Graham McIntire 5fd37a17fd feat(propagation): per-band weight calibration from full-corpus correlation ... Ran recalibrate_algo.py against the full local prop_dev (81,994 contacts, 18.6M HRRR rows) and derived per-band composite weights for the nine bands with >=200 matched contacts. Moisture (dewpoint/PWAT/surface N) is consistently beneficial through 5.76 GHz, reverses at 24 GHz; rain scales sqrt(rain_k) not linearly so 24 GHz gets 0.215 rain weight instead of the linear-ratio 0.95. 10 GHz stays as the reference band (defaults); 47+ GHz inherits defaults (n<200). - BandConfig.weights/1 returns per-band override or default fallback - @band_configs carries :weights on 222/432/902/1296/2304/3400/5760/24G - Recalibrator.compute_factors/3 + fit(band_mhz:) band-aware fitting - Scorer + ContactLive.Show pass band_config to weights/1 - algo.md Part 2d documents the 2026-04-18 analysis + derivation rule - scripts/derive_band_weights.py turns correlations into weight maps - report preserved at docs/algo-reports/2026-04-18-recalibration.md		2026-04-18 10:19:26 -05:00
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2026-04-13-recalibration.md	Refresh 2026-04-13 recalibration report	2026-04-13 12:29:51 -05:00
2026-04-18-recalibration.md	feat(propagation): per-band weight calibration from full-corpus correlation	2026-04-18 10:19:26 -05:00