Publish exact Mini <2024 identity, UTC proportions and total/post-2024 comparison charts

.gitattributes

@@ -197,3 +197,4 @@ evaluation/release_data/pressure_core/mangkhut/common-pressure.npz filter=lfs di
 evaluation/released_daily/pressure_comparison.png filter=lfs diff=lfs merge=lfs -text
 evaluation/released_daily/model_1_2_benchmark.png filter=lfs diff=lfs merge=lfs -text
 docs/trackformer_1_2_mangkhut.gif filter=lfs diff=lfs merge=lfs -text
+evaluation/released_daily/model_1_2_after_2024_benchmark.png filter=lfs diff=lfs merge=lfs -text

docs/deepmind_daily_benchmark.md

@@ -41,6 +41,29 @@ Mini has better recent-only track scores even though 1.2 has lower position/head
 
 [Full results, period breakdowns and per-lead track errors](../evaluation/deepmind_daily/benchmark.json) · [Shared snapshot and existing 1.1/1.2 uncertainty](../evaluation/released_daily/released_daily_benchmark.json) · [Completion receipt](../evaluation/deepmind_daily/verification.json) · [Independent review](../evaluation/deepmind_daily/publication_audit.json)
 
+### Strict forecast-date partition and separate charts
+
+Both README charts use **`WeatherNextCyclones_Mini_<2024`**, the same official checkpoint trained through 2023, software v0.3.0, native 1° and one CUDA member. A post-2024 forecast is not a post-2024-trained model. The following proportions refer to this evaluation cohort, not the model's training dataset.
+
+| UTC issue year | Starts / share | Storms / share |
+| --- | --- | --- |
+| `<2024` (1980–1999 here) | 1,336 / 90.7% | 230 / 85.2% |
+| Calendar 2024 | 76 / 5.2% | 22 / 8.1% |
+| `>2024` (2025–2026 here) | 61 / 4.1% | 18 / 6.7% |
+
+The original `recent_2024_onward` aggregate includes 2024 and is retained unchanged. The new **strict `after_2024`** derivative excludes 2024: track position MAE is **963.02 / 469.40 / 258.67 km**, direction error **57.88 / 33.81 / 28.73°**, and JMA pressure MAE **12.59 / 11.12 / 9.55 hPa** for 1.1 / 1.2 / Mini. Track uses 61 starts / 18 storms; pressure uses 59 starts / 18 storms / 1,174 exact common leads. The total chart still uses the original scores. Its historical 230 storms contribute 85.2% of equal-storm track weight, not 90.7%.
+
+[UTC partitions, exact case IDs, all metrics and source hashes](../evaluation/deepmind_daily/period_comparison.json) · [Total chart](../evaluation/released_daily/model_1_2_benchmark.png) · [Post-2024 chart](../evaluation/released_daily/model_1_2_after_2024_benchmark.png)
+
+Reproduce these derivative summaries without inference or raw-weather downloads:
+
+```bash
+python release_tools/build_deepmind_period_comparison.py --archive /path/to/DeepMind_RTX3070_results_20261004_125651_467960.zip
+python release_tools/plot_model_announcement.py
+```
+
+The helper checks the exact archive hash against the existing publication audit and every one of the 1,473 case-score JSON hashes against the frozen manifest. It rechecks identities, causal-time metadata, CUDA completion and common pressure masks, reproduces the original total and period scores, then recomputes only the UTC subsets using the unchanged equal-storm aggregation. It never rewrites the original worker export, changes a forecast array or claims a new raw-input audit. Chart sidecars identify their derivative source hash, checkpoint, period, exact values and missing-data policy.
+
 ## Like-for-like scoring
 
 All three models use the frozen starts, exact future label times and original issue-relative kilometre projection. Routes are unshifted. Direction is recomputed on **common moving steps across truth and all three models**, not compared using different masks.

