{
  "abstract": "Improving convective-scale skill in data-driven regional weather models requires better representing rare high-impact events, training at native resolution, broadening variable output, and demonstrating performance in real-time settings. Here, we evaluate these challenges using HRRRCast, a data-driven convection-allowing emulator trained on the High-Resolution Rapid Refresh (HRRR) analysis data. HRRRCast combines native 3-km training, moderate sigmoid-weighted loss reweighting for reflectivity and precipitation, and broader system improvements including expansion of the training archive from 3 to 9 years (2016--2025), increased vertical resolution, expanded outputs, and architectural upgrades. The weighted loss improves high-threshold skill while preserving common-event behavior. Native-resolution training avoids aliasing from 6-km subsampling and substantially improves fractions skill score at convective scales, with gains that persist across pooling windows. HRRRCast has also been running in near-real-time mode for the past year. In seven months of independent observation-based verification, HRRRCast ensemble means improve several near-surface metrics relative to operational HRRR and maintain strong precipitation and reflectivity performance. Single-member reflectivity skill is strongest at 20--30 dBZ; at 40 dBZ, skill is near parity at 6-km pooling and better at pooling radii $\\geq$30 km. Precipitation skill remains competitive through 1 in/h and extends to about 3 in/h in the best recent configuration. A focused comparison with HREF further shows useful low-cost probabilistic guidance for 3-h precipitation. At storm scale, HRRRCast exhibits a displacement--structure trade-off, with generally better storm placement than storm morphology.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36186",
  "author_total": 12,
  "authors": [
    {
      "name": "Daniel Abdi"
    },
    {
      "name": "Isidora Jankov"
    },
    {
      "name": "Vanderlei Vargas"
    },
    {
      "name": "Paul Madden"
    },
    {
      "name": "Emily Carpenter"
    },
    {
      "name": "Travis Wilson"
    },
    {
      "name": "Jeff Hamilton"
    },
    {
      "name": "Molly Smith"
    },
    {
      "name": "Lindsay Blank"
    },
    {
      "name": "Linlin Cui"
    },
    {
      "name": "Jun Wang"
    },
    {
      "name": "Matthew Mahalik"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6h56zeaz0zp7x.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36186",
  "fulltext_url": null,
  "id": "6h56zeaz0zp7x",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36186",
  "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",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7224115159",
  "pdf_url": "https://arxiv.org/pdf/2610.36186",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6h56zeaz0zp7x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.36186",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36186"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Towards operational data-driven regional weather forecasting: extreme-event skill and near-real-time verification with HRRRCast",
  "title_text": "Towards operational data-driven regional weather forecasting: extreme-event skill and near-real-time verification with HRRRCast",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/6h56zeaz0zp7x",
  "venue": "arXiv",
  "versions": []
}