update for 4 hour rain forecast

todo.md

@@ -55,3 +55,25 @@ Priority key: `P0` = critical/blocking, `P1` = important, `P2` = later optimizat
 - [x] [P0] Train baseline model on full available history and capture metrics. (completed on runtime machine)
 - [x] [P1] Add one expanded feature set and rerun evaluation. (completed on runtime machine 2026-03-12 with `feature_set=extended`, `model_version=rain-auto-v1-extended-202603120932`)
 - [x] [P0] Decide v1 threshold and define deployment interface.
+
+## 9) Extension Plan: 4-Hour Precipitation Window (Not Started)
+- [x] [P0] Lock v2 target definition for horizon extension: `rain_next_4h_mm >= <threshold_mm>` and explicitly decide whether the threshold remains `0.2mm` or is increased for 4-hour labeling. (implemented with `0.2mm` carry-forward)
+- [x] [P0] Decide rollout strategy: additive dual-horizon support (`1h` + `4h`) vs direct replacement; prefer dual-horizon for safe cutover. (implemented as additive dual-horizon)
+- [x] [P0] Parameterize label horizon in shared ML code (`scripts/rain_model_common.py`) so target columns are generated for 4-hour windows (48 x 5-minute buckets) instead of hard-coded 1-hour columns.
+- [x] [P1] Revisit persistence/context features currently tied to `rain_last_1h_mm`; decide whether to keep 1-hour context, add 4-hour context, or both for the 4-hour target. (implemented horizon-aware context column selection)
+- [x] [P0] Update training pipeline (`scripts/train_rain_model.py`) to train against the 4-hour target column, including reports, model-card content, dataset snapshot columns, and artifact metadata.
+- [x] [P0] Update audit pipeline (`scripts/audit_rain_data.py`) to report class balance and target definition for 4-hour labels.
+- [x] [P0] Update inference pipeline (`scripts/predict_rain_model.py`) to use the 4-hour target, including realized-outcome availability checks (`pred_ts + 4h`) and metadata/reporting fields.
+- [x] [P0] Finalize DB storage design for 4-hour predictions (new `predictions_rain_4h` table vs generic horizon column strategy) before migrations. (implemented dedicated `predictions_rain_4h` table)
+- [x] [P0] Create schema migration (recommended: new hypertable `predictions_rain_4h` with `rain_next_4h_mm_actual` and `rain_next_4h_actual` fields) and matching indexes.
+- [x] [P0] Update prediction upsert SQL to write to the 4-hour prediction table/columns.
+- [x] [P0] Update monitoring views in `db/init/002_rain_monitoring_views.sql` so drift/calibration/pipeline-health views include the 4-hour prediction path.
+- [x] [P0] Update Go DB query layer (`internal/db/series.go`) to read 4-hour prediction rows/fields.
+- [x] [P1] Update dashboard API defaults (`cmd/ingestd/web.go`) from `rain_next_1h` to the selected 4-hour model name (or make model name configurable).
+- [x] [P1] Update web UI labels/semantics (`cmd/ingestd/web/index.html`, `cmd/ingestd/web/app.js`) from “Rain 1h %” to “Rain 4h %” and verify chart legends/tooltips match the new horizon.
+- [x] [P1] Update worker/runtime defaults (`docker-compose.yml`, `scripts/run_rain_ml_worker.py`, `scripts/run_p0_rain_workflow.sh`) to use `rain_next_4h` naming/versioning.
+- [x] [P0] Update health-check defaults (`scripts/check_rain_pipeline_health.py`) for 4-hour evaluation latency (e.g., pending-eval age threshold > 4h).
+- [x] [P1] Update docs and runbooks (`README.md`, `docs/rain_prediction.md`, `docs/rain_model_runbook.md`) so commands, table names, and target definitions match the 4-hour system.
+- [ ] [P0] Run full retraining/evaluation for the 4-hour target and compare against current 1-hour model metrics before production cutover.
+- [ ] [P0] Execute staged rollout: deploy schema + views, deploy model + inference, verify dashboard/health checks, then switch default model name.
+- [x] [P1] Keep rollback path documented: retain `rain_next_1h` artifacts/table access until 4-hour monitoring is stable. (documented in `docs/rain_model_runbook.md` staged rollout/rollback section)