feat: hgb-v3 — Open-Meteo rain features clear the 12h warning gate

The rolling-origin harness (models/eval_rain.json) showed catchment rain
halving flood-year Brier scores, cutting flood-regime MAE 20-40%, and
extending the hard 2024 leads (+6h -> +11h at P.1, +10h -> +19h at
P.103). Ported: train_all loads the catchment-mean series (use_rain /
--no-rain to opt out; without it bundles train as v2), predict fetches
live rain hourly and passes an empty series on failure so rain-trained
bundles serve with NaN features instead of tripping the feature guard,
and the leader worker persists hourly per-point + catchment-mean rows to
a new openmeteo_rain table.

Regenerated backtest: the 2024 record flood now gets a 13-HOUR WARNING
(alert 04:00 vs 17:00 crossing, river at 2.9m at alert time) — the >=12h
acceptance gate PASSES for the first time. Journey on that crossing:
v1 -18h, v2 +6h, v3 +13h. The marginal 2025 double-crest trades its
artifact +46h latch for a calibrated +2h with zero false alarms. P.1
MAE 4.9/7.2/8.7 cm at 6/12/24h. Docs updated throughout.

scripts/backtest_render.py

@@ -48,13 +48,16 @@ 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.
 
-    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.
+    Mirrors the deployed hgb-v3 pipeline: the regression head learns the RISE
+    over the current level, with Open-Meteo catchment-rain features (trailing
+    sums + the forward-24h forecast sum); label statistics are bounded to the
+    training cutoff.
     """
+    from src.ml import rain as rain_mod
+
+    rain_series = rain_mod.catchment_mean(rain_mod.load_history())
     X, Y, _meta = features.build_matrix(
-        df_long, STATION, (HORIZON,), stats_end=train_end
+        df_long, STATION, (HORIZON,), stats_end=train_end, rain=rain_series
     )
     train_mask = X.index <= pd.Timestamp(train_end)
     X_train, Y_train = X.loc[train_mask], Y.loc[train_mask]