evaluation/deepmind_daily/period_comparison.json

@@ -0,0 +1,3959 @@
+{
+  "schema": "deepmind-completed-utc-period-comparison-v1",
+  "status": "complete_verified",
+  "model": {
+    "key": "deepmind",
+    "label": "Google DeepMind WeatherNext Cyclones Mini (<2024)",
+    "model_checkpoint": "WeatherNextCyclones_Mini_<2024",
+    "software_version": "0.3.0",
+    "software_commit": "89c4b2a77a1c57b328b909c575550fd2e5aadc9c",
+    "trained_through": 2023,
+    "resolution_degrees": 1,
+    "member_count": 1,
+    "member_policy": "One official Mini member, seed 0",
+    "checkpoint_sha256": "a1bb151457077248d70a458b1a7b19deacd2926fbcb46eee4ff2691444e3596e",
+    "method": "Official predictor and initialized-storm tracker; causal ERA5; RTX 3070 CUDA",
+    "status": "complete_verified"
+  },
+  "single_checkpoint_across_all_periods": true,
+  "cohort": {
+    "storms": 270,
+    "daily_issues": 1473,
+    "sha256": "965184f1e5ab3fcb52e304f39b38583a14f753870aa2295f2d01942a441dcfd6",
+    "leads_hours": [
+      6,
+      12,
+      18,
+      24,
+      30,
+      36,
+      42,
+      48,
+      54,
+      60,
+      66,
+      72,
+      78,
+      84,
+      90,
+      96,
+      102,
+      108,
+      114,
+      120
+    ]
+  },
+  "period_definition": {
+    "before_2024": "UTC issue year < 2024",
+    "year_2024": "UTC issue year == 2024",
+    "after_2024": "UTC issue year > 2024 (2025 onward)",
+    "total": "All 1,473 frozen daily starts"
+  },
+  "periods": {
+    "total": {
+      "daily_issues": 1473,
+      "storms": 270,
+      "daily_issue_percentage": 100.0,
+      "storm_percentage": 100.0,
+      "years": [
+        1980,
+        1981,
+        1982,
+        1983,
+        1984,
+        1985,
+        1986,
+        1987,
+        1988,
+        1989,
+        1990,
+        1991,
+        1992,
+        1993,
+        1994,
+        1995,
+        1996,
+        1997,
+        1998,
+        1999,
+        2024,
+        2025,
+        2026
+      ],
+      "case_indices": [
+        0,
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+              "value": 469.40221083866464,
+              "storms": 18
+            },
+            "track_error_120h_km": {
+              "value": 1019.115437578709,
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+            "direction_error_deg": {
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+            "shape_similarity": {
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+                }
+              },
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+                }
+              }
+            }
+          },
+          "USA": {
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+            "storms": 18,
+            "common_lead_points": 1029,
+            "models": {
+              "1.1": {
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+                  "value": 12.041161414819193,
+                  "storms": 18,
+                  "daily_cases": 59
+                },
+                "curve_similarity": {
+                  "value": 0.7295482388430138,
+                  "storms": 18,
+                  "daily_cases": 58
+                }
+              },
+              "1.2": {
+                "mae_hpa": {
+                  "value": 10.133820579981911,
+                  "storms": 18,
+                  "daily_cases": 59
+                },
+                "curve_similarity": {
+                  "value": 0.6394734569751335,
+                  "storms": 18,
+                  "daily_cases": 58
+                }
+              },
+              "deepmind": {
+                "mae_hpa": {
+                  "value": 8.96523730598073,
+                  "storms": 18,
+                  "daily_cases": 59
+                },
+                "curve_similarity": {
+                  "value": 0.7423907051779878,
+                  "storms": 18,
+                  "daily_cases": 58
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  },
+  "source_hashes": {
+    "archive_sha256": "602425d3ff9284cce44fee4d3a75f50354b6e3f26f391f5402f72afabd762d45",
+    "publication_audit_sha256": "52c767b46452a29341c024f24a774328ed6fd4169f5f28e923a81e01c641b222",
+    "snapshot_sha256": "adff1baa1dbc38a6b443cfdd436cd7d4893c04241355c9eaf5004f439ce469a4",
+    "protocol.json": "91b31582f24ffdd4f2309eddb0cc68498a2c31cab7cbabaf06f43dfbff413606",
+    "verification.json": "8f29a767ab5db61cf6773f5806cc110789329b04db48361715e5b4c295545c89",
+    "case-manifest.json": "30d35cfb79e809227fd0d255f9b5173ad18e781c7450ed4bbf0211553f8e63bd",
+    "benchmark.json": "85a0965b7a87d400ccad67b437d05b2ac651055b8f4a473ff30fdd85ac778645"
+  },
+  "verified_case_score_files": 1473,
+  "aggregation": "Valid leads within day, days within storm, then equal storm weight; identical original masks.",
+  "proportion_definition": "Fractions of evaluation starts and storms, not proportions of model training data.",
+  "pressure_definition": "Agency-specific central-pressure MAE/centred time-curve similarity, not map-grid MAE.",
+  "missing_results_scored_as_zero": 0,
+  "new_inference_performed": false,
+  "model_weights_changed": false,
+  "forecast_arrays_modified": false,
+  "raw_inputs_reaudited": false,
+  "notes": [
+    "Historical years overlap Mini fitting years; do not call the combined cohort an unused temporal test.",
+    "2024 is already after the checkpoint training cutoff, but is excluded from strict >2024.",
+    "All-time pressure has smaller valid coverage than all-time track; counts are metric-specific.",
+    "Different causal input pipelines and 50-member 1.2 versus one-member Mini; not an equal-compute comparison.",
+    "Post-2024 dates alone do not certify an untouched holdout across earlier development experiments."
+  ]
+}

evaluation/released_daily/model_1_2_after_2024_benchmark.json

@@ -0,0 +1,55 @@
+{
+  "source": "evaluation/deepmind_daily/period_comparison.json",
+  "source_sha256": "0aef2eb8b5f808e5100cbbe132af3dc3e5d2de6fefe7d92d916190049cdf25cb",
+  "chart": "evaluation/released_daily/model_1_2_after_2024_benchmark.png",
+  "chart_sha256": "e62fb38b56fdf47876efc6d748884bb95e81108dfb3bd7ac6e930db86ca3b5dc",
+  "period": "after_2024",
+  "period_definition": "UTC issue year > 2024 (2025 onward)",
+  "metrics": [
+    "pressure_JMA_hpa",
+    "mean_track_error_km",
+    "direction_error_deg"
+  ],
+  "values": {
+    "pressure_JMA_hpa": {
+      "1.1": 12.587267857142855,
+      "1.2": 11.124838837640858,
+      "deepmind": 9.554410767097337
+    },
+    "mean_track_error_km": {
+      "1.1": 963.0191970365182,
+      "1.2": 469.40221083866464,
+      "deepmind": 258.6707762778929
+    },
+    "direction_error_deg": {
+      "1.1": 57.8772574074533,
+      "1.2": 33.813490658008476,
+      "deepmind": 28.733081775997544
+    }
+  },
+  "deepmind_status": "complete_verified",
+  "deepmind_model": {
+    "key": "deepmind",
+    "label": "Google DeepMind WeatherNext Cyclones Mini (<2024)",
+    "model_checkpoint": "WeatherNextCyclones_Mini_<2024",
+    "software_version": "0.3.0",
+    "software_commit": "89c4b2a77a1c57b328b909c575550fd2e5aadc9c",
+    "trained_through": 2023,
+    "resolution_degrees": 1,
+    "member_count": 1,
+    "member_policy": "One official Mini member, seed 0",
+    "checkpoint_sha256": "a1bb151457077248d70a458b1a7b19deacd2926fbcb46eee4ff2691444e3596e",
+    "method": "Official predictor and initialized-storm tracker; causal ERA5; RTX 3070 CUDA",
+    "status": "complete_verified"
+  },
+  "models": [
+    "1.1",
+    "1.2",
+    "deepmind"
+  ],
+  "bars_per_panel": 3,
+  "error_bars": false,
+  "zero_fill": false,
+  "model_forecasts_modified": false,
+  "publication_audit_sha256": "52c767b46452a29341c024f24a774328ed6fd4169f5f28e923a81e01c641b222"
+}

