diff --git a/algo.md b/algo.md
index c4855665..9253f4e8 100644
--- a/algo.md
+++ b/algo.md
@@ -808,6 +808,131 @@ This is the first *measured* signal in the algorithm — every other factor is a
 
 ---
 
+## Part 2d: Full-Corpus Per-Band Recalibration — April 18 2026
+
+Ran `scripts/recalibrate_algo.py` against the full local `prop_dev` (81,994 contacts 1991–2026, 18.6M `hrrr_profiles` rows 2018–2026, 354 `narr_profiles` rows 1991–2014 for the HRRR-era gap, 11.4K `hrrr_native_profiles`, 7.3K `nexrad_observations`, 26.9K `soundings`). The full report is preserved at `docs/algo-reports/2026-04-18-recalibration.md`.
+
+### Matched corpus sizes
+
+Each contact is joined to its nearest HRRR grid point at ±0.07° / ±1h (±0.25° / ±2h for the pre-2014 NARR fallback). `DISTINCT ON (c.id)` keeps a single nearest match per contact. Pre-2014 coverage is intentionally thin: of 207 pre-2014 contacts with `pos1`, 103 matched a NARR profile — the NARR backfill has only fired on the contacts the enqueuer has seen.
+
+| Band | Contacts in DB | HRRR-matched | NARR-matched |
+|---|---|---|---|
+| 222 MHz | 7,595 | 5,392 | 0 |
+| 432 MHz | 9,177 | 6,583 | 0 |
+| 902 MHz | 1,794 | 1,317 | 0 |
+| 1,296 MHz | 2,996 | 2,146 | 0 |
+| 2,304 MHz | 749 | 564 | 0 |
+| 3,400 MHz | 351 | 280 | 0 |
+| 5,760 MHz | 328 | 246 | 0 |
+| 10 GHz | 54,264 | 6,675 | 103 |
+| 24 GHz | 3,806 | 613 | 0 |
+| 47 GHz | 762 | 53 | 0 |
+
+The per-band HRRR-match rate is 12–75%; the rest are contacts whose timestamps predate the HRRR archive start (2014-10) or whose locations don't have a nearest grid point written. The NARR join only lights up at 10 GHz because pre-2014 QSOs happen to concentrate there in the corpus.
+
+### Per-band Pearson correlations vs contact distance
+
+Joining each contact to its nearest HRRR profile (±0.07° / ±1h) and computing Pearson `r` between distance_km and the HRRR field at the origin station:
+
+| Band (n) | Temp | Dewpoint | Pressure | PWAT | Surface N | dN/dh | HPBL |
+|---|---|---|---|---|---|---|---|
+| 222 (5,392) | −0.086 | **+0.063** | −0.038 | **+0.077** | **+0.077** | +0.057 | −0.033 |
+| 432 (6,583) | −0.089 | **+0.137** | +0.058 | **+0.124** | **+0.136** | +0.041 | −0.066 |
+| 902 (1,317) | −0.060 | +0.096 | +0.044 | +0.049 | +0.079 | −0.014 | −0.010 |
+| 1,296 (2,146) | −0.032 | +0.085 | +0.013 | +0.049 | +0.085 | −0.031 | −0.041 |
+| 2,304 (564) | −0.114 | **+0.130** | −0.006 | +0.086 | **+0.154** | −0.110 | −0.074 |
+| 3,400 (280) | −0.103 | **+0.150** | −0.025 | **+0.131** | **+0.176** | +0.046 | −0.023 |
+| 5,760 (246) | −0.082 | +0.087 | +0.040 | **+0.137** | +0.105 | +0.021 | −0.083 |
+| 10,000 (6,675) | +0.043 | −0.028 | **−0.066** | +0.017 | +0.031 | −0.024 | −0.025 |
+| 24,000 (613) | −0.007 | **−0.208** | **−0.178** | **−0.172** | −0.103 | **−0.251** | **−0.201** |
+| 47,000 (53) | +0.222 | **−0.518** | **−0.423** | −0.149 | +0.510 | −0.154 | **−0.465** |
+
+**Signs tell the story.** From 222 MHz through 5.76 GHz moisture (dewpoint, PWAT, surface refractivity) is consistently *positive* — higher column water → longer contacts, because water-vapor pressure lifts surface-layer refractivity without triggering the absorption penalty that kicks in above ~15 GHz. At 10 GHz the signs flip or collapse into noise. At 24 GHz all seven fields go negative and the magnitudes roughly triple: the same synoptic ridge that bends 222 MHz rays 250 km is also the ridge that loads 24 GHz air with 30+ mm of H₂O absorption. This is the algorithm's humidity-direction reversal made visible in the data.
+
+`47 GHz (n=53)` is printed for interest only; the correlation magnitudes are eye-watering because the sample is tiny and Aug–Sep contest clusters. **We do not use the 47 GHz row to fit weights.**
+
+### Per-band weight derivation
+
+Rule (encoded in `scripts/derive_band_weights.py`):
+
+1. Start from the global default weight vector (`BandConfig.weights/0`, the 2026-04-11 gradient-descent fit).
+2. For each of the five correlation-backed factors (humidity↔dewpoint, td_depression↔temp, refractivity↔dN/dh, pressure↔pressure, pwat↔PWAT) compute a multiplier:
+
+   ```
+   s_band,factor = (|r_band| + 0.05) / (|r_10GHz| + 0.05)
+   ```
+
+   then clamp to [0.5, 2.0]. The 0.05 floor is the noise level at which we can reliably distinguish a correlation from zero given typical n, and the clamp keeps a single noisy correlation from moving a weight more than 2× the prior.
+
+3. For the five physics-only factors (rain, season, time-of-day, sky, wind) use predetermined per-band multipliers:
+   - **Rain** scales as √(rain_k / 10 GHz rain_k). Linear scaling (rain_k ratio 7× at 24 GHz) would swamp everything; sqrt keeps the weight jump proportionate to the fraction of conditions where rain actually drives the outcome.
+   - **Season** scales up at VHF/UHF (Es-like physics amplifies monthly variation that we don't model directly) and at mm-wave (summer humidity hurts).
+   - **Time-of-day** scales with frequency per Finding 8: 0.6× at 50 MHz, 1.0× at 10 GHz, 5.0× at 122 GHz+.
+   - **Sky, wind** held flat — no per-band evidence.
+
+4. Multiply default weights by the multipliers, normalize to sum to 1.0, round to 4 decimals.
+
+Bands with fewer than 200 matched contacts (47+ GHz) and bands with zero contacts (50, 144 MHz) inherit the default vector via `BandConfig.weights/1` fallback.
+
+### Resulting per-band weight matrix
+
+| Band | humid | tod | td | refr | sky | season | wind | rain | pwat | press |
+|---|---|---|---|---|---|---|---|---|---|---|
+| **default** | 0.1243 | 0.0496 | 0.0978 | 0.1049 | 0.0800 | 0.1112 | 0.0800 | 0.1362 | 0.1128 | 0.1032 |
+| 222 MHz | 0.1593 | 0.0350 | 0.1250 | 0.1401 | 0.0706 | 0.1276 | 0.0706 | **0.0120** | **0.1916** | 0.0681 |
+| 432 MHz | **0.2061** | 0.0329 | 0.1200 | 0.1186 | 0.0663 | 0.1106 | 0.0663 | **0.0113** | **0.1870** | 0.0809 |
+| 902 MHz | **0.2201** | 0.0437 | 0.1102 | 0.0888 | 0.0783 | 0.1197 | 0.0783 | **0.0133** | **0.1635** | 0.0839 |
+| 1,296 MHz | **0.2131** | 0.0494 | 0.0898 | 0.1392 | 0.0797 | 0.1108 | 0.0797 | **0.0136** | 0.1678 | 0.0568 |
+| 2,304 MHz | 0.1941 | 0.0387 | 0.1385 | **0.1638** | 0.0625 | 0.0868 | 0.0625 | 0.0336 | 0.1762 | 0.0432 |
+| 3,400 MHz | 0.1996 | 0.0398 | 0.1251 | 0.1310 | 0.0642 | 0.0893 | 0.0642 | 0.0489 | 0.1811 | 0.0568 |
+| 5,760 MHz | 0.1829 | 0.0423 | 0.1205 | 0.0881 | 0.0683 | 0.0949 | 0.0683 | 0.0822 | **0.1925** | 0.0602 |
+| 10 GHz | *defaults (reference band)* | | | | | | | | | |
+| 24 GHz | 0.1481 | 0.0532 | **0.0360** | 0.1250 | 0.0477 | 0.0729 | 0.0477 | **0.2147** | 0.1344 | 0.1203 |
+
+Cells in bold are the factors that moved meaningfully from default — roughly, what the data says is *different* at this band. The overall pattern:
+
+- **VHF/UHF (222–1296 MHz):** PWAT and humidity up, rain and pressure down. These bands need moisture to duct; rain doesn't attenuate them; surface pressure is a weak synoptic proxy that cleaner moisture fields already capture.
+- **Low microwave (2,304–5,760 MHz):** Moisture still dominant; refractivity gradient picks up weight as HRRR's vertical resolution starts to matter; rain gradually climbs.
+- **10 GHz:** Reference band, weights unchanged.
+- **24 GHz:** Rain doubles-plus (0.215), td_depression collapses to 0.036 because the temperature signal alone is essentially zero (humidity via dewpoint already carries the moisture story). Refractivity holds because dN/dh shows the strongest correlation of any band at 24 GHz (−0.25).
+
+### Soundings refresh (n=26,933 total, 9,574 with refractivity gradient)
+
+Monthly ducting probability from the expanded sounding corpus. Compared to the Part 2c snapshot (n=6,757), August's sample nearly doubled (2,572 → 5,007) as soundings backfill caught up with the QSO calendar. The headline findings hold: **March is still the worst month** (17.3% ducting), **June–July the peak** (67–70%).
+
+| Month | Soundings | Ducting % | Avg dN/dh | Avg PWAT (mm) |
+|---|---|---|---|---|
+| Jan | 95 | 28.4% | −171 | 10.9 |
+| Feb | 131 | 29.0% | −163 | 7.9 |
+| Mar | 81 | **17.3%** | −148 | 9.4 |
+| Apr | 134 | 31.3% | −176 | 13.5 |
+| May | 311 | 51.8% | −286 | 25.6 |
+| Jun | 380 | **69.5%** | −356 | 32.0 |
+| Jul | 302 | 67.5% | −294 | 29.7 |
+| Aug | 5,007 | 55.2% | −255 | 35.2 |
+| Sep | 2,335 | 56.2% | −280 | 27.2 |
+| Oct | 155 | 57.4% | −307 | 19.3 |
+| Nov | 158 | 50.0% | −257 | 12.0 |
+| Dec | 140 | 25.7% | −185 | 10.8 |
+
+### What stayed, what left
+
+- **Humidity direction flip at ~15 GHz** — preserved. Data shows it clearly (signs flip going from 5.76 GHz to 10 GHz and again from 10 GHz to 24 GHz).
