About NTMS Propagation Prediction
What we're building and why.
Line-of-sight is wrong for the microwave bands. Every operator on 902 MHz and up has made contacts over hills, through trees, and 200 km past the horizon — contacts that shouldn't have worked if LOS were the whole story. What's actually going on is the lower atmosphere: temperature inversions, humidity gradients, ducting layers, and rain cells that bend, trap, and absorb signals in ways that change by the hour. The window for a good contact is often minutes, not days.
Jim KM5PO started this project to try to predict those windows from weather data. Graham W5ISP picked it up and is iterating on it from there. The goal: a map that tells you when to get on the air, per band, based on real atmospheric conditions and what we've learned from the contacts you and everyone else have already made.
Heads up: the scoring you see is hand-calibrated from the data we have so far. It isn't updated in real time — I (Graham) re-fit the weights manually when there's enough new data to move the needle. The more contacts and beacon reports we collect, the better it gets. See the algorithm page for what's actually under the hood, and please submit your contacts.
How it works, roughly
Two things, paired up. Most propagation tools keep them separate.
- The weather. Hourly 3 km HRRR forecasts give surface fields and pressure-level profiles. We derive refractivity profiles, minimum refractivity gradient, ducting detection, boundary-layer depth, and precipitable water for every grid cell. ASOS surface obs, 12-hourly upper-air soundings, and gridded IEMRE reanalysis fill in the gaps. A 9-factor composite score is written out for every 0.125° cell across CONUS, for each hour of an 18-hour forecast.
- The contacts. 58k+ amateur microwave QSOs, each tagged with the atmosphere at both ends of the path (and along it) at the time the contact happened. That's a ground-truth dataset we can actually fit against — "when you had this score, how far did the contact go, and did it happen at all?"
The scoring is a weighted sum of nine factors: humidity, time of day, T–Td depression, refractivity gradient, sky cover, season, rain, wind, pressure. The weights are band-dependent because the physics changes as you go up: humidity helps at 10 GHz (more refractivity) and hurts at 24+ GHz (absorption). Every coefficient is a hypothesis I'm checking against the contact and beacon data.
What we've collected so far
Straight from the production database:
How it's built
- Elixir / Phoenix 1.8 + LiveView for everything web-facing. Ecto on Postgres for storage. Oban runs the background pipelines (HRRR pulls, terrain, ASOS, soundings, IEMRE, solar indices, commercial links).
- Leaflet + Canvas tile layers for the propagation heatmap — we render 0.125° cells at interactive frame rates. Tailwind v4 + daisyUI for layout, esbuild for JS bundling.
- Physics: ITU-R P.526-16 knife-edge + Deygout 3-edge terrain diffraction, ITU-R P.838-3 rain attenuation, dynamic k-factor from live HRRR refractivity gradients, great-circle geometry, FSPL with frequency-dependent O₂ and H₂O absorption.
- Data sources: NOAA HRRR (AWS S3, hourly analysis + 18 h forecasts), Iowa Environmental Mesonet (ASOS and upper-air soundings), IEMRE gridded reanalysis, SRTM 90 m terrain, SNMP polling on seven commercial microwave links near DFW at 5-minute intervals, and Copernicus ERA5 for pre-2014 contact enrichment.
- There's Nx/Axon/EXLA scaffolding for a small feed-forward model (13 features → 64 → 32 → 1 sigmoid). Not trained yet — waiting on enough calibrated data to not overfit.
What's next
- Beacon calibration. A distributed network of amateur receivers reporting CW beacon signal levels on a schedule. Beacon submissions are already open to anyone via the Beacons page.
- Weight calibration. Fit the 9 factor weights against recorded distances / QSO counts per band so the score reflects real propagation, not my intuition.
- Training the model. Once there are enough clean (contact, conditions) pairs, train the Axon model to augment or replace the hand-tuned scoring.
- Better inputs. MRMS for precipitation at 24+ GHz where rain attenuation dominates, RTMA/URMA surface analysis blending, GOES total precipitable water.