{
  "abstract": "Vecchia approximations enable scalable likelihood-based inference for large Gaussian process models, but their accuracy depends on the choice of conditioning sets. For spatio-temporal processes with advection, target-centered conditioning can omit informative past observations. Motivated by total column ozone observations from the Geostationary Environment Monitoring Spectrometer (GEMS), we propose a data-driven, advection-aligned block Vecchia approximation that incorporates estimated transport direction into temporal conditioning geometry. Observation locations need not lie on a regular grid. An initial advection vector is estimated from the data and used once to select transport-aligned past-time conditioning blocks. The resulting graph is held fixed during optimization, while covariance and advection parameters remain free to vary. This separates neighbor selection from covariance estimation, avoids repeated parameter-dependent neighbor searches, and provides an interpretable connection between conditioning geometry and estimated transport. Batched graphics processing unit (GPU) operations evaluate the likelihood and its gradients. In a matched comparison of daily GEMS datasets from July 2024 and July 2025, advection-aligned conditioning attained a lower fitted Vecchia negative log-likelihood than target-centered conditioning on 53 of 61 days. In six Matérn simulation scenarios with 120 matched eight-hour fields each, advection-aligned conditioning reduced advection-vector root mean squared error relative to target-centered conditioning at the same conditioning-block budget, with the largest reduction under strong transport.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.08938",
  "author_total": 2,
  "authors": [
    {
      "name": "Joonwon Lee"
    },
    {
      "name": "Michael L. Stein"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dv38kv4f4cbew.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08938",
  "fulltext_url": null,
  "id": "dv38kv4f4cbew",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08938",
  "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",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08938",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dv38kv4f4cbew.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T06:21:13Z",
      "native_id": "2611.08938",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08938"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Advection-aligned block Vecchia inference for spatio-temporal Gaussian processes",
  "title_text": "Advection-aligned block Vecchia inference for spatio-temporal Gaussian processes",
  "type": "preprint",
  "updated_at": "2026-11-10T06:21:13Z",
  "url": "https://research.draconitic.com/paper/dv38kv4f4cbew",
  "venue": "arXiv",
  "versions": []
}