{
  "abstract": "This paper describes an approach to constructing nowcast and forecast predictive models for the occurrence of high-latitude decameter ionospheric irregularities based on SuperDARN and SECIRA radar data and space weather parameters. The method relies on self-learning pattern recognition in the dynamics of space weather parameters, that influence the irregularities formation. The proposed approach is a data-driven, multiscale method in which the spatial distribution of irregularities is approximated using a neural network of an original architecture, inspired by graph neural networks. The model was trained and tested using data from 11 radars during the period 2010-2025 (SuperDARN: CVW, ICE, ICW, INV, PGR, PYK, RKN, SAS, STO; SECIRA: EKB, MAGW). It is shown that the model achieves optimal performance when using geomagnetic coordinates MLT,MLAT. The optimal set of parameters for the best nowcast and forecast over studied set of parameters is a combination of geomagnetic and solar disturbance indices ($a_{p}$, $K_{p}$, $AE$, F10.7) from ground-based observations, and satellite-measured solar wind and interplanetary magnetic field parameters ($P_{sw}$, $E_{sw}$, $\\widetilde{B_{z}}$, $T_{sw}$). Hourly mean values for all the parameters were used in the analysis. The results indicate that acceptable forecast quality is achieved with a lead time of up to 7-9 hours, with the highest quality reached for nowcast (zero lead time). It is shown that the parameters $\\widetilde{B_{z}}$ and F10.7 are significant for nowcasting only and can be neglected for other lead times. It is shown that $AE$ and $T_{sw}$ are significant for mid-term forecast (\\textgreater 2hours ahead) and can be neglected for short-term predictions. Statistical relationships between the maximum latitude of the equatorial boundary of the irregularities and space weather parameters are also discussed.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.29630",
  "author_total": 2,
  "authors": [
    {
      "name": "Oleg I. Berngardt"
    },
    {
      "name": "Ivan A. Lavygin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a1fhmsz1d7y44.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.29630",
  "fulltext_url": null,
  "id": "a1fhmsz1d7y44",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.29630",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "arxiv",
      "physics.geo-ph"
    ],
    [
      "openalex",
      "3103|Ionosphere and magnetosphere dynamics"
    ]
  ],
  "openalex": "W7223244463",
  "pdf_url": "https://arxiv.org/pdf/2610.29630",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a1fhmsz1d7y44.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:19Z",
      "native_id": "2610.29630",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.29630"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    },
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "Space weather dependence of decameter-scale ionospheric irregularities spatial distribution",
  "title_text": "Space weather dependence of decameter-scale ionospheric irregularities spatial distribution",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:19Z",
  "url": "https://research.draconitic.com/paper/a1fhmsz1d7y44",
  "venue": "arXiv",
  "versions": []
}