{ "created_utc": "2026-10-12T08:17:24.666365+00:00", "config": "config/bias_correction/may_jjas.json", "config_contents": { "schema_version": 1, "name": "may_jjas", "description": "Rainfall-amount bias-correction benchmark: May-initialized ECMWF SEAS5, JJAS season and its calendar months, every 0.25 deg cell of Ethiopia (docs/47).", "cycle": "config/operational.json", "project_config": "config/project.json", "targets": [ "Jun", "Jul", "Aug", "Sep", "JJAS" ], "training_years": [ 1993, 2016 ], "test_years": [ 2017, 2025 ], "all_years": [ 1993, 2025 ], "forecast_year": 2026, "cells": { "path": "data/masks/ethiopia_common.nc", "variable": "region_mask" }, "regions": { "ethiopia": { "label": "All Ethiopia", "path": "data/masks/ethiopia_common.nc", "variable": "region_mask" }, "jjas_domain": { "label": "JJAS R1+R2 rainfall domain", "path": "data/masks/init05_JJAS_regime_domain.nc", "variable": "season_domain" } }, "primary_region": "ethiopia", "reference_method": "mvs", "smoothing_alpha": 0.5, "dry_threshold_mm": 1.0, "pit_bins": 10, "methods": { "raw": {}, "ls": { "min_model_mean": 0.1 }, "mvs": { "ratio_bounds": [ 0.5, 2.0 ], "min_model_sd": 1.0 }, "vs": { "ratio_bounds": null, "min_model_sd": 1.0 }, "sqrt_mvs": { "ratio_bounds": [ 0.5, 2.0 ], "min_model_sd": 0.1, "space": "sqrt" }, "pt": { "b_bounds": [ 0.1, 3.0 ] }, "dc": { "ratio_bounds": [ 0.2, 5.0 ], "min_model_mean": 0.1 }, "loci": { "wet_threshold": 1.0 }, "eqm": { "extrapolation": "constant_ratio", "wet_threshold": 1.0 }, "gqm": { "wet_threshold": 1.0 }, "dqm": { "extrapolation": "constant_ratio", "ratio_bounds": [ 0.2, 5.0 ], "min_model_mean": 0.1, "wet_threshold": 1.0 }, "qdm": { "max_ratio": 5.0, "wet_threshold": 1.0 }, "sdm": { "wet_threshold": 1.0 }, "cdft": { "min_model_mean": 0.1 }, "mvs_zero_guard": { "ratio_bounds": [ 0.5, 2.0 ], "min_model_sd": 1.0, "zero_guard": true } }, "example_cell_quantiles": [ 0.1, 0.5, 0.9 ], "confirmation": { "methods": [ "mvs", "ls", "vs", "sqrt_mvs", "pt", "dc", "loci", "eqm", "gqm", "dqm", "qdm", "sdm", "cdft" ], "variants": [ "mvs_zero_guard" ], "selection_region": "ethiopia", "reliability_bins": 10, "reliability_methods": [ "mvs", "sqrt_mvs", "gqm", "eqm" ], "output_root": "outputs/bias_correction_benchmark/may_jjas/confirmation" }, "output_root": "outputs/bias_correction_benchmark/may_jjas" }, "provenance": { "commit": "9f397927f5d4ddf2be3a1fa52595f34fc8fcb56a", "uncommitted_changes": false, "code_sha256": { "scripts/bias_methods.py": "03b2fdd8a645b96f27e4087a96408c0b412b135c80bfc23637757c6c44f9c370", "scripts/bias_benchmark.py": "b2cbc28349379aa016ef81d0255ac9edd21b38ffc5425b9a12c93a1df52e9084", "scripts/bias_confirmation.py": "f2fd9f9c0a73d3ed54e670f7770b47139793173cbdcd42dda1ba642be90848cc", "scripts/significance.py": "3143761d5d201e4e62df9ad923757b952a3ea768b060c8a722afcd9b3d6a9963", "scripts/calibration_core.py": "f0635cc563e6d6aedb62799847b83fbb0f5cc12ee9f8c48ad48dda15a054fb62", "scripts/compare_calibration.py": 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"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": "d4f6e297ca276421b1d67bfeb02975f0b46767778625c079f42f6d68192da3f3", "python": "3.11.9", "numpy": "1.26.4" }, "protocol": "Benchmark rainfall-amount bias-correction methods member by member, for every grid cell and target (docs/47).\n\nRead-only experiment: never touches the operational forecasts. Configured by config/bias_correction/.json.