{
  "abstract": "Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challenging. This study compares statistical and machine-learning-based methods for post-processing ECMWF 2-m temperature and 10-m wind speed forecasts at observed and unobserved stations in Germany. We consider EMOS-based approaches, distributional regression networks, Transformers, and graph neural networks under both limited and extended predictor settings. For temperature, we also investigate linear forecast combinations and propose an altitude-aware linear pool (ALP). The results show that post-processing improves upon the raw ensemble in most settings, but no single method performs best across all variables, station groups, and evaluation metrics. The proposed ALP provides a small but significant improvement over the standard linear pool at unobserved locations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.07512",
  "author_total": 1,
  "authors": [
    {
      "name": "Mária Lakatos"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bz06296ad78kf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.07512",
  "fulltext_url": null,
  "id": "bz06296ad78kf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.07512",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7212068565",
  "pdf_url": "https://arxiv.org/pdf/2609.07512",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bz06296ad78kf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:07Z",
      "native_id": "2609.07512",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.07512"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts",
  "title_text": "Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/bz06296ad78kf",
  "venue": "arXiv",
  "versions": []
}