Two wins on the hourly propagation pipeline:
1. Parallelize the chain. seed_chain was enqueuing only f00 and
each step self-enqueued f00+1, serializing ~2.5 min × 19
forecast hours into a ~48 min chain. Fan out all 19 jobs at
once — they're genuinely independent (different HRRR URLs,
different output files) — and let queue concurrency (2 slots
× 3 pods = 6 parallel workers) drop wall time to ~10 min.
Side effect: one permanently-failing step no longer takes out
the rest of the chain, so the rescue-chain logic I added last
commit becomes unnecessary. Removed.
The :final cleanup (retain_window + purge) used to run on the
fh=18 transition; with parallel execution we don't know which
step finishes last. Cleanup now relies on the existing
PropagationPruneWorker 15-min cron for time-based pruning.
Stale chain leftovers are already a minor concern with the
15-min prune; if file bloat becomes real, PruneWorker can
gain retain_window later.
2. Hoist band-invariant factors. score_time_of_day, score_sky,
score_wind, score_pressure depend on conditions only, not the
band — but composite_score was recomputing them 17 times per
point. Added Scorer.precompute_band_invariants/1, called once
per point before the bands loop; composite_score uses the
cached values when present and falls back to computing for
any caller that doesn't pre-warm (test harness, path
integrator). Saves ~30% of the scoring inner loop.
Added 5-arity Scorer.score_refractivity/5 that takes bulk_richardson
alongside best_duct_band_ghz. The existing 1.15× boost now only
applies when Richardson is in the stable regime (< 25) — native-
profile duct cells average 8.7-18.4 for ducting vs 38.3 for
non-ducting, so a duct-band reading under turbulent conditions is
more likely to be torn up by mechanical mixing than to carry a
beyond-LOS signal.
Wiring:
- Weather.nearest_native_duct_info/3 returns {ghz, richardson}; the
bare nearest_native_duct_ghz/3 now delegates.
- PathLive and Propagation.score_with_algorithm/7 both pull the
Richardson value into the conditions map alongside best_duct_band.
- Scorer.composite_score/2 reads conditions[:bulk_richardson] and
passes it into score_refractivity/5.
- Backward compat: 4-arity variant and nil Richardson keep the old
unconditional-boost behaviour so existing callers don't regress.
Three signal sources we already collect but weren't using:
* NEXRAD composite reflectivity → rain rate via Marshall-Palmer, taken
as max of HRRR-derived and NEXRAD-derived rate so fast convective
cells between HRRR hourly analyses can still trigger the rain penalty.
Only active on f00 — forecast hours can't see future radar. New
Scorer.dbz_to_rain_rate_mmhr/1 with 5 dBZ noise floor and 150 mm/hr
hail-safe ceiling.
* hrrr_native_profiles.best_duct_band_ghz → Scorer.score_refractivity/4
applies a 1.15× boost when the cell's native-resolution duct supports
the target band's frequency. HRRR pressure-level gradients
systematically under-read thin trapping layers the native profile can
resolve. Sub-band ducts do NOT boost — they're evidence that the
gradient we have is all there is at the target frequency.
* Commercial LOS link rx_power fading → inverse tropo sensor.
Commercial.link_degradation_at/3 computes the average 7-day-baseline
vs current delta across enabled links within 75 km, ignoring links
where link_state != 1. Scorer.commercial_link_boost/2 adds +2 to +25
to the composite score for 3+ dB of fading. ~150 km radius around
DFW is the only zone this helps today, but it's the first *measured*
signal in the algorithm vs the model-derived proxies.
Also fix a latent test bug exposed by the earlier ERA5 poll-timeout
bump: era5_batch_client_test's "uncached path returns error" tests
hung for up to an hour when run with direnv's real CDS key. New
describe-level setup explicitly unsets the env var so the tests stay
hermetic.
1,359 tests, 0 failures.
Direct queries against prod (75M HRRR profiles, 16,864 soundings, 392k
surface obs, new hrrr_native_profiles table) drive these changes:
* Reinstate shallow-BL bonus as an HPBL multiplier on the refractivity
score (1.10× <200m → 0.78× ≥2000m). The Apr 11 "shallow BL bonus
removed" conclusion was an artifact of the prior matching strategy;
with the cleaner contact↔HRRR join (n=680) the binned data goes 230 km
avg at HPBL <200m vs 100 km at ≥2000m, monotonic across all bins.
* Add 6 missing bands (142, 145, 288, 322, 403, 411 GHz) so contacts in
those bands stop being silently dropped. Coefficients extrapolate
ITU-R P.676/P.838 trends from the existing 134/241 GHz entries.
* Bump ERA5 poll timeout 10 min → 1 hour and Era5MonthBatchWorker
max_attempts 3 → 5 so CDS slowness stops discarding tiles. Also dump
the full failure body when CDS omits the message field — the prior
"ERA5 job failed: nil" was masking real error reasons in oban_jobs.
* Add scripts/recalibrate_algo.py so this analysis can be re-run any
time new data lands without manual SQL. Reads PROP_PROD_DB_URL from
.envrc, drops a Markdown report into docs/algo-reports/.
* Append a dated Part 2c to algo.md documenting the corpus expansion,
the honest accounting of historical HRRR coverage (only ~1,020 of 58k
contacts are precision-matchable), and the empty era5/rtma/climatology
tables. Update the gaseous-absorption and rain-attenuation tables to
include the new bands.
Test suite: 1,335 tests, 0 failures.
Add Propagation.Region module with 8 CONUS climate zones (gulf_coast,
southeast, southern_plains, corn_belt, northeast, desert_southwest,
pacific_northwest, mountain_west) and per-region monthly seasonal
adjustment multipliers.
The scorer's score_season now takes lat/lon and applies a regional
multiplier from Region.seasonal_adjustment on top of the band's
seasonal_base + seasonal_adj. Gulf coast August gets a 1.15x boost
(drier, better for ducting) while Corn Belt August gets a 0.80x
penalty (corn evapotranspiration = miserable dewpoints).
Adjustments are hand-tuned starting points from the meteorologist's
qualitative guidance. Phase 9 recalibration will refine them from
backtest data.
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.
Previous thresholds (-500 to -60) were calibrated for radiosonde data.
HRRR profiles have coarser vertical resolution, with gradients clustering
between -40 and -130 N/km (median -70). Nearly all grid points were
falling through to the default score of 42, wasting the refractivity
factor. New thresholds (-200 to -40) spread across HRRR percentiles.
