HRRR forecast hours (f00-f18) with timeline map UI

- HrrrClient.hrrr_url accepts forecast_hour param (wrfsfcfHH.grib2)
- PropagationGridWorker fetches all 19 forecast hours per run
- Propagation.scores_at/3 queries scores at specific valid_time
- Propagation.available_valid_times/1 returns all forecast times for timeline
- Pruning keeps scores with valid_time >= now - 2h (forecast-aware)
- MapLive: select_time event, timeline data pushed to JS
- JS: forecast timeline bar at bottom of map with clickable hour buttons
- PubSub broadcast sends list of valid_times instead of single time

2026-03-31 16:44:47 -05:00

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Raw Blame History

GOES-R Satellite Data for Propagation Prediction

Summary

Verdict: Low priority. GOES-R data is already baked into HRRR, and the satellite cannot resolve the vertical gradients that cause ducting.

What is "GOES-R Rapid Refresh"?

There is no product called "GOES-R Rapid Refresh" or "GRR." These are two separate systems:

GOES-R — geostationary satellite series (GOES-16, 17, 18, 19) with the Advanced Baseline Imager (ABI)
HRRR — High-Resolution Rapid Refresh, a ground-based NWP model

They are complementary: NOAA assimilates GOES-R satellite observations (cloud-top data, satellite winds) into the HRRR model. By consuming HRRR output, we are already indirectly consuming GOES-R data — processed through full 3D physics.

Relevant GOES-R Products

Product	Resolution	Cadence	Clear Sky Only?	Relevance
Derived Stability Indices (K-Index, LI, CAPE)	10 km	5 min	Yes	Moderate — but K-index inversely correlated with ducting per our analysis
Total Precipitable Water	10 km	5 min	Yes	Low — our analysis found PWAT is NOT a ducting discriminator
Vertical Temperature Profile	10 km	5 min	Yes	Very low — see critical caveat below
Vertical Moisture Profile	10 km	5 min	Yes	Same limitation as temperature
Land Surface Temperature	2 km	5 min	Yes	Minor — land-sea contrasts drive coastal ducting
Sea Surface Temperature	2 km	hourly	Yes	Same — coastal ducting indicator
Rain Rate	2 km	5 min	No	Redundant — HRRR already provides precip
Derived Motion Winds	varies	5 min	Partially	Redundant — HRRR already provides wind fields
Cloud Imagery (water vapor)	2 km	1-5 min	No	Visual supplement only — moisture plume tracking

Critical Limitation: Cannot Resolve Ducting

The ABI is an imager with 16 spectral bands, not a hyperspectral sounder. Key problems:

The "vertical profiles" lean heavily on the NWP model first guess. The ABI lacks the spectral resolution to independently resolve temperature/moisture structure. The 54-level temperature profile sounds impressive, but with only 16 IR bands, the independent information content is very low. The retrieved profiles "retain features of the first guess" — meaning they're essentially a slightly nudged version of the same NWP model we're already using.
Ducting requires detecting gradients over 50-200m vertical scales. The ABI simply cannot resolve this. A 100m-thick temperature inversion looks identical to a smooth profile from the satellite's perspective.
Clear-sky only. All the useful derived products (TPW, stability indices, profiles) only work under cloud-free conditions. HRRR gives us data everywhere, always.

Comparison: What GOES-R Adds vs. What We Have

What We Need	HRRR (current)	GOES-R (proposed)
Surface temp/dewpoint	3 km, hourly, all-sky	10 km, 5 min, clear-sky only
Vertical profiles	8 pressure levels with T/Td/height, all-sky	54 levels but low information content, clear-sky only
Refractivity gradient	Computed from HRRR profiles — our key ducting indicator	Cannot resolve the vertical structure needed
Boundary layer depth	HPBL from model physics	No direct product
Wind	10m wind components, all-sky	Cloud-tracked winds at various levels
Precipitation	Model physics + radar assimilation	IR-only rain rate (less accurate)
Forecast capability	18-48 hours ahead	None — observation only

Where GOES-R Could Help (Minor)

Real-time nowcasting between HRRR cycles: HRRR updates hourly with ~60 min latency, meaning displayed data can be up to 2+ hours old. GOES-R's 5-minute cadence could fill the gap for:

Cloud cover changes (water vapor imagery)
Rapid destabilization (stability indices flagging convective onset)
Land-sea temperature contrasts (coastal ducting indicator)

These would be supplementary monitoring products, not improvements to the core scoring algorithm.

Data Access

All GOES-R data is freely available on AWS S3 (no auth):

s3://noaa-goes19/ (current GOES-East, operational since April 2025)
s3://noaa-goes18/ (current GOES-West)
Path: ABI-L2-{PRODUCT}{REGION}/{YEAR}/{DOY}/{HOUR}/{filename}.nc
Format: NetCDF4
Also available on Microsoft Planetary Computer (Azure)

Future: GeoXO Hyperspectral Sounder

The upcoming GeoXO satellite series (late 2020s/early 2030s) will carry a true hyperspectral IR sounder (GXS) with ~1,550 spectral bands. This would dramatically improve vertical temperature/moisture profiling from geostationary orbit and could genuinely detect inversion layers relevant to ducting. Worth revisiting when GeoXO data becomes available.

Recommendation

Don't integrate GOES-R now. The HRRR-based approach already consumes better data for ducting prediction than GOES-R can provide directly. The complexity cost (S3 polling, NetCDF parsing, spatial interpolation, handling clear-sky gaps) is not justified by the marginal benefit.

Priority order for improving predictions:

HRRR forecast hours (f01-f18) — already designed in prediction.md
Radiosonde data (already partially ingested — better vertical profiles than any satellite)
GeoXO hyperspectral sounder (when available, late 2020s)
GOES-R products (low value-add given HRRR already assimilates them)

5.3 KiB Raw Blame History