evaluation/released_daily/model_1_2_benchmark.json

@@ -1,8 +1,10 @@
 {
-  "source": "evaluation/released_daily/released_daily_benchmark.json",
-  "source_sha256": "adff1baa1dbc38a6b443cfdd436cd7d4893c04241355c9eaf5004f439ce469a4",
+  "source": "evaluation/deepmind_daily/period_comparison.json",
+  "source_sha256": "0aef2eb8b5f808e5100cbbe132af3dc3e5d2de6fefe7d92d916190049cdf25cb",
   "chart": "evaluation/released_daily/model_1_2_benchmark.png",
-  "chart_sha256": "a1aec843491af2c81c587410de9d9a478a7aae75c4b79ee4b95d2fd18ef9b2de",
+  "chart_sha256": "ef201e3187496b476bef0281f854ac5d5e6f3143e7e2152d3f9795e3b20ccc1f",
+  "period": "total",
+  "period_definition": "All 1,473 frozen daily starts",
   "metrics": [
     "pressure_JMA_hpa",
     "mean_track_error_km",
@@ -21,7 +23,7 @@
     },
     "direction_error_deg": {
       "1.1": 51.58419207122625,
-      "1.2": 34.95994745459279,
+      "1.2": 34.959947454592786,
       "deepmind": 39.76380497312257
     }
   },

