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arXiv Machine Learning By Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen, Georgios Leontidis, Arnt-B{\o}rre Salberg, S\'ebastien Lef\`evre, Robert Jenssen, Kristoffer Wickstr{\o}m

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

Read the original on arXiv Machine Learning →

The study evaluates three machine‑learning weather‑prediction models—FourCastNet3, GraphCast, and the ECMWF High‑Resolution Forecast—for wind‑speed forecasting in Northern Norway using multi‑year station data. Results show the ECMWF model slightly outperforms the ML models (RMSE 2.89 m s⁻¹ vs. 2.96 m s⁻¹ for FourCastNet3 and 2.94 m s⁻¹ for GraphCast), yet all models maintain comparable performance beyond their training periods and underestimate strong winds. FourCastNet3 performs best under high‑wind conditions, indicating ML models are competitive with traditional numerical weather prediction but still need improvement for complex terrain.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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