A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
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 outp...
| Authors | Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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