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

• URL: http://arxiv.org/abs/2609.13292v1
• Date: Wed, 09 Sep 2026 11:24:00 GMT
• Title: Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry
• Abstract summary: We present Aries, a SwinTransformer-based machine-learned weather prediction model developed at InCommodities.<n>Aries is trained on ERA5 reanalysis data at 0.25degree resolution, predicting 74 prognostic and 11 diagnostic atmospheric variables.<n>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.
• Score: 2.083477641291698
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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