{
  "abstract": "This study presents an AI-based dynamical downscaling system for Tropical Cyclones (TCs). The system incorporates an AI-based limited-area model that downscales 3-hourly low-resolution boundary forcings into hourly high-resolution fields autoregressively, and a diffusion model that converts the outputs into ensembles of hazard-relevant variables. The system is trained on the regridded CONUS404 data with ERA5 forcings, and is evaluated on 20 TCs in 2020--2024. Verification shows stable downscaling performance across Atlantic hurricane seasons, with energy spectra closely matching the CONUS404 reference. The system is also verified to produce skillful TC-relevant weather extremes, largely improved over a deterministic AI baseline. The system performs well with forcing data from other models (GDAS/FNL) and can produce detailed eyewall, rainband, and landfall structures in TC case studies. The study provides a good example of how AI-based dynamical downscaling systems can be designed to resolve small-scale extreme weather events.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02034",
  "author_total": 5,
  "authors": [
    {
      "name": "Yingkai Sha"
    },
    {
      "name": "Talea L. Mayo"
    },
    {
      "name": "Ethan D. Gutmann"
    },
    {
      "name": "Lulin Xue"
    },
    {
      "name": "Andrew Newman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0yzvk49qxrxrf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02034",
  "fulltext_url": null,
  "id": "0yzvk49qxrxrf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02034",
  "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": "W7207749219",
  "pdf_url": "https://arxiv.org/pdf/2609.02034",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0yzvk49qxrxrf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02034",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02034"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Kilometer-Scale AI Downscaling of Atlantic Hurricanes with Generative Ensembles",
  "title_text": "Kilometer-Scale AI Downscaling of Atlantic Hurricanes with Generative Ensembles",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/0yzvk49qxrxrf",
  "venue": "arXiv",
  "versions": []
}