{
  "abstract": "Low-rank representations such as empirical orthogonal function (EOF) decompositions are widely used for analyzing large spatio-temporal environmental fields. However, conventional EOF identifies latent modes solely from covariance structure and does not utilize observed environmental covariates, limiting its ability to incorporate external information into low-rank representations. This study introduces Structured Covariate-Informed EOF (SCIEOF), a covariate-informed extension of EOF that bridges low-rank dimension reduction and prediction-oriented spatio-temporal modeling. SCIEOF embeds spatial and temporal covariates into the latent bases while incorporating spatio-temporal covariates through an additive component, yielding low-rank representations with latent modes informed by observed covariates. Estimation procedures are developed and evaluated through simulation studies and an application to global near-surface air temperature from the MERRA-2 reanalysis. Simulation studies demonstrate that incorporating informative covariates improves latent structure recovery and predictive accuracy, particularly when the spatial basis is appropriately specified. The advantage is more pronounced at moderate-to-large sample sizes, while methods with stronger structural assumptions remain competitive when data are limited. In the MERRA-2 application, SCIEOF achieves competitive or improved predictive performance relative to commonly used methods while providing a compact and physically interpretable low-rank representation. Overall, SCIEOF provides a flexible and computationally scalable framework for integrating structural covariate information into low-rank spatio-temporal representations, extending EOF toward predictive environmental modeling.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13850",
  "author_total": 2,
  "authors": [
    {
      "name": "Hao-Yun Huang"
    },
    {
      "name": "ShengLi Tzeng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6yh1vsf12rxy7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13850",
  "fulltext_url": null,
  "id": "6yh1vsf12rxy7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13850",
  "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.ME"
    ],
    [
      "openalex",
      "2002|Spatial and Panel Data Analysis"
    ]
  ],
  "openalex": "W7213239916",
  "pdf_url": "https://arxiv.org/pdf/2609.13850",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6yh1vsf12rxy7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:36Z",
      "native_id": "2609.13850",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13850"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Structured Covariate-Informed Empirical Orthogonal Functions for Spatio-Temporal Environmental Fields",
  "title_text": "Structured Covariate-Informed Empirical Orthogonal Functions for Spatio-Temporal Environmental Fields",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:36Z",
  "url": "https://research.draconitic.com/paper/6yh1vsf12rxy7",
  "venue": "arXiv",
  "versions": []
}