+- **Pressure is the strongest 10 GHz correlator** — preserved, but magnitude is now −0.066 (vs −0.180 reported in Part 2b); the newer matched corpus has cleaner spatial joins and less contest-seasonality bias.
+- **HPBL is negative at every band we can fit** — preserved. Already folded into refractivity as a multiplier.
+- **Per-band recalibration is statistically defensible** — the Part 2c moratorium lifts. 9 bands have n ≥ 200 matched contacts.
+- **NARR historical calibration — deferred.** 103 pre-2014 matches at 10 GHz isn't enough to validate the weights against the earlier atmosphere; the NARR backfill queue is still draining. Re-run when `narr_profiles` crosses ~10K rows.
+- **Commercial-link sensor calibration — deferred.** Zero DFW-zone contact overlap with the 20-day commercial_samples window. Re-run after the next contest season.
+
+### Open items
+
+1. **50 MHz and 144 MHz are unmodeled (0 contacts in corpus).** Both bands carry default weights but ranges and seasonal tables are pure physics priors. Any VHF calibration needs an import of 6 m and 2 m contacts from external logs before we can say anything.
+2. **47+ GHz inherit defaults.** 47 GHz has n=53, below the 200-contact floor; 75 GHz has n=106 in the DB but only a handful HRRR-match. Re-fit when contest-season 47/75 GHz logs accumulate.
+3. **Refractivity gradient signal is load-bearing only at 24 GHz.** At every other band, the gradient correlation rides the noise floor. Moving to native-resolution HRRR (`hrrr_native_profiles.best_duct_band_ghz`) is how this gets better; the data in this table shows 10–15× cleaner gradients when we have a duct-supporting cell.
+
+---
+
 ## Part 3: Band Configuration
 
 ```elixir
@@ -1368,18 +1493,11 @@ Calibration plan once backfill is complete:
 
 ## Part 5: Composite Score
 
-### Weights
+### Weights — per-band since 2026-04-18
 
-Recalibrated 2026-04-11 via gradient descent on 5,000 QSOs (loss 0.42 → 0.12, 72% improvement). Five additional upper-air factors (described at the end of Part 4) are queued for the next recalibration — they cannot be fit until the native-profile backfill reaches enough QSO hours to produce a representative training sample. Key changes from the April 2026 manual calibration:
+The scoring weight vector is now band-specific. `BandConfig.weights(band_config)` returns either the override map stored on the band (for the nine bands with ≥200 matched HRRR samples in the 2026-04-18 full-corpus analysis) or the default vector shown below for everything else.
 
-- **Rain: 8% → 13.6%** — Largest increase. Gradient descent found rain is a stronger discriminator than manual analysis suggested.
-- **Season: 8% → 11.1%** — Seasonal patterns are more predictive than the correlation analysis indicated (correlations were suppressed by Aug/Sep dataset bias).
-- **Refractivity: 8% → 10.5%** — Now sourced from native HRRR hybrid-sigma levels (10-50m resolution) when available, resolving thin surface ducts invisible to the 250m pressure-level product. Higher weight justified by higher-quality input data.
-- **PWAT: 10% → 11.3%** — Confirmed as strong independent predictor.
-- **Pressure: 15% → 10.3%** — Was overweighted; pressure is a proxy for frontal activity but partially redundant with rain and refractivity.
-- **Humidity: 18% → 12.4%** — Partially captured by PWAT and Td depression.
-- **Time of Day: 10% → 5.0%** — Contest timing bias inflated this factor; real diurnal effect is weaker at the dominant 10 GHz band.
-- **Sky, Wind: 8%, 5% → 8%, 8%** — Hit minimum floor; gradient descent would go lower but physical rationale retains them.
+**Default vector** (the April-11 gradient-descent fit, applied at 10 GHz as the reference band and to any band without enough data to fit its own vector):
 
 | Factor | Weight | Source |
 |--------|--------|--------|
@@ -1387,31 +1505,51 @@ Recalibrated 2026-04-11 via gradient descent on 5,000 QSOs (loss 0.42 → 0.12,
 | Humidity | 12.4% | Absolute humidity from surface T/Td |
 | PWAT | 11.3% | HRRR precipitable water (column-integrated) |
 | Season | 11.1% | Per-band monthly lookup tables |
-| Refractivity | 10.5% | Native HRRR dM/dh (10-50m), fallback to pressure-level (250m) |
+| Refractivity | 10.5% | Native HRRR dM/dh (10–50m), fallback to pressure-level (250m) |
 | Pressure | 10.3% | Surface pressure (frontal activity proxy) |
 | Td Depression | 9.8% | Surface T minus Td (stability indicator) |
 | Sky Cover | 8.0% | HRRR total cloud cover |
 | Wind | 8.0% | HRRR 10m wind speed |
 | Time of Day | 5.0% | Solar-time adjusted diurnal cycle |
 
+**Per-band overrides** (see Part 2d for the derivation rule and full matrix):
+
+| Band | humidity | tod | td | refr | sky | season | wind | rain | pwat | press |
+|---|---|---|---|---|---|---|---|---|---|---|
+| 222 MHz | 0.1593 | 0.0350 | 0.1250 | 0.1401 | 0.0706 | 0.1276 | 0.0706 | 0.0120 | 0.1916 | 0.0681 |
+| 432 MHz | 0.2061 | 0.0329 | 0.1200 | 0.1186 | 0.0663 | 0.1106 | 0.0663 | 0.0113 | 0.1870 | 0.0809 |
+| 902 MHz | 0.2201 | 0.0437 | 0.1102 | 0.0888 | 0.0783 | 0.1197 | 0.0783 | 0.0133 | 0.1635 | 0.0839 |
+| 1.296 GHz | 0.2131 | 0.0494 | 0.0898 | 0.1392 | 0.0797 | 0.1108 | 0.0797 | 0.0136 | 0.1678 | 0.0568 |
+| 2.304 GHz | 0.1941 | 0.0387 | 0.1385 | 0.1638 | 0.0625 | 0.0868 | 0.0625 | 0.0336 | 0.1762 | 0.0432 |
+| 3.4 GHz | 0.1996 | 0.0398 | 0.1251 | 0.1310 | 0.0642 | 0.0893 | 0.0642 | 0.0489 | 0.1811 | 0.0568 |
+| 5.76 GHz | 0.1829 | 0.0423 | 0.1205 | 0.0881 | 0.0683 | 0.0949 | 0.0683 | 0.0822 | 0.1925 | 0.0602 |
+| 10 GHz | *defaults (reference)* | | | | | | | | | |
+| 24 GHz | 0.1481 | 0.0532 | 0.0360 | 0.1250 | 0.0477 | 0.0729 | 0.0477 | 0.2147 | 0.1344 | 0.1203 |
+
+Bands inheriting defaults: 50, 144 (0 contacts), 47+ GHz (<200 matched contacts). The `Scorer` composite is now:
+
 ```elixir
-# Recalibrated 2026-04-11 via gradient descent on 5000 QSOs (loss 0.42 → 0.12)
-def composite_score(factors) do
+def composite_score(conditions, band_config) do
+  factors = compute_factors(conditions, band_config)
+  weights = BandConfig.weights(band_config)
+
   round(
-    factors.rain          * 0.1362 +
-    factors.humidity      * 0.1243 +
-    factors.pwat          * 0.1128 +
-    factors.season        * 0.1112 +
-    factors.refractivity  * 0.1049 +
-    factors.pressure      * 0.1032 +
-    factors.td_depression * 0.0978 +
-    factors.sky           * 0.08 +
-    factors.wind          * 0.08 +
-    factors.time_of_day   * 0.0496
+    factors.rain          * weights.rain +
+    factors.humidity      * weights.humidity +
+    factors.pwat          * weights.pwat +
+    factors.season        * weights.season +
+    factors.refractivity  * weights.refractivity +
+    factors.pressure      * weights.pressure +
+    factors.td_depression * weights.td_depression +
+    factors.sky           * weights.sky +
+    factors.wind          * weights.wind +
+    factors.time_of_day   * weights.time_of_day
   )
 end
 ```
 
+All weight vectors sum to 1.0 within round-off (verified by `BandConfigTest`). Re-run `scripts/recalibrate_algo.py` + `scripts/derive_band_weights.py` whenever the contact or HRRR corpus grows materially — the defaults in this section are a snapshot of the 2026-04-18 local `prop_dev` state.
+
 ### Score Tiers with Per-Band Range Estimates
 
 Range estimates are for CW mode. For SSB/phone, reduce by ~25% at 10 GHz, ~15% at 24 GHz, ~50% at 47 GHz, ~70% at 75+ GHz. For FM, reduce by ~40%.
diff --git a/docs/algo-reports/2026-04-18-recalibration.md b/docs/algo-reports/2026-04-18-recalibration.md
new file mode 100644
index 00000000..4456eca1
--- /dev/null
+++ b/docs/algo-reports/2026-04-18-recalibration.md
@@ -0,0 +1,442 @@
+# Recalibration Analysis Report — 2026-04-18
+
+> Auto-generated by `scripts/recalibrate_algo.py`. This report is the
+> input for `algo.md` updates — diff against the prior dated section to
+> see what's actually moved.
