{
  "abstract": "Accurate models of gas radiation are required for the analysis of atmospheric remote sensing data. These models need to be CPU efficient in order to comply with the exponential growth of data provided by the most recent generations of satellite instruments. In this paper, we describe two methods to treat gaseous absorption in satellite-based remote sensing scenarios. The two approaches are founded on statistical/machine learning (SL/ML) strategies. Compared to most works that only focus on a single SL/ML model, a detailed comparison is provided in this paper between a physics-informed SL method founded on a double mathematical induction strategy, called the dependent ALD (Augmented L-Distribution) model, and a purely data driven ML model (DDML). Results of the two approaches are assessed against reference LBL calculations and compared with a Correlated K-Distribution (CKD) model both in terms of accuracy and CPU cost. The two SL/ML models are shown to reach an accuracy higher than 0.5% at a small fraction (three orders of magnitude below) of the CPU cost of high-resolution calculations. The two methods open new possibilities for fast though accurate forward modeling of satellite remote sensing data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Frédéric André"
    },
    {
      "name": "Xijiade Aihemaiti"
    },
    {
      "name": "Tao Ren"
    },
    {
      "name": "Loïc Guilmard"
    },
    {
      "name": "François Thieuleux"
    },
    {
      "name": "Nada Mourtaday"
    },
    {
      "name": "Antoine Rimboud"
    },
    {
      "name": "Somesh Roy"
    },
    {
      "name": "Céline Cornet"
    },
    {
      "name": "Laurent Labonnotte"
    },
    {
      "name": "Jérôme Riedi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6m1g94svrmak6.bib",
  "cited_by": 1,
  "doi": "10.1016/j.jqsrt.2026.110011",
  "fulltext_url": null,
  "id": "6m1g94svrmak6",
  "issns": [
    "0022-4073",
    "1879-1352"
  ],
  "landing_url": "https://hal.science/hal-05805159v1",
  "language": "en",
  "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": [
    [
      "hal",
      "0.math"
    ],
    [
      "hal",
      "0.sdu"
    ],
    [
      "hal",
      "1.math.math-mp"
    ],
    [
      "hal",
      "1.sdu.ocean"
    ],
    [
      "openalex",
      "2206|Radiative Heat Transfer Studies"
    ]
  ],
  "openalex": "W7162567011",
  "pdf_url": "https://hal.science/hal-05805159/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/6m1g94svrmak6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:01Z",
      "native_id": "hal-05805159",
      "source": "hal",
      "url": "https://hal.science/hal-05805159v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:1.sdu.ocean",
      "subfield": "oceanography"
    },
    {
      "field": "mathematics",
      "origin": "hal:1.math.math-mp",
      "subfield": "mathematical-physics"
    }
  ],
  "title": "A comparison of data-driven and physics-informed statistical learning models for atmospheric remote sensing applications",
  "title_text": "A comparison of data-driven and physics-informed statistical learning models for atmospheric remote sensing applications",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:01Z",
  "url": "https://research.draconitic.com/paper/6m1g94svrmak6",
  "venue": "Journal of Quantitative Spectroscopy and Radiative Transfer",
  "versions": []
}