{
  "annotation_id": "30ecbc05-4e16-43f1-8341-0e58a987be64",
  "date_created": "2026-03-02T18:01:21.851000Z",
  "date_modified": "2026-03-02T18:01:21.851000Z",
  "file_hash": "bc515effde8ba90081b64ea7905c6ce88a047fbf35efb709b70839987a8c2b92",
  "private": false,
  "record": {
    "abstract": "Tomographic techniques are successfully applied to obtain 4D images of the\ntropospheric refractivity in a local dense network. In the lower atmosphere\nboth the small height and time scales and the non-dispersive nature of\ntropospheric delays require a more careful analysis of the data. We show how\nGPS data is processed to obtain the tropospheric slant delays using the\nGIPSY-OASIS II software and define the concept of pseudo-wet delays, which will\nbe the observables in the tomographic software. We then discuss the inverse\nproblem in the 3D stochastic tomography, using simulated refractivity fields to\ntest the system and the impact of noise. Finally, we use data from the Kilauea\nnetwork in Hawaii and a local 4x4x41-voxel grid on a region of 400 Km$^2$ and\n15 Km in height to produce 4D refractivity fields. Results are compared with\nECMWF forecast.",
    "arxiv_id": "physics/9901004",
    "authors": [
      "A. Flores",
      "G. Ruffini",
      "A. Rius"
    ],
    "categories": [
      "physics.geo-ph",
      "physics.ao-ph"
    ],
    "doi": "10.1007/s00585-000-0223-7",
    "title": "4D Tropospheric Tomography using GPS Estimated Slant Delays",
    "url": "https://arxiv.org/abs/physics/9901004"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b806c9fb-9781-49e9-a3e4-30b7a04d72d4",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}