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grabowski 160617e87b feat: openmeteo_rain 2021+ backfill entry point
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rain.backfill_db pushes the full cached Open-Meteo archive into the
openmeteo_rain table in 5k-row idempotent upsert chunks;
scripts/backfill_rain_db.py is the thin CLI (DB from Config/.env or
--db-url). Safe to re-run and safe alongside the hourly live writer.
2026-08-12 17:28:44 +07:00
grabowski df0ae8cda3 feat: hgb-v3 — Open-Meteo rain features clear the 12h warning gate
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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.
2026-08-12 17:05:01 +07:00
grabowski cbb3bf7369 feat: Open-Meteo catchment rainfall series + rain features + harness variant
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src/ml/rain.py fetches hourly precipitation for five upper-Ping
catchment points (Chiang Dao, Mae Taeng, Mae Ngat, Mae Rim, city) from
the Open-Meteo forecast-model archive (2021-03 onward, no API key,
Bangkok-local timestamps, year-chunked local cache) plus the live
forecast endpoint for serving (trailing days + next 48h).

features.build_features/build_matrix accept the catchment-mean series
and add rain_6h/24h/72h trailing sums and rain_fc24 — the forward 24h
sum, a genuine forecast feature (archived forecasts at training time, a
real weather forecast at serving; never contains river data). Columns
exist only when a series is provided: HGB rejects all-NaN columns at
fit, so no-rain training omits them and serving passes an empty series
for alignment. evaluate.py gains the rise_rain variant (and --no-rain)
so the harness can judge whether rain beats the deployed rise baseline.
2026-08-12 16:39:36 +07:00