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Author	SHA1	Message	Date
Graham McIntire	e8ae407be9	Add rain scatter prediction to map detail panel When clicking a grid point, fetches the latest NEXRAD composite reflectivity and identifies rain cells within 300 km that could enable rain scatter contacts. Shows: - Scatter classification (excellent/good/marginal/none) - Top 3 cells with dBZ, distance, bearing, and relative signal - Colored circle markers on the map at rain cell locations - Markers sized by reflectivity, colored by intensity Uses simplified bistatic radar equation accounting for reflectivity, frequency-dependent scattering (Rayleigh/Mie), and R^4 path loss. NEXRAD cells sampled every ~5 km within bounding box for efficiency.	2026-04-11 15:19:19 -05:00
Graham McIntire	e7a7ae073d	Phase 9.3, 9.4, and Phase 3 NEXRAD pipeline Task 9.3 - Weight recalibration via gradient descent: - Recalibrator module fits logistic regression weights using Nx - Trains on QSO positives vs random baseline negatives - Cross-validates by month, normalizes weights to sum to 1.0 - Mix task: mix recalibrate_scorer --sample 5000 --epochs 2000 Task 9.4 - Side-by-side scorer comparison: - ScorerDiff.compare/3 re-scores grid with old vs new weights - Reports mean diff, regressions, improvements, per-band breakdown - Mix task: mix scorer_diff --new-weights '{...}' Phase 3 - NEXRAD ingestion pipeline: - NexradClient fetches IEM n0q composite PNGs, extracts per-point box statistics (mean/max dBZ, texture variance) - NexradObservation schema with unique (lat, lon, observed_at) - NexradWorker on :nexrad queue for background processing - nexrad_texture backtest feature in Features module - mix nexrad_backfill --limit 200 All tasks added to AdminTaskWorker and Release for production use. 1116 tests, 0 failures.	2026-04-10 12:48:36 -05:00

Author

SHA1

Message

Date

Graham McIntire

e8ae407be9

Add rain scatter prediction to map detail panel

When clicking a grid point, fetches the latest NEXRAD composite
reflectivity and identifies rain cells within 300 km that could
enable rain scatter contacts. Shows:
- Scatter classification (excellent/good/marginal/none)
- Top 3 cells with dBZ, distance, bearing, and relative signal
- Colored circle markers on the map at rain cell locations
- Markers sized by reflectivity, colored by intensity

Uses simplified bistatic radar equation accounting for reflectivity,
frequency-dependent scattering (Rayleigh/Mie), and R^4 path loss.
NEXRAD cells sampled every ~5 km within bounding box for efficiency.

2026-04-11 15:19:19 -05:00

Graham McIntire

e7a7ae073d

Phase 9.3, 9.4, and Phase 3 NEXRAD pipeline

Task 9.3 - Weight recalibration via gradient descent:
- Recalibrator module fits logistic regression weights using Nx
- Trains on QSO positives vs random baseline negatives
- Cross-validates by month, normalizes weights to sum to 1.0
- Mix task: mix recalibrate_scorer --sample 5000 --epochs 2000

Task 9.4 - Side-by-side scorer comparison:
- ScorerDiff.compare/3 re-scores grid with old vs new weights
- Reports mean diff, regressions, improvements, per-band breakdown
- Mix task: mix scorer_diff --new-weights '{...}'

Phase 3 - NEXRAD ingestion pipeline:
- NexradClient fetches IEM n0q composite PNGs, extracts per-point
  box statistics (mean/max dBZ, texture variance)
- NexradObservation schema with unique (lat, lon, observed_at)
- NexradWorker on :nexrad queue for background processing
- nexrad_texture backtest feature in Features module
- mix nexrad_backfill --limit 200

All tasks added to AdminTaskWorker and Release for production use.
1116 tests, 0 failures.

2026-04-10 12:48:36 -05:00

2 commits