# Rain Prediction (Next 1 Hour)

This project now includes a starter training script for a **binary rain prediction**:

> **Will we see >= 0.2 mm of rain in the next hour?**

It uses local observations (WS90 + barometric pressure) and trains a lightweight
logistic regression model. This is a baseline you can iterate on as you collect
more data.

## What the script does
- Pulls data from TimescaleDB.
- Resamples observations to 5-minute buckets.
- Derives **pressure trend (1h)** from barometer data.
- Computes **future 1-hour rainfall** from the cumulative `rain_mm` counter.
- Trains a model and prints evaluation metrics.

The output is a saved model file (optional) you can use later for inference.

## Requirements
Python 3.10+ and:

```
pandas
numpy
scikit-learn
psycopg2-binary
joblib
```

Install with:

```sh
python3 -m venv .venv
source .venv/bin/activate
pip install -r scripts/requirements.txt
```

## Usage

```sh
python scripts/train_rain_model.py \
  --db-url "postgres://postgres:postgres@localhost:5432/micrometeo?sslmode=disable" \
  --site "home" \
  --start "2026-01-01" \
  --end "2026-02-01" \
  --out "models/rain_model.pkl"
```

You can also provide the connection string via `DATABASE_URL`:

```sh
export DATABASE_URL="postgres://postgres:postgres@localhost:5432/micrometeo?sslmode=disable"
python scripts/train_rain_model.py --site home
```

## Output
The script prints metrics including:
- accuracy
- precision / recall
- ROC AUC
- confusion matrix

If `joblib` is installed, it saves a model bundle:

```
models/rain_model.pkl
```

This bundle contains:
- The trained model pipeline
- The feature list used during training

## Data needs / when to run
For a reliable model, you will want:
- **At least 2-4 weeks** of observations
- A mix of rainy and non-rainy periods

Training with only a few days will produce an unstable model.

## Features used
The baseline model uses:
- `pressure_trend_1h` (hPa)
- `humidity` (%)
- `temperature_c` (C)
- `wind_avg_m_s` (m/s)
- `wind_max_m_s` (m/s)

These are easy to expand once you have more data (e.g. add forecast features).

## Notes / assumptions
- Rain detection is based on **incremental rain** derived from the WS90
  `rain_mm` cumulative counter.
- Pressure comes from `observations_baro`.
- All timestamps are treated as UTC.

## Next improvements
Ideas once more data is available:
- Add forecast precipitation and cloud cover as features
- Try gradient boosted trees (e.g. XGBoost / LightGBM)
- Train per-season models
- Calibrate probabilities (Platt scaling / isotonic regression)