{
  "created_utc": "2026-10-13T10:01:47.327917+00:00",
  "config": "config/probability_calibration/feb_fmam.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/feb_fmam.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/feb_fmam",
    "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/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": 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",
    "fmam_domain": "FMAM rainfall contribution domain"
  },
  "primary_region": "ethiopia",
  "training_years": [
    1993,
    2016
  ],
  "test_years": [
    2017,
    2025
  ],
  "targets": {
    "Feb": {
      "tag": "init02_Feb",
      "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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                  0.45303788000804884,
                  0.33268488659805484,
                  0.42701279488378235,
                  0.41841242170453297,
                  0.3194159203844659,
                  0.4195852458115674,
                  0.4933798948985926,
                  0.3628012208168902,
                  0.41680502038707523,
                  0.5840367302696414,
                  0.4417653447212832,
                  0.41271573599677913,
                  0.48098544742430965,
                  0.27655931887302454,
                  0.3995465457948823,
                  0.3807631876545,
                  0.25379998428819606,
                  0.3698774989164374,
                  0.368023724004967,
                  0.4260350678332725,
                  0.3934260565549052,
                  0.39600519902120807,
                  0.458420218085758
                ],
                "rps_clim": [
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                  0.4798896228256101,
                  0.4849075864486966,
                  0.41241504425749453,
                  0.5029558847077034,
                  0.5029939351629127,
                  0.4590515135406598,
                  0.5225990904174145,
                  0.3820603897566554,
                  0.4436804399577896,
                  0.4801714460795853,
                  0.3809037399509187,
                  0.40151024353467196,
                  0.4700844665522386,
                  0.466118920990152,
                  0.4707394720701949,
                  0.4008721558163862,
                  0.516086904524994,
                  0.38688232612428824,
                  0.4662425666589424,
                  0.43843885609330674,
                  0.41402493320976497,
                  0.41674923264831454,
                  0.4416090925394558
                ],
                "brier_below": [
                  0.04221720431507358,
                  0.3284723564783085,
                  0.09449198112746521,
                  0.23545488087522462,
                  0.31268251653905127,
                  0.10973245558133637,
                  0.3123785146108837,
                  0.3947560343537717,
                  0.1467278490078051,
                  0.29138679124905853,
                  0.2450842196191028,
                  0.18765297231510944,
                  0.2106380938121853,
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                  0.06893882450367653,
                  0.31333849742400843,
                  0.17016727086583722,
                  0.05321450342416322,
                  0.25450773133628557,
                  0.2632548476300475,
                  0.3297357370753349,
                  0.1346457946646722,
                  0.2680095286259636,
                  0.18640214216948392
                ],
                "brier_below_clim": [
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                  0.3240468021697362,
                  0.14785333547007734,
                  0.213053356723655,
                  0.3586999017921628,
                  0.15821566468317896,
                  0.3243944706740214,
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                  0.2874936534337067,
                  0.202814560835241,
                  0.1934689386829828,
                  0.21938847245972978,
                  0.15272693725494546,
                  0.13190277579879756,
                  0.3369986467976019,
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                  0.12718492747837762,
                  0.2526297611075025,
                  0.31415579752145567,
                  0.30060479918432803,
                  0.1409360490589818,
                  0.2547240986771653,
                  0.1707498076532336
                ],
                "brier_above": [
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                  0.12456552508953123,
                  0.2381929043474934,
                  0.1915579152730495,
                  0.10572990720866672,
                  0.20968346302823018,
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                  0.09862386282723855,
                  0.21607337250237724,
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                  0.09629933319812338,
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                "brier_above_clim": [
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                  0.15208676912310592,
                  0.13783405689458672,
                  0.2730888841507831,
                  0.16202513395676843,
                  0.2708592848862224
                ],
                "log_loss": [
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                  1.1129328627042012,
                  0.8683252045766034,
                  1.1379133054989075,
                  1.0087894328146714,
                  0.7933999238272711,
                  1.0772335772485726,
                  1.109341475603793,
                  1.0442430741310502,
                  1.0787211651585267,
                  1.2668314998738042,
                  1.1947358564679251,
                  1.0940809365138338,
                  1.1234620859845763,
                  0.7966736400422899,
                  1.0254459618910219,
                  1.027639336288975,
                  0.6721100040289911,
                  1.078608840387264,
                  0.9730169819349336,
                  1.153971499224365,
                  1.0504282333975246,
                  1.098013758744755,
                  1.1071124707168107
                ],
                "log_loss_clim": [
                  1.0636365215990604,
                  1.0851020934385713,
                  1.0829845357788488,
                  1.114915471528915,
                  1.0751910196539225,
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                  1.0941195291347958,
                  1.0665730030228233,
                  1.1278992668815964,
                  1.1009887350094487,
                  1.084851863174709,
                  1.1285317750993535,
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                  1.1201255184468752,
                  1.069304218453464,
                  1.1263882943170247,
                  1.0909646546286205,
                  1.103642856085365,
                  1.1140840166062393,
                  1.1131588909468124,
                  1.101981515597516
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              "rps": 0.39567972440710797,
              "rpss": 0.1263736164396203,
              "bss_below": 0.06512527212516095,
              "bss_above": 0.18796867271874562,
              "log_loss": 1.019170916797012,
              "log_loss_skill": 0.07100797054236485,
              "reliability": {
                "below": {
                  "weight": [
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                    0.113968,
                    0.139339,
                    0.141315,
                    0.050593,
                    0.0,
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                  "mean_forecast": [
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                    0.2508,
                    0.3548,
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                    0.5484,
                    0.6285,
                    NaN,
                    NaN,
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                  "observed_frequency": [
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                    0.3282,
                    0.4375,
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                },
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                  "mean_forecast": [
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                  "observed_frequency": [
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                    0.3617,
                    0.5128,
                    0.7344,
                    0.6883,
                    NaN,
                    NaN,
                    NaN
                  ]
                }
              }
            },
            "fmam_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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                  0.32483112662579694,
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                ],
                "rps_clim": [
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                "brier_below": [
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                "brier_below_clim": [
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