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.

tests/test_flood_forecast.py

@@ -197,7 +197,7 @@ def test_train_smoke_and_roundtrip(tmp_path):
     df = make_synth(n, data_stations, seed=7, pulses=pulses)
 
     metrics = train.train_all(
-        df, target_stations, models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 20}
+        df, target_stations, models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 20}, use_rain=False
     )
     assert metrics["stations"]["P.1"]["status"] == "trained"
     assert metrics["stations"]["P.20"]["status"] == "trained"
@@ -237,7 +237,7 @@ def test_feature_name_stability(tmp_path):
     upstream = [code for code, _lead in features.UPSTREAM_LEADS["P.1"]]
     data_stations = ["P.1"] + upstream
     df = make_synth(300, data_stations, seed=11, pulses={"P.1": [(100, 20, 2.0)]})
-    train.train_all(df, ["P.1"], models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 10})
+    train.train_all(df, ["P.1"], models_dir=tmp_path, skip_eval=True, hgb_overrides={"max_iter": 10}, use_rain=False)
     # Safe: loading the bundle this same test just wrote to tmp_path, not an external file.
     bundle = joblib.load(tmp_path / "flood_P.1.joblib")