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.
The slow CDS submit/poll/download cycle of the month-batch worker was
sharing the era5 queue with the cheap Era5FetchWorker router, and its
2 concurrent slots were permanently pinned by long-running batch jobs
while hundreds of router jobs starved. Give the batch worker its own
era5_batch queue (also 2 concurrent per pod) so the router has
dedicated capacity.
Per-point ERA5 fetches were tragically slow because every point-hour
triggered its own asynchronous CDS job (submit → poll → assemble →
download). For backfill this meant thousands of independent jobs
queued against Copernicus. The new path groups requests by calendar
month and a 2° × 2° lat/lon tile so one CDS cycle populates ~60k
profiles at once, and Oban uniqueness on (year, month, tile_lat,
tile_lon) collapses every duplicate enqueue.
- Era5BatchClient builds the monthly CDS requests, extracts every
(lat, lon, hour) from the GRIB2 blob with wgrib2, derives
refractivity params, and bulk-inserts in 2k-row chunks with
on_conflict: :nothing. fetch_month_into_db/1 short-circuits when
the month-tile already has any cached profile.
- Era5MonthBatchWorker runs the batch on the :era5 queue with a
generous backoff (10m → 1d) and the uniqueness key above.
- Era5FetchWorker is now a thin router: cache hit → :ok, cache miss
→ enqueue the month-batch for the point's tile-month and return.
No more per-point CDS calls.
- Wgrib2 grows extract_grid_messages/3 which preserves per-message
datetimes by parsing `d=YYYYMMDDHH[MMSS]` from the inventory, so a
single GRIB2 file carrying a whole month decodes correctly.
- The era5_backfill mix task enqueues month-tile batches directly.
