{
  "abstract": "A regime- and horizon-adaptive probabilistic ensemble is proposed for local multi-horizon weather forecasting. The theoretical formulation links conditional second-moment error matrices to posterior probabilities of latent atmospheric-state components, while the Soft_Final scheme combines horizon adaptation, regime-dependent weighting, stabilization, and soft posterior mixing. For the posterior-weighted quadratic model, we derive an exact excess-risk decomposition, a Karush–Kuhn–Tucker (KKT) optimality certificate, an active-boundary decomposition on the probability simplex, and deterministic spectral stability bounds. The experimental dataset comprises 5184 hourly meteorological observations and 1296 forecast records across nine lead times; confirmatory inference is based on six station–season blocks. After Holm correction, statistically significant superiority of Soft_Final over the globally fixed static ensemble is not established, whereas ablation analysis indicates that horizon adaptation contributes more consistently than additional regime conditioning. Probabilistic performance exhibits a substantive trade-off among coverage, interval width, and the continuous ranked probability score (CRPS). The main contribution is a rigorous separation of model optimality, operational approximation, and out-of-sample predictive error. Same-protocol comparisons with ensemble model output statistics (EMOS), Bayesian model averaging (BMA), quantile regression forests (QRFs), and residual conformal post-processing demonstrate competitive but non-uniform performance rather than universal dominance.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrotechnical Complexes, Novosibirsk State Technical University, 630073 Novosibirsk, Russia",
    "Department of Information Technology, Tomsk Polytechnic University, 634050 Tomsk, Russia",
    "Yurga Technological Institute (Branch), Tomsk Polytechnic University, 652055 Yurga, Russia",
    "Department of Mechanical Engineering, Tomsk Polytechnic University, 634050 Tomsk, Russia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Boris V. Malozyomov",
      "orcid": "0000-0001-8686-9556"
    },
    {
      "affs": [
        1
      ],
      "name": "Nikita V. Martyushev",
      "orcid": "0000-0003-0620-9561"
    },
    {
      "affs": [
        2
      ],
      "name": "Denis V. Valuev",
      "orcid": "0000-0001-5131-8754"
    },
    {
      "affs": [
        3
      ],
      "name": "Svetlana N. Sorokova",
      "orcid": "0000-0002-0328-899X"
    },
    {
      "affs": [
        3
      ],
      "name": "Egor A. Efremenkov",
      "orcid": "0000-0001-6617-9152"
    },
    {
      "affs": [
        1
      ],
      "name": "Anton Y. Demin"
    },
    {
      "affs": [
        1
      ],
      "name": "Alexander V. Pogrebnoy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7tmc4b0jgqbca.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203711",
  "fulltext_url": null,
  "id": "7tmc4b0jgqbca",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3711",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7221696422",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7tmc4b0jgqbca.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203711",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3711"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1902|Meteorological Phenomena and Simulations",
      "subfield": "atmospheric"
    }
  ],
  "title": "Conditional-Risk Optimization and KKT Geometry of a Regime- and Horizon-Adaptive Probabilistic Ensemble for Local Weather Forecasting",
  "title_text": "Conditional-Risk Optimization and KKT Geometry of a Regime- and Horizon-Adaptive Probabilistic Ensemble for Local Weather Forecasting",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/7tmc4b0jgqbca",
  "venue": "Mathematics",
  "versions": []
}