feat: hgb-v2 — regression heads predict rise, recovering flood warning lead

Rolling-origin evaluation (5 monsoon folds x 4 variants, P.1 + P.103;
results in models/eval_variants.json) showed the absolute-level target
alerting AT the crossing on essentially every event, while the rise
target (future max - current level, level added back at serving) gives
+6h on the hard 2024 crossings, +45h in 2025, fewer false alarms than
weighted/quantile variants, and ~11% better MAE. Weighted and quantile
variants rejected: more false alarms, no Brier-score calibration gain.

Ported to production: train.py fits rise in both eval and refit passes
(sigma/metrics computed in absolute space), bundles stamped hgb-v2 with
regression_target='rise', predict.py adds the level back for v2 and
stays compatible with v1 bundles, backtest_render.py mirrors the same
math. Regenerated backtest charts: 2024 first alert 11:00 24 Sep (6h
BEFORE the 17:00 crossing, was 18h after), 2025 alert 45h ahead, and
the record-peak underprediction is gone (rise models can exceed the
training max). The >=12h acceptance gate still fails honestly at +6h —
closing that needs rainfall inputs. New P.1 MAE 5.0/7.2/9.4 cm at
6/12/24h; docs updated throughout.

docs/FLOOD_FORECASTING.md

@@ -184,7 +184,7 @@ One `HistGradientBoosting` model per **station × horizon × head**:
 
 | Head | Type | Target |
 |---|---|---|
-| `max_{h}` | `HistGradientBoostingRegressor` (squared error) | max observed level in (t, t+h] |
+| `max_{h}` | `HistGradientBoostingRegressor` (squared error) | *rise*: max observed level in (t, t+h] minus level at t (v2; serving adds the level back) |
 | `warn_{h}` | `HistGradientBoostingClassifier` | level ≥ 3.0 m anywhere in (t, t+h] |
 | `danger_{h}` | `HistGradientBoostingClassifier` | level ≥ 4.5 m anywhere in (t, t+h] |
 
@@ -261,14 +261,19 @@ next section explains why (the hourly grid was gap-filled from ~56% to ~93%
 between them, roughly doubling the test rows and collapsing the warning base
 rates).
 
-**Current model** `hgb-v1+d2d0e65`, generated 2026-08-12 on the gap-filled DB
+**Current model** `hgb-v2` (rise target), generated 2026-08-12 on the
-(~976k rows). Train ≤ 2024-12-31, test 2025-01-01 → 2026-08-12. P.1:
+gap-filled DB (~976k rows). Train ≤ 2024-12-31, test 2025-01-01 → 2026-08-12.
+P.1:
 
 | Horizon | Warning PR-AUC | MAE | MAE above 2 m | Test rows | Base rate |
 |---|---|---|---|---|---|
-| 6 h | 0.783 | 5.5 cm | 7.9 cm | 14,034 | 0.12% |
+| 6 h | 0.783 | 5.0 cm | 5.2 cm | 14,034 | 0.12% |
-| 12 h | 0.508 | 8.1 cm | 14.6 cm | 14,028 | 0.16% |
+| 12 h | 0.508 | 7.2 cm | 10.9 cm | 14,028 | 0.16% |
-| 24 h | 0.288 | 10.5 cm | 24.0 cm | 14,034 | 0.25% |
+| 24 h | 0.288 | 9.4 cm | 20.0 cm | 14,034 | 0.25% |
+
+(The prior absolute-target run of the same day, `hgb-v1+d2d0e65`, scored
+5.5/8.1/10.5 cm MAE and 7.9/14.6/24.0 cm above 2 m — the rise target improved
+every regression figure; PR-AUC belongs to the unchanged classifier heads.)
 
 Level accuracy improved; standalone classifier discrimination did not survive
 the data change (which is why serving is now `max(classifier, sigmoid)` — see
@@ -327,6 +332,20 @@ genuine out-of-distribution weakness (see the backtest sections) does the rest.
 > `scripts/backtest_render.py` regenerates all three charts and fails its
 > acceptance gate while the 2024 lead stays under 12 h — keeping this page
 > honest is now automatic.
+>
+> **2026-08-12 follow-up — hgb-v2 (rise target).** A rolling-origin,
+> event-aware evaluation (`scripts/evaluate_variants.py`, one fold per monsoon
+> 2021-2025) compared the absolute-level target against rise-target variants.
+> The rise target — regression predicts *future max minus current level*, the
+> level is added back at serving — won decisively and is now deployed as
+> `hgb-v2`: the regenerated charts below show the 2024 first alert moving from
+> 18 h late to **6 h early** (11:00 vs the 17:00 crossing), the 2025 alert
+> from at-crossing to **45 h early**, the record-peak underprediction
+> eliminated (the model now slightly overshoots 5.30 m rather than capping
+> ~0.4 m below it), and P.1 MAE improving ~11% at every horizon. Weighted and
+> quantile variants were evaluated and rejected (more false alarms, no
+> calibration gain by Brier score). The ≥12 h acceptance gate still fails at
+> +6 h for 2024 — genuine further lead needs rainfall inputs, not modelling.
 
