{ "created_utc": "2026-10-13T11:29:21.530974+00:00", "config": "config/probability_calibration/sep_ondj.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/sep_ondj.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/sep_ondj", "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/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": 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", "ondj_domain": "ONDJ R3 (Deyr) domain" }, "primary_region": "ethiopia", "training_years": [ 1993, 2016 ], "test_years": [ 2017, 2024 ], "targets": { "Oct": { "tag": "init09_Oct", "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 ], 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