{
  "abstract": "A lightweight neural network is described that produces six-hourly, spatially detailed probabilistic quantitative precipitation forecasts (PQPF) from a sequence of hourly spatially detailed probabilistic forecasts, for lead times from 6 to 72\\,h. The hourly forecasts were generated by a ``zero-inflated'' Attention Residual U-Net that outputs the parameters of a probability distribution including a probability of no precipitation and a weighted mixture of two Gamma distributions at each grid point for each hourly forecast lead time. The six-hourly network passes the six consecutive hourly parameter sets, together with summary statistics of the spatial texture of the raw deterministic forecast, through a small recurrent (GRU) layer and a multi-layer perceptron, and maps them to a corresponding zero-inflated mixture of three Gamma distributions describing the six-hourly accumulated precipitation. Training uses sets of hourly probabilistic forecasts, drawn preferentially from wet and heavy-precipitation cases (importance sampling), and matched Multi-Radar Multi-Sensor (MRMS) six-hourly precipitation accumulations, subject to a quality threshold. The resulting six-hourly probabilistic forecasts are well calibrated and skillful, as demonstrated by reliability diagrams, performance diagrams and Brier Skill Scores evaluated over an out-of-sample period spanning day 12 through the end of every month of 2025. The Brier Skill Scores are consistently and significantly higher than those of a baseline that sums independent draws from the hourly distributions. Forecasts show realistic modulation of precipitation related to terrain variations",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03326",
  "author_total": 1,
  "authors": [
    {
      "name": "Thomas M Hamill"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8n9cejhtwk414.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03326",
  "fulltext_url": null,
  "id": "8n9cejhtwk414",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03326",
  "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"
    ],
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03326",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8n9cejhtwk414.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:04Z",
      "native_id": "2611.03326",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03326"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Six-hourly Probabilistic Precipitation Forecasts Created From Hourly Probabilities",
  "title_text": "Six-hourly Probabilistic Precipitation Forecasts Created From Hourly Probabilities",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/8n9cejhtwk414",
  "venue": "arXiv",
  "versions": []
}