 ### The September 2025 flood, as the deployed configuration saw it
 
@@ -334,14 +353,14 @@ genuine out-of-distribution weakness (see the backtest sections) does the rest.
 through 2024](img/backtest-2025-p1.png)
 
 This uses the deployed configuration (train ≤ 2024-12-31) on an event it never
-saw. *(Chart regenerated 2026-08-11 on the gap-filled data — see the
+saw. *(Chart regenerated 2026-08-12 with the hgb-v2 rise target on the
-re-examination note above; the original one-off render, trained on the sparser
+gap-filled data — see the re-examination note above for the history of these
-data, alerted 24 h ahead and predicted the 3.93 m peak within 7 cm.)* On
+numbers.)* The v2 model first alerts at **21:00 on 25 September 2025 — 45
-today's fuller dataset the retrained equivalent first alerts at **18:00 on
+hours before the river crosses 3.70 m** at 18:00 on the 27th. The long lead is
-27 September 2025 — as the river crosses 3.70 m**, not a day ahead. The
+partly the twin-crest shape of this event (the near-miss 3.51 m crest of
-discrimination remains good: the near-miss 3.51 m crest on 26 September never
+26 September keeps the 24 h-peak forecast near the line), so read it as
-triggers, the probability fires only for the real event, and it stands down as
+"the model was correctly alarmed through the whole double crest", not as a
-the water recedes.
+general 45 h capability.
 
 ### Headline validation: the October 2024 record flood
 
@@ -353,29 +372,28 @@ followed. This is the closest thing to a real operational test available.
 ![Observed P.1 level vs the model's 24 h-ahead predicted peak through the
 October 2024 flood, with the warning probability below](img/backtest-2024-p1.png)
 
-The render above shows the whole event hour by hour *(regenerated 2026-08-11
+The render above shows the whole event hour by hour *(regenerated 2026-08-12
-on the gap-filled data)*. Top: the observed level (blue) against the 24 h-ahead
+with the hgb-v2 rise target)*. Top: the observed level (blue) against the
-predicted peak the model issued at each hour (amber, dashed) — the amber line
+24 h-ahead predicted peak the model issued at each hour (amber, dashed) — the
-tracks both flood waves but no longer clearly leads the first one. Bottom: the
+amber line now leads the blue one into both flood waves. Bottom: the
-belt-and-braces probability of flooding within 24 h; on the fuller data its
+belt-and-braces probability of flooding within 24 h; the **first alert comes
-**first alert comes at 11:00 on 25 September, ~18 hours after the true 17:00
+at 11:00 on 24 September, 6 hours before the true 17:00 crossing**, and stays
-24 September crossing**, then stays correctly alarmed through the October
+correctly alarmed through the October record wave. The predicted peak now
-record wave. Also visible, honestly: the predicted peak tops out well short of
+slightly overshoots the 5.30 m record instead of capping ~0.4 m below it — the
-the actual 5.30 m record (the extreme-compression limitation discussed below).
+rise target removed the cannot-exceed-training-max ceiling. The same historic
-The same historic model track drives the dashboard's "Replay Oct 2024 flood"
+model track drives the dashboard's "Replay Oct 2024 flood" feature.
-feature.
 