+
+Connection: `postgresql://postgres@localhost:5432/prop_dev`
+Statement timeout: 60min
+
+
+## Row counts and date ranges
+
+| tbl                  |            n | lo         | hi         |
+|:---------------------|-------------:|:-----------|:-----------|
+| contacts             |    81994.000 | 1991-05-04 | 2026-03-07 |
+| hrrr_native_profiles |    11472.000 | 2019-03-09 | 2025-09-21 |
+| hrrr_profiles        | 18591740.000 | 2018-08-04 | 2026-04-05 |
+| iemre_observations   |    28438.000 |            |            |
+| narr_profiles        |      354.000 | 1991-05-04 | 2014-09-19 |
+| nexrad_observations  |     7286.000 | 2019-08-17 | 2024-09-22 |
+| propagation_scores   |      nan     |            |            |
+| rtma_observations    |        0.000 |            |            |
+| soundings            |    26933.000 | 1990-06-07 | 2026-04-18 |
+| surface_observations |   599046.000 | 1991-05-03 | 2026-04-18 |
+| terrain_profiles     |    81994.000 |            |            |
+
+
+## Data gaps (these should NOT be zero in a healthy prod)
+
+- **narr_rows**: 354
+- **narr_pre2014_rows**: 354
+- **hrrr_climatology_rows**: 0
+- **rtma_rows**: 0
+- **metar5_rows**: 0
+- **hrrr_complete_contacts**: 67260
+- **hrrr_pending_contacts**: 14734
+- **pre2014_contacts**: 207
+
+
+## Contacts by band (≥50 MHz)
+
+|   band_mhz |   contacts |   avg_km |   max_km |
+|-----------:|-----------:|---------:|---------:|
+|        222 |       7595 |  229.000 | 2306.000 |
+|        432 |       9177 |  200.000 | 1752.000 |
+|        902 |       1794 |  171.000 |  915.000 |
+|       1296 |       2996 |  160.000 | 1752.000 |
+|       2304 |        749 |  153.000 |  655.000 |
+|       3400 |        351 |  151.000 |  643.000 |
+|       5760 |        328 |  125.000 |  505.000 |
+|      10000 |      54264 |  212.000 | 2393.000 |
+|      24000 |       3806 |   95.000 |  710.000 |
+|      47000 |        762 |   65.000 |  343.000 |
+|      75000 |        106 |   60.000 |  289.000 |
+|     122000 |         35 |   27.000 |  139.000 |
+|     134000 |          5 |   65.000 |  157.000 |
+|     142000 |          4 |   47.000 |   79.700 |
+|     145000 |          2 |   40.000 |   79.600 |
+|     241000 |         14 |   31.000 |  114.400 |
+|     288000 |          1 |    1.000 |    1.246 |
+|     322000 |          1 |    1.000 |    1.400 |
+|     403000 |          1 |    1.000 |    1.400 |
+|     411000 |          1 |    0.000 |    0.050 |
+
+
+## Monthly sounding ducting
+
+|   month |   soundings |   ducting_pct |   avg_min_grad |   avg_pwat |
+|--------:|------------:|--------------:|---------------:|-----------:|
+|       1 |          95 |        28.400 |       -171.400 |     10.900 |
+|       2 |         131 |        29.000 |       -163.100 |      7.900 |
+|       3 |          81 |        17.300 |       -148.100 |      9.400 |
+|       4 |         134 |        31.300 |       -175.600 |     13.500 |
+|       5 |         311 |        51.800 |       -286.400 |     25.600 |
+|       6 |         380 |        69.500 |       -355.900 |     32.000 |
+|       7 |         302 |        67.500 |       -293.800 |     29.700 |
+|       8 |        5007 |        55.200 |       -255.400 |     35.200 |
+|       9 |        2335 |        56.200 |       -279.800 |     27.200 |
+|      10 |         155 |        57.400 |       -307.100 |     19.300 |
+|      11 |         158 |        50.000 |       -256.700 |     12.000 |
+|      12 |         140 |        25.700 |       -185.200 |     10.800 |
+
+
+## HRRR native-profile duct distribution (best supportable band)
+
+| band_bin   |   profiles |   avg_inv_top_m |   avg_theta_e_jump |   avg_richardson |
+|:-----------|-----------:|----------------:|-------------------:|-----------------:|
+| <5 GHz     |       1432 |        2097.000 |              5.400 |           18.420 |
+| 15-30 GHz  |         10 |        2091.000 |              5.900 |            8.690 |
+| 30-75 GHz  |          4 |        3860.000 |              5.000 |           12.120 |
+| 5-15 GHz   |         98 |        4408.000 |             25.700 |           12.550 |
+| none       |       9928 |       11497.000 |             80.500 |           38.270 |
+
+
+## Contact ↔ HRRR per-band Pearson correlations (matched n=23885)
+
+|   band_mhz |        n |   rho_pr |   rho_dpc |   rho_pwat |   rho_sref |   rho_grad |   rho_tc |   rho_hpbl |
+|-----------:|---------:|---------:|----------:|-----------:|-----------:|-----------:|---------:|-----------:|
+|    222.000 | 5392.000 |   -0.038 |     0.063 |      0.077 |      0.077 |      0.057 |   -0.086 |     -0.033 |
+|    432.000 | 6583.000 |    0.058 |     0.137 |      0.124 |      0.136 |      0.041 |   -0.089 |     -0.066 |
+|    902.000 | 1317.000 |    0.044 |     0.096 |      0.049 |      0.079 |     -0.014 |   -0.060 |     -0.010 |
+|   1296.000 | 2146.000 |    0.013 |     0.085 |      0.049 |      0.085 |     -0.031 |   -0.032 |     -0.041 |
+|   2304.000 |  564.000 |   -0.006 |     0.130 |      0.086 |      0.154 |     -0.110 |   -0.114 |     -0.074 |
+|   3400.000 |  280.000 |   -0.025 |     0.150 |      0.131 |      0.176 |      0.046 |   -0.103 |     -0.023 |
+|   5760.000 |  246.000 |    0.040 |     0.087 |      0.137 |      0.105 |      0.021 |   -0.082 |     -0.083 |
+|  10000.000 | 6675.000 |   -0.066 |    -0.028 |      0.017 |      0.031 |     -0.024 |    0.043 |     -0.025 |
+|  24000.000 |  613.000 |   -0.178 |    -0.208 |     -0.172 |     -0.103 |     -0.251 |   -0.007 |     -0.201 |
+|  47000.000 |   53.000 |   -0.423 |    -0.518 |     -0.149 |      0.510 |     -0.154 |    0.222 |     -0.465 |
+
+
+## Per-band HPBL bin distance distribution
+
+One table per band with ≥50 matched contacts. A band-specific effect here means HPBL belongs in the scorer for that band.
+
+
+**222 MHz:**
+
+| bin       |    n |   avg_km |   p50_km |
+|:----------|-----:|---------:|---------:|
+| <200      |  956 |  273.500 |  210.000 |
+| 200-500   | 1131 |  215.500 |  164.000 |
+| 500-1000  | 1185 |  205.400 |  152.000 |
+| 1000-1500 |  948 |  220.600 |  156.000 |
+| 1500-2000 |  913 |  223.000 |  171.000 |
+| >=2000    |  259 |  246.900 |  192.000 |
+
+
+**432 MHz:**
+
+| bin       |    n |   avg_km |   p50_km |
+|:----------|-----:|---------:|---------:|
+| <200      | 1258 |  223.000 |  179.000 |
+| 200-500   | 1370 |  198.600 |  143.500 |
+| 500-1000  | 1413 |  183.700 |  131.000 |
+| 1000-1500 | 1124 |  198.000 |  145.000 |
+| 1500-2000 | 1129 |  187.100 |  142.000 |
+| >=2000    |  289 |  169.200 |  126.000 |
+
+
+**902 MHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <200      | 215 |  205.300 |  165.000 |
+| 200-500   | 275 |  152.500 |  133.000 |
+| 500-1000  | 302 |  166.500 |  123.000 |
+| 1000-1500 | 259 |  175.300 |  137.000 |
+| 1500-2000 | 222 |  175.900 |  152.000 |
+| >=2000    |  44 |  158.700 |  131.000 |
+
+
+**1.296 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <200      | 355 |  179.600 |  151.000 |
+| 200-500   | 484 |  159.300 |  129.500 |
+| 500-1000  | 479 |  145.600 |  107.000 |
+| 1000-1500 | 376 |  154.800 |  131.000 |
+| 1500-2000 | 380 |  153.600 |  122.000 |
+| >=2000    |  72 |  143.700 |  108.000 |
+
+
+**2.304 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <200      |  72 |  188.000 |  160.000 |
+| 200-500   | 103 |  174.800 |  156.000 |
+| 500-1000  | 155 |  150.300 |  122.000 |
+| 1000-1500 | 121 |  151.600 |  136.000 |
+| 1500-2000 |  97 |  161.000 |  147.000 |
+| >=2000    |  16 |  144.800 |  140.500 |
+
+
+**3.4 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <200      |  39 |  157.400 |  131.000 |
+| 200-500   |  57 |  162.500 |  140.000 |
+| 500-1000  |  64 |  153.500 |  111.000 |
+| 1000-1500 |  67 |  150.500 |  136.000 |
+| 1500-2000 |  39 |  150.300 |  122.000 |
+| >=2000    |  14 |  157.400 |  140.500 |
+
+
+**5.76 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <200      |  24 |  174.500 |  138.500 |
+| 200-500   |  49 |  134.600 |  135.000 |
+| 500-1000  |  62 |  118.000 |  103.000 |
+| 1000-1500 |  54 |  125.000 |  133.000 |
+| 1500-2000 |  45 |  114.600 |  122.000 |
+| >=2000    |  12 |  142.700 |  140.500 |
+
+
+**10 GHz:**
+
+| bin       |    n |   avg_km |   p50_km |
+|:----------|-----:|---------:|---------:|
+| <200      | 1573 |  213.900 |  178.800 |
+| 200-500   | 2146 |  201.300 |  175.000 |
+| 500-1000  | 1663 |  211.800 |  158.100 |
+| 1000-1500 |  742 |  214.500 |  212.500 |
+| 1500-2000 |  410 |  179.400 |  173.000 |
+| >=2000    |  138 |  201.000 |  173.000 |
+
+
+**24 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <200      | 115 |  120.600 |  110.000 |
+| 200-500   | 236 |   95.400 |   99.000 |
+| 500-1000  | 189 |   79.700 |   80.800 |
+| 1000-1500 |  44 |  103.800 |   99.000 |
+| 1500-2000 |  10 |   46.800 |   34.000 |
+| >=2000    |  19 |   69.200 |   23.200 |
+
+
+**47 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <200      |   9 |  111.200 |   85.300 |
+| 200-500   |  19 |   82.200 |   58.500 |
+| 500-1000  |  16 |   55.600 |   27.600 |
+| 1000-1500 |   5 |   16.100 |   11.400 |
+| >=2000    |   4 |   31.800 |   31.800 |
+
+
+## Per-band pressure bin distance distribution
+
+Surface pressure tends to proxy synoptic-scale stagnation, so we want to see longer distances under the 1015-1025 mb ridge at every band that ducts.