release_tools/build_deepmind_period_comparison.py

@@ -0,0 +1,171 @@
+"""Audit and partition completed CUDA scores; never run or change a forecast.
+
+The frozen worker export stays byte-for-byte intact. This derivative uses UTC
+issue years, not model version strings or storm-name matching. Calendar 2024
+is a separate boundary group: after_2024 always means 2025 onward.
+"""
+from __future__ import annotations
+
+import argparse
+from collections import defaultdict
+from datetime import datetime
+import hashlib
+import json
+from pathlib import Path
+import zipfile
+
+from deepmind_daily_benchmark import aggregate, STEP_NS
+from import_deepmind_release_results import ROOT, same, validate_identity
+
+FOLDER = ROOT / 'evaluation/deepmind_daily'
+DEST = FOLDER / 'period_comparison.json'
+MODELS = ('1.1', '1.2', 'deepmind')
+
+
+def sha(path):
+    with Path(path).open('rb') as stream:
+        return hashlib.file_digest(stream, 'sha256').hexdigest()
+
+
+def require(condition, message):
+    if not condition:
+        raise ValueError(message)
+
+
+def period_key(issue_time):
+    issue = datetime.fromisoformat(issue_time.replace('Z', '+00:00'))
+    require(issue.utcoffset().total_seconds() == 0, 'Issue date must be UTC')
+    return 'before_2024' if issue.year < 2024 else 'year_2024' if issue.year == 2024 else 'after_2024'
+
+
+def summarize(records, total_issues, total_storms):
+    groups = defaultdict(list)
+    for record in records:
+        groups[record['storm_id']].append(record)
+    return {
+        'daily_issues': len(records),
+        'storms': len(groups),
+        'daily_issue_percentage': 100 * len(records) / total_issues,
+        'storm_percentage': 100 * len(groups) / total_storms,
+        'years': sorted({int(r['issue_time_utc'][:4]) for r in records}),
+        'case_indices': sorted(r['case_index'] for r in records),
+        'scores': aggregate(groups),
+    }
+
+
+def build(archive):
+    protocol = json.loads((FOLDER / 'protocol.json').read_text())
+    exported = json.loads((FOLDER / 'benchmark.json').read_text())
+    receipt = json.loads((FOLDER / 'verification.json').read_text())
+    audit = json.loads((FOLDER / 'publication_audit.json').read_text())
+    snapshot_path = ROOT / 'evaluation/released_daily/released_daily_benchmark.json'
+    snapshot = json.loads(snapshot_path.read_text())
+    validate_identity(protocol, receipt, exported)
+    require(audit['status'] == 'complete_verified', 'Missing original publication audit')
+    require(sha(archive) == audit['archive_sha256'], 'Different result archive')
+    require(sha(snapshot_path) == audit['snapshot_sha256'], 'Different original comparison')
+    for name, expected in audit['imported_files_sha256'].items():
+        require(sha(FOLDER / name) == expected, 'Changed frozen export: ' + name)
+
+    records = []
+    with zipfile.ZipFile(archive) as bundle:
+        names = bundle.namelist()
+        require(len(names) == len(set(names)), 'Duplicate ZIP entries')
+        manifest_bytes = bundle.read('results/case-manifest.json')
+        require(hashlib.sha256(manifest_bytes).hexdigest() == sha(FOLDER / 'case-manifest.json'),
+                'Archive case manifest differs')
+        items = {r['path']: r for r in json.loads(manifest_bytes)['files']}
+        for case in protocol['cases']:
+            path = f"cases/{case['case_index']:05d}.json"
+            content = bundle.read('results/' + path)
+            require(len(content) == items[path]['bytes'] and
+                    hashlib.sha256(content).hexdigest() == items[path]['sha256'],
+                    'Case score hash differs: ' + path)
+            record = json.loads(content)
+            require(all(record.get(k) == v for k, v in case.items()), 'Case identity differs: ' + path)
+            require(record['status'] == 'complete' and record['backend'] == 'cuda' and
+                    record['members'] == 1 and record['gpu_verified_steps'] == 20 and
+                    record['verified_device_steps'] == 20, 'Incomplete CUDA case: ' + path)
+            require(record['checkpoint_sha256'] == protocol['checkpoint_sha256'] and
+                    record['protocol_sha256'] == receipt['protocol_sha256'], 'Model identity differs')
+            require(record['input_time_ns'] == [case['issue_ns'] - STEP_NS, case['issue_ns']] and
+                    not record['future_weather_used'] and not record['future_storm_labels_used'],
+                    'Noncausal input metadata')
+            for agency, pressure in record['pressure_metrics'].items():
+                if not pressure['valid_leads']:
+                    continue
+                masks = [[value is not None for value in pressure['models'][m]['error_by_lead_hpa']]
+                         for m in MODELS]
+                require(masks[0] == masks[1] == masks[2] and sum(masks[0]) == pressure['valid_leads'],
+                        'Different pressure support: ' + agency)
+                shapes = [pressure['models'][m]['curve_similarity'] is not None for m in MODELS]
+                require(shapes[0] == shapes[1] == shapes[2], 'Different pressure-curve support')
+            records.append(record)
+
+    total_issues, total_storms = 1473, 270
+    require(len(records) == total_issues and len({r['storm_id'] for r in records}) == total_storms,
+            'Incomplete case/storm coverage')
+    bins = {key: [r for r in records if period_key(r['issue_time_utc']) == key]
+            for key in ('before_2024', 'year_2024', 'after_2024')}
+    periods = {'total': summarize(records, total_issues, total_storms),
+               **{key: summarize(rows, total_issues, total_storms) for key, rows in bins.items()}}
+    same(periods['total']['scores'], exported['aggregate_equal_complete_storm'], 'Original total scores')
+    original_groups = defaultdict(list)
+    for record in records:
+        original_groups[record['storm_id']].append(record)
+    for key, expected in exported['periods'].items():
+        same(aggregate({sid: days for sid, days in original_groups.items() if days[0]['period'] == key}),
+             expected, 'Original period scores: ' + key)
+    require(set(periods['before_2024']['case_indices']).isdisjoint(periods['after_2024']['case_indices']),
+            'Overlapping temporal subsets')
+    require(sum(len(rows) for rows in bins.values()) == total_issues, 'Partition does not cover total')
+    require(sum(len({r['storm_id'] for r in rows}) for rows in bins.values()) == total_storms,
+            'Storm crosses year bins; do not imply additive equal-storm weights')
+    reference = next(m for m in snapshot['models'] if m['key'] == 'deepmind')
+    return {
+        'schema': 'deepmind-completed-utc-period-comparison-v1',
+        'status': 'complete_verified',
+        'model': reference,
+        'single_checkpoint_across_all_periods': True,
+        'cohort': exported['cohort'],
+        'period_definition': {'before_2024': 'UTC issue year < 2024',
+                              'year_2024': 'UTC issue year == 2024',
+                              'after_2024': 'UTC issue year > 2024 (2025 onward)',
+                              'total': 'All 1,473 frozen daily starts'},
+        'periods': periods,
+        'source_hashes': {'archive_sha256': audit['archive_sha256'],
+                          'publication_audit_sha256': sha(FOLDER / 'publication_audit.json'),
+                          'snapshot_sha256': sha(snapshot_path),
+                          **{name: sha(FOLDER / name) for name in
+                             ('protocol.json', 'verification.json', 'case-manifest.json', 'benchmark.json')}},
+        'verified_case_score_files': total_issues,
+        'aggregation': 'Valid leads within day, days within storm, then equal storm weight; identical original masks.',
+        'proportion_definition': 'Fractions of evaluation starts and storms, not proportions of model training data.',
+        'pressure_definition': 'Agency-specific central-pressure MAE/centred time-curve similarity, not map-grid MAE.',
+        'missing_results_scored_as_zero': 0,
+        'new_inference_performed': False,
+        'model_weights_changed': False,
+        'forecast_arrays_modified': False,
+        'raw_inputs_reaudited': False,
+        'notes': ['Historical years overlap Mini fitting years; do not call the combined cohort an unused temporal test.',
+                  '2024 is already after the checkpoint training cutoff, but is excluded from strict >2024.',
+                  'All-time pressure has smaller valid coverage than all-time track; counts are metric-specific.',
+                  'Different causal input pipelines and 50-member 1.2 versus one-member Mini; not an equal-compute comparison.',
+                  'Post-2024 dates alone do not certify an untouched holdout across earlier development experiments.'],
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--archive', type=Path, required=True)
+    args = parser.parse_args()
+    result = build(args.archive)
+    DEST.write_text(json.dumps(result, indent=2, allow_nan=False) + '\n')
+    print(json.dumps({'status': result['status'], 'output': str(DEST),
+                      'periods': {k: {n: v[n] for n in
+                                     ('daily_issues', 'storms', 'daily_issue_percentage', 'storm_percentage')}
+                                  for k, v in result['periods'].items()}}, indent=2))
+
+
+if __name__ == '__main__':
+    main()

