ML Integration:
- Load trained model at app startup, cache compiled predict fn in persistent_term
- Grid worker uses batched ML prediction (10K chunks) when model loaded,
falls back to algorithm scorer when not
- ML score replaces composite, algorithm factor scores preserved for detail view
- Fix process explosion: single EXLA call per chunk instead of per-grid-point
QSO Features:
- Callsign search (ILIKE on station1/station2) with trigram indexes
- Reciprocal QSO grouping (same pair, same band, same hour)
- Wider layout (max-w-7xl) for data table pages
- QSO Training Data link on map page
Infrastructure:
- Re-enable hourly propagation grid worker in dev
- Track ML model weights in git for Docker builds
- Add btree indexes on qsos (timestamp, band, distance_km)
- Remove nav icons from layout header
Enqueue worker now gathers atmospheric data at pos1, midpoint, and pos2
along each QSO path instead of only pos1. Existing has_* guards prevent
duplicate fetches at each grid point.
- Add Radio.qso_path_points/1 for path point extraction
- Update hrrr_job_for_qso, iemre_job_for_qso, jobs_for_qso to iterate path points
- Refactor Weather into find_nearest_hrrr/3 and find_nearest_iemre/3
- Add hrrr_profiles_for_path/1 and iemre_for_path/1 query functions
- Add mix reset_enrichment task to trigger re-processing
- Add precip_1h_in and wx_codes fields to ASOS surface observations
- Add IEMRE reanalysis schema for radar-derived hourly precipitation
- Add IemreFetchWorker with exponential backoff and idempotency
- Integrate IEMRE enqueue into cron weather backfill pipeline
- All existing QSOs marked iemre_queued=false for automatic backfill
- Defensive grid-to-coordinate resolution in create_qso (case instead of crash on match failure)
- Log warning when Oban weather enqueue fails instead of silently dropping
- Add 30s per-session submission cooldown to prevent rapid-fire submissions
Maidenhead grid module converts grid squares to lat/lon coordinates.
Submission form validates grids, bands, modes, and email, computes
positions and distance, then triggers the weather/HRRR/terrain
processing pipeline via Oban.
Fetch elevation data along the path between two stations via the
Open-Meteo Elevation API (with Open-Topo-Data fallback), compute
Fresnel zone clearance, earth bulge, and knife-edge diffraction loss,
and store the results per QSO.
- terrain_profiles table with migration and TerrainProfile schema
- ElevationClient with batched API calls and fallback
- TerrainAnalysis with Fresnel/diffraction physics (ITU-R P.526-15)
- TerrainProfileWorker on Oban :terrain queue
- QsoWeatherEnqueueWorker enqueues terrain jobs automatically
- QSO show page displays verdict badge and collapsible elevation table
- Reorder show page: terrain, soundings, solar, HRRR, surface obs
- Fix Dockerfile wgrib2 build (add cmake dependency)
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.
