Beaumontmeteo

Shiny app and helper scripts to cache Meteo France station data around Beaumont in a local SQLite database.

What is stored

The cache now uses one unified dataset called weather_measurements inside data/rain_history.sqlite.

Each row stores:

• location,
• station,
• observation timestamp,
• metric id,
• numeric value,
• source metadata.

That means rain, temperature, humidity, wind and pressure can all be compared on the same time axis later.

App

Run from the project root:

shiny::runApp()

The UI now has two panels:

• a rain panel backed by historical DPClim rainfall,
• a second panel backed by the same SQLite dataset, with a selector for temperature and other station metrics.

Syncing the cache

Put your Meteo France API key in data/secrets as token4, then run:

Rscript scripts/update_rain_db.R

For a slow historical rainfall backfill into the same SQLite dataset:

Rscript scripts/backfill_two_years.R

Useful options:

• update_rain_db.R --location=vignasses
• update_rain_db.R --location=vignasses,la_mure
• update_rain_db.R --days=60
• update_rain_db.R --db-path=/somewhere/else/rain.sqlite
• backfill_two_years.R --location=vignasses
• backfill_two_years.R --db-path=/somewhere/else/rain.sqlite
• backfill_two_years.R --chunk-days=30
• backfill_two_years.R --resume=false
• backfill_two_years.R --between-chunk-sleep=20

Important note about frequency

Historical rain can be backfilled from the climatology endpoint, so a weekly sync is fine there.

Temperature, humidity, wind and pressure come from the rolling station observation feed. To keep those metrics continuous in the database, run the sync daily rather than weekly.

Cron examples

Daily full weather sync:

0 7 * * * cd /path/to/beaumontmeteo && Rscript scripts/update_rain_db.R >> /tmp/beaumontmeteo-sync.log 2>&1

If you only care about rain history, the same script can still be run weekly because rainfall is backfilled on each sync.

Why SQLite

SQLite is a good fit here because this is a local, read-heavy app with a small periodic write job.

• No database server to install or operate.
• One file to move, back up and inspect.
• Proper indexed queries and deduplication, unlike CSV or RDS files.
• The app still works offline once data has been cached.

If this ever becomes a shared multi-user service with concurrent writers, Postgres would be the next upgrade.

Notes

• The database path can be overridden with BEAUMONT_RAIN_DB.
• The repo ignores data/*.sqlite so the cache file stays local.