release_tools/plot_model_announcement.py

@@ -10,6 +10,22 @@ import numpy as np
 ROOT = Path(__file__).resolve().parents[1]
 SOURCE = ROOT/'evaluation/released_daily/released_daily_benchmark.json'
 DEST = ROOT/'evaluation/released_daily/model_1_2_benchmark.png'
+PERIOD_SOURCE = ROOT/'evaluation/deepmind_daily/period_comparison.json'
+POST_DEST = ROOT/'evaluation/released_daily/model_1_2_after_2024_benchmark.png'
+
+
+def period_metrics(period):
+    scores = period['scores']
+    pressure = scores['pressure']['JMA']
+    return {
+        'pressure_JMA_hpa': {
+            'values': {m: pressure['models'][m]['mae_hpa']['value'] for m in ('1.1', '1.2', 'deepmind')},
+            'coverage': {'daily_issues': pressure['daily_cases'], 'storms': pressure['storms']},
+        },
+        **{key: {'values': {m: scores['route'][m][key]['value'] for m in ('1.1', '1.2', 'deepmind')},
+                  'coverage': {'daily_issues': period['daily_issues'], 'storms': period['storms']}}
+           for key in ('mean_track_error_km', 'direction_error_deg')},
+    }
 
 
 def main():
@@ -20,17 +36,28 @@ def main():
     audit = json.loads((ROOT/'evaluation/deepmind_daily/publication_audit.json').read_text())
     if reference['status'] != 'complete_verified' or audit['snapshot_sha256'] != hashlib.sha256(SOURCE.read_bytes()).hexdigest():
         raise ValueError('DeepMind bars require the matching independent publication audit')
-    metrics = {m['key']: m for m in source['metrics']}
+    periods = json.loads(PERIOD_SOURCE.read_text())
+    if periods['status'] != 'complete_verified' or periods['source_hashes']['snapshot_sha256'] != hashlib.sha256(SOURCE.read_bytes()).hexdigest():
+        raise ValueError('Period comparison requires the exact original audited snapshot')
+    if periods['model'] != reference or not periods['single_checkpoint_across_all_periods']:
+        raise ValueError('Period groups are not different checkpoint generations')
+    for key, dest, title in (('total', DEST, 'Total · mixed-year comparison'),
+                             ('after_2024', POST_DEST, 'After 2024 · 2025–2026 comparison')):
+        metrics = period_metrics(periods['periods'][key])
+        draw_chart(metrics, reference, periods, key, dest, title)
+
+
+def draw_chart(metrics, reference, periods, period_key, dest, title):
     models = ('1.1', '1.2', 'deepmind')
     plt.rcParams.update({'font.family': 'DejaVu Sans', 'font.size': 11,
                          'axes.spines.top': False, 'axes.spines.right': False,
                          'axes.spines.left': False, 'axes.spines.bottom': False})
     fig, axes = plt.subplots(1, 3, figsize=(15.2, 6.1))
     fig.set_facecolor('#f6f8fc')
-    specs = [('pressure_JMA_hpa', 'Pressure intensity', 'Central-pressure MAE · hPa', 18, 2),
-             ('mean_track_error_km', 'Track position', 'Mean position error · km', 1050, 1),
-             ('direction_error_deg', 'Track direction', 'Six-hour heading error · degrees', 65, 2)]
-    for ax, (key, title, label, maximum, digits) in zip(axes, specs):
+    specs = [('pressure_JMA_hpa', 'Pressure intensity', 'Central-pressure MAE · hPa', 20, 2),
+             ('mean_track_error_km', 'Track position', 'Mean position error · km', 1200, 1),
+             ('direction_error_deg', 'Track direction', 'Six-hour heading error · degrees', 75, 2)]
+    for ax, (key, panel_title, label, maximum, digits) in zip(axes, specs):
         row = metrics[key]
         values = [row['values'][k] for k in models]
         if any(v is None or not np.isfinite(v) or v < 0 for v in values):
@@ -43,29 +70,35 @@ def main():
             ax.text(x, value+maximum*.038, f'{value:,.{digits}f}', ha='center',
                     fontsize=17, fontweight='bold', color='#21334c')
         ax.set_ylim(0, maximum)
-        ax.set_xticks([0, 1, 2], ['1.1', '1.2\nmean of 50', 'DeepMind Mini\none member'])
+        ax.set_xticks([0, 1, 2], ['1.1', '1.2\nmean of 50', 'DeepMind Mini\n<2024 · 1 member'])
         ax.tick_params(axis='both', length=0, labelcolor='#53647c')
-        ax.set_title(title, loc='left', fontsize=16, fontweight='bold', color='#21334c', pad=38)
+        ax.set_title(panel_title, loc='left', fontsize=16, fontweight='bold', color='#21334c', pad=38)
         ax.text(0, 1.03, label, transform=ax.transAxes, color='#53647c', fontsize=10)
         coverage = row['coverage']
-        ax.text(.5, -.225, f"{coverage['daily_issues']:,} daily starts / {coverage['storms']} storms",
+        ax.text(.5, -.33, f"{coverage['daily_issues']:,} daily starts / {coverage['storms']} storms",
                 transform=ax.transAxes, ha='center', color='#53647c', fontsize=10)
-    fig.text(.055, .945, 'Trackformer 1.2 · matched model comparison', fontsize=23, fontweight='bold', color='#182b45')
-    fig.text(.055, .895, 'Matched five-day forecasts · equal weight per storm · lower is better', fontsize=12, color='#53647c')
-    fig.text(.055, .095, 'DeepMind: WeatherNext Cyclones Mini <2024 · software v0.3.0 · 1° · RTX 3070 CUDA · all 1,473 forecasts verified.', fontsize=10, color='#53647c')
-    fig.text(.055, .057, 'Mean errors, not significance claims. Mixed historical/recent cohort; Mini has better recent-only track scores. Different pipelines/member policies.', fontsize=10, color='#53647c')
-    fig.subplots_adjust(left=.055, right=.975, top=.735, bottom=.29, wspace=.36)
-    fig.savefig(DEST, dpi=180, facecolor=fig.get_facecolor())
+    fig.text(.055, .945, title, fontsize=23, fontweight='bold', color='#182b45')
+    fig.text(.055, .89, 'DeepMind = WeatherNext Cyclones Mini <2024 · software v0.3.0 · 1° · one CUDA member', fontsize=12, color='#53647c')
+    fig.text(.055, .84, 'Same saved forecasts and valid times · +6 to +120 h · equal storm weight · lower is better', fontsize=11, color='#53647c')
+    if period_key == 'total':
+        note = 'Storm proportions: <2024 85.2% · 2024 8.1% · >2024 6.7%. Historical dates overlap Mini fitting years.'
+    else:
+        note = 'Strict UTC issue year >2024: 61 starts / 18 storms. Calendar 2024 is excluded, not counted as >2024.'
+    fig.text(.055, .09, note, fontsize=10, color='#53647c')
+    fig.text(.055, .052, 'Pressure coverage is smaller than track coverage. Missing is not zero. Different pipelines/member counts; no significance or unused-holdout claim.', fontsize=9.5, color='#53647c')
+    fig.subplots_adjust(left=.055, right=.975, top=.705, bottom=.32, wspace=.36)
+    fig.savefig(dest, dpi=180, facecolor=fig.get_facecolor())
     plt.close(fig)
-    receipt = {'source': SOURCE.relative_to(ROOT).as_posix(), 'source_sha256': hashlib.sha256(SOURCE.read_bytes()).hexdigest(),
-               'chart': DEST.relative_to(ROOT).as_posix(), 'chart_sha256': hashlib.sha256(DEST.read_bytes()).hexdigest(),
+    receipt = {'source': PERIOD_SOURCE.relative_to(ROOT).as_posix(), 'source_sha256': hashlib.sha256(PERIOD_SOURCE.read_bytes()).hexdigest(),
+               'chart': dest.relative_to(ROOT).as_posix(), 'chart_sha256': hashlib.sha256(dest.read_bytes()).hexdigest(),
+               'period': period_key, 'period_definition': periods['period_definition'][period_key],
                'metrics': [s[0] for s in specs], 'values': {k: metrics[k]['values'] for k, *_ in specs},
                'deepmind_status': 'complete_verified', 'deepmind_model': reference,
                'models': list(models), 'bars_per_panel': 3, 'error_bars': False,
                'zero_fill': False, 'model_forecasts_modified': False,
                'publication_audit_sha256': hashlib.sha256((ROOT/'evaluation/deepmind_daily/publication_audit.json').read_bytes()).hexdigest()}
-    DEST.with_suffix('.json').write_text(json.dumps(receipt, indent=2)+'\n')
-    print(json.dumps(receipt, indent=2))
+    dest.with_suffix('.json').write_text(json.dumps(receipt, indent=2)+'\n')
+    print(json.dumps({'chart': receipt['chart'], 'period': period_key, 'values': receipt['values']}))
 
