{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13292",
  "author_total": 5,
  "authors": [
    {
      "name": "Lukas Hedegaard Morsing"
    },
    {
      "name": "Arian Bakhtiarnia"
    },
    {
      "name": "Jonas Lynge Olesen"
    },
    {
      "name": "Tómas Bragi Björnsson Leth"
    },
    {
      "name": "Christian Gøbel Bach"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f27te1h2z10fe.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13292",
  "fulltext_url": null,
  "id": "f27te1h2z10fe",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13292",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7213270274",
  "pdf_url": "https://arxiv.org/pdf/2609.13292",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f27te1h2z10fe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.13292",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13292"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry",
  "title_text": "Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/f27te1h2z10fe",
  "venue": "arXiv",
  "versions": []
}