# Rain Model Runbook

Operational guide for training, evaluating, deploying, monitoring, and rolling back the rain model.

## 1) One-time Setup

Apply monitoring views:

```sh
docker compose exec -T timescaledb \
  psql -U postgres -d micrometeo \
  -f /docker-entrypoint-initdb.d/002_rain_monitoring_views.sql
```

## 2) Train + Evaluate

Recommended evaluation run (includes validation-only tuning, calibration comparison, naive baselines, and walk-forward folds):

```sh
python scripts/train_rain_model.py \
  --site "home" \
  --start "2026-02-01T00:00:00Z" \
  --end "2026-03-03T23:55:00Z" \
  --feature-set "extended" \
  --model-family "auto" \
  --forecast-model "ecmwf" \
  --tune-hyperparameters \
  --max-hyperparam-trials 12 \
  --calibration-methods "none,sigmoid,isotonic" \
  --walk-forward-folds 4 \
  --model-version "rain-auto-v1-extended" \
  --out "models/rain_model.pkl" \
  --report-out "models/rain_model_report.json" \
  --model-card-out "models/model_card_{model_version}.md" \
  --dataset-out "models/datasets/rain_dataset_{model_version}_{feature_set}.csv"
```

Review in report:
- `candidate_models[*].hyperparameter_tuning`
- `candidate_models[*].calibration_comparison`
- `naive_baselines_test`
- `walk_forward_backtest`

## 3) Deploy

1. Promote the selected artifact path to the inference worker (`RAIN_MODEL_PATH` or CLI `--model-path`).
2. Run one dry-run inference:

```sh
python scripts/predict_rain_model.py \
  --site home \
  --model-path "models/rain_model.pkl" \
  --model-name "rain_next_1h" \
  --dry-run
```

3. Run live inference:

```sh
python scripts/predict_rain_model.py \
  --site home \
  --model-path "models/rain_model.pkl" \
  --model-name "rain_next_1h"
```

## 4) Rollback

1. Identify the last known-good model artifact in `models/`.
2. Point deployment to that artifact (worker env `RAIN_MODEL_PATH` or manual inference path).
3. Re-run inference command and verify writes in `predictions_rain_1h`.
4. Keep the failed artifact/report for postmortem.

## 5) Monitoring

### Feature drift

```sql
SELECT *
FROM rain_feature_drift_daily
WHERE site = 'home'
ORDER BY day DESC
LIMIT 30;
```

Alert heuristic: any absolute z-score > 3 for 2+ consecutive days.

### Prediction drift

```sql
SELECT *
FROM rain_prediction_drift_daily
WHERE site = 'home'
ORDER BY day DESC
LIMIT 30;
```

Alert heuristic: `predicted_positive_rate` shifts by > 2x relative to trailing 14-day median.

### Calibration/performance drift

```sql
SELECT *
FROM rain_calibration_drift_daily
WHERE site = 'home'
ORDER BY day DESC
LIMIT 30;
```

Alert heuristic: sustained Brier-score increase > 25% from trailing 30-day average.

## 6) Pipeline Failure Alerts

Use the health-check script in cron, systemd timer, or your alerting scheduler:

```sh
python scripts/check_rain_pipeline_health.py \
  --site home \
  --model-name rain_next_1h \
  --max-ws90-age 20m \
  --max-baro-age 30m \
  --max-forecast-age 3h \
  --max-prediction-age 30m \
  --max-pending-eval-age 3h \
  --max-pending-eval-rows 200
```

The script exits non-zero on failure, so it can directly drive alerting.

## 7) Continuous Worker Defaults

`docker-compose.yml` provides these controls for `rainml`:
- `RAIN_TUNE_HYPERPARAMETERS`
- `RAIN_MAX_HYPERPARAM_TRIALS`
- `RAIN_CALIBRATION_METHODS`
- `RAIN_WALK_FORWARD_FOLDS`
- `RAIN_ALLOW_EMPTY_DATA`
- `RAIN_MODEL_CARD_PATH`

Recommended production defaults:
- Enable tuning daily or weekly (`RAIN_TUNE_HYPERPARAMETERS=true`)
- Keep walk-forward folds `0` in continuous mode, run fold backtests in scheduled evaluation jobs