The GFS (Global Forecast System) and ECMWF (European Centre for Medium-Range Weather Forecasts) are the two most widely used global weather prediction models. For weather market traders, understanding their relative accuracy is critical for interpreting edge signals.
Model Overview
### GFS (Global Forecast System) - Operator: NOAA (National Oceanic and Atmospheric Administration) - Resolution: ~13 km grid spacing - Update frequency: Every 6 hours (00Z, 06Z, 12Z, 18Z) - Forecast range: 16 days - Ensemble members: 31 (GEFS) - Data access: Free via Open-Meteo, NOMADS
### ECMWF (European Model) - Operator: European Centre for Medium-Range Weather Forecasts - Resolution: ~9 km grid spacing - Update frequency: Every 6 hours - Forecast range: 15 days - Ensemble members: 51 (ENS) - Data access: Available via Open-Meteo
Accuracy Comparison
Based on verification data from Celsi's model tracking system, here's how they compare across US cities:
### Temperature (High) ECMWF generally has a slight edge in day 1-3 temperature forecasts, with a mean absolute error (MAE) approximately 0.5-1.0°F lower than GFS. The advantage narrows for day 4+ forecasts.
Key finding: GFS tends to run slightly warm (positive bias) for southern cities like Miami and Houston, while ECMWF shows less systematic bias.
### Temperature (Low) For overnight lows, the models perform more similarly. GFS occasionally outperforms ECMWF in clear-sky winter scenarios where radiational cooling is the dominant factor.
### Precipitation Precipitation forecasting is where the models diverge most significantly. ECMWF's higher resolution and more sophisticated convective parameterization give it a measurable advantage, particularly for: - Frontal precipitation timing - Tropical moisture events - Snow-rain line placement
GFS tends to overforecast light precipitation events and underforecast heavy events.
What This Means for Trading
When GFS and ECMWF agree, the consensus signal is strong — contracts trading far from this consensus represent high-confidence edge opportunities.
When they disagree, consider: - For temperature: Lean slightly toward ECMWF for 1-3 day forecasts - For precipitation: ECMWF generally more reliable, but check both ensemble spreads - For extreme events: Neither model is consistently better — look at the ensemble spread rather than the deterministic run
Model Bias by City
Celsi tracks rolling model bias for each city. Some notable patterns: - Denver: Both models struggle with upslope snow events; GFS often underpredicts cold-air damming - Phoenix: GFS runs ~2°F warm in summer; ECMWF closer to observed - Seattle: Both models handle marine layer poorly, but ECMWF has less low-temperature bias - NYC: Models are closest to accurate here due to dense observational data
You can see live bias tracking on each city page in Celsi's verification dashboard.