\n\nDesigns (one score per year: area-weighted over a region; years weighted equally):\n training : leave-one-year-out within the training years; the scored year never enters a fit (clean; screened)\n operational : fit on the training years, scored on the test years (exploratory; these years were seen before)\n full : leave-one-year-out within all observed years, for per-cell maps and distribution checks (descriptive)\n forecast : fit on all observed years, corrected forecast-year members of every method (member-by-member comparison)\nScores: ensemble CRPS (mm) of the empirical distribution delivered to users and its CRPSS against the CHIRPS training\nclimatology (primary); the fair CRPS (Ferro 2014) as a separately labelled diagnostic; ensemble-mean bias, MAE, RMSE\nand correlation; spread-error ratio; rank histograms; tercile RPS/RPSS of alpha-smoothed member counts (no\nclimatology blend); distribution of the pooled corrected members against CHIRPS (mean, standard deviation, 10/50/90th\npercentiles, exactly-0 and at-or-below-threshold shares); coverage of the amount and probability scores; how often\neach method's limits, caps and fallbacks act.\nScreening: each method against the reference (operational MVS) per target and score (CRPS, fair CRPS, RPS):\none-sided sign-flip test on the training-design per-year scores and no harm over the test years. Two\nmultiplicity families are reported: within one method across the targets (\"screen_pass\") and across every\ndeclared method and target (\"screen_pass_family\"; sensitivity variants such as the MVS all-zero guard are screened\nwithin method only). A screening pass is not an adoption decision. For the reference comparison, each cell's\ncontribution is also split by its climate group in the fold's training years (all 0 mm, all dry, mixed, always wet).", "gate_rule": "Screening rule (docs/36 rule applied as a screen): a method passes for a target and score if, on the leave-one-year-out 1993-2016 per-year scores, its mean improvement over mvs has a one-sided sign-flip p-value below 0.05 after Holm adjustment, and its mean score over 2017-2025 (fits on 1993-2016) is not worse. 'screen_pass': Holm across the 5 targets of one method. 'screen_pass_family': Holm across every method and target of the benchmark for that score. A screening pass is not an operational adoption decision (see the confirmation run).", "methods": { "raw": { "label": "Raw ECMWF (no correction)", "family": "baseline", "definition": "x' = x (the uncorrected model; baseline, not a candidate)", "references": [ "Johnson et al. 2019" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": {} }, "ls": { "label": "Linear Scaling", "family": "scaling", "definition": "x' = x * mu_obs / mu_model (multiplicative mean correction; ratio 1 where mu_model < 0.1 mm)", "references": [ "Lenderink et al. 2007", "Teutschbein & Seibert 2012" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "min_model_mean": 0.1 } }, "mvs": { "label": "Mean-Variance Scaling (operational)", "family": "scaling", "definition": "x' = max(0, mu_obs + r (x - mu_model)), r = clip(sd_obs/sd_model, 0.5, 2); r = 1 if sd_model < 1 mm", "references": [ "Torralba et al. 2017", "Manzanas et al. 2019" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "ratio_bounds": [ 0.5, 2.0 ], "min_model_sd": 1.0 } }, "vs": { "label": "Variance Scaling (unbounded)", "family": "scaling", "definition": "Same algebra and the same r = 1 fallback (sd_model < 1 mm) as MVS, without the 0.5-2 ratio limits", "references": [ "Teutschbein & Seibert 2012" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "ratio_bounds": null, "min_model_sd": 1.0 } }, "sqrt_mvs": { "label": "Square-root-space Mean-Variance Scaling", "family": "scaling", "definition": "MVS applied to sqrt(x) (r limits 0.5-2; r = 1 if the sqrt-space sd < 0.1), clipped at 0 and squared back (docs/36)", "references": [ "Manzanas et al. 2019" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "ratio_bounds": [ 0.5, 2.0 ], "min_model_sd": 0.1, "space": "sqrt" } }, "pt": { "label": "Power Transformation", "family": "scaling", "definition": "x' = a x^b: b (0.1-3) matches the coefficient of variation where reachable (bound hits reported), a