How do you forecast the weather?
We set up a model to represent the current state of the atmosphere based on many observations. From that, the model projects forward in time and calculates how the atmosphere may evolve. The outcome of the forecast is very sensitive to small errors in the initial state, so we run what we call an ensemble forecast. Instead of just running the model once, we make a series of small changes to the initial state and re-run the model a large number of times to get a set of forecasts. On some days the model runs may be similar, which gives us a high level of confidence in the forecast; on other days, the model runs can differ radically so we have to be more cautious.
How certain can you be about forecasts?
The level of confidence varies from day to day and from forecast to forecast. In some circumstances you can get big differences between the forecasts in the ensemble. The biggest uncertainties are often around big storms and the dramatic weather everyone cares about, because the atmosphere has to be in a sensitive, unstable state to generate that high-impact weather. The chaotic nature of the atmospheric system does impose fundamental limits on predictability. In terms of day-to-day weather, that limit is typically between 10 days and two weeks using probabilistic forecasts.
“Often you cannot – for good scientific reasons – say definitely that it will or will not be raining”
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Can you give an example of how forecasting influences behaviour?
In December 2013, a big storm hit the east coast of England. High sea levels coincided with a high tide, resulting in a massive storm surge and coastal flooding. Seven days before, the forecasting system had flagged a small but significant risk of that happening: one or two of the 24 forecasts in the ensemble showed a sea level way above danger levels – enough to alert our professional colleagues. Over the following days the probability of flooding rose to around 50 per cent and warnings were raised to amber level. About two days ahead, we got to the point where we had high confidence in the forecast, so people were evacuated, which helped to protect lives and property.
Video: How the Met Office predicts the weather
From 2011, the Met Office started presenting rain forecasts using probabilities. Was that controversial?
We’d been debating it for a long time. The Americans have been putting out probability of precipitation forecasts for many years, and it’s quite accepted there. The argument in favour is that often you cannot – for good scientific reasons – say definitely that it will or will not be raining. So you are giving people much better information if you tell them the probability of rainfall. While we recognise that some people find probabilities difficult to understand, lots of people do understand them and make better decisions as a result.
How effectively do people use your probabilities in their decision-making?
We have done research on this with the Finance and Economics Experimental Laboratory at the University of Exeter. We found that students who were given temperature probability forecasts consistently made better predictions about what the temperature would be, and that was true whether they were in science and engineering, business and economics, or humanities. In 2011 we ran an online game in which 8000 people were asked to respond to rainfall and temperature forecasts presented as probabilities. We showed that the more information you gave people, the better decisions they made, and that was true across different age groups, genders and educational backgrounds.
Which are the most challenging parts of weather forecasting?
The one that is proving particularly difficult at the moment is visibility and fog. Visibility is related to things like temperature, pressure and humidity in a highly non-linear way. A very small difference in temperature or wind speed can make a huge difference to visibility, so fog is often very patchy and localised. This create a big challenge for models or human forecasters. Our customers, airports for example, want really specific forecasts. They don’t care if there’s fog a quarter of a mile away, they care if there’s fog on the runway.
Read more: “Chance: How randomness rules our world“
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is head of weather science numerical modelling at the UK Met Office
