{
  "annotation_id": "d5751f42-4e5b-45aa-bbcd-e28b7065b5a9",
  "date_created": "2026-03-02T18:01:21.517000Z",
  "date_modified": "2026-03-02T18:01:21.517000Z",
  "file_hash": "c167e16900c1f5e8e332c7e7fe0d2acd8ea44bf7a56762770d3140de9bb38dad",
  "private": false,
  "record": {
    "abstract": "We develop the formalism for a PIM-based functional for stochastic tomography\nwith a Kalman filter, in which the inversion problem associated with\nfour-dimensional ionospheric stochastic tomography is regularized. For\nconsistency, GPS data is used to select dynamically the best PIM parameters, in\na 3DVAR fashion. We demonstrate the ingestion of GPS (IGS and GPS/MET) data\ninto a parameterized ionospheric model, used to select the set of parameters\nthat minimize a suitable cost functional. The resulting PIM-fitted model is\ncompared to direct 3D voxel tomography. We demonstrate the value of this method\nanalyzing IGS and GPS/MET GPS data, and present our results in terms of a 4D\nmodel of the ionospheric electronic density.",
    "arxiv_id": "physics/9807026",
    "authors": [
      "Giulio Ruffini",
      "Lidia Cucurull",
      "Alex Flores",
      "Antonio Rius"
    ],
    "categories": [
      "physics.geo-ph",
      "physics.ao-ph"
    ],
    "doi": "10.1016/S1464-1917(99)00013-6",
    "title": "A PIM-aided Kalman Filter for GPS Tomography of the Ionospheric Electron   Content",
    "url": "https://arxiv.org/abs/physics/9807026"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "05e7d676-8545-4921-8964-249e4722ad94",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}