{
  "annotation_id": "87d87dc7-1396-449c-9e94-e4b737051a10",
  "date_created": "2026-03-02T18:00:46.968000Z",
  "date_modified": "2026-03-02T18:00:46.968000Z",
  "file_hash": "ef93788ec03db8491cc840601b47a3f5d609a61cd27f6762295c04f50e2c03be",
  "private": false,
  "record": {
    "abstract": "The gaussian spread regression model for the calibration of site specific\nensemble temperature forecasts depends on the apparently restrictive assumption\nthat the uncertainty around temperature forecasts is normally distributed. We\ngeneralise the model using the kernel density to allow for much more flexible\ndistribution shapes. However, we do not find any meaningful improvement in the\nresulting probabilistic forecast when evaluated using likelihood based scores.\nWe conclude that the distribution of uncertainty is either very close to\nnormal, or if it is not close to normal, then the non-normality is not being\npredicted by the ensemble forecast that we test.",
    "arxiv_id": "physics/0310060",
    "authors": [
      "Stephen Jewson"
    ],
    "categories": [
      "physics.ao-ph"
    ],
    "title": "Do probabilistic medium-range temperature forecasts need to allow for   non-normality?",
    "url": "https://arxiv.org/abs/physics/0310060"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4108fb36-34a5-4267-8063-ad8c01bb0b92",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}