{
  "annotation_id": "9e516278-52ba-4c7c-85cf-7e485a71c66c",
  "date_created": "2026-03-02T18:00:39.814000Z",
  "date_modified": "2026-03-02T18:00:39.814000Z",
  "file_hash": "8dc502f088386da5789603f914ccb57678a349dc993e98f87a861271d7d7b0f9",
  "private": false,
  "record": {
    "abstract": "The feasibility of global ocean state estimation by sequential data\nassimilation is demonstrated. The model componenet of the assimilator is the\nGROB version of the MPIMET ocean circulation model HOPE. Assimilation uses the\nFokker-Planck representation of the Kalman Filter. This approach determines the\ntemporal evolution of error statistics by integration of the Fokker-Planck\nEquation. Phase space advection and diffusion are obtained from histogram\ntechniques considering the model as a black box. For efficiency, the estimate\ncombines nudging and Kalman Filtering. The ocean state is estimated for the El\nNino year 1997 by dynamical extrapolation of observed sea-surface temperatures\nand TAO/Triton subsurface temperatures. The model-data combination yields\nimproved estimates of the ocean\u0027s mean state and a realistic record of El Nino\nrelated variability. The assimilator proves as an efficient, viable and thus\npractical approach to operational global ocean state estimation.",
    "arxiv_id": "physics/0207097",
    "authors": [
      "Konstantin P. Belyaev",
      "Detlev Mueller"
    ],
    "categories": [
      "physics.ao-ph",
      "physics.data-an"
    ],
    "title": "A Kalman Filter for Ocean Monitoring",
    "url": "https://arxiv.org/abs/physics/0207097"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9713ca7c-bc0c-418c-9475-f93a262a3603",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}