# 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:

1. **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.

2. **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.

3. **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:**
1. HRRR forecast hours (f01-f18) — already designed in `prediction.md`
2. Radiosonde data (already partially ingested — better vertical profiles than any satellite)
3. GeoXO hyperspectral sounder (when available, late 2020s)
4. GOES-R products (low value-add given HRRR already assimilates them)