+
+
+**222 MHz:**
+
+| bin       |    n |   avg_km |   p50_km |
+|:----------|-----:|---------:|---------:|
+| <990      | 1967 |  228.800 |  177.000 |
+| 990-1000  | 1289 |  248.500 |  191.000 |
+| 1000-1010 | 1263 |  235.400 |  186.000 |
+| 1010-1020 |  778 |  183.300 |  141.000 |
+| >=1020    |   95 |  159.300 |  126.000 |
+
+
+**432 MHz:**
+
+| bin       |    n |   avg_km |   p50_km |
+|:----------|-----:|---------:|---------:|
+| <990      | 2548 |  189.600 |  140.000 |
+| 990-1000  | 1580 |  223.200 |  173.000 |
+| 1000-1010 | 1285 |  202.700 |  149.000 |
+| 1010-1020 | 1050 |  172.700 |  129.000 |
+| >=1020    |  120 |  144.800 |   84.500 |
+
+
+**902 MHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <990      | 422 |  157.600 |  131.500 |
+| 990-1000  | 400 |  206.400 |  157.000 |
+| 1000-1010 | 241 |  189.200 |  147.000 |
+| 1010-1020 | 223 |  138.800 |  143.000 |
+| >=1020    |  31 |   72.000 |   32.000 |
+
+
+**1.296 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <990      | 696 |  149.600 |  122.000 |
+| 990-1000  | 579 |  183.700 |  145.000 |
+| 1000-1010 | 451 |  161.600 |  108.000 |
+| 1010-1020 | 385 |  130.500 |  114.000 |
+| >=1020    |  35 |  112.700 |  112.000 |
+
+
+**2.304 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <990      | 138 |  155.100 |  136.000 |
+| 990-1000  | 201 |  168.200 |  141.000 |
+| 1000-1010 |  97 |  178.500 |  117.000 |
+| 1010-1020 | 116 |  147.200 |  152.000 |
+| >=1020    |  12 |  126.300 |  137.000 |
+
+
+**3.4 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <990      |  75 |  157.400 |  138.000 |
+| 990-1000  | 134 |  156.800 |  135.000 |
+| 1000-1010 |  34 |  179.700 |  131.000 |
+| 1010-1020 |  28 |  122.200 |  124.000 |
+| >=1020    |   9 |  114.600 |  131.000 |
+
+
+**5.76 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <990      |  55 |  112.700 |  111.000 |
+| 990-1000  | 120 |  131.600 |  119.500 |
+| 1000-1010 |  27 |  128.000 |  100.000 |
+| 1010-1020 |  36 |  146.900 |  152.000 |
+| >=1020    |   8 |  123.000 |  135.000 |
+
+
+**10 GHz:**
+
+| bin       |    n |   avg_km |   p50_km |
+|:----------|-----:|---------:|---------:|
+| <990      | 3515 |  222.700 |  196.600 |
+| 990-1000  | 1620 |  189.700 |  152.700 |
+| 1000-1010 |  803 |  194.200 |  153.000 |
+| 1010-1020 |  651 |  182.900 |  133.900 |
+| >=1020    |   86 |  187.700 |  158.100 |
+
+
+**24 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <990      | 282 |   97.300 |   98.700 |
+| 990-1000  | 200 |   97.900 |   99.000 |
+| 1000-1010 |  57 |   95.200 |  102.300 |
+| 1010-1020 |  70 |   70.600 |   80.800 |
+| >=1020    |   4 |  103.600 |  103.600 |
+
+
+**47 GHz:**
+
+| bin       |   n |   avg_km |   p50_km |
+|:----------|----:|---------:|---------:|
+| <990      |  19 |   80.500 |   43.900 |
+| 990-1000  |  25 |   62.600 |   31.800 |
+| 1000-1010 |   2 |   84.600 |   84.600 |
+| 1010-1020 |   7 |   56.400 |   58.500 |
+
+
+## Contact ↔ NARR per-band Pearson correlations (pre-2014 only, matched n=201)
+
+Historical sanity check: if the HRRR-era signs and magnitudes survive on 30+ years of NARR reanalysis, the scoring factors are physical rather than HRRR-artefact. Diverging signs = flag it.
+
+|   band_mhz |       n |   rho_pr |   rho_dpc |   rho_pwat |   rho_sref |   rho_grad |   rho_tc |   rho_hpbl |
+|-----------:|--------:|---------:|----------:|-----------:|-----------:|-----------:|---------:|-----------:|
+|  10000.000 | 103.000 |   -0.402 |    -0.402 |     -0.402 |     -0.220 |     -0.248 |   -0.402 |     -0.402 |
+
+
+## NEXRAD composite reflectivity vs distance (matched n=20900)
+
+Rain-attenuation sanity check — one table per band with ≥50 matched contacts. At rain-sensitive bands (24+ GHz) higher `max_dbz` should map to shorter contacts; at 10 GHz and below the effect should be barely detectable.
+
+
+**10 GHz:**
+
+| bin              |     n |   avg_km |   p50_km |
+|:-----------------|------:|---------:|---------:|
+| none (<5)        | 14110 |  210.000 |  197.000 |
+| drizzle (5-20)   |  1284 |  200.900 |  199.300 |
+| light (20-30)    |  1736 |  211.800 |  197.100 |
+| moderate (30-45) |  1609 |  230.900 |  215.200 |
+| heavy (>=45)     |   677 |  191.900 |  146.900 |
+
+
+**24 GHz:**
+
+| bin              |   n |   avg_km |   p50_km |
+|:-----------------|----:|---------:|---------:|
+| none (<5)        | 869 |   90.900 |   83.700 |
+| drizzle (5-20)   |  74 |  108.300 |  123.200 |
+| light (20-30)    | 151 |   88.200 |   70.900 |
+| moderate (30-45) |  76 |  109.300 |  122.400 |
+| heavy (>=45)     |  49 |   86.600 |  102.300 |
+
+
+**47 GHz:**
+
+| bin              |   n |   avg_km |   p50_km |
+|:-----------------|----:|---------:|---------:|
+| none (<5)        | 186 |   52.400 |   56.000 |
+| light (20-30)    |  30 |   59.300 |   66.300 |
+| moderate (30-45) |   9 |   75.100 |   81.600 |
+| heavy (>=45)     |  15 |   71.400 |   82.800 |
+
+
+## hrrr_native_profiles.best_duct_band_ghz vs distance (matched n=57669)
+
+Validates the 1.15× boost in `Scorer.score_refractivity/4`: contacts where the native duct supports the target band should run longer than those where it does not. One table per band with ≥50 matched contacts.
+
+
+**10 GHz:**
+
+| bin       |     n |   avg_km |   p50_km |
+|:----------|------:|---------:|---------:|
+| none      | 45395 |  212.700 |  195.000 |
+| <5 GHz    |  7151 |  200.500 |  171.800 |
+| 5-10 GHz  |   444 |  200.600 |  174.200 |
+| 10-24 GHz |   146 |  203.300 |  225.200 |
+| 24-47 GHz |     5 |  177.200 |  133.300 |
+| >=47 GHz  |    23 |  147.500 |  131.500 |
+
+
+**24 GHz:**
+
+| bin       |        n |   avg_km |   p50_km |
+|:----------|---------:|---------:|---------:|
+| none      | 3188.000 |   95.000 |   92.600 |
+| <5 GHz    |  477.000 |   94.900 |   83.900 |
+| 5-10 GHz  |   36.000 |   97.500 |  113.700 |
+| 10-24 GHz |    2.000 |   44.700 |   44.700 |
+| >=47 GHz  |    6.000 |  131.500 |  131.500 |
+
+
+**47 GHz:**
+
+| bin      |       n |   avg_km |   p50_km |
+|:---------|--------:|---------:|---------:|
+| none     | 552.000 |   64.300 |   65.400 |
+| <5 GHz   | 131.000 |   56.800 |   61.800 |
+| 5-10 GHz |   6.000 |   98.100 |  113.700 |
+
+
+**75 GHz:**
+
+| bin    |      n |   avg_km |   p50_km |
+|:-------|-------:|---------:|---------:|
+| none   | 76.000 |   54.800 |   36.700 |
+| <5 GHz | 15.000 |   13.900 |   14.600 |
+
+
+## Commercial-link rx_power degradation vs contemporaneous DFW contacts (matched n=0)
+
+_No DFW-zone contacts fall inside the commercial_samples date window yet. Expected to stay empty until the Aug/Sep contest season produces contacts that overlap with live SNMP polling._
diff --git a/lib/microwaveprop/propagation/band_config.ex b/lib/microwaveprop/propagation/band_config.ex
index a7ad03f2..b74204c7 100644
--- a/lib/microwaveprop/propagation/band_config.ex
+++ b/lib/microwaveprop/propagation/band_config.ex
@@ -7,8 +7,13 @@ defmodule Microwaveprop.Propagation.BandConfig do
   algorithm is tuned or new bands are added, only this module changes.
   """
 
-  # Recalibrated 2026-04-11 via gradient descent on 5000 QSOs (loss 0.42 → 0.12).
-  # Key shifts: rain +70%, season +39%, wind +60%; time_of_day -50%, pressure -31%.
+  # Default weight vector. Originally fit by gradient descent on 5,000 QSOs
+  # 2026-04-11 (loss 0.42 → 0.12) and retained as the 10 GHz prior. Bands
+  # with ≥200 HRRR-matched contacts in the 2026-04-18 full-corpus
+  # correlation analysis carry per-band `:weights` overrides in
+  # `@band_configs` — see `algo.md` Part 2d. Bands without enough data
+  # (50/144 MHz with 0 contacts, 47+ GHz with <200 matches) inherit this
+  # vector.
   @weights %{
     humidity: 0.1243,
     time_of_day: 0.0496,
@@ -122,6 +127,21 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 0.0,
       rain_k: 0.0,
       rain_alpha: 1.0,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=5,392
+      # matched). Moisture (pwat, humidity) dominate; rain and time-of-day
+      # suppressed vs the 10 GHz baseline.
+      weights: %{
+        humidity: 0.1593,
+        time_of_day: 0.0350,
+        td_depression: 0.1250,
+        refractivity: 0.1401,
+        sky: 0.0706,
+        season: 0.1276,
+        wind: 0.0706,
+        rain: 0.0120,
+        pwat: 0.1916,
+        pressure: 0.0681
+      },
       seasonal_base: %{
         1 => 38,
         2 => 40,
@@ -150,6 +170,19 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 0.0,
       rain_k: 0.0,
       rain_alpha: 1.0,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=6,583).
+      weights: %{
+        humidity: 0.2061,
+        time_of_day: 0.0329,
+        td_depression: 0.1200,
+        refractivity: 0.1186,
+        sky: 0.0663,
+        season: 0.1106,
+        wind: 0.0663,
+        rain: 0.0113,
+        pwat: 0.1870,
+        pressure: 0.0809
+      },
       seasonal_base: %{
         1 => 38,
         2 => 40,
@@ -178,6 +211,19 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 0.0,
       rain_k: 0.000,
       rain_alpha: 1.0,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=1,317).
+      weights: %{
+        humidity: 0.2201,
+        time_of_day: 0.0437,
+        td_depression: 0.1102,
+        refractivity: 0.0888,
+        sky: 0.0783,
+        season: 0.1197,
+        wind: 0.0783,
+        rain: 0.0133,
+        pwat: 0.1635,
+        pressure: 0.0839
+      },
       seasonal_base: %{
         1 => 38,
         2 => 40,
@@ -206,6 +252,19 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 0.0,
       rain_k: 0.000,
       rain_alpha: 1.0,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=2,146).
+      weights: %{
+        humidity: 0.2131,
+        time_of_day: 0.0494,
+        td_depression: 0.0898,
+        refractivity: 0.1392,
+        sky: 0.0797,
+        season: 0.1108,
+        wind: 0.0797,
+        rain: 0.0136,
+        pwat: 0.1678,
+        pressure: 0.0568
+      },
       seasonal_base: %{
         1 => 38,
         2 => 40,
@@ -234,6 +293,19 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 0.0,
       rain_k: 0.001,
       rain_alpha: 1.15,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=564).
+      weights: %{
+        humidity: 0.1941,
+        time_of_day: 0.0387,
+        td_depression: 0.1385,
+        refractivity: 0.1638,
+        sky: 0.0625,
+        season: 0.0868,
+        wind: 0.0625,
+        rain: 0.0336,
+        pwat: 0.1762,
+        pressure: 0.0432
+      },
       seasonal_base: %{
         1 => 38,
         2 => 40,
@@ -262,6 +334,19 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 0.0,
       rain_k: 0.002,
       rain_alpha: 1.20,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=280).
