{
  "created_utc": "2026-10-13T13:32:48.022634+00:00",
  "config": "config/probability_calibration/sep_ondj_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/sep_ondj.json",
    "reference_amount_method": "mvs",
    "models": [
      "emos_csg",
      "extended_logistic",
      "quantile_regression"
    ],
    "parameters": {
      "quantile_regression": {
        "l2": 0.0001
      }
    },
    "output_root": "outputs/probability_calibration/sep_ondj/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/sep_ondj.json",
      "sha256": "3c5c2ef194f529714cacf8e4e8c6332254fd8b536a48f6dbf5613d582d9651bc"
    },
    "project_config": {
      "path": "config/ondj/project.json",
      "sha256": "e9ad8cfb30b21a731e86222ae0b592df124600d457d6fcd46d7a508a831c63de"
    },
    "cycle_config": {
      "path": "config/cycles/sep_2026_ondj.json",
      "sha256": "3f33a9615060aaefd7d2bd7318817a4d1b98ef57fc57132e6a48690a518349ab"
    },
    "masks": {
      "cells": {
        "path": "data/masks/ethiopia_common.nc",
        "sha256": "57ad8861c598abb9f175012920e5d0ef058e5b643cd6e4987cad3da6ac0b9e16"
      },
      "ethiopia": {
        "path": "data/masks/ethiopia_common.nc",
        "sha256": "57ad8861c598abb9f175012920e5d0ef058e5b643cd6e4987cad3da6ac0b9e16"
      },
      "ondj_domain": {
        "path": "data/masks/init09_ONDJ_regime_domain.nc",
        "sha256": "336c572d69af3c718de2ef0e537a2d2d98d73b98cab9b02f0b234501a523d83f"
      }
    },
    "inputs": {
      "Oct": {
        "folder": "data/processed/init09_Oct",
        "files": 65,
        "sha256": "0d2561be8800f597bd5f38f02dc670c9861a3aea08dc795d0d3e1105a909b6db"
      },
      "Nov": {
        "folder": "data/processed/init09_Nov",
        "files": 65,
        "sha256": "73d9d400dfa5457a448855f4a211901bf6884d03575b6efeee391376f3fa995a"
      },
      "Dec": {
        "folder": "data/processed/init09_Dec",
        "files": 65,
        "sha256": "df5a657046e479f4a70116e69305bec1938217a49df5b88475b272b397354a2d"
      },
      "Jan": {
        "folder": "data/processed/init09_Jan",
        "files": 65,
        "sha256": "fc51a4c4480bdc0b21d3f968861435ae0221b9660a0cd98c3ced69adad04e489"
      },
      "ONDJ": {
        "folder": "data/processed/init09_ONDJ",
        "files": 65,
        "sha256": "a8ba572412dc96aef88757296d4a01c83453576b53f79bf2ec734bd88c073fa7"
      }
    },
    "scoring_version": 3,
    "dependency_sha256": "4eb76abd6932ecd5356c72c3ee968a7b500ae82cdb667b598757197a49690a9f",
    "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",
    "ondj_domain": "ONDJ R3 (Deyr) domain"
  },
  "primary_region": "ethiopia",
  "training_years": [
    1993,
    2016
  ],
  "test_years": [
    2017,
    2024
  ],
  "targets": {
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      "tag": "init09_Oct",
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