{
  "created_utc": "2026-10-12T21:00:50.903942+00:00",
  "config": "config/bias_correction/feb_fmam_ratio_limits.json",
  "protocol": "Selection-aware confirmation of rainfall-amount bias-correction methods through the full probability pipeline (docs/47).\n\nRead-only experiment. Configured by the \"confirmation\" block of config/bias_correction/<name>.json; any cycle, any\nmethod of the registry (scripts/bias_methods.py).\n\nPipeline for one amount method (the operational one, with the amount correction swapped):\n    corrected members -> tercile counts against the training CHIRPS terciles -> alpha smoothing\n    -> blend with climatology, p = (1 - lambda) p_s + lambda p_clim, one lambda per target fitted by\n       leave-one-year-out within the training years (local_blend.fit_weights, over the full rectangle as operationally)\nDesign:\n    outer leave-one-year-out over the training years. For outer year y every fit (amount correction, terciles,\n    climatology, lambda) uses only the other training years; lambda comes from inner leave-one-year-out within them.\n    Selection-aware procedures choose a method for year y with the inner (training-only) scores:\n        SEL-P: lowest inner blended RPS (probability product); SEL-A: lowest inner CRPS (amount product).\n    Their outer scores therefore include the effect of choosing among the declared candidate family.\n    Test years: all fits and the selection on the training years, scored on the test years (exploratory).\nScores per year (area-weighted over a region, equal years): blended RPS and RPSS, category Brier scores and skill\n(below and above normal), reliability tables; ensemble CRPS of the amounts (and the fair CRPS).\nTests against the operational method: paired whole-year sign-flip, Holm across the declared family\n(every method and target for the method comparisons; the targets for SEL-P, SEL-A and each sensitivity variant).\nVariants (e.g. the MVS all-zero guard) run through the same pipeline but are never chosen by SEL-P / SEL-A.\nCheckpoints (one per target and method) are reused only when the complete dependency fingerprint of the target\n(configuration contents, masks, input files, calculation code; bias_benchmark.dependencies) is unchanged.",
  "provenance": {
    "commit": "fb7425f593b8bc5f2550f0f47d4929e76eb3fb5c",
    "uncommitted_changes": false,
    "uncommitted_check": "code and configuration files this result depends on",
    "code_sha256": {
      "scripts/bias_methods.py": "f964fac9e23a92ba01fc39368d996aa660561cd8708f7552fbedc0627f6b3706",
      "scripts/bias_benchmark.py": "bd5d0cce44fa15275cc64fbaa1d9317aaefb43626b0f3a0b9c6a719c3b4fa24f",
      "scripts/bias_confirmation.py": "bbc95e4cb64256b101b5c0b30e2f351c5cd2b36893d4dfb0505c63577243f558",
      "scripts/significance.py": "3143761d5d201e4e62df9ad923757b952a3ea768b060c8a722afcd9b3d6a9963",
      "scripts/calibration_core.py": "f0635cc563e6d6aedb62799847b83fbb0f5cc12ee9f8c48ad48dda15a054fb62",
      "scripts/compare_calibration.py": "9aa137c9a02672ffacc2541cfd23cdab9b3294bf7cc77580a11e21208560866a",
      "scripts/local_blend.py": "ada5c571fb7762d2c333f83ad119d35f0784b82aa2cc35449a44cdf00c53c17d",
      "scripts/run_calibration.py": "1f34e8fc92fdd15983f1633de67fcb6a35882911b6b7072ca865df99d907afd0",
      "scripts/run_monthly.py": "dc13dfb54b78773afb6d0c25ebac8769552e5c071f5d2aed9ba2a01400e44f79",