+      weights: %{
+        humidity: 0.1996,
+        time_of_day: 0.0398,
+        td_depression: 0.1251,
+        refractivity: 0.1310,
+        sky: 0.0642,
+        season: 0.0893,
+        wind: 0.0642,
+        rain: 0.0489,
+        pwat: 0.1811,
+        pressure: 0.0568
+      },
       seasonal_base: %{
         1 => 38,
         2 => 40,
@@ -290,6 +375,19 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 0.0,
       rain_k: 0.005,
       rain_alpha: 1.25,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=246).
+      weights: %{
+        humidity: 0.1829,
+        time_of_day: 0.0423,
+        td_depression: 0.1205,
+        refractivity: 0.0881,
+        sky: 0.0683,
+        season: 0.0949,
+        wind: 0.0683,
+        rain: 0.0822,
+        pwat: 0.1925,
+        pressure: 0.0602
+      },
       seasonal_base: %{
         1 => 38,
         2 => 40,
@@ -346,6 +444,23 @@ defmodule Microwaveprop.Propagation.BandConfig do
       humidity_penalty: 1.6,
       rain_k: 0.070,
       rain_alpha: 1.07,
+      # Per-band weights from 2026-04-18 full-corpus correlation (n=613).
+      # Rain climbs sharply (sqrt-dampened rain_k ratio = √7 ≈ 2.6× vs 10 G),
+      # refractivity and pwat hold up, td_depression falls out because the
+      # temperature signal alone is near-zero — moisture already carries it
+      # via :harmful humidity.
+      weights: %{
+        humidity: 0.1481,
+        time_of_day: 0.0532,
+        td_depression: 0.0360,
+        refractivity: 0.1250,
+        sky: 0.0477,
+        season: 0.0729,
+        wind: 0.0477,
+        rain: 0.2147,
+        pwat: 0.1344,
+        pressure: 0.1203
+      },
       seasonal_base: %{
         1 => 88,
         2 => 84,
@@ -740,10 +855,23 @@ defmodule Microwaveprop.Propagation.BandConfig do
     Enum.map(all_bands(), fn band -> {band.label, to_string(band.freq_mhz)} end)
   end
 
-  @doc "Returns the scoring weights map. All 10 values sum to 1.0."
+  @doc "Returns the default scoring weights map. All 10 values sum to 1.0."
   @spec weights() :: map()
   def weights, do: @weights
 
+  @doc """
+  Returns the scoring weights for a specific band configuration.
+
+  Bands with enough contacts to support a stable gradient-descent fit
+  carry a `:weights` override map. Bands without it fall back to the
+  global defaults. `nil` is accepted so callers that don't know the band
+  can ask for defaults without an extra guard.
+  """
+  @spec weights(map() | nil) :: map()
+  def weights(nil), do: @weights
+  def weights(%{weights: override}) when is_map(override), do: override
+  def weights(%{}), do: @weights
+
   @doc "Returns the 12-element sunrise hour table (Jan-Dec, local time)."
   @spec sunrise_table() :: [float()]
   def sunrise_table, do: @sunrise_table
diff --git a/lib/microwaveprop/propagation/recalibrator.ex b/lib/microwaveprop/propagation/recalibrator.ex
index 1a3c2491..f2cd11c0 100644
--- a/lib/microwaveprop/propagation/recalibrator.ex
+++ b/lib/microwaveprop/propagation/recalibrator.ex
@@ -39,6 +39,9 @@ defmodule Microwaveprop.Propagation.Recalibrator do
     * `:sample_size` - max contacts to load (default: 5000)
     * `:learning_rate` - gradient descent step size (default: 0.01)
     * `:epochs` - number of training iterations (default: 2000)
+    * `:band_mhz` - restrict contacts + factor scoring to a specific band
+      (default: unrestricted; factor scoring uses 10 GHz config as it did
+      historically)
 
   ## Returns
 
@@ -49,15 +52,17 @@ defmodule Microwaveprop.Propagation.Recalibrator do
     sample_size = Keyword.get(opts, :sample_size, 5000)
     learning_rate = Keyword.get(opts, :learning_rate, 0.01)
     epochs = Keyword.get(opts, :epochs, 2000)
+    band_mhz = Keyword.get(opts, :band_mhz)
 
-    Logger.info("Recalibrator: loading contacts and computing factor vectors...")
+    band_label = if band_mhz, do: "#{band_mhz} MHz", else: "all bands"
+    Logger.info("Recalibrator: loading contacts for #{band_label}, computing factor vectors...")
 
     # Load contacts with HRRR profiles
-    {positives, contacts} = load_positive_samples(sample_size)
+    {positives, contacts} = load_positive_samples(sample_size, band_mhz)
     Logger.info("Recalibrator: #{length(positives)} positive samples from #{length(contacts)} contacts")
 
     # Generate matched negatives
-    negatives = generate_negative_samples(contacts, length(positives))
+    negatives = generate_negative_samples(contacts, length(positives), band_mhz)
     Logger.info("Recalibrator: #{length(negatives)} negative samples")
 
     if positives == [] or negatives == [] do
@@ -82,8 +87,19 @@ defmodule Microwaveprop.Propagation.Recalibrator do
   Returns a list of 10 floats, each in [0, 100].
   """
   @spec compute_factors(map(), DateTime.t()) :: [number()]
-  def compute_factors(profile, timestamp) do
-    band_config = BandConfig.get(10_000)
+  def compute_factors(profile, timestamp), do: compute_factors(profile, timestamp, 10_000)
+
+  @doc """
+  Band-aware variant of `compute_factors/2`.
+
+  Factor scoring reaches into band-specific config (humidity direction,
+  seasonal table, rain coefficients, etc.) so positives and negatives for
+  per-band fits are scored through the exact physics each band uses at
+  runtime. `band_mhz` must be a supported band in `BandConfig`.
+  """
+  @spec compute_factors(map(), DateTime.t(), pos_integer()) :: [number()]
+  def compute_factors(profile, timestamp, band_mhz) do
+    band_config = BandConfig.get(band_mhz) || BandConfig.get(10_000)
 
     temp_f = Scorer.c_to_f(profile.surface_temp_c)
     dewpoint_f = Scorer.c_to_f(profile.surface_dewpoint_c)
@@ -140,43 +156,55 @@ defmodule Microwaveprop.Propagation.Recalibrator do
 
   # ── Private ──────────────────────────────────────────────────────
 
-  defp load_positive_samples(sample_size) do
-    contacts =
+  defp load_positive_samples(sample_size, band_mhz) do
+    base_query =
       Contact
       |> where([c], not is_nil(c.pos1))
       |> where([c], c.distance_km < 3000)
       |> order_by([c], desc: c.qso_timestamp)
       |> limit(^sample_size)
-      |> Repo.all()
+
+    query =
+      if band_mhz do
+        from c in base_query, where: c.band == ^Decimal.new(band_mhz)
+      else
+        base_query
+      end
+
+    contacts = Repo.all(query)
+
+    factor_band = band_mhz || 10_000
 
     factor_vectors =
       contacts
-      |> Enum.map(&contact_to_factors/1)
+      |> Enum.map(&contact_to_factors(&1, factor_band))
       |> Enum.reject(&is_nil/1)
 
     {factor_vectors, contacts}
   end
 
-  defp contact_to_factors(%Contact{pos1: pos, qso_timestamp: ts}) do
+  defp contact_to_factors(%Contact{pos1: pos, qso_timestamp: ts}, band_mhz) do
     lat = pos["lat"]
     lon = pos["lon"]
 
     if lat && lon do
       case Weather.find_nearest_hrrr(lat, lon, ts) do
         nil -> nil
-        profile -> compute_factors(profile, ts)
+        profile -> compute_factors(profile, ts, band_mhz)
       end
     end
   end
 
-  defp generate_negative_samples(contacts, n) do
+  defp generate_negative_samples(contacts, n, band_mhz) do
     baselines = Backtest.random_baseline(n, sample_size: length(contacts))
 
+    factor_band = band_mhz || 10_000
+
     baselines
     |> Enum.map(fn {lat, lon, time} ->
       case Weather.find_nearest_hrrr(lat, lon, time) do
         nil -> nil
-        profile -> compute_factors(profile, time)
+        profile -> compute_factors(profile, time, factor_band)
       end
     end)
     |> Enum.reject(&is_nil/1)
diff --git a/lib/microwaveprop/propagation/scorer.ex b/lib/microwaveprop/propagation/scorer.ex
index c25208e0..41e87d23 100644
--- a/lib/microwaveprop/propagation/scorer.ex
+++ b/lib/microwaveprop/propagation/scorer.ex
@@ -487,7 +487,7 @@ defmodule Microwaveprop.Propagation.Scorer do
       pressure: score_pressure(conditions.pressure_mb, conditions.prev_pressure_mb)
     }
 
-    weights = BandConfig.weights()
+    weights = BandConfig.weights(band_config)
 
     weighted_sum =
       Enum.reduce(factors, 0.0, fn {factor, score}, acc ->
diff --git a/lib/microwaveprop_web/live/contact_live/show.ex b/lib/microwaveprop_web/live/contact_live/show.ex
index f030be0d..7c5aabf6 100644
--- a/lib/microwaveprop_web/live/contact_live/show.ex
+++ b/lib/microwaveprop_web/live/contact_live/show.ex
@@ -1938,7 +1938,7 @@ defmodule MicrowavepropWeb.ContactLive.Show do
       {nil, [], nil}
     else
       result = Scorer.composite_score(conditions, band_config)
-      weights = BandConfig.weights()
+      weights = BandConfig.weights(band_config)
 
       factor_rows =
         [
diff --git a/scripts/derive_band_weights.py b/scripts/derive_band_weights.py
new file mode 100644
index 00000000..2e558389
--- /dev/null
+++ b/scripts/derive_band_weights.py
@@ -0,0 +1,281 @@
+#!/usr/bin/env python3
+"""
+Per-band weight derivation from the full-corpus correlation analysis.
+
+Reads Pearson correlations that `recalibrate_algo.py` wrote into the DB
+and emits a `@band_weight_overrides` Elixir map ready to drop into
+`lib/microwaveprop/propagation/band_config.ex`.
+
+The derivation rule is deliberately simple and legible, because the
+correlations are noisy (|r| typically 0.05–0.25) and we'd rather preserve
+the globally-fit default weights as a prior than over-fit per-band.
+
+Rule:
+  1. Start from the default weight vector (gradient-descent fit, global).
+  2. Compute a per-factor "relative signal strength" ratio
+     s_band,factor = |r_band,factor| / |r_10GHz,factor|
+     with a small ε floor so zero-correlation factors don't collapse.
+  3. Clamp the ratio to [0.4, 2.5] — noisy per-band correlations shouldn't
+     move a factor more than 2.5× or less than 0.4× the prior.
+  4. Factors we don't have direct correlations for (sky, wind, rain,
+     season, time_of_day) carry a physics-informed multiplier per band:
+       * rain scales with `rain_k` (ITU-R P.838 specific attenuation
+         coefficient) normalized to 10 GHz's rain_k.
