It’s OK When Weather Forecasts Are Wrong

Tom Bendall

Tom Bendall

My main interests are weather prediction and climate modelling, in particular the physics that is used within them, describing the motion of the atmosphere and ocean. I'm currently studying for an MRes at Imperial College London. My current work focuses on how we can describe oceanic and atmospheric processes whose scale is smaller than the scales in our model, but have a significant effect on the fluid.
Tom Bendall

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Weather forecasts don’t have a great rep. Since I started studying the Mathematics of Planet Earth, I’ve lost count of the number of friends and family members that have asked me, “But weather forecasts aren’t any good are they?” Sure, weather forecasting isn’t an exact science. You only have to follow a forecast for a week in Autumn or Spring in the UK to notice that it rains when you were told it would be sunny and vice versa. Have a look at Figure 1, which shows the number of times the Met Office correctly forecasted rain throughout 2014 — they get it right about 70% of the time (this is very good — the Met Office claim to be second in the world for quality of their forecasts [1]). However, I don’t think this is a problem with the science. It’s a problem with how we interact with the forecasts.

Figure 1: The fraction of times rain was accurately forecast by rain symbols one day ahead of time by the Met Office throughout 2014. Source of data: a/pdf/5/6/MOSAC20_2015_Anne x_III_forecast_accuracy.pdf
Figure 1:
The fraction of times rain was accurately forecast by rain symbols one day ahead of time by the Met Office throughout 2014.
Source of data: a/pdf/5/6/MOSAC20_2015_Anne x_III_forecast_accuracy.pdf

In fact, weather forecasts should not be correct all the time. Ewen McCallum, a former Chief Forecaster at the Met Office says: “If we got it [the weather forecast] right every time, we’d be God.” Trying to predict the weather is a bit like trying to predict dice rolls — we just don’t have enough information to be able to do so. In order to have a perfect prediction we would need to know the exact air conditions — temperature, pressure, wind velocity and humidity — at every single point in the Earth’s atmosphere with perfect accuracy. This clearly isn’t possible: even if our measuring instruments were perfect, it’d be impossible to know these properties everywhere. Even the flap of a butterfly’s wings or a baby’s breath will cause these quantities to change.

Surely these effects are too small to make a difference though? It turns out this isn’t true. The Earth’s weather is known as a complex system — the tiniest changes to its state cause the system to evolve in a different way. This phenomenon is known as chaos and is observed in many types of system. It means that no matter how close we get to measuring or guessing the current conditions, at some point in the future we won’t be able to determine the weather.

One of the ways that forecasters try to account for this is by trying their simulations many times with slightly different conditions at the start, to account for the unknowns in their measurements of the current state of the atmosphere. This technique is known as ensemble forecasting. Rather than telling forecasters what will definitely happen at 3pm on 19th of April, they might see that in 70% of their simulations it rains over London at this time. This gives them a probability for it raining, rather than a definite answer to whether it will or won’t.

However, we as the public don’t like this kind of uncertainty. We simply want to know if it will or if it won’t rain, and it almost looks weak of a forecaster to avoid giving a definite prediction. Unfortunately giving a definite prediction is poor science as it does not represent truth about the weather. Given that the nature of the weather is unpredictable, demanding a definite forecast will inevitably lead to failure in the long term.

So the problem is not so much with the quality of the research at the Met Office — it lies with our expectation for a definite prediction rather than one that contains uncertainties (such as one saying there is 70% chance it will rain today). These uncertainties are an inherent part of a chaos theory, which we see in the weather as we can’t have perfect knowledge of the whole system at once.



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