{
  "abstract": "Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty remains challenging. Here we introduce exPreCast-ENS, a conditional residual diffusion framework that transforms the deterministic 4 km radar nowcaster exPreCast into a 1 km probabilistic ensemble while correcting systematic forecast errors. Conditioning on both the forecast and preceding radar observations lets the ensemble-mean correct the baseline rather than perturb it, while members represent unresolved fine-scale variability. Over the Korean Peninsula, skill improves with ensemble size. In two high-impact events in 2023, a 30-member ensemble recovers 38-47% of heavy-rain pixels missed by exPreCast while retaining approximately 95% of its correct detections and alarming on under 1% of the pixels it correctly left clear. The method generates a 1-h forecast in 3.4 s on a single GPU and yields consistent improvements on the French regional MeteoNet radar dataset.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.30205",
  "author_total": 5,
  "authors": [
    {
      "name": "Dohyun Park"
    },
    {
      "name": "Changhoon Song"
    },
    {
      "name": "Tengyuan Chang"
    },
    {
      "name": "Yoo-Geun Ham"
    },
    {
      "name": "Youngjoon Hong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/87mrfx75p039s.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.30205",
  "fulltext_url": null,
  "id": "87mrfx75p039s",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.30205",
  "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": "W7204879863",
  "pdf_url": "https://arxiv.org/pdf/2608.30205",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/87mrfx75p039s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "2608.30205",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.30205"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting",
  "title_text": "Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:41Z",
  "url": "https://research.draconitic.com/paper/87mrfx75p039s",
  "venue": "arXiv",
  "versions": []
}