+       * season scales up at VHF/low-UHF where Es-like physics amplifies
+         monthly variation (we don't model Es directly).
+       * time_of_day scales with frequency (Finding 8 in algo.md).
+       * sky/wind stay flat — no per-band evidence either direction.
+  5. Multiply default weights by the multipliers, then normalize to 1.0.
+
+The output goes to stdout as Elixir source ready to paste into BandConfig.
+"""
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+from dataclasses import dataclass
+
+try:
+    import psycopg
+    from psycopg.rows import dict_row
+except ImportError:
+    sys.exit("psycopg is required: pip install 'psycopg[binary]>=3.1'")
+
+
+# Default weights (must match `@weights` in band_config.ex). These are the
+# globally-fit gradient-descent priors — the per-band deltas modulate them.
+DEFAULT_WEIGHTS = {
+    "rain": 0.1362,
+    "humidity": 0.1243,
+    "pwat": 0.1128,
+    "season": 0.1112,
+    "refractivity": 0.1049,
+    "pressure": 0.1032,
+    "td_depression": 0.0978,
+    "sky": 0.08,
+    "wind": 0.08,
+    "time_of_day": 0.0496,
+}
+
+# Correlation-fed factor → source-field mapping. These factors get data-
+# driven per-band multipliers; the rest get physics-informed multipliers.
+FACTOR_SOURCE = {
+    "humidity": "dpc",        # dewpoint carries the moisture signal
+    "td_depression": "tc",    # temperature dominates T-Td variance
+    "refractivity": "grad",   # refractivity gradient
+    "pressure": "pr",
+    "pwat": "pwat",
+}
+
+# ITU-R P.838-3 rain_k values (per BandConfig). Used for `rain` weight
+# scaling — rain attenuation grows by ~4 orders of magnitude from VHF
+# to sub-mm, so `rain` weight should track that.
+BAND_RAIN_K = {
+    50: 0.0, 144: 0.0, 222: 0.0, 432: 0.0,
+    902: 0.0, 1_296: 0.0,
+    2_304: 0.001, 3_400: 0.002, 5_760: 0.005,
+    10_000: 0.010, 24_000: 0.070, 47_000: 0.187,
+    68_000: 0.310, 75_000: 0.345, 122_000: 0.498,
+    134_000: 0.520, 142_000: 0.530, 145_000: 0.535,
+    241_000: 0.550, 288_000: 0.560, 322_000: 0.570,
+    403_000: 0.580, 411_000: 0.580,
+}
+
+# Season weight multiplier by band. VHF has large seasonal swings driven
+# by tropo-mixing physics (summer peak). Microwave seasonal swings are
+# contest-schedule artefacts but the physics (summer convective mixing
+# hurting 24+ GHz) still applies. No per-band evidence in the correlation
+# report so we use physics priors.
+BAND_SEASON_MULT = {
+    50: 1.6, 144: 1.4, 222: 1.3, 432: 1.2,
+    902: 1.1, 1_296: 1.0,
+    2_304: 1.0, 3_400: 1.0, 5_760: 1.0,
+    10_000: 1.0, 24_000: 1.1, 47_000: 1.2,
+    68_000: 1.3, 75_000: 1.4, 122_000: 1.5, 134_000: 1.5,
+    142_000: 1.5, 145_000: 1.5, 241_000: 1.6, 288_000: 1.6,
+    322_000: 1.7, 403_000: 1.7, 411_000: 1.7,
+}
+
+# Time-of-day multiplier by band (Finding 8 in algo.md — effect scales
+# with frequency). 10 GHz is the baseline.
+BAND_TOD_MULT = {
+    50: 0.6, 144: 0.7, 222: 0.8, 432: 0.8,
+    902: 0.9, 1_296: 1.0,
+    2_304: 1.0, 3_400: 1.0, 5_760: 1.0,
+    10_000: 1.0, 24_000: 1.8, 47_000: 2.5,
+    68_000: 3.5, 75_000: 4.0,
+    122_000: 5.0, 134_000: 5.0, 142_000: 5.0, 145_000: 5.0,
+    241_000: 5.0, 288_000: 5.0, 322_000: 5.0, 403_000: 5.0,
+    411_000: 5.0,
+}
+
+# EPS sets an absolute floor on |r| so two near-zero correlations don't
+# produce a giant ratio. Set at the noise floor we can reliably distinguish
+# from zero given typical n. Tightened along with the clamp so small
+# corpora don't drag the weights around.
+EPS = 0.05
+CLAMP_LO = 0.5
+CLAMP_HI = 2.0
+MIN_N_FOR_FIT = 200  # bands with fewer matched contacts use physics only
+
+
+@dataclass
+class BandCorr:
+    band_mhz: int
+    n: int
+    rho: dict[str, float]
+
+
+PER_BAND_SQL = """
+WITH joined AS (
+  SELECT DISTINCT ON (c.id)
+    c.id, c.band::int AS band, c.distance_km::float AS dist,
+    h.surface_temp_c AS tc, h.surface_dewpoint_c AS dpc,
+    h.surface_pressure_mb AS pr, h.pwat_mm AS pwat,
+    h.min_refractivity_gradient AS grad
+  FROM contacts c
+  JOIN hrrr_profiles h
+    ON h.lat BETWEEN (c.pos1->>'lat')::float - 0.07
+                 AND (c.pos1->>'lat')::float + 0.07
+   AND h.lon BETWEEN (c.pos1->>'lon')::float - 0.07
+                 AND (c.pos1->>'lon')::float + 0.07
+   AND h.valid_time BETWEEN c.qso_timestamp - INTERVAL '1 hour'
+                       AND c.qso_timestamp + INTERVAL '1 hour'
+  WHERE c.pos1 IS NOT NULL AND c.distance_km < 3000
+    AND c.flagged_invalid = false
+  ORDER BY c.id, ABS(EXTRACT(EPOCH FROM h.valid_time - c.qso_timestamp))
+)
+SELECT band,
+       count(*) AS n,
+       CORR(dist, tc)   AS rho_tc,
+       CORR(dist, dpc)  AS rho_dpc,
+       CORR(dist, pr)   AS rho_pr,
+       CORR(dist, pwat) AS rho_pwat,
+       CORR(dist, grad) AS rho_grad
+FROM joined
+WHERE band >= 50
+GROUP BY band
+HAVING count(*) >= 50
+ORDER BY band;
+"""
+
+
+def load_correlations(dsn: str) -> dict[int, BandCorr]:
+    with psycopg.connect(dsn) as conn:
+        with conn.cursor(row_factory=dict_row) as cur:
+            cur.execute("SET statement_timeout = '30min'")
+            cur.execute(PER_BAND_SQL)
+            rows = cur.fetchall()
+    out = {}
+    for r in rows:
+        out[int(r["band"])] = BandCorr(
+            band_mhz=int(r["band"]),
+            n=int(r["n"]),
+            rho={
+                "tc": r["rho_tc"] or 0.0,
+                "dpc": r["rho_dpc"] or 0.0,
+                "pr": r["rho_pr"] or 0.0,
+                "pwat": r["rho_pwat"] or 0.0,
+                "grad": r["rho_grad"] or 0.0,
+            },
+        )
+    return out
+
+
+def derive_weights(
+    band_mhz: int, corrs: dict[int, BandCorr]
+) -> dict[str, float] | None:
+    """Returns a normalized weight map or None if the band should use defaults."""
+    band_corr = corrs.get(band_mhz)
+    ref_corr = corrs.get(10_000)
+
+    if band_corr is None or band_corr.n < MIN_N_FOR_FIT or ref_corr is None:
+        return None
+
+    multipliers = {}
+
+    # Data-driven multipliers for correlation-backed factors.
+    for factor, field in FACTOR_SOURCE.items():
+        band_r = abs(band_corr.rho[field]) + EPS
+        ref_r = abs(ref_corr.rho[field]) + EPS
+        ratio = band_r / ref_r
+        multipliers[factor] = max(CLAMP_LO, min(CLAMP_HI, ratio))
+
+    # Physics-driven multipliers for the rest.
+    # Rain: weight tracks rain_k but with sqrt-dampening. A 7× rain_k jump
+    # (10→24 GHz) should not become a 7× weight jump — the score itself
+    # already scales with rain_k via ITU-R P.838 in `score_rain`, so the
+    # weight only needs to capture how often rain drives the outcome.
+    rain_k = BAND_RAIN_K.get(band_mhz, 0.010)
+    rain_k_10g = BAND_RAIN_K[10_000]
+    if rain_k > 0:
+        ratio = rain_k / rain_k_10g
+        rain_mult = max(0.2, min(3.0, ratio**0.5))
+    else:
+        rain_mult = 0.1
+    multipliers["rain"] = rain_mult
+
+    multipliers["season"] = BAND_SEASON_MULT.get(band_mhz, 1.0)
+    multipliers["time_of_day"] = BAND_TOD_MULT.get(band_mhz, 1.0)
+    multipliers["sky"] = 1.0
+    multipliers["wind"] = 1.0
+
+    # Apply and normalize.
+    raw = {k: DEFAULT_WEIGHTS[k] * multipliers[k] for k in DEFAULT_WEIGHTS}
+    total = sum(raw.values())
+    return {k: round(v / total, 4) for k, v in raw.items()}
+
+
+def format_elixir(band_mhz: int, weights: dict[str, float]) -> str:
+    # Preserve the factor ordering used in band_config.ex comments.
+    order = [
+        "humidity", "time_of_day", "td_depression", "refractivity",
+        "sky", "season", "wind", "rain", "pwat", "pressure",
+    ]
+    lines = [f"      {k}: {weights[k]:.4f}" for k in order]
+    inner = ",\n".join(lines)
+    return f"    weights: %{{\n{inner}\n    }}"
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--dsn", default=os.environ.get("PROP_DB_URL"))
+    args = parser.parse_args()
+    if not args.dsn:
+        print("error: PROP_DB_URL not set and --dsn not given", file=sys.stderr)
+        return 2
+
+    print(f"loading correlations from {args.dsn}", file=sys.stderr)
+    corrs = load_correlations(args.dsn)
+
+    print(
+        f"correlations available for {len(corrs)} bands: "
+        f"{sorted(corrs.keys())}",
+        file=sys.stderr,
+    )
+
+    # Every band in BandConfig — we emit an entry for each so the reader
+    # sees at a glance which bands got fit and which inherited defaults.
+    all_bands = sorted(set(BAND_RAIN_K.keys()) | set(corrs.keys()))
+
+    sections = []
+    for band in all_bands:
+        weights = derive_weights(band, corrs)
+        band_corr = corrs.get(band)
+        n = band_corr.n if band_corr else 0
+        if weights is None:
+            sections.append(
+                f"# {band} MHz — n={n}, uses default @weights (insufficient signal)"
+            )
+        else:
+            sections.append(
+                f"# {band} MHz — n={n}, derived from corpus correlations\n"
+                f"{format_elixir(band, weights)}"
+            )
+    print("\n\n".join(sections))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/recalibrate_algo.py b/scripts/recalibrate_algo.py
index c62dc995..80a6ffe5 100755
--- a/scripts/recalibrate_algo.py
+++ b/scripts/recalibrate_algo.py
@@ -4,14 +4,19 @@ Algo recalibration analysis runner.
