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Why one weather forecast is never enough

When looking more than a few days ahead at the UK weather prospects, deterministic computer models can generate widely differing solutions, both from each other and from their own previous updates. This is why ensemble models are used. Although they don't provide a silver bullet, they help to improve forecast reliability and highlight confidence levels.

Compare and contrast

To provide a real example, I'll use two charts showing forecast temperatures on Sunday 21st June, 2026. The first is generated from the GFS model and shows record-breaking June heat, with 36°C reached in the London area. The second is generated from the ECMWF AIFS model, but on this chart temperatures struggle to reach 20°C.

There is a huge difference between these two deterministic models. They're called deterministic because they provide a single forecast for any given time. When the lead time is short they are generally quite accurate and usually show similar outcomes. However, in the medium term small differences can quickly become magnified, leading to the much larger discrepancies further down the line that these charts illustrate.

Step in ensemble models

That's where ensemble models come in. An ensemble model contains many runs, each starting from slightly different initial conditions. This helps account for uncertainty in the current state of the atmosphere and shows how those small differences can affect the forecast further down the line. If many of the solutions are similar, forecast confidence is relatively high. If they diverge significantly, confidence is lower. So, coming back to this example, what do the ensembles favour?

The two graphs below show the GEFS and AIFS ensemble temperature forecasts. Each line represents the forecast from one of the individual runs. The important point to note is that the lines quickly begin diverging, indicating a widening range of possible outcomes. While these charts use lines, ensemble data can be presented in several ways, for example, the "postage stamp" chart at the top of this article displays the forecast from all the individual runs together in a single grid.

In other words, confidence is low regarding how conditions will develop, even from quite an early stage. Even by day four there is a large spread. For example, on 14 June (the forecast start date is 10 June) the AIFS range is approximately 16°C to 28°C in the London area. Even after excluding the outliers, a substantial range remains.

Uncertainty can be king

The spread increases even further by day 11, meaning it's not possible to predict with any degree of confidence how things will develop. So how is this useful? Knowing that confidence is low is useful in itself, because it suggests that developments are sensitive to minor changes early in the forecast period.

I will try and update this article with a postscript, so the actual outcome is detailed. I'll then leave the article as a reference, because the principles apply regardless of the location and date.