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OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations — arXiv:2608.16373

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16373
License (as archived): CC0-1.0
Archived: 2026-10-18T18:07:11.672Z
SHA-256 of the archived text: ae1cc22046024aa0008b7f9d0cff9828df4e489802f36e6d009e1e1e07a25db2
Stable URL of this page: https://metraipolis.com/documento/dc_6104fad2120b

How to cite

OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations — arXiv:2608.16373 — arXiv (en), https://arxiv.org/abs/2608.16373 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6104fad2120b (Metraipolis archive dc_6104fad2120b, sha256 ae1cc22046024aa0008b7f9d0cff9828df4e489802f36e6d009e1e1e07a25db2)

BibTeX

@misc{dc6104fad2120b,
  title = {OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations — arXiv:2608.16373},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16373},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6104fad2120b; sha256 ae1cc22046024aa0008b7f9d0cff9828df4e489802f36e6d009e1e1e07a25db2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16373. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere. Understanding the global ocean requires jointly observing its surface and subsurface structure, yet no standardized, high-resolution dataset couples satellite surface fields to co-located in situ depth profiles in an AI-ready format. Existing resources either consist of model-reconstructed gridded products rather than observations, cover only a single variable or basin, or operate at resolutions too coarse for mesoscale dynamics. We introduce OceanDepths, the first open, global, regridded AI-ready dataset that pairs satellite-derived sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) L4 products with co-located EN4 subsurface temperature and salinity profiles, complemented by matched GLORYS12 ocean reanalysis data to support comparisons or multi-stage learning. The dataset spans 2000-2024 at 0.1 degrees x 0.1 degrees spatial resolution and at weekly temporal resolution, covering the entire globe's sea surface and with over 9.5 million paired profiles interpolated to 50 standardized depth levels. We provide a configurable system to split the globe in equally sized spatial patches. The 4D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations (approximately 0.01% per depth level) make OceanDepths a challenging testbed for novel AI methods. We demonstrate subsurface state reconstruction as an example task with simple baseline models, but also envision OceanDepths to support the development of observation-based forecast methods and other related tasks. Available at: this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.16373 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T16:29:14.110Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).