Four changes sized by measured prod telemetry (83m of spans):
1. propagation queue: 2 → 1 slot per pod. Two concurrent forecast-hour
steps per pod stacked HRRR grid + native duct grid + scored band
map into ~5-6 GiB RSS, OOM-killing every ~15 min. 3-way parallelism
cluster-wide still finishes the chain inside the hourly interval.
2. weather queue: 3 → 1 slot per pod. ASOS backfill was 429-thrashing
IEM (1,296 retryable jobs; logs were nothing but 429 backoffs).
3. PropagationGridWorker: skip native-level duct fetch on f01..f18.
At ~7-11 min/fh and 18 forecast hours, this was the largest single
cost per chain. Forecast hours fall back to
derived[:min_refractivity_gradient] from the pressure-level
profile. f00 still gets full native-level duct analysis.
4. HrrrClient.download_grib_ranges_to_file: parallelize with
Task.async_stream (max_concurrency 8). The file-backed variant was
sequential, dominating native-duct fetch time on the remaining f00
path. ~20s → ~3s per call.
Telemetry showed ~66 PropagationGridWorker exceptions per 6h with
55 ArgumentErrors and 11 TimeoutErrors, producing ~13 discarded
chain steps. Each discard broke the chain: subsequent forecast
hours were never enqueued, leaving the score store with huge gaps
(e.g. at 14:11 UTC the earliest available forecast was 18:00,
because f00-f05 all failed somewhere upstream and nothing ran
after them).
Three changes:
1. PropagationGridWorker: on the final attempt, still enqueue
fh+1 even when this step failed. Oban discards the current
job normally — but the rest of the chain keeps running, so
one bad hour doesn't take out the remaining 12-18. The
rescue is factored into a tested public helper.
2. HrrrClient.parse_idx: skip malformed idx lines instead of
raising. NOAA S3 occasionally serves an HTML error page as
the idx body, and the old strict String.to_integer path
raised ArgumentError on the first non-numeric line and took
down the chain step. This is the root cause of the 55
ArgumentErrors.
3. JS renderTimeline: when no forecast hour is at-or-before
wall-clock (all times are future — the gap scenario the
fixes above are designed to prevent), stop labeling the
earliest future slot "Now". Lets the user see honest
"+Nh" offsets instead of a lie on the pill.
The skew-T commit (30c1018) doubled @pressure_levels from 13 to 25 so
new contact fetches would cover the full troposphere. That list is
also what PropagationGridWorker pulls per forecast hour, which
doubled the GRIB footprint (~57 MB compressed + 92k points × 25
levels × 3 vars decoded through wgrib2) and pushed prod pods over
their 4 Gi OOMKill threshold. Every chain died during f00 and the
map timeline never got beyond now and now+1h because the .ntms files
for f02-f18 were never written.
Split the constant:
* @profile_pressure_levels (25 levels, 1000-100 mb) drives the
per-contact HrrrClient.fetch_profile path so the skew-T plot
keeps its full-atmosphere trace.
* @grid_pressure_levels (13 levels, 1000-700 mb) drives the grid
hot path. That's the band SoundingParams.derive reads for
min_refractivity_gradient, and native hybrid-sigma data
(native_min_gradient) takes priority over the pressure-level
fallback anyway, so upper-air levels contribute nothing to
scoring — pure memory waste on this path.
build_profile/1 still iterates the full 25-level list; grid fetches
simply populate the 13 near-surface slots and skip the rest.
Makes HrrrClient.pressure_messages public with a :grid | :profile
variant so the split is testable from outside the module.
Historical contacts showed a skew-T log-P diagram that stopped at 700 mb
because HrrrClient and Era5Client only fetched pressure-level data down
to the top of the boundary layer. The chart canvas already ran up to
100 mb, so the trace clipped mid-atmosphere.
Two complementary fixes:
1. Extend @pressure_levels in HrrrClient and Era5Client with
650/600/550/500/450/400/350/300/250/200/150/100 mb so new fetches
cover the full troposphere + lower stratosphere.
2. Prefer the native hybrid-sigma profile for the contact-detail
skew-T when one has been backfilled for the contact's hour. The
native profile already stores all 50 hybrid levels up to ~19 km,
so historical contacts covered by the native backfill get a full
trace without re-hitting S3. A new HrrrNativeProfile.to_skew_t_profile/1
converts the parallel arrays into the %{"pres","tmpc","dwpc","hght"}
list shape the renderer expects, deriving dewpoint from SPFH via the
Magnus inverse. Weather.find_nearest_native_profile/3 mirrors
find_nearest_hrrr/3 for the lookup.
Add fetch_grid/3 for batch HRRR data retrieval across multiple lat/lon
points in a single download pass. Expand surface messages to include
wind (UGRD/VGRD), cloud cover (TCDC), and precipitation (APCP). Add
extract_grid/2 to the GRIB2 extractor for multi-point extraction from
a single GRIB binary, and add GRIB2 variable identifiers for the new
surface fields.
AWS S3 doesn't support multi-range HTTP requests. When given
Range: bytes=0-999, 2000-2999 it ignores the header and returns
the full file (200 instead of 206). Download each range separately.
Eliminates the external wgrib2 C tool dependency that blocked HRRR
processing. Implements Lambert Conformal projection, simple packing
(Template 5.0), complex packing with spatial differencing (Template 5.3),
and GRIB2 section parsing — enough to extract point values from HRRR
grid data using only Elixir.
HRRR provides hourly 3km-resolution atmospheric profiles, filling the
temporal gaps (12-hourly soundings) and spatial gaps (only 9 sounding
stations) in our current weather data.
- Add hrrr_profiles table and hrrr_queued flag on QSOs
- HrrrClient fetches GRIB2 data via HTTP Range requests + wgrib2
- HrrrFetchWorker derives refractivity/ducting params via SoundingParams
- QsoWeatherEnqueueWorker now also enqueues HRRR jobs
- QSO show page displays HRRR section with collapsible profile
- Dockerfile builds wgrib2 from source for production