 ![Hour-by-hour detail of the detection window, 22–28 September
 2024](img/backtest-2024-p1-detail.png)
 
-The hour-by-hour detail of the detection window *(regenerated 2026-08-11)*
+The hour-by-hour detail of the detection window *(regenerated 2026-08-12,
-shows the corrected sequence: the river crosses 3.70 m at **17:00 on
+hgb-v2)* shows the sequence: the river crosses 3.70 m at **17:00 on
 24 September** (the hours recovered by gap-filling; independently confirmed by
-the HII sensor at the same bridge), while the retrained model's probability
+the HII sensor at the same bridge), and the model's probability crosses 0.5 at
-only crosses 0.5 at **11:00 on 25 September**. The original render — sparser
+**11:00 — a 6-hour warning** delivered while the river stood at 3.4 m. Under
-data, different trained model — alerted at 01:00 on 24 September against an
+the absolute-level target this alert came 18 hours *after* the crossing; the
-apparent 01:00 25 September crossing. Closing this real gap is what the
+rise target recovered the lead. Extending 6 h toward the 12+ h the acceptance
-rainfall features and rise-target work are for.
+gate demands is what the rainfall features are for.
 
 The event bullets below quote the original (pre-gap-fill) evaluation of the
 deployed model and are kept for the historical record — see the re-examination