 
 if __name__ == '__main__':

release_tools/sync_public_model_cards.py

@@ -23,6 +23,7 @@ HF = 'https://huggingface.co/' + REPO
 FILMS = ('mangkhut',)
 CURRENT_FIGURES = frozenset((
     'evaluation/released_daily/model_1_2_benchmark.png',
+    'evaluation/released_daily/model_1_2_after_2024_benchmark.png',
     'docs/trackformer_1_2_architecture.svg',
 ))
 SYNC_FILES = (
@@ -40,6 +41,8 @@ SYNC_FILES = (
     'release_tools/plot_release_pressure_benchmark.py',
     'release_tools/plot_daily_storm_final.py',
     'release_tools/plot_model_announcement.py',
+    'release_tools/build_deepmind_period_comparison.py',
+    'release_tools/test_deepmind_period_comparison.py',
     'release_tools/import_deepmind_release_results.py',
     'release_tools/test_deepmind_release_results.py',
     'release_tools/build_mangkhut_gif.py',
@@ -63,12 +66,15 @@ SYNC_FILES = (
     'evaluation/released_daily/pressure_comparison.png',
     'evaluation/released_daily/model_1_2_benchmark.png',
     'evaluation/released_daily/model_1_2_benchmark.json',
+    'evaluation/released_daily/model_1_2_after_2024_benchmark.png',
+    'evaluation/released_daily/model_1_2_after_2024_benchmark.json',
     'evaluation/deepmind_daily/benchmark.json',
     'evaluation/deepmind_daily/verification.json',
     'evaluation/deepmind_daily/publication_audit.json',
     'evaluation/deepmind_daily/protocol.json',
     'evaluation/deepmind_daily/case-manifest.json',
     'evaluation/deepmind_daily/canary.json',
+    'evaluation/deepmind_daily/period_comparison.json',
 )
 
