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.
We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and glob...
Varda‑single‑1.0 is a medium‑range, data‑driven weather prediction system designed for Switzerland’s Alpine region, delivering hourly deterministic forecasts at 1 km resolution and global forecasts at 31 km. It uses two independently trained stretched‑grid Graph Transformer models—an autoregressive forecaster and a temporal downscaler—trained through a curriculum that starts with ERA5 reanalysis, proceeds to a 20‑year regional reanalysis, and ends with fine‑tuning on operational analyses. Over a one‑year verification, Varda‑single matches or surpasses MeteoSwiss’s numerical weather prediction baselines for most key metrics, though it underestimates local wind peaks and produces smoother convective precipitation fields. whyItMatters":"The system demonstrates that high‑resolution machine‑learning models can rival traditional numerical weather prediction in complex terrain, offering a complementary tool for operational forecasting and research."
arXiv:2511. 23043v2 Announce Type: replace-cross Abstract: We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length.
arXiv:2610.28512v1 Announce Type: new Abstract: Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models. We introduce...
arXiv:2611.06130v1 Announce Type: new Abstract: Accurate short-term wind power forecasting is critical for grid stability and electricity trading. While transformer-based deep learning is state of th...
arXiv:2504. 20238v2 Announce Type: replace-cross Abstract: Atmospheric predictability research has long held that rapid error growth at small spatial scales imposes an intrinsic limit of roughly two weeks on deterministic weather forecast skill.
Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models. We introduce GraphCast--Mamba, which augments a frozen, pretra...
The paper introduces Nested‑EAGLE, a 0.25° global weather model with a 6 km refinement over the contiguous United States, designed to merge short‑ and medium‑range forecasts into a single system. It shows lower mean‑squared error for near‑surface and low‑level variables over the U.S. compared to NOAA’s GFS and HRRR, while remaining competitive globally. Although precipitation forecasts are less skillful than HRRR’s deterministic training, Nested‑EAGLE delivers the most accurate storm‑location predictions at longer lead times, with blurred extrema.
arXiv:2606. 08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates.
The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.
Aries is a proprietary medium‑range weather prediction model built with a SwinTransformer architecture by InCommodities. Trained on ERA5 reanalysis data at 0.25° resolution, it forecasts 74 prognostic and 11 diagnostic atmospheric variables. In out‑of‑sample tests using 2025 ECMWF Analysis initializations, Aries achieves lower RMSE than ECMWF HRES and AIFS for 10‑metre wind speed up to four days ahead, and comparable RMSE to AIFS for 2‑metre temperature.
arXiv:2607. 27152v1 Announce Type: new Abstract: Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy.