{
  "abstract": "Earth System Models rely on tunable subgrid-scale parameterizations, but optimizing these parameters is computationally expensive, particularly when nonlinear interactions require many simulations. We present a hybrid Physics-ML multi-fidelity framework that combines Green's Function Optimization (GFO) with Gaussian Process or Neural Network surrogate optimization. Using a quasi-geostrophic turbulence model, GFO first ranks parameter sensitivities in normalized coordinates and selects a reduced active subset. Nonlinear surrogates then explore this subset using inexpensive 30-day simulations before refining promising candidates with 180-day simulations. Across seven strategies and a 35-member ensemble, GFO-MultiGP and GFO-MultiNN achieved mean improvements of 64.6 percent and 65.2 percent, respectively, while reaching practical saturation after 3,060 and 2,520 simulation-days. The corresponding standalone GP and NN achieved 61.1 percent and 42.0 percent improvements and required 7,740 and 6,660 simulation-days. These results demonstrate an end-to-end sample-efficiency advantage for the tested hybrid pipelines. Because screening, dimensionality reduction, initialization, and fidelity scheduling change simultaneously, their individual contributions are not isolated.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13275",
  "author_total": 5,
  "authors": [
    {
      "name": "Abdullah A. Fahad"
    },
    {
      "name": "Manmeet Singh"
    },
    {
      "name": "Donifan Barahona"
    },
    {
      "name": "Anton Darmenov"
    },
    {
      "name": "Andrea Molod"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/64ganchf52fb7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13275",
  "fulltext_url": null,
  "id": "64ganchf52fb7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13275",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7213340357",
  "pdf_url": "https://arxiv.org/pdf/2609.13275",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/64ganchf52fb7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.13275",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13275"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "A Physics--ML Multi-Fidelity Strategy for Earth System Model Parameter Optimization: A QG Proof-of-Concept",
  "title_text": "A Physics--ML Multi-Fidelity Strategy for Earth System Model Parameter Optimization: A QG Proof-of-Concept",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/64ganchf52fb7",
  "venue": "arXiv",
  "versions": []
}