{
  "abstract": "NASA’s aquatic and marine research enterprise within the overarching NASA Biosphere Program is entering an era of unprecedented data abundance. MODIS and the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission already generate ocean color data at rates that strain legacy processing and archiving workflows, and forthcoming missions will add further volume and complexity. These satellite streams are joined by rapidly growing in situ networks (e.g., BGC-Argo), suborbital campaigns, genomic surveys, citizen science, and increasingly high-resolution numerical models. The result is a Big Data regime in which the volume, variety, and acquisition rate of ocean relevant Earth system data exceed the capacity of legacy processing, storage, and synthesis approaches. An important goal is to fully integrate observing workflows, understanding, and models across the terrestrial, aquatic, and atmosphere domains to fully enable understanding of life and the realization of improved information to manage uses of ecosystem services. This white paper addresses Leveraging Ocean Data and Models, a cross-cutting Grand Challenge that underlies and enables progress on the four other foundational grand challenges under the NASA Biosphere, Atmosphere, Cryosphere, and Hydrosphere framework - Global Biosphere, Carbon and the Elements of Life, Interface Habitats, and Transient Events - each of which depends on the ability to acquire, harmonize, and synthesize disparate satellite, suborbital, in situ, and model data streams. We recommend a decadal strategy built on two complementary pillars: Access and Utility of Ocean Observational Data, covering tiered cloud-based facilities, workforce training, international data sharing, and machine learning; and Numerical Models and Data Assimilation, covering mechanistic and statistical models, coupled physical-biogeochemical assimilation, hybrid approaches, mission emulators, and a NASA Digital Twin Ocean. Without concerted, proactive investment in this cross-cutting infrastructure, the scientific and societal returns on the coming decade’s expanded observing system will be substantially diminished.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Susanne Elizabeth Craig"
    },
    {
      "name": "Frank E. Muller-Karger"
    },
    {
      "name": "Leonid Shumilo"
    },
    {
      "name": "Maria Tzortziou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b119jyhxke25a.bib",
  "cited_by": 0,
  "doi": "10.31223/x5bv4n",
  "fulltext_url": null,
  "id": "b119jyhxke25a",
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  "landing_url": "https://eartharxiv.org/repository/view/15207/",
  "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",
      "Artificial Intelligence and Robotics"
    ],
    [
      "bepress",
      "Atmospheric Sciences"
    ],
    [
      "bepress",
      "Biogeochemistry"
    ],
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      "bepress",
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    ],
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      "bepress",
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    ],
    [
      "bepress",
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    ],
    [
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    ],
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    ],
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      "bepress",
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    ],
    [
      "bepress",
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    ],
    [
      "bepress",
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    ],
    [
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    ],
    [
      "bepress",
      "Meteorology"
    ],
    [
      "bepress",
      "Numerical Analysis and Scientific Computing"
    ],
    [
      "bepress",
      "Oceanography"
    ],
    [
      "bepress",
      "Other Earth Sciences"
    ],
    [
      "bepress",
      "Other Oceanography and Atmospheric Sciences and Meteorology"
    ],
    [
      "bepress",
      "biogeochemical models"
    ],
    [
      "bepress",
      "cloud computing"
    ],
    [
      "bepress",
      "data assimilation"
    ],
    [
      "bepress",
      "digital twin ocean"
    ],
    [
      "bepress",
      "machine learning"
    ],
    [
      "bepress",
      "ocean data infrastructure"
    ],
    [
      "bepress",
      "workforce development"
    ],
    [
      "openalex",
      "1910|Oceanographic and Atmospheric Processes"
    ]
  ],
  "openalex": "W7214342264",
  "pdf_url": "https://eartharxiv.org/repository/object/15207/download/26372/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b119jyhxke25a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:39Z",
      "native_id": "15207",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15207/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "bepress:Atmospheric Sciences",
      "subfield": "atmospheric"
    },
    {
      "field": "earth",
      "origin": "bepress:Climate",
      "subfield": "climate"
    },
    {
      "field": "earth",
      "origin": "bepress:Environmental Sciences",
      "subfield": "environmental-science"
    },
    {
      "field": "earth",
      "origin": "bepress:Biogeochemistry",
      "subfield": "geology-geochemistry"
    },
    {
      "field": "earth",
      "origin": "bepress:Hydrology",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "Leveraging Ocean Data and Models: Building the Data Infrastructure, Workforce, and Modeling Capacity to Synthesize Earth’s Ocean Observations",
  "title_text": "Leveraging Ocean Data and Models: Building the Data Infrastructure, Workforce, and Modeling Capacity to Synthesize Earth’s Ocean Observations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:39Z",
  "url": "https://research.draconitic.com/paper/b119jyhxke25a",
  "venue": "EarthArXiv",
  "versions": []
}