 
@@ -181,13 +187,13 @@ def run(output, publish=False):
         # immutable commit in the card. Never pin new bytes to the old movie
         # revision or let a missing future image render as a stale figure.
         assets = api.create_commit(repo_id=REPO, repo_type='model', parent_commit=parent,
-            commit_message='Publish completed WeatherNext Cyclones Mini daily comparison and audited three-model chart',
+            commit_message='Publish exact Mini <2024 identity, UTC proportions and total/post-2024 comparison charts',
             operations=[CommitOperationAdd(path_in_repo=name, path_or_fileobj=str(ROOT/name)) for name in SYNC_FILES])
         card_path.write_text(render_card(original.read_text(), github, assets.oid))
         receipt['files']['README.md'] = sha(card_path)
         receipt['figure_revision'] = assets.oid
         committed = api.create_commit(repo_id=REPO, repo_type='model', parent_commit=assets.oid,
-            commit_message='Update Trackformer 1.2 model card with completed DeepMind CUDA benchmark',
+            commit_message='Show completed Mini <2024 results with separate total and post-2024 benchmarks',
             operations=[CommitOperationAdd(path_in_repo='README.md', path_or_fileobj=str(card_path))])
         for name, expected in receipt['files'].items():
             saved = hf_hub_download(REPO, name, revision=committed.oid, cache_dir=CACHE)

release_tools/test_deepmind_period_comparison.py

@@ -0,0 +1,101 @@
+"""Offline temporal/identity/coverage checks; no model or network imports."""
+import hashlib
+import json
+import unittest
+
+from build_deepmind_period_comparison import FOLDER, MODELS, ROOT, period_key, summarize
+from import_deepmind_release_results import same
+from plot_model_announcement import period_metrics
+
+
+class DeepMindPeriodComparisonTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.report = json.loads((FOLDER / 'period_comparison.json').read_text())
+        cls.protocol = json.loads((FOLDER / 'protocol.json').read_text())
+        cls.original = json.loads((FOLDER / 'benchmark.json').read_text())
+
+    def test_strict_utc_boundary_not_2024_onward(self):
+        self.assertEqual(period_key('2023-12-31T18:00:00Z'), 'before_2024')
+        self.assertEqual(period_key('2024-01-01T00:00:00Z'), 'year_2024')
+        self.assertEqual(period_key('2024-12-31T18:00:00Z'), 'year_2024')
+        self.assertEqual(period_key('2025-01-01T00:00:00Z'), 'after_2024')
+        with self.assertRaises(ValueError):
+            period_key('2024-12-31T23:00:00+08:00')
+
+    def test_counts_and_proportions_are_exact_partition(self):
+        expected = {'before_2024': (1336, 230), 'year_2024': (76, 22), 'after_2024': (61, 18)}
+        all_ids = []
+        by_id = {c['case_index']: c for c in self.protocol['cases']}
+        for key, (days, storms) in expected.items():
+            period = self.report['periods'][key]
+            self.assertEqual(period['daily_issues'], days)
+            self.assertEqual(period['storms'], storms)
+            self.assertAlmostEqual(period['daily_issue_percentage'], days / 1473 * 100)
+            self.assertAlmostEqual(period['storm_percentage'], storms / 270 * 100)
+            self.assertEqual(len(period['case_indices']), days)
+            self.assertEqual(len({by_id[i]['storm_id'] for i in period['case_indices']}), storms)
+            for index in period['case_indices']:
+                self.assertEqual(period_key(by_id[index]['issue_time_utc']), key)
+            all_ids.extend(period['case_indices'])
+        self.assertEqual(sorted(all_ids), list(range(1473)))
+
+    def test_total_preserves_all_original_scores(self):
+        same(self.report['periods']['total']['scores'],
+             self.original['aggregate_equal_complete_storm'], 'original totals')
+
+    def test_pressure_coverage_never_becomes_track_coverage(self):
+        total = self.report['periods']['total']
+        post = self.report['periods']['after_2024']
+        self.assertEqual(total['scores']['pressure']['JMA']['daily_cases'], 134)
+        self.assertEqual(total['scores']['pressure']['JMA']['common_lead_points'], 2637)
+        self.assertEqual(post['scores']['pressure']['JMA']['daily_cases'], 59)
+        self.assertEqual(post['scores']['pressure']['JMA']['common_lead_points'], 1174)
+        self.assertEqual(period_metrics(post)['mean_track_error_km']['coverage']['daily_issues'], 61)
+        self.assertEqual(period_metrics(post)['pressure_JMA_hpa']['coverage']['daily_issues'], 59)
+        missing = self.report['periods']['before_2024']['scores']['pressure']['JMA']
+        for model in MODELS:
+            self.assertIsNone(missing['models'][model]['mae_hpa']['value'])
+        self.assertIsNone(period_metrics(self.report['periods']['before_2024'])['pressure_JMA_hpa']['values']['deepmind'])
+
+    def test_empty_coverage_is_null_not_zero(self):
+        empty = summarize([], 1473, 270)
+        self.assertIsNone(empty['scores']['route']['deepmind']['mean_track_error_km']['value'])
+        self.assertIsNone(empty['scores']['pressure']['JMA']['models']['deepmind']['mae_hpa']['value'])
+
+    def test_equal_storm_not_pooled_daily_average(self):
+        def record(storm, index, score):
+            values = dict(mean_track_error_km=score, track_error_120h_km=score,
+                          direction_error_deg=score, shape_similarity=.5,
+                          track_error_by_lead_km=[score] * 20)
+            return {'storm_id': storm, 'case_index': index, 'issue_time_utc': '2025-01-01T00:00:00Z',
+                    'route_metrics': {m: values for m in MODELS}, 'pressure_metrics': {}}
+        rows = [record('a', 0, 10), record('a', 1, 10), record('a', 2, 10), record('b', 3, 30)]
+        self.assertEqual(summarize(rows, 4, 2)['scores']['route']['deepmind']['mean_track_error_km']['value'], 20)
+
+    def test_same_checkpoint_and_immutable_source_hashes(self):
+        self.assertTrue(self.report['single_checkpoint_across_all_periods'])
+        self.assertEqual(self.report['model']['model_checkpoint'], 'WeatherNextCyclones_Mini_<2024')
+        self.assertEqual(self.report['model']['trained_through'], 2023)
+        self.assertEqual(self.report['model']['software_version'], '0.3.0')
+        for name in ('protocol.json', 'verification.json', 'case-manifest.json', 'benchmark.json'):
+            self.assertEqual(hashlib.sha256((FOLDER / name).read_bytes()).hexdigest(), self.report['source_hashes'][name])
+        for key in ('new_inference_performed', 'model_weights_changed', 'forecast_arrays_modified', 'raw_inputs_reaudited'):
+            self.assertFalse(self.report[key])
+        self.assertEqual(self.report['missing_results_scored_as_zero'], 0)
+
+    def test_chart_receipts_match_exact_data_and_bytes(self):
+        for name, key in (('model_1_2_benchmark', 'total'), ('model_1_2_after_2024_benchmark', 'after_2024')):
+            base = ROOT / 'evaluation/released_daily' / name
+            receipt = json.loads(base.with_suffix('.json').read_text())
+            self.assertEqual(receipt['period'], key)
+            self.assertEqual(receipt['deepmind_model'], self.report['model'])
+            self.assertEqual(receipt['source_sha256'], hashlib.sha256((FOLDER / 'period_comparison.json').read_bytes()).hexdigest())
+            self.assertEqual(receipt['chart_sha256'], hashlib.sha256(base.with_suffix('.png').read_bytes()).hexdigest())
+            expected = period_metrics(self.report['periods'][key])
+            self.assertEqual(receipt['values'], {k: v['values'] for k, v in expected.items()})
+            self.assertFalse(receipt['zero_fill'])
+
+
+if __name__ == '__main__':
+    unittest.main()