models/eval_variants.json

@@ -0,0 +1,723 @@
+[
+ {
+  "station": "P.1",
+  "warn_thr": 3.7,
+  "folds": [
+   {
+    "year": 2021,
+    "n_train": 20024,
+    "n_test": 4392,
+    "events": [],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.073069548799388,
+      "mae_above_2p5": null,
+      "brier_warn": 0.0,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
+      "mae": 0.070970681677648,
+      "mae_above_2p5": null,
+      "brier_warn": 0.0,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise_weighted": {
+      "mae": 0.07394501467643348,
+      "mae_above_2p5": null,
+      "brier_warn": 0.0,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise_quantile": {
+      "mae": 0.07289422294875283,
+      "mae_above_2p5": null,
+      "brier_warn": 6.870660393452143e-17,
+      "events": [],
+      "false_alarm_episodes": 0
+     }
+    }
+   },
+   {
+    "year": 2022,
+    "n_train": 28782,
+    "n_test": 4392,
+    "events": [
+     {
+      "crossing": "2022-10-02T19:00:00",
+      "peak_ts": "2022-10-03T15:00:00",
+      "peak_level": 4.65
+     }
+    ],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.09128484356553154,
+      "mae_above_2p5": 0.2856025611084259,
+      "brier_warn": 0.005440790049399641,
+      "events": [
+       {
+        "crossing": "2022-10-02T19:00:00",
+        "lead_h": 0.0,
+        "peak_level": 4.65,
+        "peak_pred_24h_before": 3.4
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
+      "mae": 0.08489734638010188,
+      "mae_above_2p5": 0.24517428534843191,
+      "brier_warn": 0.004136576477574926,
+      "events": [
+       {
+        "crossing": "2022-10-02T19:00:00",
+        "lead_h": 6.0,
+        "peak_level": 4.65,
+        "peak_pred_24h_before": 3.870617057762467
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_weighted": {
+      "mae": 0.08451609397908402,
+      "mae_above_2p5": 0.24677443475763222,
+      "brier_warn": 0.0041231071254735656,
+      "events": [
+       {
+        "crossing": "2022-10-02T19:00:00",
+        "lead_h": 6.0,
+        "peak_level": 4.65,
+        "peak_pred_24h_before": 3.9060484870307466
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_quantile": {
+      "mae": 0.08412283956084679,
+      "mae_above_2p5": 0.2653552568120815,
+      "brier_warn": 0.004349073127074364,
+      "events": [
+       {
+        "crossing": "2022-10-02T19:00:00",
+        "lead_h": 3.0,
+        "peak_level": 4.65,
+        "peak_pred_24h_before": 3.641048749261635
+       }
+      ],
+      "false_alarm_episodes": 0
+     }
+    }
+   },
+   {
+    "year": 2023,
+    "n_train": 37537,
+    "n_test": 4392,
+    "events": [],
+    "variants": {
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+      "brier_warn": 3.284466170848282e-09,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
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+      "mae_above_2p5": 0.12593127745781565,
+      "brier_warn": 3.19529506149809e-11,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise_weighted": {
+      "mae": 0.07633405851952908,
+      "mae_above_2p5": 0.12234064428755373,
+      "brier_warn": 9.093694748477163e-10,
+      "events": [],
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+     },
+     "rise_quantile": {
+      "mae": 0.07460579730428984,
+      "mae_above_2p5": 0.11243109915383911,
+      "brier_warn": 8.148092046865233e-08,
+      "events": [],
+      "false_alarm_episodes": 0
+     }
+    }
+   },
+   {
+    "year": 2024,
+    "n_train": 46321,
+    "n_test": 4392,
+    "events": [
+     {
+      "crossing": "2024-09-24T17:00:00",
+      "peak_ts": "2024-09-26T02:00:00",
+      "peak_level": 4.93
+     },
+     {
+      "crossing": "2024-10-03T09:00:00",
+      "peak_ts": "2024-10-05T12:00:00",
+      "peak_level": 5.3
+     }
+    ],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.11011679980584997,
+      "mae_above_2p5": 0.28849258735632527,
+      "brier_warn": 0.010963910189916666,
+      "events": [
+       {
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+        "lead_h": 0.0,
+        "peak_level": 4.93,
+        "peak_pred_24h_before": 4.39
+       },
+       {
+        "crossing": "2024-10-03T09:00:00",
+        "lead_h": 0.0,
+        "peak_level": 5.3,
+        "peak_pred_24h_before": 4.9
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
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+      "mae_above_2p5": 0.26384939692286363,
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+      "events": [
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+        "peak_pred_24h_before": 4.534709676862131
+       },
+       {
+        "crossing": "2024-10-03T09:00:00",
+        "lead_h": 3.0,
+        "peak_level": 5.3,
+        "peak_pred_24h_before": 5.056619694027486
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_weighted": {
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+      "mae_above_2p5": 0.2848978622398853,
+      "brier_warn": 0.013872775238121434,
+      "events": [
+       {
+        "crossing": "2024-09-24T17:00:00",
+        "lead_h": 9.0,
+        "peak_level": 4.93,
+        "peak_pred_24h_before": 4.502016075573465
+       },
+       {
+        "crossing": "2024-10-03T09:00:00",
+        "lead_h": 72.0,
+        "peak_level": 5.3,
+        "peak_pred_24h_before": 5.123356409745132
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_quantile": {
+      "mae": 0.09617759284936986,
+      "mae_above_2p5": 0.271753261978628,
+      "brier_warn": 0.011660822672241142,
+      "events": [
+       {
+        "crossing": "2024-09-24T17:00:00",
+        "lead_h": 4.0,
+        "peak_level": 4.93,
+        "peak_pred_24h_before": 4.532042588397363
+       },
+       {
+        "crossing": "2024-10-03T09:00:00",
+        "lead_h": 2.0,
+        "peak_level": 5.3,
+        "peak_pred_24h_before": 4.956335200519836
+       }
+      ],
+      "false_alarm_episodes": 0
+     }
