{
  "abstract": "AETHER-P\\textsuperscript{3} Nowcast v1.0 is a machine-learning-based global thermospheric neutral-density model developed for low-Earth-orbit applications and prepared for onboarding to NASA's Community Coordinated Modeling Center (CCMC). The model provides pointwise neutral-density estimates together with predictive uncertainty using a deep evidential regression framework driven by causal solar, solar-wind, geomagnetic, spatial, temporal, and empirical-model inputs. This report documents the released model configuration, training-data construction, software traceability, output products, validation strategy, benchmark performance, and known limitations. The training archive combines accelerometer- and mission-derived density observations from CHAMP, GRACE-A, GOCE, Swarm-C, and GRACE-FO spanning 2000--2023. More than 40 million eligible 30-s observations are available, but the archive is strongly dominated by consecutive quiet-time measurements. To preserve coverage of physically important regimes, the final 1.67-million-sample training set is constructed using deterministic regime-aware sampling that progressively subsamples quiet conditions while retaining all available extreme-condition observations. Validation uses temporally disjoint chronological blocks with exclusion guards to reduce information leakage. Evaluation across quiet, moderate, and extreme conditions shows competitive performance relative to HASDM, JB2008, NRLMSISE-00, and available WAM-IPE cases, while also identifying limitations associated with sparse training coverage, mission-dependent density products, and condition-dependent uncertainty calibration. The report provides a reproducible technical description of the AETHER-P\\textsuperscript{3} Nowcast v1.0 research release and its current CCMC onboarding configuration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.06777",
  "author_total": 2,
  "authors": [
    {
      "name": "Ruochen Wang"
    },
    {
      "name": "Xiaoli Bai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/35c2gg0wk20ek.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.06777",
  "fulltext_url": null,
  "id": "35c2gg0wk20ek",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.06777",
  "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.space-ph"
    ],
    [
      "openalex",
      "3103|Ionosphere and magnetosphere dynamics"
    ]
  ],
  "openalex": "W7212051948",
  "pdf_url": "https://arxiv.org/pdf/2609.06777",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/35c2gg0wk20ek.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.06777",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.06777"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:physics.space-ph",
      "subfield": "stellar"
    }
  ],
  "title": "AETHER-P3 Nowcast v1.0: Model Description, Training-Data Construction, and Validation Technical report prepared in support of CCMC onboarding",
  "title_text": "AETHER-P3 Nowcast v1.0: Model Description, Training-Data Construction, and Validation Technical report prepared in support of CCMC onboarding",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/35c2gg0wk20ek",
  "venue": "arXiv",
  "versions": []
}