HRRR provides hourly 3km-resolution atmospheric profiles, filling the
temporal gaps (12-hourly soundings) and spatial gaps (only 9 sounding
stations) in our current weather data.
- Add hrrr_profiles table and hrrr_queued flag on QSOs
- HrrrClient fetches GRIB2 data via HTTP Range requests + wgrib2
- HrrrFetchWorker derives refractivity/ducting params via SoundingParams
- QsoWeatherEnqueueWorker now also enqueues HRRR jobs
- QSO show page displays HRRR section with collapsible profile
- Dockerfile builds wgrib2 from source for production
Adds haversine_km/4 to Radio context and backfill_distances/1 which
calculates distance_km from pos1/pos2 for QSOs missing it. Called
by QsoWeatherEnqueueWorker before enqueueing weather fetches.
Oban cron worker runs every 4 hours, finds QSOs without weather data,
discovers nearby ASOS/sounding stations, and enqueues WeatherFetchWorker
jobs. Migrations run automatically on app start in production.
IEM was returning 503s under 10 concurrent workers. Three fixes:
- Req transient retry with exponential backoff on IEM requests
- WeatherFetchWorker Oban job retries failed ASOS/RAOB fetches 2-3h later
with random jitter to avoid thundering herd
- Import script concurrency reduced 10→5, weather queue capped at 3
Store surface observations (ASOS) and upper-air soundings (RAOB) alongside
QSOs for atmospheric propagation correlation. Three new tables: weather_stations,
surface_observations, and soundings with JSONB profiles and pre-computed derived
parameters (refractivity, gradients, duct detection, stability indices).
Includes IEM API client for historical data import and import script seeded
with 95 ASOS + 9 sounding stations from PropCast coverage area.
