{
  "created_utc": "2026-10-13T11:04:47.497649+00:00",
  "config": "config/probability_calibration/may_jjas.json",
  "config_contents": {
    "description": "Phase 1 probability-calibration benchmark (docs/48): Pathway A, the amount correction fixed at the operational MVS; the tercile-probability calibration replaced.",
    "benchmark_config": "config/bias_correction/may_jjas.json",
    "amount_method": "mvs",
    "methods": [
      "climatology_blend",
      "pseudocount",
      "temperature",
      "dirichlet",
      "multinomial_logistic",
      "ordinal_logistic"
    ],
    "parameters": {
      "multinomial_logistic": {
        "l2": 0.001
      },
      "ordinal_logistic": {
        "l2": 0.001
      }
    },
    "selection": true,
    "selection_region": "ethiopia",
    "output_root": "outputs/probability_calibration/may_jjas",
    "selection_folds": 6
  },
  "protocol": "Phase 1 probability-calibration benchmark (docs/48): which tercile-probability method gives the best published probabilities?\n\nPathway A: the amount correction is fixed (the operational MVS, configurable); the probability-calibration stage is replaced.\nFor each target, nested year-based validation over the training years:\n  outer leave-one-year-out: for the left-out year y every fit uses only the other training years \u2014 the amount correction and\n  terciles (each inner year z from a fit without y and z), and every calibrator's parameters, fitted on the inner out-of-fold\n  records of the other years; the calibrated probabilities of year y are then scored.\n  test years: fits on all training years (calibrators on their leave-one-year-out records), scored on the test years\n  (exploratory).\n  SEL: a calibrator is chosen for each outer year by inner cross-validation of the calibrators (blocks of consecutive\n  years, 'selection_folds') on the inner records only (lowest inner RPS), so its score includes the choice among the methods.\nScores (per year, area-weighted over a region; equal years): RPS/RPSS (primary), category Brier scores and skill, log loss\nand its skill against climatology, reliability tables. Tests: each method against the incumbent climatology blend, paired\nwhole-year sign-flip, Holm across every method and target; SEL against the incumbent, Holm across the targets; no harm over\nthe test years.\n\n  python scripts/prob_benchmark.py --config config/probability_calibration/sep_ondj.json",
  "provenance": {
    "commit": "0849f4436ec4f79abcf169365a2730ae07b86b75",
    "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": 1,
  "pathway": "A: fixed amount correction, replaced probability calibration",
  "amount_method": "mvs",
  "incumbent": "climatology_blend",
  "methods": [
    "climatology_blend",
    "pseudocount",
    "temperature",
    "dirichlet",
    "multinomial_logistic",
    "ordinal_logistic"
  ],
  "version": 1,
  "registry": {
    "pseudocount": {
      "name": "Pseudocount smoothing (no blend)",
      "inputs": "member counts: p_k = (n_k + alpha) / (M + 3 alpha)",
      "multiclass": "joint (counts)",
      "parameters": "alpha fixed (0.5)",
      "references": [
        "Wilks 2019"
      ],
      "incumbent": false,
      "outputs": "tercile probabilities only (no CDF)"
    },
    "climatology_blend": {
      "name": "Climatology blend (incumbent)",
      "inputs": "smoothed probabilities and training climatology",
      "multiclass": "joint",
      "parameters": "lambda: one per target, minimum RPS",
      "references": [
        "Rajagopalan et al. 2002"
      ],
      "incumbent": true,
      "outputs": "tercile probabilities only (no CDF)"
    },
    "temperature": {
      "name": "Temperature scaling",
      "inputs": "log smoothed probabilities",
      "multiclass": "joint (softmax)",
      "parameters": "one temperature T in 0.25-4, log loss",
      "references": [
        "Guo et al. 2017"
      ],
      "incumbent": false,
      "outputs": "tercile probabilities only (no CDF)"
    },
    "dirichlet": {
      "name": "Regularized Dirichlet calibration",
      "inputs": "log smoothed probabilities only",
      "multiclass": "joint (softmax of a linear map)",
      "parameters": "3x3 map + bias, identity-regularized, log loss",
      "references": [
        "Kull et al. 2019"
      ],
      "incumbent": false,
      "outputs": "tercile probabilities only (no CDF)"
    },
    "multinomial_logistic": {
      "name": "Regularized multinomial logistic regression",
      "inputs": "standardized ensemble mean, log spread ratio (no probabilities)",
      "multiclass": "joint (softmax; near = reference)",
      "parameters": "6 coefficients, L2 on slopes, log loss",
      "references": [
        "Wilks 2019"
      ],
      "incumbent": false,
      "outputs": "tercile probabilities only (no CDF)"
    },
    "ordinal_logistic": {
      "name": "Ordinal (cumulative-logit) logistic regression",
      "inputs": "standardized ensemble mean, log spread ratio",
