Predicting the weather

16 July 2019

Interview with 

Chris Bell, Weatherquest

IS_IT_RAINING

A girl holding an umbrella checks if it is still raining

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Let’s look at an example of one of the most powerful computer simulations that affects every single one of us every day: the weather forecast. Chris Bell is a forecaster from Weatherquest and a lecturer at the University of East Anglia and he explained to Adam Murphy how this happens...

Chris - Well it starts with observation. So you can't make a good prediction of the weather without observing the weather correctly and that's one of the biggest challenges, actually, in terms of getting a weather forecast correct. So we first take every bit of weather observations we can get. That goes for the traditional weather stations that we would see at the ground, to offshore observations from ships, and aeroplanes as well in the sky, weather balloons and also from satellites. Increasingly so from satellites, actually, and all of this data is fed back into computer simulations and that's really how we forecast the weather.

Adam - What's the simulation actually doing to spit out the forecast?

Chris - So we know, generally, the equations of the atmosphere in terms of how moisture and temperature and air moves around in the atmosphere from basic physics equations. You can put those physics equations into these big computer models and input the data that we observe into those equations and run them out for an hour, and see what the answer is, and then run them for another hour, and see what the answer is and then so on and so on. You can go out to, you know, some computer models go out for a couple of weeks and some out to a couple of months. And as you know, climate models are simulating weather all the way out for several decades.

Adam - Now, when I look at the weather app on my phone, it can say something like a 50 percent chance of rain. But what does that number mean? Is it like a coin flip whether it will rain or not?

Chris - That is a good question. In fact, more and more people nowadays are getting their weather forecast from these mobile phone apps and one of the things about that is so there's two different ways you could come up with a percentage in a mobile phone app for a weather forecast. One of them can be, you can take a computer model and there are areas of the atmosphere and around the Earth that we don't know the actual weather observation for. So we have to estimate using what we do know.

And so it turns out if you tweak those estimations just slightly. So for example, maybe there's an area south of Iceland and out to the to the southeast of Greenland where we don't have observations and you tweak the estimations you make just slightly, it can have a big impact on the forecast out several days in advance. So you do these tweaks and you run the same computer model over and over and over, maybe 50 times, and then you have a whole solution of possibilities out to day one, even to day 15 in advance. And if your question is “is it going to rain?”, you can see how many of those computer models are forecasting it to rain and then that's how you get your answer for percentage. The other way of doing it is to look at how much rain is around your area in the computer model and if all of the grid points around your area are covered with rain then that's a high chance of rain and if only one or two of them are. Then you've got a lower percentage.

Adam - How good are our predictions of weather and why is it important to make sure my barbecue doesn't get rained on?

Chris - Say back in the 1950s and 60s when weather forecasters used to sit down and draw up a weather chart and then they would see new observations come in, and they would draw up another weather chart. They would do that hour after hour and they would look at those weather charts and they would project how the different weather systems were moving and they would use that to make their forecast. When they do that they would be lucky to get the weather forecast right more than a day or two in advance because you simply didn't have all the information that we do nowadays. But, obviously, with the invention of weather satellites we can see clouds moving, we can input that data into these big computer models that we have now and it allows us to make weather forecasts from much further in advance and also expect more from the accuracy of the weather forecast.

So I would say nowadays getting the forecast right two to three days in advance you should be able to almost do that to an hourly time step. You know, to get within a couple of degrees of the temperature and wind speed and whether it's raining on that hour or not. Obviously that can be affected a lot by whatever weather pattern you're in but three or four days fairly accurately to the hour. Beyond that, there starts to be the uncertainty that creeps in and once you get past about seven to ten days out things start to get much more tricky.

Adam - And why is it important to have that level of accuracy?

Chris - Well, I mean, you have lots of big organizations making massive decisions on the weather. So let's just take an example of maybe a port for example. So there might be a strong wind event coming up that keeps the cranes from being able to operate. So that has an impact on the ships that are offshore that are coming to that port. The lorries that are coming from the different distribution places within the country to the port to pick up the goods coming from the ships. If a port can know that there is a disruptive spell of weather coming, say five or six days in advance, that might shut down their operations for 24 hours, being able to adjust what they do leading up to that can make a big difference. And that's the kind of things that the big companies are doing to try to minimize their their loss in terms of costings from the weather forecast.

Adam - Now despite all your hard work, the weather forecast isn't always right. So how might we improve predictions in the future?

Chris - Yeah, that's a really good question because we are starting to see that computer models are getting much much quicker. We are having more and more data that we're putting into them, but actually we're having fewer and fewer weather observations from the surface of the earth and more reliance on satellite. So I think that's probably the way forward in future is to improve our ability to monitor the weather from a satellite. Because it's still quite difficult for a satellite to see through the atmosphere so it can have a good idea of what the temperatures are at the cloud tops and what the temperatures are at the surface of the Earth.

But seeing what's happening in between is is quite difficult for a satellite to do and we're constantly improving the ability to do that and I think that's probably the way forward. That, combined with something else that was mentioned in the show; machine learning. So I think machine learning is another thing that's going to be happening in the future for weather forecasting. So you look at loads of different weather variables; wind, temperature, pressure, pressure anomalies and that sort of thing and you put all that data into a machine and let it kind of come up with the solution rather than the traditional style of weather forecasting where you're running a bunch of equations.

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