Why do weather forecasts disagree?
Two apps, two answers: usually it's different models, different update times and different ways of turning raw data into an icon.
Weather forecasts disagree because different apps use different models, updated at different times, then simplify the raw output in different ways. One app may run the American GFS, another ECMWF or the Met Office model; one may have refreshed an hour ago and another last night. The underlying atmosphere is genuinely uncertain, and each app makes different choices about how to present that uncertainty as a single icon.
Reason one: they're running different models
This is the biggest cause. There is no single "the forecast" - there are several major numerical weather prediction models run by different centres: the Met Office Unified Model (UKMO/UKV), ECMWF's IFS from Europe, NOAA's GFS from the United States, and DWD's ICON from Germany. Each uses different observations, resolutions, physics and data assimilation, so they genuinely produce different answers. A weather app is only as distinctive as the model it happens to pull from. If your phone's default app uses GFS and a friend's uses the Met Office, you should expect them to differ, sometimes markedly, especially several days out.
Reason two: they updated at different times
Models run on a schedule - typically several times a day - and each new run ingests fresh observations. Two apps can therefore show different forecasts simply because one is displaying an older run than the other. During a fast-changing situation, a forecast from this morning's run can look quite different from one made last night. When two sources disagree, checking how recently each was updated often explains most of the gap.
Reason three: deterministic runs versus ensembles
Many consumer apps show a single deterministic model run - one possible future - rendered as a crisp icon. But professional forecasting uses ensembles: many runs of the model with slightly varied starting conditions, revealing the spread of possible outcomes. A single deterministic run can look confident even when the ensemble is scattered. Two apps pulling different members, or one using an ensemble mean while another uses a single run, will disagree - and the disagreement is actually telling you something true: the atmosphere is uncertain.
Reason four: the atmosphere is chaotic
Even with perfect apps, some disagreement is unavoidable because weather is chaotic. Tiny differences in the starting conditions grow over time - the "butterfly effect" - so small, legitimate differences between models amplify as the forecast reaches further ahead. In the UK this is magnified by the North Atlantic jet stream: a slight difference in where a model puts the jet sends a depression onto a completely different track. That is why apps often agree about tomorrow but split about next weekend.
Reason five: turning data into a single icon
The last mile is presentation. Raw model output is rich - probabilities, rates, ranges - but an app has to compress it into one icon and one number per hour or day. The rules for that compression differ. What one app calls "light rain" another may render as "cloudy with a shower". Rounding a 40% and a 60% chance of rain to a sun-or-cloud icon can make two forecasts look opposite when the underlying numbers are close. Local high-resolution models like the Met Office UKV may also place a shower on your town that a coarse global model spreads thinly across the county.
| Why they differ | What it means for you |
|---|---|
| Different model (Met Office, ECMWF, GFS, ICON) | Expect differences; no single model is always right. |
| Different update time | Check which is more recent before trusting it. |
| Deterministic vs ensemble | A crisp icon can hide real uncertainty. |
| Atmospheric chaos + the jet stream | Disagreement grows with lead time. |
| Icon and rounding rules | Small number differences look like big disagreements. |
So which forecast should you believe?
Rather than picking a favourite app, treat disagreement as information. When several major models agree, confidence is high and you can plan firmly. When they diverge, the honest reading is that the forecast is genuinely uncertain and you should stay flexible. Favour the more recently updated source, lean on high-resolution local guidance like the Met Office UKV for next-day detail, and for anything hazardous follow the Met Office's National Severe Weather Warning Service and Environment Agency flood alerts rather than any single app icon.
This is the core idea behind checkweather.io: instead of hiding the disagreement, it lines the Met Office, ECMWF, GFS and ICON up side by side, converts how much they agree into a plain-English confidence score, and grades an accuracy scoreboard against recorded weather - "no black boxes". When your two apps clash, that clash is not a bug; it is the atmosphere telling you how sure the forecast really is.
Does disagreement mean forecasting is unreliable?
It is tempting to conclude that if apps disagree, the whole enterprise is guesswork - but the opposite is true. Modern forecasting has improved dramatically over recent decades, to the point where a present-day multi-day forecast is broadly comparable in skill to a much shorter-range forecast from a generation ago, according to the national services that measure it. Disagreement is not a symptom of failure; it is the science being honest about the genuine uncertainty in a chaotic atmosphere. A forecast that always sounded certain would be lying. The value lies in knowing when the models agree - and can be trusted firmly - and when they diverge and you should hold plans loosely. Far from undermining forecasting, visible disagreement is one of its most useful outputs, provided you know how to read it.
The takeaway
Forecasts disagree because they are different models, run at different times, distilled into icons by different rules, all describing a chaotic atmosphere that Britain's jet-stream weather makes especially hard to pin down. The smart response is not to hunt for the one "right" app but to read the level of agreement between the major models - and to trust the forecast most when they converge.