{
  "abstract": "We develop a framework for offline full-window branch discovery, optionally followed by online continuation with an ensemble Kalman filter (EnKF). Three mechanisms drive the branch search: adjoint path-kernel (APK) differentiation balances kernel differentiation and correction-stabilized path perturbation, shifting the optimization from exploration to exploitation; an optimized Gaussian initial law broadens the search over initial-state basins; and loss-weighted mixing across independent runs recombines successful path components. We may then select an interior state using a local loss and continue online with an EnKF. In 40-dimensional Lorenz-96 experiments, the mean offline path RMSE of APK is 4.3 times smaller than that of population weak-$\\mathrm{4D\\text{-}Var}_x$. The resulting APK-EnKF method has a mean online RMSE 64 times smaller than that of ordinary EnKF.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.10949",
  "author_total": 1,
  "authors": [
    {
      "name": "Angxiu Ni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b6et07anykaqr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.10949",
  "fulltext_url": null,
  "id": "b6et07anykaqr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.10949",
  "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.NA"
    ],
    [
      "arxiv",
      "math.DS"
    ],
    [
      "arxiv",
      "math.NA"
    ],
    [
      "arxiv",
      "nlin.CD"
    ],
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      "openalex",
      "1902|Meteorological Phenomena and Simulations"
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  "openalex": "W7212242112",
  "pdf_url": "https://arxiv.org/pdf/2609.10949",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b6et07anykaqr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2609.10949",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.10949"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.DS",
      "subfield": "dynamical-systems"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    },
    {
      "field": "physics",
      "origin": "arxiv:nlin.CD",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Full-window branch discovery and loss-selected EnKF continuation for data assimilation",
  "title_text": "Full-window branch discovery and loss-selected EnKF continuation for data assimilation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:30Z",
  "url": "https://research.draconitic.com/paper/b6et07anykaqr",
  "venue": "arXiv",
  "versions": []
}