release_tools/test_public_model_cards.py

@@ -16,7 +16,7 @@ class PublicModelCardsTest(unittest.TestCase):
         self.assertTrue(rendered.startswith(self.header))
         for text in ('# Introducing Trackformer 1.2', '1,473 days / 270 storms',
                      '134 days / 40 storms', '13.53', '12.84',
-                     'WeatherNext software v0.3.0', '10.20 hPa', '288.5 km',
+                     'WeatherNext software v0.3.0', '10.20 hPa', '258.7 km',
                      'no native wind-radius forecast head'):
             self.assertIn(text, rendered)
         self.assertNotIn('README revision', rendered)
@@ -134,18 +134,34 @@ class PublicModelCardsTest(unittest.TestCase):
             self.assert_metric_row(release, label, metrics[key], places)
 
     def test_recent_group_is_not_confused_with_combined_cohort(self):
-        exported = json.loads((ROOT / 'evaluation/deepmind_daily/benchmark.json').read_text())
-        recent = exported['periods']['recent_2024_onward']['route']
-        section = self.source.split('| Recent storms beginning in 2024+', 1)[1]
-        for label, key, places in (('Mean track error', 'mean_track_error_km', 1),
-                                   ('Six-hour direction error', 'direction_error_deg', 2)):
-            values = {model: recent[model][key]['value'] for model in ('1.1', '1.2', 'deepmind')}
-            # The recent-only table has four columns, unlike the main coverage table.
-            rows = [line for line in section.splitlines() if line.startswith('| ')
-                    and line.split('|')[1].strip().startswith(label)]
-            self.assertEqual(len(rows), 1)
-            row = rows[0].rstrip('|').rstrip() + ' | recent-only |'
-            self.assert_metric_row(row, label, values, places)
+        report = json.loads((ROOT / 'evaluation/deepmind_daily/period_comparison.json').read_text())
+        post = report['periods']['after_2024']
+        self.assertEqual(post['daily_issues'], 61)
+        self.assertEqual(post['storms'], 18)
+        self.assertEqual(post['years'], [2025, 2026])
+        section = self.source.split('### After 2024', 1)[1]
+        for label, key, places in (('Track position MAE', 'mean_track_error_km', 1),
+                                   ('Track-direction error', 'direction_error_deg', 2),
+                                   ('Route-shape similarity', 'shape_similarity', 4)):
+            values = {model: post['scores']['route'][model][key]['value']
+                      for model in ('1.1', '1.2', 'deepmind')}
+            self.assert_metric_row(section, label, values, places)
+        pressure = post['scores']['pressure']['JMA']['models']
+        for label, key, places in (('JMA central-pressure MAE', 'mae_hpa', 2),
+                                   ('JMA pressure-curve similarity', 'curve_similarity', 4)):
+            self.assert_metric_row(section, label,
+                                   {m: pressure[m][key]['value'] for m in ('1.1', '1.2', 'deepmind')}, places)
+
+    def test_checkpoint_identity_and_proportions_precede_separate_charts(self):
+        for source in (self.source, render_card(self.original, self.source)):
+            self.assertLess(source.index('**DeepMind checkpoint:'), source.index('model_1_2_benchmark.png'))
+            self.assertEqual(source.count('model_1_2_benchmark.png'), 1)
+            self.assertEqual(source.count('model_1_2_after_2024_benchmark.png'), 1)
+            for text in ('90.7%', '85.2%', '5.2%', '8.1%', '4.1%', '6.7%',
+                         'Calendar 2024', '59 days / 18 storms / 1,174',
+                         'not training-data composition', 'same Mini `<2024` checkpoint',
+                         '100% Mini `<2024`; 0% other checkpoints'):
+                self.assertIn(text, source)
 
     def test_evaluation_index_and_worker_handoff_metadata_are_explained(self):
         index = (ROOT / 'evaluation/README.md').read_text()