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.

src/ml/predict.py

@@ -127,6 +127,7 @@ def _model_forecast(
     bundle: dict,
     as_of: pd.Timestamp,
     current_level: float,
+    rain: Optional[pd.Series] = None,
 ) -> List[dict]:
     warn_thr = bundle["thresholds"]["warning"]
     danger_thr = bundle["thresholds"]["danger"]
@@ -145,7 +146,7 @@ def _model_forecast(
         )
         warn_thr, danger_thr = cfg_warn, cfg_danger
 
-    feature_row = features.build_features(grid, station_code).loc[[as_of]]
+    feature_row = features.build_features(grid, station_code, rain=rain).loc[[as_of]]
     expected_columns = bundle["feature_names"]
     missing = [c for c in expected_columns if c not in feature_row.columns]
     if missing:
@@ -229,6 +230,7 @@ def _forecast_station(
     models_dir: Path,
     now: pd.Timestamp,
     horizons: Tuple[int, ...],
+    rain: Optional[pd.Series] = None,
 ) -> List[dict]:
     level_col = (station_code, "water_level")
     if level_col not in grid.observed.columns:
@@ -264,7 +266,9 @@ def _forecast_station(
         )
 
     bundle = _load_bundle(bundle_path)
-    model_results = _model_forecast(station_code, grid, bundle, as_of, current_level)
+    model_results = _model_forecast(
+        station_code, grid, bundle, as_of, current_level, rain=rain
+    )
     if model_results is None:
         return _heuristic_forecast(
             station_code,
@@ -301,6 +305,7 @@ def get_forecasts(
     readings_by_station: Dict[str, List[dict]],
     models_dir: Union[str, Path] = DEFAULT_MODELS_DIR,
     now: Optional[Union[datetime.datetime, str]] = None,
+    rain: Optional[pd.Series] = None,
 ) -> List[dict]:
     """Produce flood forecasts for every station present in `readings_by_station`.
 
@@ -323,7 +328,9 @@ def get_forecasts(
     for station_code in readings_by_station.keys():
         try:
             results.extend(
-                _forecast_station(station_code, grid, models_dir, now, DEFAULT_HORIZONS)
+                _forecast_station(
+                    station_code, grid, models_dir, now, DEFAULT_HORIZONS, rain=rain
+                )
             )
         except Exception as error:
             logger.error(f"Forecast failed for station {station_code}: {error}")
@@ -359,4 +366,14 @@ def get_latest_forecasts(
             f"No recent data for station {missing_station}; omitting from forecasts"
         )
 
-    return get_forecasts(readings_by_station, models_dir=models_dir)
+    # Live rain: trailing days + next-48h forecast. On fetch failure pass an
+    # EMPTY series (not None) so rain-trained bundles still find their columns
+    # (as NaN) and serve model output instead of tripping the feature guard.
+    from .rain import serving_series
+
+    rain = serving_series()
+    if rain is None:
+        logger.warning("live rain unavailable; rain features will be NaN")
+        rain = pd.Series(dtype=float)
+
+    return get_forecasts(readings_by_station, models_dir=models_dir, rain=rain)