{ "created_utc": "2026-10-13T12:57:17.473726+00:00", "config": "config/probability_calibration/may_jjas_phase2.json", "config_contents": { "description": "Phase 2, Pathway A: one-vs-rest binary calibrators (Platt, beta, isotonic) against the incumbent climatology blend; amount correction fixed (docs/48).", "benchmark_config": "config/bias_correction/may_jjas.json", "amount_method": "mvs", "methods": [ "climatology_blend", "platt_ovr", "beta_ovr", "isotonic_ovr" ], "parameters": { "platt_ovr": { "l2": 0.0001 } }, "selection": true, "selection_region": "ethiopia", "output_root": "outputs/probability_calibration/may_jjas/phase2_binary", "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": "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": 1, "pathway": "A: fixed amount correction, replaced probability calibration", "amount_method": "mvs", "incumbent": "climatology_blend", "methods": [ "climatology_blend", "platt_ovr", "beta_ovr", "isotonic_ovr" ], "version": 1, "registry": { "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)" }, "platt_ovr": { "name": "Platt (sigmoid) calibration, one-vs-rest + normalization", "inputs": "logit of each smoothed category probability", "multiclass": "one-vs-rest, then divided by the sum", "parameters": "2 per category (intercept, slope), L2 1e-4 on the slope, binary log loss", "references": [ "Platt 1999" ], "incumbent": false, "outputs": "tercile probabilities only (no CDF)" }, "beta_ovr": { "name": "Beta calibration, one-vs-rest + normalization", "inputs": "ln p_k and ln(1 - p_k) of each smoothed category probability", "multiclass": "one-vs-rest, then divided by the sum", "parameters": "3 per category (c, a >= 0, b >= 0), binary log loss", "references": [ "Kull et al. 2017" ], "incumbent": false, "outputs": "tercile probabilities only (no CDF)" }, "isotonic_ovr": { "name": "Isotonic regression, one-vs-rest + normalization", "inputs": "each smoothed category probability", "multiclass": "one-vs-rest, then divided by the sum", "parameters": "monotone step map per category, pooled over cells and years (weighted pool-adjacent-violators)", "references": [ "Zadrozny & Elkan 2002" ], "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": { "Jun": { "tag": "init05_Jun", "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": [ 0.42931540966969034, 0.47065644575323196, 0.455007189552589, 0.4309998811722683, 0.5782387558747842, 0.4580698251660113, 0.4673562439957732, 0.4713222547069534, 0.4173031681210436, 0.4140214911447917, 0.411735574757159, 0.5098099865306472, 0.420683955250026, 0.4215406625981025, 0.4820695377199034, 0.41557494494958475, 0.45906283129268155, 0.4330666963668821, 0.48851801158464425, 0.4373224426094803, 0.46643924902319445, 0.4391641594949776, 0.4671247188545888, 0.4392022539345633 ], "rps_clim": [ 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