- 3 hidden layers instead of 2 for better feature interaction learning
- Target is within-band distance percentile (0-1) instead of raw
normalized distance — reduces noise from operator/equipment variation
Features: HRRR conditions averaged at both QSO endpoints + solar time
Target: distance_km normalized per-band (distance / p99_range, capped at 1.0)
This trains on actual propagation outcomes from 57K+ QSOs, not the
hand-tuned algorithm output.
Raw features had vastly different scales (pressure ~1013, sin/cos ~[-1,1])
causing gradient explosion. Normalize all atmospheric features to ~[0,1]
using known physical bounds. Add Polaris dep for optimizer.
Score time-of-day per grid point using longitude/15 solar offset instead of
hardcoded CST/CDT. Add PWAT as 10th scoring factor. Refine pressure thresholds.
Update ML model and training pipeline to use local solar time.
