- 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
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.
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.
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.
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.
Add Microwaveprop.Backtest: a feature-evaluation framework that runs
a (lat, lon, valid_time) -> float function over the historical QSO
corpus and a matched random-time baseline, reporting distribution
statistics, distance-binned lift, and band-stratified lift.
Adds four baseline feature wrappers around the current scorer inputs
(naive_gradient, td_depression, time_of_day, pressure), a mix backtest
CLI, and the first set of baseline reports under priv/backtest_reports
so downstream phases have a frozen reference point to compare against.
