{
  "abstract": "We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and global forecasts on a 31 km mesh. The system comprises two independently trained stretched-grid Graph Transformer models with encoder-processor-decoder architecture, developed in the Anemoi framework: a 6-hourly autoregressive forecaster and a temporal downscaler reconstructing hourly forecasts between the forecaster's steps. Its training curriculum includes pre-training on ERA5 reanalysis data, followed by training on a 20-year kilometre-scale regional reanalysis, and finally fine-tuning on operational kilometre-scale analyses. Verified over one year against operational analyses and surface station observations, Varda-single is competitive with or improves on MeteoSwiss' operational numerical weather prediction baselines for most headline scores and variables. It broadly matches the skill of the high-resolution 1 km ICON-CH1-EPS control at lead times up to +33 h and generally outperforms the 2 km ICON-CH2-EPS control at lead times up to +120 h. Despite competitive aggregate scores, Varda-single underestimates some local wind maxima and produces overly smooth convective precipitation fields, consistent with the smoothing associated with squared-error training. To gain insight into the model's behaviour, we investigate three case studies beyond the aggregated headline scores, and find particular weaknesses in Varda-single's representation of local winds over complex terrain. Varda-single represents an important step in the development of high-resolution ML forecasting over complex terrain, in complementing the operational regional numerical weather prediction models of MeteoSwiss with data-driven models and in providing a pretrained model for researchers and user-specific applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.01835",
  "author_total": 31,
  "authors": [
    {
      "name": "Alberto Pennino"
    },
    {
      "name": "Francesco Zanetta"
    },
    {
      "name": "Michele Cattaneo"
    },
    {
      "name": "Claire Merker"
    },
    {
      "name": "Radi Radev"
    },
    {
      "name": "Jonas Bhend"
    },
    {
      "name": "Louis Frey"
    },
    {
      "name": "Hugues de Laroussilhe"
    },
    {
      "name": "Ophélia Miralles"
    },
    {
      "name": "Carlos Osuna"
    },
    {
      "name": "Daniele Nerini"
    },
    {
      "name": "Andreas Pauling"
    },
    {
      "name": "Daniel Hupp"
    },
    {
      "name": "Ulrich Hamann"
    },
    {
      "name": "Mary McGlohon"
    },
    {
      "name": "Marti Bosch"
    },
    {
      "name": "Luca Lanzilao"
    },
    {
      "name": "Marco Arpagaus"
    },
    {
      "name": "Lukas Jansing"
    },
    {
      "name": "Daniel Leuenberger"
    },
    {
      "name": "Mark A. Liniger"
    },
    {
      "name": "Katrin Ehlert"
    },
    {
      "name": "Matthew Chantry"
    },
    {
      "name": "Håvard Homleid Haugen"
    },
    {
      "name": "Gert Mertes"
    },
    {
      "name": "Ana Prieto Nemesio"
    },
    {
      "name": "Mario Santa Cruz"
    },
    {
      "name": "Jasper Wijnands"
    },
    {
      "name": "Gabriel Moldovan"
    },
    {
      "name": "Harrison Cook"
    },
    {
      "name": "Oliver Fuhrer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4c45kq4m7r6h2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.01835",
  "fulltext_url": null,
  "id": "4c45kq4m7r6h2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.01835",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7215945999",
  "pdf_url": "https://arxiv.org/pdf/2610.01835",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4c45kq4m7r6h2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2610.01835",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.01835"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography",
  "title_text": "Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/4c45kq4m7r6h2",
  "venue": "arXiv",
  "versions": []
}