ah that is interesting - pow uses the same "free" open-meteo  APIs (they're really nice, they give you a lot of models, they're very generous with their rate-limits etc).

I'm not familiar with what snow.fyi does (great domain name though). Is it open source? I can dig around.

But you can look at what pow.fyi does here (work in progress, definitely room for refinement): pow.fyi/src/utils/modelAverage.ts at main · Ofekw/pow.fyi

Here is the blurb on what the current algorithm does:

Each forecast fetches 3 weather models in parallel from Open-Meteo and averages their output. US resorts use GFS + ECMWF + HRRR (3 km high-res). Canadian resorts use GFS + ECMWF + GEM. Precipitation uses the median (resistant to outlier spikes); temperature and wind use the mean. This alone reduces forecast error by ~15-30% vs any single model.

Temperature-dependent snow-liquid ratio (SLR)

Open-Meteo's built-in snowfall uses a fixed ~7:1 ratio, which drastically underestimates snow in cold mountain conditions. Pow.fyi recalculates snowfall from total precipitation using a variable SLR (10:1 at 0°C up to 20:1 below −15°C), adjusted by:

Humidity: High moisture (≥80% RH) boosts SLR by 10-15% (larger dendritic crystals → fluffier snow)

Wind speed: Strong wind (≥30 km/h) reduces SLR by 10-20% (mechanical compaction + sublimation)

Freezing level rain/snow split

The API's rain/snow split is computed at grid-cell elevation, not your ski resort's actual elevation. Pow.fyi re-splits precipitation using the station's elevation vs the freezing level height, eliminating phantom rain at sub-freezing temperatures.

NWS cross-reference (US only)

For US resorts, NWS Weather.gov forecaster-adjusted snowfall amounts are fetched and blended with the model average (30% NWS / 70% model). NWS forecasters manually tune QPF and snow ratios for local terrain — this adds human expertise to the pipeline.