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.
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BibTeX
@misc{morsing2026aries,
title = {{Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry}},
author = {Lukas Hedegaard Morsing and Arian Bakhtiarnia and Jonas Lynge Olesen and Tómas Bragi Björnsson Leth and Christian Gøbel Bach},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13292},
eprint = {2609.13292},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13292},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry
AU - Lukas Hedegaard Morsing
AU - Arian Bakhtiarnia
AU - Jonas Lynge Olesen
AU - Tómas Bragi Björnsson Leth
AU - Christian Gøbel Bach
PY - 2026
DA - 2026/09/09
PB - arXiv
DO - 10.48550/arxiv.2609.13292
UR - https://arxiv.org/abs/2609.13292
AB - 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.
ER -