      "scripts/cycle.py": "00e076fc93dc2c8a5caebf6a209fc3de09a5f69e16c2e7cb20bd7b36d2ecdcba",
      "scripts/common.py": "72b83f5c530017e317ea43af3a0e430e6d03f361bc032de374a0f942435cb0a1"
    },
    "config": {
      "path": "config/bias_correction/feb_fmam_ratio_limits.json",
      "sha256": "ff2b846daa31c1b3e99e462710430e76c6eabea959653f3dab63c22e69df67fb"
    },
    "project_config": {
      "path": "config/fmam_feb/project.json",
      "sha256": "9999e9e5e03f95998e3d22396d2d2e5bf2f8c79decfc571c715caf2878daa4a1"
    },
    "cycle_config": {
      "path": "config/cycles/feb_2026_fmam.json",
      "sha256": "120955984a14d292912b9a8d48f83af060290d40d6b54ed9d627b36280c0652b"
    },
    "masks": {
      "cells": {
        "path": "data/masks/ethiopia_common.nc",
        "sha256": "57ad8861c598abb9f175012920e5d0ef058e5b643cd6e4987cad3da6ac0b9e16"
      },
      "ethiopia": {
        "path": "data/masks/ethiopia_common.nc",
        "sha256": "57ad8861c598abb9f175012920e5d0ef058e5b643cd6e4987cad3da6ac0b9e16"
      },
      "fmam_domain": {
        "path": "data/masks/fmam_coverage_chirps_v3_1993_2025_v1.nc",
        "sha256": "c386d127f7041ed6b383c5f46b9537e3740bfee6b85f383e3a8b0792cac58fbb"
      }
    },
    "inputs": {
      "Feb": {
        "folder": "data/processed/init02_Feb",
        "files": 67,
        "sha256": "aa05fc058f64121f726ee7f06608768bafb243ef9702ccd6c154217f2a9a13d5"
      },
      "Mar": {
        "folder": "data/processed/init02_Mar",
        "files": 67,
        "sha256": "41b2c07eb813ed00ca81d1c21d2a620cd1cdbc2d2725e806669bd035f1a8f7cf"
      },
      "Apr": {
        "folder": "data/processed/init02_Apr",
        "files": 67,
        "sha256": "7c29ca912e693cabdd2d1626e106f967dd554116d18f98a18faa33f957dfb15e"
      },
      "May": {
        "folder": "data/processed/init02_May",
        "files": 67,
        "sha256": "c590b53da737506d77661bb04aa6caf2a91d62a0ab9b88d8a2ecbb7c49b925c0"
      },
      "FMAM": {
        "folder": "data/processed/init02_FMAM",
        "files": 67,
        "sha256": "6915035b5cc71c72bb89702c935f922163818566aa3868159a9183f7326db742"
      }
    },
    "scoring_version": 3,
    "dependency_sha256": "b615f9e7a75850b565cd0960a8ab21a5ac936431ba099126ae7a085a44ab7bbf",
    "python": "3.11.9",
    "numpy": "1.26.4",
    "checkpoint_keys": {
      "Feb": "fd1bd33288b162a975587420678ff4cedea59cfeee17037b89b6048218e5832a",
      "Mar": "a43d11ff9e05906d1ba10599aeb3b808b8aa11790276b95a6cfe78bc596aedd1",
      "Apr": "915be880e7585fa4bd36d3734b081b48d613c0e0fc66612c0806379e5ad80718",
      "May": "0c2721db16bf0a3d3c360cdb9f52a6cef9ad4c75af194adf7d264716236c2781",
      "FMAM": "abaa460ac89d17572e7a23f99644e9dd606ee8d6de663fe3ee027617354417a8"
    }
  },
  "methods": [
    "mvs",
    "mvs_r3",
    "mvs_r4",
    "vs"
  ],
  "family": [
    "mvs",
    "mvs_r3",
    "mvs_r4",
    "vs"
  ],
  "variants": [],
  "reference": "mvs",
  "targets": {
    "Feb": {
      "tag": "init02_Feb",
      "methods": {
        "mvs": {
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            "training": [
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            "fmam_domain": {
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          "test": {
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        "SEL-A": {
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