Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry

Abstract:Medium-range weather forecasting underpins operational and planning decisions across the energy industry. Developing competitive weather models was once the domain of national meteorological centers, but recent advances in machine-learned weather prediction (MLWP) have opened the field to industry. We present Aries, a SwinTransformer-based MLWP model developed at InCommodities. Aries is trained on ERA5 reanalysis data at 0.25\textdegree{} resolution, predicting 74 prognostic and 11 diagnostic atmospheric variables. We evaluate the model on 2025 ECMWF Analysis initializations, ensuring a recent and strictly out-of-sample test period for all models compared. On 10-metre wind speed, Aries outperforms both ECMWF HRES and AIFS in terms of RMSE for lead times up to four days, while on 2-metre temperature it achieves RMSE on par with AIFS operational. These results demonstrate that proprietary development of competitive weather models is technically viable, supporting a broader set of forecasts available for operational and planning applications in the energy industry.

Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)

arXiv:2609.13292 [physics.ao-ph]

(or arXiv:2609.13292v1 [physics.ao-ph] for this version)

https://doi.org/10.48550/arXiv.2609.13292 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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