- 15 features: add surface_refractivity and latitude
- Bigger network: 128→64→32 (3 hidden layers)
- Phase 1: pretrain on 500K stratified algorithm scores (all seasons/locations)
- Phase 2: fine-tune on 57K real QSO-HRRR matched data (percentile target)
- Lower LR (0.0003) for fine-tuning to preserve pretrained knowledge
- Model.train accepts :initial_state option for transfer learning
- 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
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.
Solar time (longitude/15) replaces fixed CDT/CST offset for time-of-day
scoring. Correlation analysis shows dramatic improvement at higher
frequencies: 24 GHz rho jumps from 0.056 (UTC) to 0.188 (solar), and
75 GHz corrects from spurious -0.39 to physically correct +0.24.
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.
