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Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry — arXiv:2609.13292

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13292
License (as archived): CC0-1.0
Archived: 2026-10-16T00:12:19.773Z
SHA-256 of the archived text: 4f4c5b99e02cd71d40bef4c294fc05d1a6b4b9ea088531ab9196ab3c93ce585a
Stable URL of this page: https://metraipolis.com/documento/dc_2c307dd7cd95

How to cite

Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry — arXiv:2609.13292 — arXiv (en), https://arxiv.org/abs/2609.13292 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2c307dd7cd95 (Metraipolis archive dc_2c307dd7cd95, sha256 4f4c5b99e02cd71d40bef4c294fc05d1a6b4b9ea088531ab9196ab3c93ce585a)

BibTeX

@misc{dc2c307dd7cd95,
  title = {Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry — arXiv:2609.13292},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13292},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2c307dd7cd95; sha256 4f4c5b99e02cd71d40bef4c294fc05d1a6b4b9ea088531ab9196ab3c93ce585a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13292. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.LG).
Categorie arXiv: Atmospheric and Oceanic Physics (physics.ao-ph) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.13292 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T23:54:14.831Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).