Comparing GEFS, ECMWF EPS and AIFS ENS
An ensemble is a collection of forecasts representing different possible atmospheric evolutions. Comparing systems adds another perspective, but agreement is evidence to weigh—not a guarantee that the common answer is right.
Three systems in the same viewing workflow
| System | What it represents | Using it here |
|---|---|---|
| GEFS | NOAA’s Global Ensemble Forecast System, a physics-based ensemble. | Free maps and precipitation plumes; temperature plumes require Premium. |
| ECMWF EPS | ECMWF’s physics-based IFS ensemble. | Free map guidance through F024; later hours and its plumes require Premium. |
| AIFS ENS | ECMWF’s data-driven Artificial Intelligence Forecasting System ensemble. | Free map guidance through F024; later hours and its plumes require Premium. |
The inspected SKC processing uses 31 GEFS members and 51 members for each ECMWF ensemble, including a control member. Product availability can change; use the displayed catalog rather than assuming identical products or forecast lengths across systems.
Make the comparison fair
- Match valid times, not just the slider position. Different initialization times can produce different lead times for the same event.
- Match the variable, units, threshold and accumulation period.
- Look at the distribution as well as the mean. A similar mean can hide different timing or two distinct groups of outcomes.
- Check successive runs. A single change does not establish a trend.
Differences can be useful
If one system places a wet band farther north, use the maps to identify where the forecasts diverge and the plume to examine timing and amounts near your location. Avoid averaging displayed probabilities across systems as though every member were independent and equally calibrated.
AIFS ENS is not automatically more accurate because it uses machine learning. Physics-based guidance is not automatically more reliable for every variable either. Local terrain, season, lead time and the event matter; this site does not publish a universal model skill ranking.
What a shared display does not change
SKC Weather’s common map interface helps comparison, but it does not make the source grids or model behavior identical. Zooming into a global map reveals the display at a larger scale; it does not create neighborhood-scale forecast skill.
Source descriptions and attribution: About SKC Weather, NOAA GEFS and ECMWF Open Data.