{
  "abstract": "This study conducts a hydro-climatic comparative analysis of two regions prone to heavy urban snowfall: the eastern shores of the North American Great Lakes (Lake-Effect Snow: LES) and the coast of the Sea of Japan (Sea-Effect Snow: SES). We introduce a quantitative metric—water vapor conversion efficiency (η= snowfall amount /PW)—and apply a data-driven causal discovery framework (DirectLiNGAM). Building upon challenges identified in previous studies (e.g., Fujisaki-Manome et al., 2022; West et al., 2019; Steenburgh and Nakai, 2020), we present the following five new findings: (1)Mathematical modeling of a discontinuous \"risk jump\" (Jrisk) using the Heaviside step functions. (2)Construction of causal Directed Acyclic Graphs (DAGs) that distinguish between physical topological differences. (3)Characterization of properties through non-stationary Generalized Extreme Value (GEV) distribution analysis spanning over 80 years (confirming a Weibull-type distribution with an upper bound (ξ= -0.12 < 0) for the Great Lakes and a heavy-tailed Fréchet-type distribution (ξ= +0.18 > 0) for Japan). (4)Identification of a memory effect regarding water vapor accumulation over 1–2 days (lag 1–2: r = 0.78). (5)Quantification of differences in amplification effects attributable to topographic conditions, Japan's steep mountain ranges (elevations of 2,000–3,000 m) versus the US's gentle plateaus (elevations of 300–500 m).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "hiroshi matsuda"
    },
    {
      "name": "Shin'ichi HOMMA"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0769wtnjmbmfj.bib",
  "cited_by": 0,
  "doi": "10.31223/x58f8d",
  "fulltext_url": null,
  "id": "0769wtnjmbmfj",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15061/",
  "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/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Causal Network Analysis"
    ],
    [
      "bepress",
      "DAGs"
    ],
    [
      "bepress",
      "Direct LiNGAM"
    ],
    [
      "bepress",
      "Heavy Snowfall Systems"
    ],
    [
      "bepress",
      "Lake-Effect Snow"
    ],
    [
      "bepress",
      "Non-stationary GEV"
    ],
    [
      "bepress",
      "North American Great Lakes"
    ],
    [
      "bepress",
      "Physical Sciences and Mathematics"
    ],
    [
      "bepress",
      "Sea of Japan Coast"
    ],
    [
      "bepress",
      "Sea-Effect Snow"
    ],
    [
      "bepress",
      "maximam snowfall"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7213538791",
  "pdf_url": "https://eartharxiv.org/repository/object/15061/download/26150/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0769wtnjmbmfj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "15061",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15061/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1902|Meteorological Phenomena and Simulations",
      "subfield": "atmospheric"
    }
  ],
  "title": "Causal Network Analysis of Heavy Snowfall Systems: North American Great Lakes vs. Sea of Japan Coast",
  "title_text": "Causal Network Analysis of Heavy Snowfall Systems: North American Great Lakes vs. Sea of Japan Coast",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/0769wtnjmbmfj",
  "venue": "EarthArXiv",
  "versions": []
}