Without -s, wgrib2 -lon only outputs msg:offset:lon=X,lat=Y,val=Z
with no variable name or level. The -s flag adds the short inventory
(d=DATE:VAR:LEVEL:...) so the parser can identify which variable
each value belongs to.
setup-buildx-action downloads from GitHub Releases which is slow
and unreliable from the Forgejo runner. Plain docker build/push
works fine since Docker is already on the runner host.
Points spread coast-to-coast created a ~476k cell bounding grid
(350 messages × 476k cells × 4 bytes ≈ 665 MB), causing OOM.
Switch to -lon which extracts values at specific lat/lon points
with text output. One wgrib2 call, one file scan, negligible
BEAM memory regardless of point geographic spread.
- Stream profile storage and score upsert instead of materializing
full 20k+ item lists (propagation_grid_worker, propagation.ex)
- GC between forecast hours and store/compute phases to reclaim
~400 MB of grid data between steps
- Single-pass field extraction in scorer.ex path_integrated_conditions
instead of 6 separate Enum traversals
- Eliminate intermediate merged map in fetch_grid by combining
merge + profile build into one pipe
- Fix UUID bug: bingenerate → generate in native grid worker
(same issue previously fixed in nexrad_worker)
The parallel download was holding all ~530MB of range responses in
memory before writing to disk. Now each range is fetched and written
one at a time, so only one chunk is in memory at a time.
Instead of holding ~530MB GRIB binary in BEAM memory, download
ranges directly to a temp file and run wgrib2 on it. Peak memory
drops from ~530MB to just HTTP chunk buffers.
Release.backtest_all, climatology, native_derive now enqueue an
AdminTaskWorker job on the new :admin queue and return immediately.
Progress visible in Oban Web at /admin/oban.
- Add `mix backtest --all` for consolidated pass/fail table across all features
- Add Backtest.consolidated_report/2 and to_consolidated_markdown/1
- Add Features.all_features/0 to auto-discover backtestable features
- Add `mix import_contest_logs` for bulk ARRL contest CSV import with dedup
- Fix hrrr_climatology to batch by (month, hour) to avoid query timeout
- Fix Repo.query! result pattern (Postgrex.Result, not tuple)
- Backtest reports for all Phase 1-6 features
Mix tasks that call app.start were also booting Oban's cron scheduler,
causing PropagationGridWorker and other cron jobs to fire during
backfills. Add Oban.pause_all_queues(Oban) immediately after app.start
in every mix task that only needs Repo access.
FrontalAnalysis module (Weather.FrontalAnalysis):
- detect_fronts/3 computes the Thermal Front Parameter (TFP) from
2D grids of surface temperature and pressure using Nx vectorized
ops. TFP = -nabla|nabla(theta)| . nabla(theta)/|nabla(theta)|.
Most negative values mark cold fronts.
- central_gradient/1 for 2D finite differences with edge handling
- nearest_front/3 finds closest front point with distance and bearing
- path_front_angle/2 computes angle between a QSO path and the
front (0 = parallel = good, 90 = crosses = dead)
Backtest feature stubs for distance_to_front and parallel_to_front
(return nil until the pipeline caches per-cell frontal features from
the hourly HRRR grid run). The FrontalAnalysis module itself is
tested and ready for integration.
NEXRAD spike docs also included in this commit.
NCEI ASOS 5-minute data client (Weather.NceiMetarClient):
- fetch/3 pulls per-station monthly .dat files from NCEI C00418
- parse/1 decodes the fixed-width METAR format including precise
T-group temperatures (T02110094 → 21.1/9.4°C)
- metar_5min_observations table: schema-identical to
surface_observations, separate table to avoid mixing cadences
Weather.recent_surface_obs/3 prefers 5-min data when available,
falls back to the hourly surface_observations table.
Data URL: https://www.ncei.noaa.gov/data/automated-surface-observing-system-five-minute/access/YYYY/MM/asos-5min-KXXX-YYYYMM.dat
Available back to 1996.