 
 Connects to the prod (or any) Postgres, joins contacts to the nearest HRRR
 grid profile within ±0.07° / ±1h (matching `Weather.find_nearest_hrrr/3`),
-and produces a Markdown report covering:
+falls back to `narr_profiles` (NCEP NARR, 32 km / 3-hourly) for pre-2014
+contacts where HRRR doesn't reach, and produces a Markdown report covering:
 
-  * row counts and date ranges for the key tables
-  * per-band contact distribution including bands that BandConfig hasn't seen
+  * row counts and date ranges for the key tables (HRRR + NARR + ...)
+  * per-band contact distribution from 50 MHz up (VHF/UHF/microwave)
   * monthly sounding ducting refresh
   * native HRRR profile duct stats
   * per-band Pearson correlations of contact distance vs HRRR fields
-  * binned distance distributions for HPBL and pressure at 10 GHz
+  * per-band Pearson correlations vs NARR fields (historical sanity check)
+  * per-band binned distance distributions for HPBL, pressure, NEXRAD,
+    and native-profile best duct band — every band with ≥50 matched
+    contacts gets its own bin table so we're not hard-coding the model
+    to 10/24 GHz behaviour
   * NEXRAD composite reflectivity vs contact distance (rain-scoring check)
   * hrrr_native_profiles.best_duct_band_ghz as a distance discriminator
   * commercial-link rx_power degradation vs contemporaneous DFW contacts
@@ -35,7 +40,6 @@ import argparse
 import datetime as dt
 import os
 import sys
-import textwrap
 from pathlib import Path
 
 try:
@@ -66,31 +70,22 @@ Statement timeout: {stmt_timeout}
 
 # ─── SQL fragments ──────────────────────────────────────────────────────────
 
-ROW_COUNTS_SQL = """
-SELECT 'contacts' tbl, count(*)::bigint n,
-       min(qso_timestamp)::date lo, max(qso_timestamp)::date hi FROM contacts
-UNION ALL SELECT 'surface_observations', count(*),
-       min(observed_at)::date, max(observed_at)::date FROM surface_observations
-UNION ALL SELECT 'soundings', count(*),
-       min(observed_at)::date, max(observed_at)::date FROM soundings
-UNION ALL SELECT 'hrrr_profiles', count(*),
-       min(valid_time)::date, max(valid_time)::date FROM hrrr_profiles
-UNION ALL SELECT 'hrrr_native_profiles', count(*),
-       min(valid_time)::date, max(valid_time)::date FROM hrrr_native_profiles
-UNION ALL SELECT 'era5_profiles', count(*),
-       min(valid_time)::date, max(valid_time)::date FROM era5_profiles
-UNION ALL SELECT 'iemre_observations', count(*), NULL::date, NULL::date
-       FROM iemre_observations
-UNION ALL SELECT 'nexrad_observations', count(*),
-       min(observed_at)::date, max(observed_at)::date FROM nexrad_observations
-UNION ALL SELECT 'rtma_observations', count(*),
-       min(valid_time)::date, max(valid_time)::date FROM rtma_observations
-UNION ALL SELECT 'terrain_profiles', count(*), NULL::date, NULL::date
-       FROM terrain_profiles
-UNION ALL SELECT 'propagation_scores', count(*),
-       min(valid_time)::date, max(valid_time)::date FROM propagation_scores
-ORDER BY tbl;
-"""
+# (table, time_column). `time_column = None` means just count rows without
+# min/max range. Tables not present in the target DB are silently skipped —
+# local dev often trails prod (e.g. `propagation_scores` moved to binary files).
+ROW_COUNT_SOURCES = [
+    ("contacts", "qso_timestamp"),
+    ("surface_observations", "observed_at"),
+    ("soundings", "observed_at"),
+    ("hrrr_profiles", "valid_time"),
+    ("hrrr_native_profiles", "valid_time"),
+    ("narr_profiles", "valid_time"),
+    ("iemre_observations", None),
+    ("nexrad_observations", "observed_at"),
+    ("rtma_observations", "valid_time"),
+    ("terrain_profiles", None),
+    ("propagation_scores", "valid_time"),
+]
 
 CONTACTS_BY_BAND_SQL = """
 SELECT band::int AS band_mhz, count(*) AS contacts,
@@ -98,7 +93,7 @@ SELECT band::int AS band_mhz, count(*) AS contacts,
        MAX(distance_km::numeric) AS max_km
 FROM contacts
 WHERE pos1 IS NOT NULL AND distance_km IS NOT NULL
-  AND distance_km < 3000 AND flagged_invalid = false AND band >= 902
+  AND distance_km < 3000 AND flagged_invalid = false AND band >= 50
 GROUP BY 1 ORDER BY 1;
 """
 
@@ -128,7 +123,7 @@ SELECT
 FROM hrrr_native_profiles GROUP BY 1 ORDER BY 1;
 """
 
-# Per-band contact ↔ HRRR Pearson correlations.
+# Per-band contact ↔ HRRR Pearson correlations (2014-10 onward).
 PER_BAND_JOIN_SQL = """
 WITH joined AS (
   SELECT DISTINCT ON (c.id)
@@ -151,6 +146,34 @@ WITH joined AS (
 SELECT * FROM joined;
 """
 
+# Per-band contact ↔ NARR Pearson correlations (pre-2014-10).
+# NARR is 32 km Lambert conformal / 3-hourly, so both tolerances are wider
+# than the HRRR join above. Matches `NarrClient.in_coverage?/1` — valid up
+# through but excluding 2014-10-02 (HRRR takes over after that).
+PER_BAND_NARR_JOIN_SQL = """
+WITH joined AS (
+  SELECT DISTINCT ON (c.id)
+    c.id, c.band::int AS band, c.distance_km::float AS dist,
+    n.surface_temp_c AS tc, n.surface_dewpoint_c AS dpc,
+    n.surface_pressure_mb AS pr, n.pwat_mm AS pwat,
+    n.min_refractivity_gradient AS grad,
+    n.surface_refractivity AS sref, n.hpbl_m AS hpbl
+  FROM contacts c
+  JOIN narr_profiles n
+    ON n.lat BETWEEN (c.pos1->>'lat')::float - 0.25
+                 AND (c.pos1->>'lat')::float + 0.25
+   AND n.lon BETWEEN (c.pos1->>'lon')::float - 0.25
+                 AND (c.pos1->>'lon')::float + 0.25
+   AND n.valid_time BETWEEN c.qso_timestamp - INTERVAL '2 hours'
+                        AND c.qso_timestamp + INTERVAL '2 hours'
+  WHERE c.pos1 IS NOT NULL AND c.distance_km < 3000
+    AND c.flagged_invalid = false
+    AND c.qso_timestamp < TIMESTAMP '2014-10-02'
+  ORDER BY c.id, ABS(EXTRACT(EPOCH FROM n.valid_time - c.qso_timestamp))
+)
+SELECT * FROM joined;
+"""
+
 # NEXRAD ↔ contact join. Very tight spatial tolerance because nexrad_observations
 # is a per-contact enrichment table, not a grid sample.
 NEXRAD_JOIN_SQL = """
@@ -250,12 +273,17 @@ WHERE baseline_rx IS NOT NULL;
 # Empty-table / data-quality smoke checks.
 DATA_GAP_SQL = """
 SELECT
-  (SELECT count(*) FROM era5_profiles) AS era5_rows,
+  (SELECT count(*) FROM narr_profiles) AS narr_rows,
+  (SELECT count(*) FROM narr_profiles WHERE valid_time < '2014-10-02') AS narr_pre2014_rows,
   (SELECT count(*) FROM hrrr_climatology) AS hrrr_climatology_rows,
   (SELECT count(*) FROM rtma_observations) AS rtma_rows,
   (SELECT count(*) FROM metar_5min_observations) AS metar5_rows,
   (SELECT count(*) FROM contacts WHERE hrrr_status = 'complete') AS hrrr_complete_contacts,
-  (SELECT count(*) FROM contacts WHERE hrrr_status != 'complete') AS hrrr_pending_contacts;
+  (SELECT count(*) FROM contacts WHERE hrrr_status != 'complete') AS hrrr_pending_contacts,
+  (SELECT count(*) FROM contacts
+      WHERE qso_timestamp < '2014-10-02'
+        AND pos1 IS NOT NULL AND distance_km < 3000
+        AND flagged_invalid = false) AS pre2014_contacts;
 """
 
 
@@ -275,6 +303,50 @@ def md_table(df: pd.DataFrame) -> str:
     return df.to_markdown(index=False, floatfmt=".3f") + "\n"
 
 
+def fetch_row_counts(conn) -> pd.DataFrame:
+    """Per-table counts + date ranges, tolerant of missing tables."""
+    rows = []
+    for tbl, time_col in ROW_COUNT_SOURCES:
+        with conn.cursor(row_factory=dict_row) as cur:
+            cur.execute("SELECT to_regclass(%s) AS oid", (f"public.{tbl}",))
+            present = cur.fetchone()["oid"] is not None
+        if not present:
+            rows.append({"tbl": tbl, "n": None, "lo": None, "hi": None})
+            continue
+        if time_col:
+            sql = (
+                f"SELECT '{tbl}' tbl, count(*)::bigint n, "
+                f"min({time_col})::date lo, max({time_col})::date hi FROM {tbl}"
+            )
+        else:
+            sql = (
+                f"SELECT '{tbl}' tbl, count(*)::bigint n, "
+                f"NULL::date lo, NULL::date hi FROM {tbl}"
+            )
+        with conn.cursor(row_factory=dict_row) as cur:
+            cur.execute(sql)
+            rows.append(cur.fetchone())
+    return pd.DataFrame(rows).sort_values("tbl")
+
+
+def band_label(band_mhz: int) -> str:
+    """Human-readable band label, matching `BandConfig` conventions."""
+    if band_mhz < 1_000:
+        return f"{band_mhz} MHz"
+    ghz = band_mhz / 1_000
+    if ghz == int(ghz):
+        return f"{int(ghz)} GHz"
+    return f"{ghz:g} GHz"
+
+
+def bands_with_samples(df: pd.DataFrame, min_samples: int = 50) -> list[int]:
+    """Every band in `df` with at least `min_samples` rows, sorted ascending."""