the mean", "references": [ "Leander & Buishand 2007", "Teutschbein & Seibert 2012" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "b_bounds": [ 0.1, 3.0 ] } }, "dc": { "label": "Delta Change", "family": "change-preserving", "definition": "Training-year CHIRPS totals times a = forecast ensemble mean / model mean (a in 0.2-5): historical scenarios, not corrected members", "references": [ "Hay et al. 2000", "Teutschbein & Seibert 2012" ], "member_wise": false, "uses_forecast_distribution": true, "parameters": { "ratio_bounds": [ 0.2, 5.0 ], "min_model_mean": 0.1 } }, "loci": { "label": "Local Intensity Scaling", "family": "scaling", "definition": "Model threshold with the observed frequency of totals above 1 mm, then intensity scaling above it (designed for daily rainfall; here monthly or seasonal totals)", "references": [ "Schmidli et al. 2006" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "wet_threshold": 1.0 } }, "eqm": { "label": "Empirical Quantile Mapping", "family": "quantile mapping", "definition": "x' = Q_obs(F_model(x)) with the dry policy; constant-ratio extrapolation outside the training range", "references": [ "Boe et al. 2007", "Themessl et al. 2011", "Gudmundsson et al. 2012" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "extrapolation": "constant_ratio", "wet_threshold": 1.0 } }, "gqm": { "label": "Gamma Quantile Mapping", "family": "quantile mapping", "definition": "x' = G_obs^-1(G_model(x)), G = dry mass (<= 1 mm) + gamma by weighted maximum likelihood, with the dry policy; linear scaling where fewer than 3 wet values", "references": [ "Piani et al. 2010" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "wet_threshold": 1.0 } }, "dqm": { "label": "Detrended Quantile Mapping", "family": "change-preserving", "definition": "a * EQM(x / a), a = forecast ensemble mean / model mean (0.2-5): removes and reapplies the bounded ratio; exact preservation of the final mean anomaly is not guaranteed", "references": [ "Cannon et al. 2015" ], "member_wise": true, "uses_forecast_distribution": true, "parameters": { "extrapolation": "constant_ratio", "ratio_bounds": [ 0.2, 5.0 ], "min_model_mean": 0.1, "wet_threshold": 1.0 } }, "qdm": { "label": "Quantile Delta Mapping", "family": "change-preserving", "definition": "x' = Q_obs(tau) * x / Q_model(tau), tau = rank of x in the forecast ensemble; change capped at 5 and replaced by Q_obs(tau) where Q_model(tau) <= 1 mm (both reported), so preservation is partial there", "references": [ "Cannon et al. 2015" ], "member_wise": true, "uses_forecast_distribution": true, "parameters": { "max_ratio": 5.0, "wet_threshold": 1.0 } }, "sdm": { "label": "Scaled Distribution Mapping", "family": "change-preserving", "definition": "Switanek et al. (2017) precipitation steps 1-7: gamma fits, recurrence intervals interpolated to the forecast's length, scaled CDF, rain-member count; model climatology with equal-year weights", "references": [ "Switanek et al. 2017" ], "member_wise": true, "uses_forecast_distribution": true, "parameters": { "wet_threshold": 1.0 } }, "cdft": { "label": "CDF-transform", "family": "change-preserving", "definition": "F_obs,f = F_obs(F_model^-1(F_forecast)); x' = F_obs,f^-1(F_forecast(x)); implementation choices: model and forecast first scaled to the CHIRPS mean, values stay within the scaled forecast range", "references": [ "Michelangeli et al. 2009", "Vrac et al. 2012" ], "member_wise": true, "uses_forecast_distribution": true, "parameters": { "min_model_mean": 0.1 } }, "mvs_zero_guard": { "label": "MVS with all-zero guard (variant)", "family": "variant", "definition": "Operational MVS, but corrected totals are 0 mm where every training year has exactly 0 mm (a documented modelling choice, not a physical rule)", "references": [ "Torralba et al. 2017", "Manzanas et al. 