{
  "created_utc": "2026-10-13T13:13:52.259626+00:00",
  "config": "config/probability_calibration/may_jjas_distribution.json",
  "config_contents": {
    "description": "Phase 2, Pathway B: rainfall-distribution models from the raw ensemble against the published pipeline (docs/48).",
    "benchmark_config": "config/bias_correction/may_jjas.json",
    "reference_amount_method": "mvs",
    "models": [
      "emos_csg",
      "extended_logistic",
      "quantile_regression"
    ],
    "parameters": {
      "quantile_regression": {
        "l2": 0.0001
      }
    },
    "output_root": "outputs/probability_calibration/may_jjas/distribution"
  },
  "protocol": "Phase 2, Pathway B: rainfall-distribution models against the published pipeline (docs/48).\n\nEach model (dist_calibration.py) predicts a full rainfall distribution from the raw ensemble; terciles, quantiles and intervals\nare derived from it. Design per target (national cells), as in Phase 1:\n  outer leave-one-year-out over the training years: every model is fitted on the other training years only (pooled over cells,\n  equal weight per year); the left-out year is predicted and scored.\n  test years: fitted on all training years, scored on the test years (exploratory).\nReferences, computed in the same folds:\n  probabilities: the incumbent published pipeline (MVS -> smoothed counts -> climatology blend, blend weight from the inner\n                 out-of-fold years, as in Phase 1);\n  amounts:       the MVS-corrected ensemble (CRPS of its empirical distribution, empirical quantiles).\nScores (per year, area-weighted over a region; equal years): tercile RPS/RPSS, CRPS/CRPSS (training CHIRPS climatology),\npinball loss at 10/50/90% and coverage of the 10-90% interval. Tests: RPS of each model against the incumbent and CRPS of\neach model against the MVS ensemble, paired whole-year sign-flip, Holm across the models and targets, no harm over the test\nyears.\n\n  python scripts/dist_benchmark.py --config config/probability_calibration/sep_ondj_distribution.json",
  "provenance": {
    "commit": "6d8236d4a4451887bffc84aaee53f54723a9d820",
    "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/may_jjas.json",
      "sha256": "2cfeeebc7d4d91f3bee67e440c9f74f41b2fca3705c95a0bfd1d7e7b2c568efa"
    },
    "project_config": {
      "path": "config/project.json",
      "sha256": "e4f0865e03a0a8349a5c01a48120cd7e4ef41bb865e2370733a3b81ddf862160"
    },
    "cycle_config": {
      "path": "config/operational.json",
      "sha256": "44ba9357752249ef879af3aad10cec30f02165ac3589ce626123f5b8b3286b14"
    },
    "masks": {
      "cells": {
        "path": "data/masks/ethiopia_common.nc",
        "sha256": "57ad8861c598abb9f175012920e5d0ef058e5b643cd6e4987cad3da6ac0b9e16"
      },
      "ethiopia": {
        "path": "data/masks/ethiopia_common.nc",
        "sha256": "57ad8861c598abb9f175012920e5d0ef058e5b643cd6e4987cad3da6ac0b9e16"
      },
      "jjas_domain": {
        "path": "data/masks/init05_JJAS_regime_domain.nc",
        "sha256": "65b8d6bf9c3ac5ef408688ea9f79e028e421f7fdd371d093beb693db0fcfe802"
      }
    },
    "inputs": {
      "Jun": {
        "folder": "data/processed/init05_Jun",
        "files": 67,
        "sha256": "ee40e28ce79056d49d6074e4180c2a5275a52d5b6564a4a09edbcaf8850a4ec2"
      },
      "Jul": {
        "folder": "data/processed/init05_Jul",
        "files": 67,
        "sha256": "27e6089a6cbb581469a59d2e5a551c8e0bd7df0751a95e8c844f1eb63ba22d26"
      },
      "Aug": {
        "folder": "data/processed/init05_Aug",
        "files": 67,
        "sha256": "5089f3c813e80e8227cd20d1587a857bc545c3c4ff092cd0f53715a2b7a50c0a"
      },
      "Sep": {
        "folder": "data/processed/init05_Sep",
        "files": 67,
        "sha256": "e29f812041c61bcdae92d419fcec6f05a888f570a5e05b2090dd49a6f11795fb"
      },
      "JJAS": {
        "folder": "data/processed/init05_JJAS",
        "files": 67,
        "sha256": "8b484f2d4d7c869da0d764bc930dd14a9f34666590affb072f4d073058a61c4d"
      }
    },
    "scoring_version": 3,
    "dependency_sha256": "4ff037337550c323f604433358db4479f40d36f7febbb5f2da044fa3477494bc",
    "python": "3.11.9",
    "numpy": "1.26.4"
  },
  "phase": 2,
  "pathway": "B: rainfall-distribution models from the raw ensemble",
  "models": [
    "emos_csg",
    "extended_logistic",
    "quantile_regression"
  ],
  "registry": {
    "emos_csg": {
      "name": "EMOS, censored shifted gamma",
      "estimates": "rainfall distribution with a probability of zero",
      "predictors": "ensemble mean m' and mean absolute difference md' (relative)",
      "fitting": "maximum likelihood, pooled over cells",
      "references": [
        "Scheuerer & Hamill 2015"
      ],
      "outputs": "CDF, quantiles, tercile probabilities"
    },
    "extended_logistic": {
      "name": "Heteroscedastic extended logistic regression",
      "estimates": "coherent CDF across all thresholds",
      "predictors": "sqrt of the ensemble mean m', log spread s' (relative)",
      "fitting": "log loss over each cell\u2019s CHIRPS deciles, pooled",
      "references": [
        "Wilks 2009",
        "Messner et al. 2014"
      ],
      "outputs": "CDF, quantiles, tercile probabilities"
    },
    "quantile_regression": {
      "name": "Regularized quantile regression",
      "estimates": "conditional quantiles at 19 levels (5\u201395%), CDF by interpolation",
      "predictors": "ensemble mean m' and spread s' (relative)",
      "fitting": "pinball loss per level (Huber-smoothed), L2 1e-4, pooled",
      "references": [
        "Koenker & Bassett 1978"
      ],
      "outputs": "CDF, quantiles, tercile probabilities"
    }
  },
  "references": {
    "incumbent": "MVS -> smoothed counts -> climatology blend (published probabilities)",
    "mvs_ensemble": "MVS-corrected ensemble (empirical distribution)"
  },
  "regions": {
    "ethiopia": "All Ethiopia",
    "jjas_domain": "JJAS R1+R2 rainfall domain"
  },
  "primary_region": "ethiopia",
  "training_years": [
    1993,
    2016
  ],
  "test_years": [
    2017,
    2025
  ],
  "targets": {
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      "tag": "init05_Jun",
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