Methodology
How PowSwirl Blend works — our forecast algorithm is fully open and transparent. No black boxes, no proprietary secrets.
The Problem with Raw Model Output
Most weather APIs return a "snowfall" variable that bakes in a fixed 7:1 snow-to-liquid ratio (SLR). This is roughly accurate for sea-level, near-freezing snow, but Colorado mountain snow typically has a 12:1 to 15:1 SLR — meaning raw API snowfall numbers can underestimate actual accumulation by 40–58%.
Additionally, no single weather model is best at all time horizons. HRRR excels at 0–24 hours but only runs 48 hours out. ECMWF is the most skillful at 3–14 days. GFS, despite its wide use, systematically underforecasts mountain snowfall. Blindly using any single model means getting the wrong answer much of the time.
Real-world example
On Feb 17–21, 2026, Winter Park received 11" of snow (confirmed on-mountain + OpenSnow). GFS predicted 0.77" (93% too low). ECMWF predicted 6.36" (still low due to 7:1 SLR). Using ECMWF precipitation with our 12:1 SLR correction yielded 10.9" — matching reality almost exactly.
Our Approach: Two Corrections
PowSwirl applies two corrections to raw model output to produce accurate snowfall forecasts:
1. Temperature-Based SLR
Instead of using the model's pre-computed snowfall (fixed 7:1 SLR), we fetch raw precipitation in mm and convert to inches of snow using the temperature at that hour. Colder temps = fluffier snow = higher ratios.
2. Multi-Model Blending
We blend four weather models with time-varying weights. The best model for each time horizon gets the highest weight. If a model is unavailable (e.g., HRRR beyond 48hr), its weight redistributes proportionally.
Snow-to-Liquid Ratio (SLR)
We estimate SLR from the 2-meter air temperature at each forecast hour. This is a simplified version of the approach used by the National Weather Service and academic meteorology.
| Temperature | SLR | Quality |
|---|---|---|
| ≤ 10°F | 15:1 | Champagne Powder |
| 10–20°F | 13:1 | Light Powder |
| 20–28°F | 10:1 | Average Density |
| 28–32°F | 7:1 | Heavy/Wet |
| > 32°F | 0:1 | Rain |
Formula: snowfall (in) = precipitation (mm) / 25.4 × SLR. For example, 10mm of precip at 15°F = (10 / 25.4) × 13 = 5.1" of light powder.
The Four Models
PowSwirl uses four numerical weather prediction (NWP) models, each with different strengths. All data comes from Open-Meteo, which mirrors NOAA/ECMWF/DWD output for free.
High-Resolution Rapid Refresh
Best short-range mountain model. Updated hourly, captures terrain-driven weather like upslope snow and localized convection.
European Centre for Medium-Range Weather Forecasts
Consistently the most accurate global model. Best at capturing large-scale storm patterns and moisture transport.
Global Forecast System
Workhorse US model. Good at synoptic-scale pattern recognition but tends to underestimate mountain snowfall.
ICOsahedral Nonhydrostatic model
Strong medium-range model with good performance in mountainous terrain. Provides a valuable third opinion.
Blend Weights by Time Horizon
The PowSwirl Blend uses different model weights depending on the forecast horizon. These weights are based on model verification studies and our own observed performance at Colorado ski resorts.
HRRR dominates because its 3km resolution captures terrain-driven mountain weather that coarser models miss. GFS is minimized due to persistent underforecasting of mountain snowfall.
ECMWF takes over as the most skillful model at medium range. HRRR is fading but still contributes recent trends. ICON and GFS provide ensemble diversity.
HRRR doesn't run beyond 48 hours. ECMWF is the gold standard for extended range. ICON and GFS provide spread indication.
Forecast Elevation
Weather models interpolate to the requested elevation. Using the summit (e.g., 12,060' at Winter Park) can lead to unrealistic values because few models resolve true summit conditions.
PowSwirl uses the mid-mountain elevation as the default forecast point — typically the midpoint between base and summit. This better represents where most skiing happens and where most snow measurement occurs. For Winter Park, this is 10,530' vs. the 12,060' summit.
Known Limitations
SLR is simplified. Real SLR depends on humidity, wind, crystal type, and more. Our temperature-only approach is a pragmatic approximation that performs well in verification but isn't meteorologically complete.
Weights are static. Our blend weights don't adapt to individual storm patterns. A future version may use recent SNOTEL verification to dynamically adjust weights toward the model performing best for the current event.
Bias correction is decaying. We apply a correction based on the gap between the last model run and the latest SNOTEL observation. This correction decays exponentially — strong for the next few hours, negligible by 48 hours out.
Wind transport not modeled. We don't account for wind-loaded slopes vs. scoured ridges. Snowfall totals represent what falls from the sky, not what accumulates on any particular slope.
See It in Action
Every resort page shows the PowSwirl Blend alongside individual model forecasts. Click "How does this work?" on any blend card to see the active weights.