One model forecasts torrential rain while another expects barely a shower in the same location. How can they disagree so sharply? A Meteorix comparison for Intipucá, El Salvador, captured on 5 November 2026, shows why no model can predict the atmosphere perfectly.
Meteorix model comparison captured on 5 November 2026 for Intipucá. Forecast for 8 November; not observed rainfall.
On 5 November 2026, Meteorix displayed sharply different rainfall forecasts for Sunday 8 November in Intipucá, eastern El Salvador. These were forecasts made before that date, not observed rainfall.
| Model or product | Forecast rainfall |
|---|---|
| GFS (United States) | 139.5 mm |
| ECMWF Ensemble | 18.4 mm |
| ECMWF AIFS | 5.1 mm |
| ARPEGE (France) | 1.2 mm |
| ECMWF IFS | 0.4 mm |
| ICON (Germany) | 0.1 mm |
The highest and lowest forecasts differed by 139.4 mm. Such a gap signals very different possibilities for the same location.
A forecast begins with an estimate of current temperature, moisture, pressure and wind across the globe. Satellites, weather stations, radiosondes and aircraft provide observations, but the complete atmosphere cannot be measured perfectly.
Each centre combines observations using its own data-assimilation methods. Small initial differences can amplify over time in the chaotic atmosphere, an idea often described as the butterfly effect.
Numerical models divide the atmosphere into three-dimensional grid cells. A thunderstorm can be smaller than a cell in a global model. One forecast may place the heaviest rain over Intipucá while another shifts it 30 kilometres away.
In Central America, tropical moisture, mountains and nearby oceans can create large rainfall contrasts across short distances. Finer resolution helps represent some details but does not guarantee accuracy.
Cloud formation, raindrop growth, ice processes and thunderstorm updrafts involve physics too complex or too small for every global grid cell to resolve. Models represent these processes through parameterisations, which differ between forecasting systems.
Machine-learning systems such as ECMWF AIFS follow a different approach from conventional physics-based models. Different methods and initial conditions can produce contrasting rainfall totals.
An intense storm may produce very heavy rain in a narrow area while nearby places stay relatively dry. Forecasting the exact location and timing is harder than predicting broad regional temperatures.
Daily totals can also diverge if one model places the storms late on Sunday and another early on Monday. Always compare the same accumulation window and forecast definition.
Not necessarily. The GFS suggested a far wetter scenario than the other displayed products. Its forecast does not prove 139.5 mm would fall, but an outlier should not automatically be dismissed.
Comparing models helps avoid relying on just one prediction. Agreement often supports greater relative confidence, while large disagreements highlight uncertainty.
But averages can hide incompatible scenarios. If five models predict little rain and one predicts an extreme amount, the mean may suggest a middle outcome none actually forecasts. ECMWF Ensemble is also a probabilistic ensemble product, not directly equivalent to a deterministic model run.
Meteorix helps identify uncertainty; it does not automatically identify which forecast will verify. Use model evolution, observations and official weather warnings together.
Weather models are powerful but cannot reproduce the atmosphere perfectly. Their differences help indicate when a forecast is relatively robust and when several outcomes deserve consideration.
The Intipucá example shows why a good forecast should explain not just what weather is expected, but how confident we can be in the prediction.
Compare ECMWF, GFS and ICON for the same place and forecast period. Search for Intipucá on Meteorix and open the Models comparison (PRO). Forecasts change as new model runs become available.
Open the Meteorix map ↗No model is consistently best for every variable, region or forecast lead time. Accuracy depends on the situation and verification against observations.
No. It is a scenario worth monitoring, not a guarantee.
It signals uncertainty, not necessarily dry weather.
No. A model range is not automatically a calibrated probability interval.
For official forecasts and warnings, consult your national weather service.
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