{
  "abstract": "We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.25512",
  "author_total": 9,
  "authors": [
    {
      "name": "Nazak Rouzegari"
    },
    {
      "name": "Vesta Afzali Gorooh"
    },
    {
      "name": "Agniv Sengupta"
    },
    {
      "name": "Phu Nguyen"
    },
    {
      "name": "Kuo-Lin Hsu"
    },
    {
      "name": "Amir AghaKouchak"
    },
    {
      "name": "Soroosh Sorooshian"
    },
    {
      "name": "F. Martin Ralph"
    },
    {
      "name": "Luca Delle Monache"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e7tt3m8v3qxc0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.25512",
  "fulltext_url": null,
  "id": "e7tt3m8v3qxc0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.25512",
  "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.AI"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7214082004",
  "pdf_url": "https://arxiv.org/pdf/2609.25512",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e7tt3m8v3qxc0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:39Z",
      "native_id": "2609.25512",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.25512"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation",
  "title_text": "West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:39Z",
  "url": "https://research.draconitic.com/paper/e7tt3m8v3qxc0",
  "venue": "arXiv",
  "versions": []
}