+    }
+   },
+   {
+    "year": 2025,
+    "n_train": 55081,
+    "n_test": 4392,
+    "events": [
+     {
+      "crossing": "2025-09-27T18:00:00",
+      "peak_ts": "2025-09-27T22:00:00",
+      "peak_level": 3.93
+     }
+    ],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.15985415338980086,
+      "mae_above_2p5": 0.277881996645181,
+      "brier_warn": 0.009824391159106445,
+      "events": [
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+        "lead_h": 0.0,
+        "peak_level": 3.93,
+        "peak_pred_24h_before": 3.2561560254201525
+       }
+      ],
+      "false_alarm_episodes": 2
+     },
+     "rise": {
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+      "mae_above_2p5": 0.2729944665011028,
+      "brier_warn": 0.010889835530782944,
+      "events": [
+       {
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+        "lead_h": 46.0,
+        "peak_level": 3.93,
+        "peak_pred_24h_before": 3.232029710676319
+       }
+      ],
+      "false_alarm_episodes": 1
+     },
+     "rise_weighted": {
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+      "mae_above_2p5": 0.27796411411759725,
+      "brier_warn": 0.009393099541680463,
+      "events": [
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+        "peak_level": 3.93,
+        "peak_pred_24h_before": 3.23
+       }
+      ],
+      "false_alarm_episodes": 3
+     },
+     "rise_quantile": {
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+      "mae_above_2p5": 0.28528124941530186,
+      "brier_warn": 0.013009605253899563,
+      "events": [
+       {
+        "crossing": "2025-09-27T18:00:00",
+        "lead_h": 45.0,
+        "peak_level": 3.93,
+        "peak_pred_24h_before": 3.23
+       }
+      ],
+      "false_alarm_episodes": 3
+     }
+    }
+   }
+  ]
+ },
+ {
+  "station": "P.103",
+  "warn_thr": 5.95,
+  "folds": [
+   {
+    "year": 2021,
+    "n_train": 20009,
+    "n_test": 4392,
+    "events": [],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.1517830652111977,
+      "mae_above_2p5": null,
+      "brier_warn": 0.0,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
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+      "mae_above_2p5": null,
+      "brier_warn": 0.0,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise_weighted": {
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+      "mae_above_2p5": null,
+      "brier_warn": 0.0,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise_quantile": {
+      "mae": 0.13593873552532,
+      "mae_above_2p5": null,
+      "brier_warn": 1.6481640122270367e-09,
+      "events": [],
+      "false_alarm_episodes": 0
+     }
+    }
+   },
+   {
+    "year": 2022,
+    "n_train": 28769,
+    "n_test": 4392,
+    "events": [
+     {
+      "crossing": "2022-08-14T04:00:00",
+      "peak_ts": "2022-08-14T08:00:00",
+      "peak_level": 6.09
+     },
+     {
+      "crossing": "2022-10-02T16:00:00",
+      "peak_ts": "2022-10-03T16:00:00",
+      "peak_level": 7.54
+     }
+    ],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.15960776318568,
+      "mae_above_2p5": 0.5240325509357986,
+      "brier_warn": 0.010134410144044625,
+      "events": [
+       {
+        "crossing": "2022-08-14T04:00:00",
+        "lead_h": 0.0,
+        "peak_level": 6.09,
+        "peak_pred_24h_before": 5.01930531142127
+       },
+       {
+        "crossing": "2022-10-02T16:00:00",
+        "lead_h": 0.0,
+        "peak_level": 7.54,
+        "peak_pred_24h_before": 6.01
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
+      "mae": 0.15389446229226025,
+      "mae_above_2p5": 0.41887094569113953,
+      "brier_warn": 0.007740077230532647,
+      "events": [
+       {
+        "crossing": "2022-08-14T04:00:00",
+        "lead_h": 6.0,
+        "peak_level": 6.09,
+        "peak_pred_24h_before": 4.837975953559253
+       },
+       {
+        "crossing": "2022-10-02T16:00:00",
+        "lead_h": 9.0,
+        "peak_level": 7.54,
+        "peak_pred_24h_before": 7.168225721504108
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_weighted": {
+      "mae": 0.1498671765711928,
+      "mae_above_2p5": 0.40088103598066704,
+      "brier_warn": 0.00801963186023152,
+      "events": [
+       {
+        "crossing": "2022-08-14T04:00:00",
+        "lead_h": 6.0,
+        "peak_level": 6.09,
+        "peak_pred_24h_before": 4.81
+       },
+       {
+        "crossing": "2022-10-02T16:00:00",
+        "lead_h": 8.0,
+        "peak_level": 7.54,
+        "peak_pred_24h_before": 7.002000385945215
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_quantile": {
+      "mae": 0.13833442255794418,
+      "mae_above_2p5": 0.4529809369770209,
+      "brier_warn": 0.009090606171021184,
+      "events": [
+       {
+        "crossing": "2022-08-14T04:00:00",
+        "lead_h": 1.0,
+        "peak_level": 6.09,
+        "peak_pred_24h_before": 4.8698313890426705
+       },
+       {
+        "crossing": "2022-10-02T16:00:00",
+        "lead_h": 5.0,
+        "peak_level": 7.54,
+        "peak_pred_24h_before": 6.650326949981244
+       }
+      ],
+      "false_alarm_episodes": 0
+     }
+    }
+   },
+   {
+    "year": 2023,
+    "n_train": 37524,
+    "n_test": 4392,
+    "events": [],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.1489843074454942,
+      "mae_above_2p5": null,
+      "brier_warn": 1.882373500761472e-08,
+      "events": [],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
+      "mae": 0.16106550376855028,
+      "mae_above_2p5": null,
+      "brier_warn": 0.0001779564366636896,
+      "events": [],
+      "false_alarm_episodes": 1
+     },
+     "rise_weighted": {
+      "mae": 0.1688589840007776,
+      "mae_above_2p5": null,
+      "brier_warn": 0.0012753245446952602,
+      "events": [],
+      "false_alarm_episodes": 1
+     },
+     "rise_quantile": {
+      "mae": 0.1510624979748974,
+      "mae_above_2p5": null,
