Registered users can suggest edits to any contact's core fields
(callsigns, grids, band, mode, timestamp). Edits enter an admin
approval queue with field-by-field diff view. On approve, changes
are applied and enrichment re-enqueued if grids/band changed.
Users receive email notification on approve or reject.
Also updates dependabot.yml for mix ecosystem.
When clicking a grid point, fetches the latest NEXRAD composite
reflectivity and identifies rain cells within 300 km that could
enable rain scatter contacts. Shows:
- Scatter classification (excellent/good/marginal/none)
- Top 3 cells with dBZ, distance, bearing, and relative signal
- Colored circle markers on the map at rain cell locations
- Markers sized by reflectivity, colored by intensity
Uses simplified bistatic radar equation accounting for reflectivity,
frequency-dependent scattering (Rayleigh/Mie), and R^4 path loss.
NEXRAD cells sampled every ~5 km within bounding box for efficiency.
Terrain elevations now include the earth bulge correction, so the
profile visually humps up in the middle relative to the straight
LOS beam — showing how the curved earth rises into the signal path.
Uses actual k-factor from HRRR refractivity when available, otherwise
standard 4/3. For a 300 km path at k=4/3, the midpoint bulge is
~2,650 m (8,700 ft).
When clicking a grid point with ducting, the panel now shows each
duct layer with base-top height in feet, thickness in meters, and
minimum trapped frequency. Data flows from Duct.analyze through
the scoring factors as a ducts array.
- Replace stale April 2026 manual weights with recalibrated values
- Document native hybrid-sigma data in data flow section
- Note refractivity factor now uses native 10-50m resolution
- Add hourly grid integration section to Part 12
- Expose duct_info (count, freq, thickness) in scoring factors for UI
The PropagationGridWorker now fetches native hybrid-sigma levels
(TMP, SPFH, HGT, PRES × 50 levels) alongside the standard surface
and pressure products. Native data provides 10-50m vertical spacing
vs 250m from pressure levels, detecting thin surface ducts invisible
to the standard product.
Key design: cell-by-cell reducer in Wgrib2.extract_grid_from_file_mapped
processes each of the 95k CONUS cells through a duct analysis function
inline, keeping only scalar metrics per cell. Peak memory ~86 MB
instead of ~1.8 GB for the full grid map.
Per-cell output: native_min_gradient, best_duct_freq_ghz,
max_duct_thickness_m, duct_count. The scorer prefers the native
gradient over the pressure-level gradient when available.
Native fetch is optional — if it fails, scoring continues with
pressure-level data only.
Backtest on 11,431 native profiles (2026-04-11):
- Drop duct_usable_10/24/47ghz (always 1.0, no discrimination)
- Drop bulk_richardson (near-identical QSO vs baseline means)
- Document all feature results with signal strength assessment
- theta_e_jump is strongest native discriminator (44% lift)
- best_duct_freq and duct_thickness show clear physical signal
- Round grid-derived lat/lon in maybe_fill_latlon changeset step
- Format lat/lon to 6 decimal places on index table
- Migrate existing beacon data to 6 decimal precision
- Add comma separators to EIRP mW display (e.g. 10,000 mW)
- Extract shared add_commas helper for format_freq and format_mw
Height in feet doesn't need decimal precision. Migrates the DB
column, updates schema type, and strips trailing .0 from form
input so the integer cast succeeds.
- Add CommaNumber JS hook for live comma formatting while typing
frequency MHz in the beacon form
- Strip commas server-side before changeset validation
- Order beacon list by most recently added (desc inserted_at)
- Round lat/lon to 6 decimal places in changeset and display
- Round height_ft to integer in changeset and display
- Display coords at 6 decimal places on show page
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.
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
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.
- Spike docs at docs/research/hrrr_native_levels.md confirming files
are on AWS S3 for 5+ years, 50 hybrid levels, and include TKE and
SPFH needed for Phase 2 turbulence features. Architectural finding:
per-point on-demand fetching is impractical (~530 MB/file), so
the ingestion worker batches per (date, hour) instead.
