MRMS
----
Layer the NOAA MRMS PrecipRate product onto the score grid so rain fade
updates every 2 minutes instead of every hour alongside HRRR. New modules:
- Microwaveprop.Weather.MrmsClient: fetches the latest .grib2.gz off the
NCEP mirror (Req auto-decompresses so no gunzip step), writes the raw
GRIB2 to a temp file, and calls the existing wgrib2 wrapper with the
0.125 propagation grid spec to get interpolated cells. Returns a
%{{lat, lon} => mm_per_hour} map with missing-value sentinels dropped.
- Microwaveprop.Weather.MrmsCache: ETS-backed GenServer mirroring
ScoreCache/GridCache. Caches a single "current" entry keyed by
valid_time with PubSub broadcast so peer nodes stay in sync and only
the Oban leader pays the fetch + regrid cost.
- Microwaveprop.Workers.MrmsFetchWorker: cron every 2 minutes, short-
circuits when the cached valid_time already matches the newest file.
Microwaveprop.Propagation.AsosNudge.compute/4 now takes an optional
rain_grid. When a cell has MRMS rain >= 0.1 mm/hr it gets patched onto
the HRRR profile's `precip_mm` field (the scorer already reads it there)
and the cell is re-scored even with no ASOS station nearby. Cells with
MRMS rain below the threshold aren't touched so dry cells keep their
raw HRRR scores (which have the wind/sky/native-gradient signal that
isn't persisted on HrrrProfile rows and would otherwise be lost).
AsosAdjustmentWorker pulls MrmsCache on every tick and passes the grid
through to AsosNudge.compute/4. Also skips the IemClient error branch
that can never happen and handles the ASOS-empty + MRMS-empty case
explicitly. MrmsCache wired into the supervision tree; MrmsFetchWorker
cron entry added to config.exs and dev.exs.
Four new AsosNudge cases cover MRMS-only re-scoring, threshold gating,
and the wet/dry score delta.
Beacons 500
-----------
Beacon.format_freq/1 and format_mw/1 crashed on whole-number floats
(e.g. 24192.0) because `frac == 0.0` could become false under float
rounding while `trim_trailing_zeros/1` stripped the decimal point,
leaving a 1-element list that couldn't be destructured as [_, frac].
Shared format_number/1 helper handles integer input directly and
pattern-matches both the "int-only" and "int + frac" shapes.
Added stream_data property tests covering the whole microwave range for
both integers and floats to catch this class of bug before prod.
UTC clock flash
---------------
The /weather and /map UTC clocks were empty until the JS hook mounted
post-WebSocket, producing a several-second blank spot on initial load
and a clobber risk on sidebar re-renders. Mount now computes a
server-rendered `initial_utc_clock` string and the template seeds the
element with that plus `phx-update="ignore"` so LiveView morphdom won't
overwrite what the hook writes.
- Add Weather.GridCache: ETS cache of derived HRRR grid rows keyed by
valid_time, cluster-synced via PubSub. Eagerly warmed from
PropagationGridWorker after each upsert so /weather map pan/zoom and
weather_point_detail hit zero DB on warm cache.
- Replace latest_weather_grid DB query path with cache-first lookup +
DB fallback. hrrr_profiles is 42M rows partitioned; pulling 3-10k
rows per viewport on every pan was the main cost.
- ContactLive.Show: defer the heavy enrichment loads (weather, solar,
HRRR, terrain, IEMRE, elevation profile, ITU-R propagation analysis,
data_sources) into a handle_info(:hydrate) that runs after the shell
renders. Initial mount now returns nil placeholders; template already
had :if guards for all of them. Shell-to-first-paint goes from
~500ms-2s down to ~20ms.
- Cache fetch_queue_counts for 5s in ContactLive.Show — oban_jobs group
by query was running on every contact page view.
- Backfill stats: wrap count_unprocessed, fetch_stats, fetch_db_stats
in Microwaveprop.Cache with 2-5s TTLs; bump refresh debounce from 1s
to 2s so bulk enrichment events don't thrash the DB.
- ScoreCache stores {band, valid_time} as %{{lat, lon} => score} map so
point lookups are O(1); adds fetch_point/4 and valid_times/1
- available_valid_times/1 reads directly from ScoreCache when warm,
falls back to DB on cold start
- point_forecast/3 iterates cached valid_times and uses fetch_point/4
instead of hitting the DB per click
- NexradCache: node-local ETS cache of decoded n0q PNG pixel buffers
keyed by 5-minute rounded timestamp; skips ~1-5s HTTP+decode on
concurrent/repeat clicks within the same window
- MapLive: start_async the rain_scatter fetch so point_detail renders
immediately with a pending marker; push rain_scatter_update when
NEXRAD resolves
- MapLive: preload all 18 remaining forecast hours for the current
viewport after mount/band change/propagation_updated; client caches
them and renders timeline scrubs instantly without a server roundtrip.
Adds set_selected_time event for fast-path state sync.
- Propagation map JS: forecastCache map + drawScatterMarkers helper,
timeline click uses preloaded cache when available
Aliases: add module aliases for 9 nested module references
Apply: replace apply/3 with direct module attribute calls
Line length: break 1 long spec line
Refactoring: extract helpers to reduce complexity and nesting
in show.ex, radio.ex, weather workers, terrain, duct detection,
backfill dashboard, contact map, and mix tasks
- Replace %Struct{} with Struct.t() in all @spec annotations
- Replace length(x) > 0 with x != [] in test assertions
- Fix multi-line spec struct references in weather.ex
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.
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.
- 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).
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.
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.