2019" ], "member_wise": true, "uses_forecast_distribution": false, "parameters": { "ratio_bounds": [ 0.5, 2.0 ], "min_model_sd": 1.0, "zero_guard": true } } }, "targets": { "Jun": { "tag": "init05_Jun", "cells": 1484, "methods": { "raw": { "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": { "crps": [ 29.73123186545845, 28.04465155842142, 26.785928892210265, 35.75671459608659, 43.085491345075724, 32.456487436336566, 23.319728714205365, 29.64485373691109, 27.758087368472598, 25.109608459154884, 35.06749695394589, 31.487925792348644, 28.06507274718875, 30.02364757168086, 33.75395687910457, 25.08982851602991, 28.425695860971544, 29.985605823046267, 32.40512005446808, 28.33799212330636, 23.237688414223083, 24.306416551493136, 32.029481703304576, 24.728604180922222 ], "crps_clim": [ 13.598628827431384, 16.1287234213203, 16.846876606138146, 30.663467984689664, 22.80447649187906, 18.226651308312636, 14.525911039990154, 14.251447116167167, 13.837640825193597, 12.137215810172771, 16.791243786004284, 15.751382993845937, 12.898768089874904, 10.917544921058786, 28.541535877041742, 12.922717363327399, 16.266275488306196, 12.276815800801508, 15.42283207898044, 14.38398343120619, 12.541645594742256, 20.245101600952477, 20.421425827020485, 13.948777217630555 ], "crps_fair": [ 29.243988979961387, 27.51948272448848, 26.260731253490814, 35.1368603646405, 42.601586778066, 31.959108292130107, 22.77829176636773, 29.12764650049459, 27.145909710038417, 24.531089340300813, 34.49078654506866, 30.889131467097645, 27.466520107745083, 29.51003687360772, 33.17849338511798, 24.501545185676477, 27.958703124795154, 29.4466902057842, 31.826705535639814, 27.814897646246994, 22.652720386028918, 23.768624789151442, 31.502940096078003, 24.19432465911316 ], "crps_fair_clim": [ 12.904996569959033, 15.439883009669698, 16.159396333095156, 30.002155498348493, 22.128279551953014, 17.541784244932863, 13.834034998829466, 13.559051256968939, 13.144461241959426, 11.44081572501621, 16.10365814777164, 15.061827922294817, 12.203810338581421, 10.218834853157688, 27.87620457989062, 12.227804970506362, 15.577695591933987, 11.580680109566593, 14.732654751908749, 13.69183858775614, 11.84601147508678, 19.564057360096648, 19.74071553356249, 13.255808119988123 ], "rps": [ 0.7250112489889046, 0.6324223894895202, 0.6347372833286056, 0.5413992434369884, 0.9551105468702191, 0.7787312512879477, 0.5557117939472559, 0.6234867421709851, 0.6606202294397442, 0.5658497707422564, 0.6993924934699656, 0.6036844569939083, 0.6145338502375445, 0.7939175372980674, 0.6511346021284617, 0.5171177499408168, 0.6499122147130312, 0.7034439954620012, 0.8215428593792655, 0.6432815689457789, 0.5035962637614073, 0.5808107006839713, 0.7747117206348227, 0.546976946478911 ], "rps_clim": [ 0.44720547123089077, 0.4589801915113287, 0.4635911453059036, 0.4595146547250173, 0.4903287387863352, 0.4582362529556338, 0.45761747407668063, 0.46494431407467296, 0.4237729823019771, 0.4155827009516894, 0.4368040922007073, 0.4627485570660647, 0.40386649932915103, 0.4219599357733368, 0.5019918976338255, 0.4318073507386062, 0.4712546280602498, 0.4328409615693309, 0.48030200333874057, 0.4457785596836651, 0.4573407337768707, 0.4724286848016833, 0.46721179707623267, 0.4529883734945551 ], "bias": [ -7.758361722367513, -3.0997895007325367, -4.302292719095161, -11.349030090895285, -47.51535554422696, -26.52445797186852, 9.560252687816277, 20.361251423681068, 9.993696074491085, 13.393621644431022, 6.828844387002807, 36.27567570864364, 15.67313083910682, 0.7317594109943185, -24.747383429336146, 7.459974931677958, -1.9951449696245604, -9.083367276737537, -2.1431623343506896, 2.2870964247418524, 0.7009972976063379, 3.6325896128981334, -0.5257921734949514, 1.8543510453280332 ], "mae": [ 38.016101912179494, 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