- hrrr_native_profiles schema: arrays per level plus cached surface
scalars and placeholder columns for Phase 2/4 derived fields.
Strictly additive — the existing hrrr_profiles table is untouched.
- HrrrNativeClient: pure URL/message-list helpers, build_native_profile/1
that turns a parsed wgrib2 map into the schema shape (TDD'd).
- Exposed HrrrClient.download_grib_ranges/2 so the native client
reuses the existing parallel byte-range download + disk cache.
- HrrrNativeGridWorker: Oban worker keyed on {year, month, day, hour},
unique at :infinity, pulls distinct (lat, lon) points from contacts
in the ±30 min window, downloads the native grib2, extracts per
point, bulk-upserts.
- mix hrrr_native_backfill --limit N enqueues the top-N hours by
contact count.
Phase 1 gate still pending Task 1.6 (sanity-check backtest after
live data lands).
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.
The slow CDS submit/poll/download cycle of the month-batch worker was
sharing the era5 queue with the cheap Era5FetchWorker router, and its
2 concurrent slots were permanently pinned by long-running batch jobs
while hundreds of router jobs starved. Give the batch worker its own
era5_batch queue (also 2 concurrent per pod) so the router has
dedicated capacity.
When a contact is linked to a logged-in user account, display that
user's callsign in the Submitted column instead of a generic "Yes".
Anonymous submissions still show "Yes"; scraped contacts show an em
dash.
Contacts now carry a user_id FK to users. When a logged-in user
submits a contact (single or CSV upload), the server injects their
user_id and the email from their account into the submission params,
and the visible email input is replaced with a hidden input holding
that value — logged-in users don't have to retype their address.
The submission changeset now requires at least one of user_id or
submitter_email (rather than hard-requiring email), and email format
is only validated when one is actually present.
Reworded the submit-page blurb from "10 GHz to 241 GHz" to "902 MHz
and up" so it matches the actual band coverage of the project.
Per-point ERA5 fetches were tragically slow because every point-hour
triggered its own asynchronous CDS job (submit → poll → assemble →
download). For backfill this meant thousands of independent jobs
queued against Copernicus. The new path groups requests by calendar
month and a 2° × 2° lat/lon tile so one CDS cycle populates ~60k
profiles at once, and Oban uniqueness on (year, month, tile_lat,
tile_lon) collapses every duplicate enqueue.
- Era5BatchClient builds the monthly CDS requests, extracts every
(lat, lon, hour) from the GRIB2 blob with wgrib2, derives
refractivity params, and bulk-inserts in 2k-row chunks with
on_conflict: :nothing. fetch_month_into_db/1 short-circuits when
the month-tile already has any cached profile.
- Era5MonthBatchWorker runs the batch on the :era5 queue with a
generous backoff (10m → 1d) and the uniqueness key above.
- Era5FetchWorker is now a thin router: cache hit → :ok, cache miss
→ enqueue the month-batch for the point's tile-month and return.
No more per-point CDS calls.
- Wgrib2 grows extract_grid_messages/3 which preserves per-message
datetimes by parsing `d=YYYYMMDDHH[MMSS]` from the inventory, so a
single GRIB2 file carrying a whole month decodes correctly.
- The era5_backfill mix task enqueues month-tile batches directly.
Uploads now show a review page with three summary cards (valid,
duplicates, invalid), tables for invalid and duplicate rows, and a
sample of the first 20 valid rows. Nothing is written to the database
until the user clicks "Looks good — submit N contacts".
Duplicate detection treats two rows as the same contact if they share
the same pair of (callsign, grid) tuples (direction-agnostic), the
same band, and timestamps within 60 minutes. Dedup runs against both
existing DB contacts (single band/time-scoped query) and earlier rows
in the same upload, and the preview labels which side matched.