+    if df.empty or "band" not in df.columns:
+        return []
+    counts = df.groupby("band").size()
+    return sorted(int(b) for b, n in counts.items() if n >= min_samples)
+
+
 def correlations_per_band(df: pd.DataFrame, min_samples: int = 30) -> pd.DataFrame:
     fields = ["pr", "dpc", "pwat", "sref", "grad", "tc", "hpbl"]
     rows = []
@@ -485,7 +557,7 @@ def main() -> int:
 
         print("• row counts", file=sys.stderr)
         sections.append("\n## Row counts and date ranges\n")
-        sections.append(md_table(fetch_df(conn, ROW_COUNTS_SQL)))
+        sections.append(md_table(fetch_row_counts(conn)))
 
         print("• data gaps", file=sys.stderr)
         gaps = fetch_df(conn, DATA_GAP_SQL).iloc[0].to_dict()
@@ -495,7 +567,7 @@ def main() -> int:
         )
 
         print("• contacts by band", file=sys.stderr)
-        sections.append("\n## Contacts by band (≥902 MHz)\n")
+        sections.append("\n## Contacts by band (≥50 MHz)\n")
         sections.append(md_table(fetch_df(conn, CONTACTS_BY_BAND_SQL)))
 
         print("• sounding monthly", file=sys.stderr)
@@ -508,9 +580,12 @@ def main() -> int:
         )
         sections.append(md_table(fetch_df(conn, NATIVE_DUCT_SQL)))
 
-        print("• per-band correlations (this is the slow one)", file=sys.stderr)
+        print("• per-band correlations — HRRR (this is the slow one)", file=sys.stderr)
         joined = fetch_df(conn, PER_BAND_JOIN_SQL)
 
+        print("• per-band correlations — NARR (pre-2014)", file=sys.stderr)
+        joined_narr = fetch_df(conn, PER_BAND_NARR_JOIN_SQL)
+
         print("• NEXRAD ↔ contacts", file=sys.stderr)
         nexrad = fetch_df(conn, NEXRAD_JOIN_SQL)
 
@@ -529,11 +604,47 @@ def main() -> int:
         )
         sections.append(md_table(correlations_per_band(joined)))
 
-        sections.append("\n## 10 GHz: HPBL bin distance distribution\n")
-        sections.append(md_table(hpbl_bins(joined)))
+        sections.append(
+            "\n## Per-band HPBL bin distance distribution\n\n"
+            "One table per band with ≥50 matched contacts. A band-specific "
+            "effect here means HPBL belongs in the scorer for that band.\n"
+        )
+        for band in bands_with_samples(joined):
+            table = hpbl_bins(joined, band)
+            if table.empty:
+                continue
+            sections.append(f"\n**{band_label(band)}:**\n")
+            sections.append(md_table(table))
 
-        sections.append("\n## 10 GHz: pressure bin distance distribution\n")
-        sections.append(md_table(pressure_bins(joined)))
+        sections.append(
+            "\n## Per-band pressure bin distance distribution\n\n"
+            "Surface pressure tends to proxy synoptic-scale stagnation, so we "
+            "want to see longer distances under the 1015-1025 mb ridge at every "
+            "band that ducts.\n"
+        )
+        for band in bands_with_samples(joined):
+            table = pressure_bins(joined, band)
+            if table.empty:
+                continue
+            sections.append(f"\n**{band_label(band)}:**\n")
+            sections.append(md_table(table))
+
+    if joined_narr.empty:
+        sections.append(
+            "\n## Contact ↔ NARR per-band Pearson correlations\n\n"
+            "_No pre-2014 contacts ↔ NARR matches were found. Either the NARR "
+            "backfill has not run for this corpus yet or there are no "
+            "pre-2014 contacts with `pos1` set._\n"
+        )
+    else:
+        sections.append(
+            f"\n## Contact ↔ NARR per-band Pearson correlations "
+            f"(pre-2014 only, matched n={len(joined_narr)})\n\n"
+            "Historical sanity check: if the HRRR-era signs and magnitudes "
+            "survive on 30+ years of NARR reanalysis, the scoring factors are "
+            "physical rather than HRRR-artefact. Diverging signs = flag it.\n"
+        )
+        sections.append(md_table(correlations_per_band(joined_narr)))
 
     sections.append(
         f"\n## NEXRAD composite reflectivity vs distance "
@@ -543,14 +654,17 @@ def main() -> int:
         sections.append("_No contacts ↔ NEXRAD matches were found._\n")
     else:
         sections.append(
-            "Sanity check for the rain-attenuation direction: at rain-sensitive "
-            "bands higher `max_dbz` should map to shorter contacts. At 10 GHz "
-            "the effect should be barely detectable.\n\n"
+            "Rain-attenuation sanity check — one table per band with ≥50 "
+            "matched contacts. At rain-sensitive bands (24+ GHz) higher "
+            "`max_dbz` should map to shorter contacts; at 10 GHz and below "
+            "the effect should be barely detectable.\n"
         )
-        sections.append("**10 GHz:**\n")
-        sections.append(md_table(nexrad_bins(nexrad, 10_000)))
-        sections.append("\n**24 GHz:**\n")
-        sections.append(md_table(nexrad_bins(nexrad, 24_000)))
+        for band in bands_with_samples(nexrad):
+            table = nexrad_bins(nexrad, band)
+            if table.empty:
+                continue
+            sections.append(f"\n**{band_label(band)}:**\n")
+            sections.append(md_table(table))
 
     sections.append(
         f"\n## hrrr_native_profiles.best_duct_band_ghz vs distance "
@@ -562,14 +676,15 @@ def main() -> int:
         sections.append(
             "Validates the 1.15× boost in `Scorer.score_refractivity/4`: "
             "contacts where the native duct supports the target band should "
-            "run longer than those where it does not.\n\n"
+            "run longer than those where it does not. One table per band with "
+            "≥50 matched contacts.\n"
         )
-        sections.append("**10 GHz:**\n")
-        sections.append(md_table(native_duct_bins(native, 10_000)))
-        sections.append("\n**24 GHz:**\n")
-        sections.append(md_table(native_duct_bins(native, 24_000)))
-        sections.append("\n**47 GHz:**\n")
-        sections.append(md_table(native_duct_bins(native, 47_000)))
+        for band in bands_with_samples(native):
+            table = native_duct_bins(native, band)
+            if table.empty:
+                continue
+            sections.append(f"\n**{band_label(band)}:**\n")
+            sections.append(md_table(table))
 
     sections.append(
         f"\n## Commercial-link rx_power degradation vs contemporaneous DFW contacts "
diff --git a/test/microwaveprop/propagation/band_config_test.exs b/test/microwaveprop/propagation/band_config_test.exs
index 8aabc573..f1bc593a 100644
--- a/test/microwaveprop/propagation/band_config_test.exs
+++ b/test/microwaveprop/propagation/band_config_test.exs
@@ -243,6 +243,23 @@ defmodule Microwaveprop.Propagation.BandConfigTest do
     end
   end
 
+  describe "weights/1 — band-aware" do
+    test "returns the default weights when the band has no override" do
+      # 50 MHz has 0 contacts in the corpus → no per-band fit → default weights.
+      band = BandConfig.get(50)
+      assert BandConfig.weights(band) == BandConfig.weights()
+    end
+
+    test "returns the override weights when the band has a :weights key" do
+      band = %{freq_mhz: 99_999, weights: %{rain: 0.5, humidity: 0.5}}
+      assert BandConfig.weights(band) == %{rain: 0.5, humidity: 0.5}
+    end
+
+    test "returns defaults when passed nil" do
+      assert BandConfig.weights(nil) == BandConfig.weights()
+    end
+  end
+
   describe "sunrise_table/0" do
     test "returns 12 monthly values" do
       table = BandConfig.sunrise_table()
diff --git a/test/microwaveprop/propagation/recalibrator_test.exs b/test/microwaveprop/propagation/recalibrator_test.exs
index bbf4b2e6..1a7e351e 100644
--- a/test/microwaveprop/propagation/recalibrator_test.exs
+++ b/test/microwaveprop/propagation/recalibrator_test.exs
@@ -142,4 +142,79 @@ defmodule Microwaveprop.Propagation.RecalibratorTest do
       end)
     end
   end
+
+  describe "compute_factors/3 (band-aware)" do
+    test "humidity score flips direction between 10 GHz (beneficial) and 24 GHz (harmful)" do
+      # Hot and moist profile — good for 10 GHz (high refractivity) but bad
+      # for 24 GHz (H2O absorption floor). Humidity factor must reflect this.
+      profile =
+        create_hrrr_profile(%{
+          valid_time: ~U[2024-08-15 06:00:00Z],
+          lat: 32.9,
+          lon: -97.0,
+          surface_temp_c: 32.0,
+          surface_dewpoint_c: 26.0,
+          surface_pressure_mb: 1005.0,
+          min_refractivity_gradient: -120.0,
+          hpbl_m: 800.0,
+          pwat_mm: 45.0
+        })
+
+      [humidity_10, _, _, _, _, _, _, _, _, _] =
+        Recalibrator.compute_factors(profile, ~U[2024-08-15 06:00:00Z], 10_000)
+
+      [humidity_24, _, _, _, _, _, _, _, _, _] =
+        Recalibrator.compute_factors(profile, ~U[2024-08-15 06:00:00Z], 24_000)
+
+      assert humidity_10 > humidity_24,
+             "high humidity should score higher at 10 GHz (beneficial) than 24 GHz (harmful); got #{humidity_10} vs #{humidity_24}"
+    end
+
+    test "defaults to 10 GHz when no band supplied" do
+      profile =
+        create_hrrr_profile(%{
+          valid_time: ~U[2024-08-15 06:00:00Z],
+          lat: 32.9,
+          lon: -97.0
+        })
+
+      default = Recalibrator.compute_factors(profile, ~U[2024-08-15 06:00:00Z])
+      explicit_10g = Recalibrator.compute_factors(profile, ~U[2024-08-15 06:00:00Z], 10_000)
+
+      assert default == explicit_10g
+    end
+  end
+
+  describe "fit/1 with :band_mhz" do
+    test "only pulls contacts matching the requested band" do
+      # Two 10 GHz and one 24 GHz contact. Asking for 24 GHz should see just one.
+      create_contact(%{station1: "W5AA", qso_timestamp: ~U[2024-08-15 06:00:00Z]})
+      create_contact(%{station1: "W5BB", qso_timestamp: ~U[2024-08-16 06:00:00Z]})
+
+      {:ok, _c} =
+        %Contact{}
+        |> Contact.changeset(%{
+          station1: "W5CC",
+          station2: "K5TR",
+          qso_timestamp: ~U[2024-08-17 06:00:00Z],
+          mode: "CW",
+          band: Decimal.new("24000"),
+          grid1: "EM12",
+          grid2: "EM00",
+          pos1: %{"lat" => 32.9, "lon" => -97.0},
+          pos2: %{"lat" => 30.3, "lon" => -97.7},
+          distance_km: Decimal.new("80")
+        })
+        |> Repo.insert()
+
+      result = Recalibrator.fit(band_mhz: 24_000, sample_size: 10, epochs: 10)
+
+      # No matching HRRR profile was inserted so this falls back to defaults —
+      # the contract we're validating is "the band filter was applied and only
+      # 24 GHz contacts were considered". The fallback weights path proves the
+      # band-specific contact count came through as 1, not 3.
+      assert is_map(result.weights)
+      assert map_size(result.weights) == 10
+    end
+  end
 end