eval: quantile heads and fc48 on top of hgb-v3 (rejected/deferred); HII gauge-rain aggregate

Rolling-origin harness gains rise_rain_quantile, rise_rain_quantile_uw,
rise_rain_qsigma (L2 point + quantile sigma) and rise_rain_fc48, all
opt-in, plus --from-cache for reproducible offline reruns. Results in
models/eval_2026-09-12*.json, write-up in docs/FLOOD_FORECASTING.md:

- quantile point prediction: better MAE, worse first-alert lead at 5 of
  11 events (P.103 2022-08-14 +6h -> +1h) -> rejected
- quantile sigma only: Brier within noise (0.0031 -> 0.0029) -> not worth 3x heads
- rain_fc48: neutral everywhere except 2024-10-03 P.1 (+21h -> +72h), n=1
  -> deferred to after the 2026 season

src/ml/hii_rain.py: catchment-mean hourly rain from the ~130 HII gauges in
the upper-Ping box and a 24h-sum comparison against Open-Meteo. Not a
training feature (table exists only since 2026-08-11, no archive); exposed
at GET /api/hii/rainfall/catchment so the two sources' agreement is on
record by the time a fold can test it.

data._read_cache now skips non-station files in models/cache/ (the shared
dir also holds rain_openmeteo / dam_* caches, which crashed the reader).
scripts/summarize_eval.py prints per-variant lead/peak-error tables.

tests/test_hii_rain.py

@@ -0,0 +1,47 @@
+"""HII gauge-rain aggregate: pure-function tests (no DB)."""
+
+import numpy as np
+import pandas as pd
+
+from src.ml import hii_rain
+
+
+def _hourly(start, n):
+    return pd.date_range(start, periods=n, freq="h")
+
+
+def test_compare_identical_series_has_zero_bias():
+    idx = _hourly("2026-08-12", 200)
+    rng = np.random.default_rng(1)
+    rain = pd.Series(rng.exponential(0.5, len(idx)), index=idx)
+    out = hii_rain.compare_with_openmeteo(rain, rain.copy(), window_h=24)
+    assert out["overlap_hours"] == 200
+    assert out["bias_mm"] == 0.0
+    assert out["mae_mm"] == 0.0
+    assert out["corr"] > 0.999
+
+
+def test_compare_reports_constant_bias():
+    idx = _hourly("2026-08-12", 100)
+    gauge = pd.Series(1.0, index=idx)
+    model = pd.Series(1.5, index=idx)  # model wetter by 0.5 mm/h
+    out = hii_rain.compare_with_openmeteo(gauge, model, window_h=24)
+    assert abs(out["bias_mm"] - 12.0) < 1e-9  # 0.5 mm/h x 24 h
+
+
+def test_compare_uses_overlap_only():
+    gauge = pd.Series(1.0, index=_hourly("2026-08-12", 100))
+    model = pd.Series(1.0, index=_hourly("2026-08-14", 100))  # 52 h overlap
+    out = hii_rain.compare_with_openmeteo(gauge, model, window_h=24)
+    assert out["overlap_hours"] == 52
+
+
+def test_compare_no_overlap():
+    gauge = pd.Series(1.0, index=_hourly("2026-01-01", 10))
+    model = pd.Series(1.0, index=_hourly("2026-06-01", 10))
+    assert hii_rain.compare_with_openmeteo(gauge, model) == {"overlap_hours": 0}
+
+
+def test_load_gauge_mean_without_db_returns_none(monkeypatch):
+    monkeypatch.setattr(hii_rain, "resolve_db_url", lambda *a, **k: None)
+    assert hii_rain.load_gauge_mean() is None