CsvImport grows preview/2 + commit/1; the old import/2 is kept as a
thin backward-compat wrapper that skips dedup. Added 11 new CsvImport
tests and reworked the SubmitLive CSV tests for the two-step flow.
Pruning used to only run at the end of a successful PropagationGridWorker
pass, so a stretch of failed compute jobs (k8s OOM kills, SIGTERM)
stopped prune from running and let the table accumulate ~5h of stale
rows. A dedicated PropagationPruneWorker now runs every 15 minutes on
its own Oban cron, and PropagationGridWorker also calls prune_old_scores
at the start of each run as a second safety net. Bumped the delete
timeout from 2m to 5m so the first catch-up pass has enough headroom.
- Beacon map defaults to just the marker at zoom 11. A daisyUI toggle
switch shows/hides the estimated-coverage cell layer; the coverage
info line and tier legend are hidden unless the toggle is on.
- Beacon detail list replaced with a compact dl grid (2-4 columns).
Lat/lon rounded to 5 decimals, power/height/beamwidth formatted via
Beacon.format_mw/1 so no more "2.5e3" scientific notation or "280.0".
format_mw/1 hoisted onto the schema so index and show share it.
- About page no longer uses prose classes (this project doesn't load
@tailwindcss/typography). Headings, lists, and paragraphs now use
explicit utility classes so they render as intended.
Beacons can now record a directional bearing ("omni" or 0-360 degrees)
and a beamwidth in degrees. The keying list grows from {on_off, fsk}
to include FM Voice, WSPR, and the full Q65A/B/C/D/E × 15/30/60/120
family, grouped via optgroups in the submission form.
- Anonymous users can submit beacons (held pending admin approval)
- Added notes field to beacons (textarea on form, shown on detail page)
- Added Beacons link to /map sidebar nav (mobile + desktop),
removed stale Rover Planner link
- Moved /backfill to /admin/backfill under the admin live_session
- Beacon detail map zooms in two extra levels after fitBounds
The Era5FetchWorker now declares an Oban unique constraint on
{lat, lon, valid_time} for any job in available/scheduled/executing/
retryable, so two backfill runs targeting the same contact grid point
can no longer spawn parallel CDS requests for the same hour.
Because Oban OSS insert_all doesn't honor unique, ERA5 jobs are now
routed through Oban.insert/1 from ContactWeatherEnqueueWorker and the
era5_backfill mix task. Other worker types still use insert_all.
Non-admin users can now submit beacons but they are held in an
unapproved state until an admin approves them. Only approved beacons
appear in the public list; pending submissions are shown in a separate
admin-only section on /beacons with Approve and Delete actions. The
show page surfaces a pending badge and an admin-only Approve button
when viewing an unapproved beacon.
Also formats EIRP (mW) on the index page without scientific notation.
Replace the idealised concentric-circle range rings with a realistic
per-HRRR-grid-cell reception map. For every 0.125° grid point within
the band's exceptional range, the estimator computes great-circle
distance, FSPL + atmospheric loss, and applies a cell-specific score
adjustment of (50 - score) * 0.3 dB — score 100 gives a 15 dB ducting
boost, score 0 a 15 dB penalty. Cells below the -145 dBm detection
floor are dropped.
The beacon map hook now renders each surviving cell as a 0.125°
filled rectangle with a tooltip showing tier, Rx dBm, distance, and
HRRR score, so the coverage footprint bulges where ducting conditions
are good and cuts off where they aren't, instead of being a perfect
circle. Falls back to score 50 for cells that don't have HRRR data
yet, so the plot is always useful.
Also fixes the prior all-grey-tiles regression by restoring an
explicit setView() at map creation and adding a post-mount
invalidateSize() for when the map is placed inside a flex container.
The power_mw field represents the beacon's effective radiated power,
not just transmitter output. Relabel it as "TX power (EIRP)" on the
form and detail list, "EIRP (mW)" in the index, and drop the now-
meaningless tx_gain_dbi constant in RangeEstimate since the stored
value already includes antenna gain.