Phase 3 NEXRAD spike: IEM n0q composite available at 5-min cadence
back to 2022+. Compression-ratio proxy shows afternoon images have
13-81% more texture than dawn (directionally correct), but the n0q
product thresholds out the faint clear-air returns needed for BL
stability detection. Parked until MRMS or Level III products can be
investigated. See docs/research/nexrad_spike.md.
Phase 6: hrrr_climatology table aggregating surface_temp_c by
(lat, lon, month, hour) from the 42M+ hrrr_profiles grid-point
rows. mix hrrr_climatology builds it via a single SQL GROUP BY +
upsert. Backtest.Features.temperature_anomaly computes current_temp
minus climatological mean — the meteorologist's "temperature
deviation above normal" predictor for summer afternoon enhancement.
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.
Duct module (Propagation.Duct):
- refractivity_profile/1: ITU-R P.453 N at each native level
- m_profile/1: modified refractivity M = N + 157*h(km)
- detect_ducts/1: find contiguous regions where dM/dh < 0, returning
base/top height, thickness, and M-deficit per duct
- min_trapped_frequency_ghz/1: waveguide approximation (Bean & Dutton)
for the minimum frequency a duct of given geometry can trap
- analyze/1: full pipeline from native profile to duct list + best
trapped frequency across all ducts
Derive task updated to also compute ducts JSONB and best_duct_band_ghz
alongside the Phase 2 turbulence fields.
Backtest features: duct_thickness, best_duct_freq, duct_usable_10ghz,
duct_usable_24ghz, duct_usable_47ghz.
Real-data validation: 2022-08-20 12Z TX profile shows 0 ducts (M
increases monotonically) — correct for a well-mixed boundary layer
on a turbulent August afternoon (Ri=0.16).
bulk_richardson, theta_e_jump, and shear_at_top feature functions
pull derived fields from the nearest hrrr_native_profile. Ready for
mix backtest once sufficient data is backfilled.
Inversion detection module (Propagation.Inversion):
- find_inversion_top/1 walks the native profile to locate the first
temperature inversion (surface-based or elevated)
- bulk_richardson/3 computes the Richardson number across the
inversion layer (Ri < 0.25 = turbulent, > 1 = laminar/good)
- shear_magnitude/3 computes the wind shear vector magnitude
- potential_temperature/2 for θ = T*(P0/P)^0.286
Theta-e module (Weather.ThetaE):
- Bolton (1980) equivalent potential temperature
- dewpoint_from_spfh/2 via Magnus-Tetens inversion
- theta_e_jump/3 for the thermodynamic decoupling metric
mix hrrr_native_derive_fields populates inversion_top_m,
bulk_richardson, theta_e_jump_k, and shear_at_top_ms on existing
hrrr_native_profiles rows.
First real data: 2022-08-20 12Z TX profile shows inversion at
186 m, Ri = 0.16 (turbulent), θ_e jump = 0.33 K — consistent with
marginal propagation conditions at that hour.
wgrib2 -lola ... bin writes Fortran unformatted records (4-byte
length header + data + 4-byte length trailer per message).
parse_lola_binary was treating the binary as tightly packed,
causing every message after the first to read from the wrong
offset — values came out as garbage across all grid points.
Fix: account for the 8-byte record overhead per message when
computing the data offset for each message's grid values.
This bug affects both the existing propagation grid extraction
(which may have been producing subtly wrong scores) and the new
native-level extraction (which was producing obviously wrong
values). The fix is a one-line stride change.
Also adds Backtest.Features.native_surface_refractivity for the
Phase 1 sanity check, plus a tighter wgrib2 match pattern that
selects only hybrid-level messages from the native file.
The Elixir GRIB2 decoder didn't map level type 105 (hybrid) or
variable IDs for SPFH and TKE, so native-level messages decoded as
"unknown:105:N" keys that build_native_profile couldn't find. Add
the three missing mappings to Section.identify_level/identify_var.
Also add HrrrNativeClient.extract_native_profiles/2 which uses
wgrib2's -lola on a tight bounding-box subgrid for speed (the pure
Elixir decoder takes ~70s per point on a 395 MB file; wgrib2 handles
40 points in seconds). The worker now routes through this path.