      "multiclass": "ordinal (ordered cutpoints)",
      "parameters": "2 ordered cutpoints + 2 slopes, L2 on slopes, log loss",
      "references": [
        "McCullagh 1980"
      ],
      "incumbent": false,
      "outputs": "tercile probabilities only (no CDF)"
    }
  },
  "regions": {
    "ethiopia": "All Ethiopia",
    "jjas_domain": "JJAS R1+R2 rainfall domain"
  },
  "primary_region": "ethiopia",
  "training_years": [
    1993,
    2016
  ],
  "test_years": [
    2017,
    2025
  ],
  "targets": {
    "Jul": {
      "tag": "init05_Jul",
      "methods": {
        "climatology_blend": {
          "training": {
            "ethiopia": {
              "years": [
                1993,
                1994,
                1995,
                1996,
                1997,
                1998,
                1999,
                2000,
                2001,
                2002,
                2003,
                2004,
                2005,
                2006,
                2007,
                2008,
                2009,
                2010,
                2011,
                2012,
                2013,
                2014,
                2015,
                2016
              ],
              "per_year": {
                "rps": [
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                  0.44685122092913226,
                  0.45270312920250516,
                  0.494517874973582,
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                  0.44530113428949447,
                  0.4629530944351337,
                  0.4070807450395527,
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                ],
                "rps_clim": [
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                  0.4668563967515232,
                  0.4722071384657149,
                  0.4700629053401188,
                  0.4416277853045537
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                "brier_below": [
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                  0.16775019859790208,
                  0.23718211912626735,
                  0.20243225252102015,
                  0.2711196602479812,
                  0.15843290829455117,
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                  0.23161519916593432
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                "brier_below_clim": [
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                  0.1772720877647993,
                  0.23250880823643677,
                  0.18364157998711608,
                  0.25298088487778414,
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                "brier_above": [
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                  0.28637357997194907,
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                  0.16627710440451882,
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                "log_loss": [
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                  1.1028371201509384,
                  1.1554386591534869,
                  1.1401093390945816,
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                  1.007054100655117,
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                "log_loss_clim": [
                  1.0871650777805744,
                  1.0895794529049794,
                  1.0993751170032733,
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              "rps": 0.4463252661853912,
              "rpss": 0.014453588830031228,
              "bss_below": 0.014653198354576569,
              "bss_above": 0.01425347552130507,
              "log_loss": 1.084985622253854,
              "log_loss_skill": 0.010997454531503692,
              "reliability": {
                "below": {
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                  "mean_forecast": [
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                  "observed_frequency": [
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                  ]
                }
              }
            },
            "jjas_domain": {
              "years": [
                1993,
                1994,
                1995,
                1996,
                1997,
                1998,
                1999,
                2000,
                2001,
                2002,
                2003,
                2004,
                2005,
                2006,
                2007,
                2008,
                2009,
                2010,
                2011,
                2012,
                2013,
                2014,
                2015,
                2016
              ],
              "per_year": {
                "rps": [
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                "brier_below": [
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