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.
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.
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
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.
