- HrrrClient.hrrr_url accepts forecast_hour param (wrfsfcfHH.grib2)
- PropagationGridWorker fetches all 19 forecast hours per run
- Propagation.scores_at/3 queries scores at specific valid_time
- Propagation.available_valid_times/1 returns all forecast times for timeline
- Pruning keeps scores with valid_time >= now - 2h (forecast-aware)
- MapLive: select_time event, timeline data pushed to JS
- JS: forecast timeline bar at bottom of map with clickable hour buttons
- PubSub broadcast sends list of valid_times instead of single time
Req default 15s receive_timeout too short for GRIB2 byte-range
downloads on prod. Also reduce HRRR queue concurrency from 20 to 5
to avoid NOAA rate limiting (was causing burst-then-stall pattern).
New AsosAdjustmentWorker runs every 10 minutes:
- Fetches latest ASOS observations from all ~2900 US stations via
IEM bulk currents API (parallel fetch across 51 state networks)
- For each grid point within 75km of a reporting station, re-scores
using fresh ASOS data (temp, dewpoint, wind, sky, pressure, precip)
with HRRR refractivity gradient from the last hourly computation
- Pushes updated scores to the map via PubSub
Also stores HRRR profiles in the database during grid computation
so the data persists for reference and ASOS blending.
Add a separate wgrib2-builder stage that compiles wgrib2 from source.
Docker layer caching means this only builds once — subsequent deploys
reuse the cached layer. The binary is copied into the final runtime
image. Also fixed wgrib2 path lookup to be runtime instead of
compile-time so it works in the Docker build pipeline.
Add Wgrib2 module that shells out to wgrib2 binary for fast GRIB2
grid extraction using -lola (nearest-neighbor to regular lat-lon grid).
Falls back to pure-Elixir decoder if wgrib2 is not installed.
Also: parallel GRIB2 range downloads, merge adjacent byte ranges,
skip corrupt messages instead of failing, pressure fetch is optional.
Both extract_points and extract_grid now skip messages with missing
data sections instead of halting. Pressure product fetch is now
optional for both grid and single-point modes — if pressure data
has corrupt messages, surface-only profiles are still stored. Fixes
historical HRRR backfill failing on old data with incomplete files.
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.
Add extract_values/3 to SimplePacking and ComplexPacking for batch
index extraction from a single GRIB2 message. Add extract_grid/2 to
Extractor which takes a list of {lat, lon} points and returns all
variable values for each point, skipping points outside the grid.
This enables extracting weather data for many grid points from a
single HRRR download instead of re-parsing per point.
- Add precip_1h_in and wx_codes fields to ASOS surface observations
- Add IEMRE reanalysis schema for radar-derived hourly precipitation
- Add IemreFetchWorker with exponential backoff and idempotency
- Integrate IEMRE enqueue into cron weather backfill pipeline
- All existing QSOs marked iemre_queued=false for automatic backfill
extract_n_values_array used Enum.take(count) on a reversed list
before reversing it, which included padding values from byte
alignment and dropped actual values. When group count * bits
wasn't a multiple of 8, the extra padding bits produced a
phantom value that shifted the entire array by one position.
This caused cascading errors in spatial differencing — values
started correct but diverged exponentially (DPT decoded as
38 billion K instead of 275 K).
Fix: reverse the list first, then take count, so padding values
at the end are discarded instead of actual values at the start.
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.
S3 returns 200 with the full ~116MB file instead of 206 for some older
HRRR data. Treat this as a permanent failure instead of trying to parse
the entire file.
Instead of returning "malformed section" on trailing bytes or truncated
sections, attempt to return parsed results. This handles older HRRR
files (2019) that have padding bytes after the last section.
consume_bits would crash with a MatchError when the remaining
bitstring had fewer bits than the group width. Added a guard
clause to stop consuming when insufficient bits remain, matching
how wgrib2 handles trailing padding in packed data sections.
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.
Previously retry: :transient only retried connection errors, not HTTP
429 rate limits. All clients now use a custom retry function that
handles 429, 500, 502, 503, 504 with 5 retries and jittered backoff.
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
IEM was returning 503s under 10 concurrent workers. Three fixes:
- Req transient retry with exponential backoff on IEM requests
- WeatherFetchWorker Oban job retries failed ASOS/RAOB fetches 2-3h later
with random jitter to avoid thundering herd
- Import script concurrency reduced 10→5, weather queue capped at 3
Store surface observations (ASOS) and upper-air soundings (RAOB) alongside
QSOs for atmospheric propagation correlation. Three new tables: weather_stations,
surface_observations, and soundings with JSONB profiles and pre-computed derived
parameters (refractivity, gradients, duct detection, stability indices).
Includes IEM API client for historical data import and import script seeded
with 95 ASOS + 9 sounding stations from PropCast coverage area.
