{
  "created_utc": "2026-10-13T12:44:52.216106+00:00",
  "config": "config/probability_calibration/feb_fmam_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/feb_fmam.json",
    "reference_amount_method": "mvs",
    "models": [
      "emos_csg",
      "extended_logistic",
      "quantile_regression"
    ],
    "parameters": {
      "quantile_regression": {
        "l2": 0.0001
      }
    },
    "output_root": "outputs/probability_calibration/feb_fmam/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/feb_fmam.json",
      "sha256": "5e74d282f6e0f984a3bb2d0527f665d535894be91f139628db7e8deb7d5374c9"
    },
    "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": "48caccb09fad2e9ffb99c16c117a2a316e3649df3d0cf9295084fe5a3101064c",
    "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",
    "fmam_domain": "FMAM rainfall contribution domain"
  },
  "primary_region": "ethiopia",
  "training_years": [
    1993,
    2016
  ],
  "test_years": [
    2017,
    2025
  ],
  "targets": {
    "Feb": {
      "tag": "init02_Feb",
      "methods": {
        "emos_csg": {
          "training": {
            "ethiopia": {
              "years": [
                1993,
                1994,
                1995,
                1996,
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                1998,
                1999,
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                2003,
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                2007,
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                2010,
                2011,
                2012,
                2013,
                2014,
                2015,
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              ],
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