Why do weather forecasts disagree?
If two apps show different temperatures for the same place on the same day, neither is necessarily lying. They are usually just reading from different weather models. Here is what that means.
You check one app and it says 18 degrees and dry on Saturday. You check another and it says 15 degrees with showers. Same town, same day. Someone must be wrong, right?
Not quite. Most of the time, the two apps are simply reporting different weather models, and those models genuinely disagree. Understanding why they disagree is the single most useful thing you can learn about forecasting, because the disagreement itself carries information. That is the whole idea behind why forecasts disagree showing several models side by side.
Every forecast starts with a model
A modern forecast is not a person looking at the sky. It is the output of a numerical weather prediction model: a giant simulation of the atmosphere running on a supercomputer. The model divides the world into a 3D grid of boxes, loads in the current conditions measured by satellites, weather balloons, aircraft and surface stations, and then steps the physics forward in time to work out what happens next.
The Met Office and other agencies describe this process in plain terms in their explainers on how forecasts are made. The key point is that no two models are built the same way. They differ in three big respects:
- Resolution. How small the grid boxes are. A finer grid can resolve individual showers and hills; a coarser one smooths them out.
- Physics. How each model approximates things it cannot simulate perfectly, such as clouds, turbulence and how the surface heats the air.
- Initial conditions. How each model takes messy, incomplete observations and turns them into a clean starting snapshot. This step is called data assimilation, and different centres do it differently.
Meet the models
When apps disagree, they are usually pulling from some mix of these global and regional systems:
- ECMWF (the 'Euro' model) run by the European Centre for Medium-Range Weather Forecasts. Widely regarded as the most skilful global model.
- GFS, the Global Forecast System from the US agency NOAA. Free, global, and updated four times a day.
- ICON, run by Germany's national weather service, DWD.
- UKMO, the Met Office's global model.
- GEM, run by Environment and Climate Change Canada.
- NBM, NOAA's National Blend of Models, which statistically blends many models into one.
Each is a serious, well-verified system. On any given day, one may handle a particular storm better than the others, but there is no single model that always wins.
The butterfly effect: why small errors grow
Even a perfect model would still eventually disagree with reality, because the atmosphere is chaotic. In the 1960s the meteorologist Edward Lorenz found that tiny differences in a simulation's starting point led to wildly different outcomes later. He summed it up with the image of a butterfly flapping its wings and, weeks later, influencing whether a distant storm forms. This is the butterfly effect.
The practical consequence is that we never know the starting state of the atmosphere perfectly. There are always gaps between the weather stations and balloons. Those small starting errors are unavoidable, and chaos amplifies them as the forecast runs forward. This is why forecasts get less certain the further ahead they look, a limit we cover in how-far-ahead-can-you-trust-a-forecast.
Why two apps disagree for the same place
Put those two facts together and the disagreement makes sense. Two apps can differ because they run different models, because those models started from slightly different snapshots of 'now', and because chaos has magnified those differences by the time you are looking at Saturday. Add in the fact that apps then post-process the raw model output differently, rounding, blending, and adjusting for local terrain, and a 3-degree gap between two apps is completely normal.
What 'model spread' means
To measure this uncertainty directly, forecasters run a model many times over, each with slightly nudged starting conditions. This is called an ensemble, and ECMWF explains it well in its ensemble forecasting materials. If all the ensemble members and the different models land close together, the atmosphere is in a predictable mood. If they scatter widely, it is not.
That scatter is the model spread, and it is the honest signal at the heart of checkweather.io.
Small spread means high confidence. Large spread means low confidence. The forecast is not just a number; it is a number plus how much the models trust it.
How to read disagreement
- Agreement is a green light. When ECMWF, GFS, ICON and the rest cluster tightly, you can plan around it with real confidence.
- Disagreement is a caution, not a coin toss. Wide spread means the outcome is genuinely uncertain. Keep a flexible plan and check again closer to the day.
- Watch the trend, not one run. If the models converge over successive updates, confidence is rising. If they keep jumping around, treat the details as provisional.
- Care about the variable that matters to you. Models often agree on temperature but disagree on rain timing, or vice versa. Look at the specific thing you are planning around.
An app that shows you a single clean number is hiding this. One that shows you where the models agree, and flags where they do not, is telling you the truth about how much to trust it. Compare the models yourself for your area on compare US forecasts or compare UK forecasts.