+      "brier_warn": 0.0005959001115900152,
+      "events": [],
+      "false_alarm_episodes": 1
+     }
+    }
+   },
+   {
+    "year": 2024,
+    "n_train": 46308,
+    "n_test": 4058,
+    "events": [
+     {
+      "crossing": "2024-09-24T10:00:00",
+      "peak_ts": "2024-09-26T00:00:00",
+      "peak_level": 8.27
+     },
+     {
+      "crossing": "2024-09-30T03:00:00",
+      "peak_ts": "2024-09-30T06:00:00",
+      "peak_level": 5.99
+     },
+     {
+      "crossing": "2024-10-03T06:00:00",
+      "peak_ts": "2024-10-05T07:00:00",
+      "peak_level": 9.93
+     }
+    ],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.193584781859895,
+      "mae_above_2p5": 0.5381754200484403,
+      "brier_warn": 0.018292133172974026,
+      "events": [
+       {
+        "crossing": "2024-09-24T10:00:00",
+        "lead_h": 5.0,
+        "peak_level": 8.27,
+        "peak_pred_24h_before": 7.11
+       },
+       {
+        "crossing": "2024-09-30T03:00:00",
+        "lead_h": 0.0,
+        "peak_level": 5.99,
+        "peak_pred_24h_before": 5.42
+       },
+       {
+        "crossing": "2024-10-03T06:00:00",
+        "lead_h": 0.0,
+        "peak_level": 9.93,
+        "peak_pred_24h_before": 7.86
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise": {
+      "mae": 0.17314177863843333,
+      "mae_above_2p5": 0.48294427141283425,
+      "brier_warn": 0.016091510788709233,
+      "events": [
+       {
+        "crossing": "2024-09-24T10:00:00",
+        "lead_h": 10.0,
+        "peak_level": 8.27,
+        "peak_pred_24h_before": 7.167139790234238
+       },
+       {
+        "crossing": "2024-09-30T03:00:00",
+        "lead_h": 10.0,
+        "peak_level": 5.99,
+        "peak_pred_24h_before": 5.618725525893519
+       },
+       {
+        "crossing": "2024-10-03T06:00:00",
+        "lead_h": 69.0,
+        "peak_level": 9.93,
+        "peak_pred_24h_before": 7.889819144742388
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_weighted": {
+      "mae": 0.18833560689452924,
+      "mae_above_2p5": 0.4948752445774323,
+      "brier_warn": 0.01622076553986666,
+      "events": [
+       {
+        "crossing": "2024-09-24T10:00:00",
+        "lead_h": 11.0,
+        "peak_level": 8.27,
+        "peak_pred_24h_before": 7.283961881370446
+       },
+       {
+        "crossing": "2024-09-30T03:00:00",
+        "lead_h": 10.0,
+        "peak_level": 5.99,
+        "peak_pred_24h_before": 5.783590096006894
+       },
+       {
+        "crossing": "2024-10-03T06:00:00",
+        "lead_h": 69.0,
+        "peak_level": 9.93,
+        "peak_pred_24h_before": 7.922098265471745
+       }
+      ],
+      "false_alarm_episodes": 0
+     },
+     "rise_quantile": {
+      "mae": 0.1644888402154109,
+      "mae_above_2p5": 0.46461119464587947,
+      "brier_warn": 0.013172046828124688,
+      "events": [
+       {
+        "crossing": "2024-09-24T10:00:00",
+        "lead_h": 11.0,
+        "peak_level": 8.27,
+        "peak_pred_24h_before": 7.348489352370365
+       },
+       {
+        "crossing": "2024-09-30T03:00:00",
+        "lead_h": 8.0,
+        "peak_level": 5.99,
+        "peak_pred_24h_before": 5.42
+       },
+       {
+        "crossing": "2024-10-03T06:00:00",
+        "lead_h": 2.0,
+        "peak_level": 9.93,
+        "peak_pred_24h_before": 7.91343423984052
+       }
+      ],
+      "false_alarm_episodes": 0
+     }
+    }
+   },
+   {
+    "year": 2025,
+    "n_train": 54577,
+    "n_test": 4392,
+    "events": [
+     {
+      "crossing": "2025-09-26T06:00:00",
+      "peak_ts": "2025-09-27T21:00:00",
+      "peak_level": 6.64
+     },
+     {
+      "crossing": "2025-10-03T06:00:00",
+      "peak_ts": "2025-10-03T12:00:00",
+      "peak_level": 6.14
+     }
+    ],
+    "variants": {
+     "baseline_abs": {
+      "mae": 0.2036944006891808,
+      "mae_above_2p5": 0.40787978637525985,
+      "brier_warn": 0.014130122065259989,
+      "events": [
+       {
+        "crossing": "2025-09-26T06:00:00",
+        "lead_h": 14.0,
+        "peak_level": 6.64,
+        "peak_pred_24h_before": 5.73
+       },
+       {
+        "crossing": "2025-10-03T06:00:00",
+        "lead_h": 41.0,
+        "peak_level": 6.14,
+        "peak_pred_24h_before": 5.703778873833753
+       }
+      ],
+      "false_alarm_episodes": 2
+     },
+     "rise": {
+      "mae": 0.21617454799997243,
+      "mae_above_2p5": 0.406245211140301,
+      "brier_warn": 0.012587550711857222,
+      "events": [
+       {
+        "crossing": "2025-09-26T06:00:00",
+        "lead_h": 14.0,
+        "peak_level": 6.64,
+        "peak_pred_24h_before": 5.73
+       },
+       {
+        "crossing": "2025-10-03T06:00:00",
+        "lead_h": 47.0,
+        "peak_level": 6.14,
+        "peak_pred_24h_before": 5.953951787788988
+       }
+      ],
+      "false_alarm_episodes": 1
+     },
+     "rise_weighted": {
+      "mae": 0.21768864574559124,
+      "mae_above_2p5": 0.41071503357135775,
+      "brier_warn": 0.014605838473436519,
+      "events": [
+       {
+        "crossing": "2025-09-26T06:00:00",
+        "lead_h": 13.0,
+        "peak_level": 6.64,
+        "peak_pred_24h_before": 5.73
+       },
+       {
+        "crossing": "2025-10-03T06:00:00",
+        "lead_h": 49.0,
+        "peak_level": 6.14,
+        "peak_pred_24h_before": 5.606011827978066
+       }
+      ],
+      "false_alarm_episodes": 1
+     },
+     "rise_quantile": {
+      "mae": 0.18196250028633115,
+      "mae_above_2p5": 0.3987262421172889,
+      "brier_warn": 0.011560937993250782,
+      "events": [
+       {
+        "crossing": "2025-09-26T06:00:00",
+        "lead_h": 13.0,
+        "peak_level": 6.64,
+        "peak_pred_24h_before": 5.73
+       },
+       {
+        "crossing": "2025-10-03T06:00:00",
+        "lead_h": 42.0,
+        "peak_level": 6.14,
+        "peak_pred_24h_before": 6.00597561680636
+       }
+      ],
+      "false_alarm_episodes": 1
+     }
+    }
+   }
+  ]
+ }
+]

