{
  "annotation_id": "38296b3b-2c90-40ee-b459-e9aa5e22a4c9",
  "date_created": "2026-03-02T18:00:53.410000Z",
  "date_modified": "2026-03-02T18:00:53.410000Z",
  "file_hash": "c1cfc042203af5fb6de76af89803a9f8183b43408abe4250eae7edc9342c6b10",
  "private": false,
  "record": {
    "abstract": "The spread of ensemble weather forecasts contains information about the\nspread of possible future weather scenarios. But how much information does it\ncontain, and how useful is that information in predicting the probabilities of\nfuture temperatures? One traditional answer to this question is to calculate\nthe spread-skill correlation. We discuss the spread-skill correlation and how\nit interacts with some simple calibration schemes. We then point out why it is\nnot, in fact, a useful measure for the amount of information in the ensemble\nspread, and discuss a number of other measures that are more useful.",
    "arxiv_id": "physics/0410039",
    "authors": [
      "Stephen Jewson"
    ],
    "categories": [
      "physics.ao-ph"
    ],
    "title": "Probabilistic forecasts of temperature: measuring the utility of the   ensemble spread",
    "url": "https://arxiv.org/abs/physics/0410039"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9c962919-ebca-442a-8df5-f82994204613",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}