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
- point_detail response includes forecast array (score per valid_time)
- SVG sparkline shows score trend across all forecast hours
- Trend indicator: Improving/Declining/Steady based on first vs last score
- Add covering index (band_mhz, lat, lon, valid_time) INCLUDE (score) for
point_forecast queries
Define 0.125-degree CONUS grid (25-50N, 125-66W) for propagation
scoring and create propagation_scores table with composite unique
index on lat/lon/valid_time/band_mhz for upsert support.
Add idempotent migration to ensure precip_1h_in and wx_codes columns
exist, then reset weather_queued flag and clear stale observations
so the cron re-fetches with precipitation data included.
- 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
Poll UBNT AirFiber radios (AF11X + AF60-LR) every 5 minutes via
net-snmp CLI, storing signal metrics in commercial_samples. Fetches
ASOS weather alongside each cycle for propagation correlation.
Includes 7 seeded link definitions, Oban cron worker, and net-snmp
in the Docker image.
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
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.
Instead of fetching inline with Task.async_stream (which hammered IEM
with concurrent requests), the import script now just checks what data
is missing and enqueues WeatherFetchWorker jobs. Oban handles all HTTP
with concurrency 3 and automatic backoff on failures.
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.