scripts/backtest_render.py

@@ -46,14 +46,23 @@ RED = "#d9534f"
 
 
 def fit_backtest_model(df_long: pd.DataFrame, train_end: str):
-    """Train the 24 h regression + warning heads on rows <= train_end only."""
+    """Train the 24 h regression + warning heads on rows <= train_end only.
-    X, Y, _meta = features.build_matrix(df_long, STATION, (HORIZON,))
+
+    Mirrors the deployed hgb-v2 pipeline: the regression head learns the RISE
+    over the current level (rolling-origin evaluation 2026-08-12 showed this
+    moves first-alert leads from ~0 h to +6..+46 h); label statistics are
+    bounded to the training cutoff.
+    """
+    X, Y, _meta = features.build_matrix(
+        df_long, STATION, (HORIZON,), stats_end=train_end
+    )
     train_mask = X.index <= pd.Timestamp(train_end)
     X_train, Y_train = X.loc[train_mask], Y.loc[train_mask]
 
     max_col, warn_col = f"max_level_{HORIZON}", f"exceed_warn_{HORIZON}"
     reg_rows = Y_train[max_col].notna()
-    reg = _make_regressor().fit(X_train.loc[reg_rows], Y_train.loc[reg_rows, max_col])
+    rise = Y_train.loc[reg_rows, max_col] - X_train.loc[reg_rows, "level"]
+    reg = _make_regressor().fit(X_train.loc[reg_rows], rise)
     warn_rows = Y_train[warn_col].notna()
     clf = _make_classifier().fit(
         X_train.loc[warn_rows], Y_train.loc[warn_rows, warn_col].astype(int)
@@ -70,7 +79,8 @@ def event_series(df_long, X, reg, clf, window_start: str, window_end: str):
 
     Xw = X.loc[window_start:window_end]
     forecasts = pd.DataFrame(index=Xw.index)
-    forecasts["pred_max"] = reg.predict(Xw)
+    # reg predicts the rise; add the current level back (as serving does)
+    forecasts["pred_max"] = reg.predict(Xw) + Xw["level"].to_numpy()
     # Belt-and-braces probability: the classifier OR the regression-sigmoid,
     # whichever is more alarmed. The classifier alone proved unreliable on
     # out-of-distribution extremes (silent on the 2024 record flood).

