update for 4 hour rain forecast

docs/rain_prediction.md

@@ -1,14 +1,14 @@
-# Rain Prediction (Next 1 Hour)
+# Rain Prediction (Next 4 Hours)
 
 This project includes a baseline workflow for **binary rain prediction**:
 
-> **Will we see >= 0.2 mm of rain in the next hour?**
+> **Will we see >= 0.2 mm of rain in the next 4 hours?**
 
 It uses local observations (WS90 + barometer), trains a logistic regression
 baseline, and writes model-driven predictions back to TimescaleDB.
 
 ## P0 Decisions (Locked)
-- Target: `rain_next_1h_mm >= 0.2`.
+- Target: `rain_next_4h_mm >= 0.2`.
 - Primary use-case: low-noise rain heads-up signal for dashboard + alert candidate.
 - Frozen v1 training window (UTC): `2026-02-01T00:00:00Z` to `2026-03-03T23:55:00Z`.
 - Threshold policy: choose threshold on validation set by maximizing recall under
@@ -40,7 +40,7 @@ pip install -r scripts/requirements.txt
 - `scripts/train_rain_model.py`: strict time-based split training and metrics report, with optional
   validation-only hyperparameter tuning, calibration comparison, naive baseline comparison, and walk-forward folds.
 - `scripts/predict_rain_model.py`: inference using saved model artifact; upserts into
-  `predictions_rain_1h`.
+  `predictions_rain_4h`.
 - `scripts/run_rain_ml_worker.py`: long-running worker for periodic training + prediction.
 - `scripts/check_rain_pipeline_health.py`: freshness/failure check for alerting.
 - `scripts/recommend_rain_model.py`: rank saved training reports and recommend a deployment candidate.
@@ -60,7 +60,15 @@ Model-family options (`train_rain_model.py`):
 
 ## Usage
 ### 1) Apply schema update (existing DBs)
-`001_schema.sql` includes `predictions_rain_1h`.
+`003_rain_predictions_4h.sql` adds `predictions_rain_4h`.
+
+```sh
+docker compose exec -T timescaledb \
+  psql -U postgres -d micrometeo \
+  -f /docker-entrypoint-initdb.d/003_rain_predictions_4h.sql
+```
+
+`001_schema.sql` still remains safe to re-run for full schema parity.
 
 ```sh
 docker compose exec -T timescaledb \
@@ -76,6 +84,8 @@ docker compose exec -T timescaledb \
   -f /docker-entrypoint-initdb.d/002_rain_monitoring_views.sql
 ```
 
+All examples below assume a 4-hour horizon (`--horizon-hours 4`) and `model-name=rain_next_4h`.
+
 ### 2) Run data audit
 ```sh
 export DATABASE_URL="postgres://postgres:postgres@localhost:5432/micrometeo?sslmode=disable"
@@ -203,7 +213,8 @@ python scripts/train_rain_model.py \
 python scripts/predict_rain_model.py \
   --site home \
   --model-path "models/rain_model.pkl" \
-  --model-name "rain_next_1h"
+  --model-name "rain_next_4h" \
+  --horizon-hours 4
 ```
 
 ### 5) One-command P0 workflow
@@ -236,7 +247,7 @@ docker compose logs -f rainml
 - Model card: `models/model_card_<model_version>.md`
 - Model artifact: `models/rain_model.pkl`
 - Dataset snapshot: `models/datasets/rain_dataset_<model_version>_<feature_set>.csv`
-- Prediction rows: `predictions_rain_1h` (probability + threshold decision + realized
+- Prediction rows: `predictions_rain_4h` (probability + threshold decision + realized
   outcome fields once available)
 
 ### 7) Recommend deploy candidate from saved reports