}m	11 Sept 2026 21:02 UTC → 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 →

11 Sept 2026
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
rss.arxiv.org

Full timeline →

Source documents
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.