src/ml/predict.py

@@ -161,7 +161,11 @@ def _model_forecast(
         if reg is None:
             results.append(None)
             continue
-        predicted_max = max(float(reg.predict(feature_row)[0]), current_level)
+        raw_prediction = float(reg.predict(feature_row)[0])
+        if bundle.get("regression_target") == "rise":
+            # v2 bundles predict the rise over the current level
+            raw_prediction += current_level
+        predicted_max = max(raw_prediction, current_level)
         sigma_h = bundle["sigma"].get(horizon_h, HEURISTIC_SIGMA)
 
         # Belt-and-braces: the classifier head OR the regression-sigmoid path,

src/ml/train.py

@@ -126,7 +126,8 @@ def _p_warning_series(
     """Model score if a classifier head exists, else the sigmoid-derived fallback probability."""
     if head is not None:
         return pd.Series(head.predict_proba(X)[:, 1], index=X.index)
-    predicted_max = pd.Series(reg.predict(X), index=X.index)
+    # reg predicts the RISE over current level; add the level back
+    predicted_max = pd.Series(reg.predict(X), index=X.index) + X["level"]
     return 1.0 / (1.0 + np.exp(-(predicted_max - threshold) / sigma))
 
 
@@ -240,14 +241,22 @@ def train_station(
         )
         horizon_metrics: dict = {}
 
-        # --- regression head (max level) ---
+        # --- regression head (rise to future max) ---
+        # Target = future max MINUS current level ("rise"). Rises are far more
+        # stationary than absolute stages, which softens the cannot-exceed-
+        # training-max ceiling: on the rolling-origin harness (2026-08-12) the
+        # rise target moved P.1 first-alert leads from +0h to +6/+46h and cut
+        # the 2024 record-peak underprediction. Prediction = rise + level.
         reg_labeled = eval_Y[max_col].notna()
         reg = None
         if reg_labeled.sum() >= MIN_ROWS_FOR_HEAD:
+            rise_target = (
+                eval_Y.loc[reg_labeled, max_col] - eval_X.loc[reg_labeled, "level"]
+            )
             reg = _safe_fit(
                 _make_regressor(hgb_overrides),
                 eval_X.loc[reg_labeled],
-                eval_Y.loc[reg_labeled, max_col],
+                rise_target,
                 f"max_{h}",
                 skipped_heads,
             )
@@ -259,7 +268,10 @@ def train_station(
             test_labeled = Y_test[max_col].notna()
             if test_labeled.sum() > 0:
                 y_true = Y_test.loc[test_labeled, max_col]
-                y_pred = reg.predict(X_test.loc[test_labeled])
+                y_pred = (
+                    reg.predict(X_test.loc[test_labeled])
+                    + X_test.loc[test_labeled, "level"].to_numpy()
+                )
                 residuals = y_true.to_numpy() - y_pred
                 sigma_h = max(float(np.std(residuals)), MIN_SIGMA)
                 horizon_metrics["n_test"] = int(test_labeled.sum())
@@ -367,7 +379,7 @@ def train_station(
             reg = _safe_fit(
                 _make_regressor(hgb_overrides),
                 X.loc[labeled],
-                Y.loc[labeled, max_col],
+                Y.loc[labeled, max_col] - X.loc[labeled, "level"],  # rise target
                 head_key,
                 skipped_heads,
             )
@@ -401,7 +413,10 @@ def train_station(
 
     bundle = {
         "station_code": station,
-        "model_version": f"hgb-v1+{_git_short_sha()}",
+        "model_version": f"hgb-v2+{_git_short_sha()}",
+        # v2: regression heads predict the RISE over the current level; the
+        # serving side must add the level back. Old v1 bundles lack this key.
+        "regression_target": "rise",
         "trained_at": datetime.datetime.now().isoformat(),
         "sklearn_version": sklearn.__version__,
         "feature_names": feature_names,
@@ -428,7 +443,7 @@ def train_all(
     """Train and save every requested station's models. Returns the metrics.json payload."""
     models_dir = Path(models_dir)
     models_dir.mkdir(parents=True, exist_ok=True)
-    model_version = f"hgb-v1+{_git_short_sha()}"
+    model_version = f"hgb-v2+{_git_short_sha()}"
 
     station_results: Dict[str, dict] = {}
     for station in stations: