Can AI help us better predict the weather?

0:00Hannah Fry:Welcome to Google Deep Mind the podcast, I'm Hannah Fry. When a hurricane is heading towards land, time becomes absolutely critical. An extra day of warning can clear the roads, empty the hospitals, open up shelters and get people to safety. Even a few hours can be the difference between a crisis and a catastrophe. But while we can calculate the exact second the sun will rise a thousand years from now, predicting the weather in the future with certainty remains one of the trickiest physics problems on the planet. How do you handle something that is so sensitive to tiny fluctuations, where a small change in today's air pressure can turn into a major storm a week later?

0:40Hannah Fry:The famous butterfly effect. Well, the traditional way is solving fluid dynamics equations with supercomputers. But now there is another way that is doing something completely different. The team at Google DeepMind have been thinking about the challenge of AI weather prediction for years. They started back in 2020 with models that use satellite images to predict short-term rainfall. Today, the latest system, WeatherNext, provides detailed hour-by-hour predictions for the entire planet. Joining me to discuss how AI models the weather is Peter Battaglia, Senior Director of Research at Google DeepMind.

1:22Hannah Fry:Welcome to the podcast, Peter. Thank you. I want to start with a really concrete example of how AI has made a difference. Tell me the story about Hurricane Melissa. Yeah, so Hurricane Melissa was a very powerful hurricane that struck Jamaica and several Caribbean islands in October 2025. And we had been working on specific models for hurricane forecasting for several years. and earlier in 2025, about a year ago, we released our best weather model and we created a website called Weather Lab that had live hurricane forecasts. We had also established these partnerships with a number of agencies, including the National Hurricane Center, which is in the U.S.

2:10under NOAA. So throughout the season, we had been seeing that the model was fairly accurate at predicting hurricanes. And when what became Hurricane Melissa started to form in the Atlantic about a week before it eventually made landfall, our forecast started showing that it was going to become a very intense hurricane.

2:30Hannah Fry:Because unusually, it started really small, didn't it? And stayed small for a while. Yeah. And this is how they work. They start as a disturbance. It's just some pattern that could eventually, given the warm sea temperature, draw up enough energy to then become a very intense and ferocious storm. So about a week before, a little less than a week before, we started to see the model becoming quite confident that it was going to turn into a Category 5 hurricane, which is the strongest category of hurricane. And what was it at this point? I don't think it was even a tropical storm yet. It might have been a tropical depression at that point.

3:06So actually, we watch the folks on Twitter or X. There's actually a pretty active meteorological community, and they follow a lot of the models. So they started to notice and say, this is, you know, the Google model is forecasting a very intense storm. And, you know, they're saying it's going to be category five and people started to notice and we were really watching intently.

3:26Hannah Fry:Were the other models, I mean, because there's numerous other simulations that are running looking at exactly that area at the same time, were they also predicting at this point that it was going to be catching fire? Some were predicting that it was going to rise in intensity, but none, to my knowledge, had the confidence and of the confidence around that it was going to become that intense and the specific trajectory and the specific level of intensity. The National Hurricane Center, their forecasters are making, they're constantly observing and making guesses and determinations and trying to refine their own official projection about what's going to happen.

4:02They're drawing on all sorts of different information, like traditional models that are well understood, newer models, and they're designed as a center. they're sort of set up to take lots of different pieces of information and then sort of stitch that all together to form their forecast. And so on Saturday, they made an official determination that it would become a Category 5 storm and hit Jamaica. It still wasn't even, I don't think it was even a Category 1 storm yet. So they had predicted that it was going to undergo rapid intensification in like a two-day, two-and-a-half-day period.

4:35Hannah Fry:So this is Saturday, and it was predicted to hit land on what day? I think it was going to reach Category 5, I think, on Monday and maybe make landfall on Tuesday, if I remember correctly. Right. So, this is still actually a fair amount of warning then? Yes. I think it had about three-day, you know, lead time. And when they issue that Category 5 warning, that also prompts, it triggers a set of preparatory responses. And then the local officials know, okay, this is, there's a lot of confidence, information suggesting this is going to be very dangerous. We're going to start evacuating. We're going to do all the different things that wouldn't have taken place had it been a less intense storm.

5:08And this was, we were told later, this was the lowest intensity storm that the National Hurricane Center had ever forecast to become Category 5. They had never made a Category 5 forecast from such a low intensity. And we learned later that they said they were heavily informed by watching our model, that our model confidence in the forecast was really increasing their own confidence.

5:32Hannah Fry:Because you were saying it with 80 % confidence, right? Yeah, I think it varied by which forecast, because we made forecasts every six hours. But about, yeah, about 80, you know, as the week unfolded, it went from, you know, a lower probability to a pretty high probability. So they issued the Category 5 warning. It did strike Jamaica. I think 95 people lost their lives, a huge amount of damage. I think on record, it was the highest wind ever recorded. Yeah, 190 mile an hour winds. Yeah. I mean, the thing is, as devastating as it was, it could have been worse had there been less warning. Yeah, we can never really know what would happen.

6:10But we do know that these types of operational centers are crucially important for protecting people and protecting property. And again, also getting the services back online once the storm passes. So, yeah, and just in general, through that whole experience, that was very, I mean, we've had this wonderful partnership with the National Hurricane Center. And our researchers talk to them, you know, all the time. And we learn a lot about how they make their determination. So then we can feed that back into how we operate. So even though it's sort of a case study in a single instance, we actually, that partnership and seeing that whole thing through lets us then learn about how to improve our models and the types of information that the decision makers need to see in order to inform their decisions.

6:50decisions.

6:51Hannah Fry:We're going to get into the model a little bit later, but I just wonder on this topic of Melissa, do you think that the AI model has a sort of a kind of advantage when it comes to predicting hurricanes that far out in the future? Or is it just that you're doing something different to the other models and that's maybe why you get a different result? The way I think about it, it's pretty simple. What's happened in the past is the same physical process that happens in the future. So when you get enough evidence of the past and you have models that are capable of really making sense of the fine, you know, sort of subtle structure among these, you know, input and output and past and future relationships, you just get more accurate predictions.

7:34So the better you do your sort of modeling and your machine learning and your evaluation and your data, you know, all the data handling, the better your models are going to be.

7:42Hannah Fry:Because it's still according to the same rules. It's still the same rules. You just have to extract as much information about that data from the past as you can and then use it to forecast the future. So tell me a bit about this cyclone forecasting that you're releasing to the world. Tell me a bit about that. What was the motivation behind that? We wanted to make a special case of the model that was designed to predict tropical cyclones. These are like some of the most intense, strongest, rarest really weather phenomena. And we felt if we could do this well, it will have impact. It'll also be a pretty significant scientific challenge that'll force us to improve our methods.

8:16This sort of phase of our project began in earnest about four years ago, and we were working on global models, sort of forecasting all the weather on the surface and the atmosphere for 10, 15-day forecasts. As we made progress on that, we wanted to have like downstream impact forecasting the actual weather events. So we looked at things like tropical cyclones have been in all of our papers. We've done little analyses. Okay, so this predicted the temperature, you know, in seven days, but can it predict the track of a tropical cyclone? So that had been going on for a while, but then we began a sort of focused line of work, a work stream, with a dedicated sub-team about two years ago or so.

8:57And we put a lot of investment in this, and we also built partnerships right from the get-go, academics, operational, meteorological centers. We had trusted testers for many months before we released it, but then we released it last year and we made improvements to it over time. When we run the experimental evaluations over the last several years and across the globe, we see about an extra day of accuracy in the sense that a forecast which would have had an accuracy of some level at a two-day forecast, we achieve that same accuracy at a three-day forecast in many cases. and this allows you to make, you know, again, preparations for the storm and, you know, plan and issue warnings earlier.

9:45And in general, this is going to, you know, potentially be a pretty impactful tool for people to use.

9:51Hannah Fry:Do people have access to these forecasts? Like if I wanted to know whether it was going to rain next week, could I look at your forecast? Yeah. So there's different ways to access the forecast. One way is we actually have a live feed of our forecast that you can access. So you can Google for weather next and you'll find it in various forms. If you want the tropical cyclone forecast, you can go on Weather Lab. We actually just released beyond the tropical cyclones, like temperature and wind and pressure as well. And if you want to know what the weather is going to be, like if you want to just use your weather app, you can use pixel or search and the weather next forecasts are also contributing to those forecasts as well so there's a lot of ways you know whether you're a enterprise customer and you want to get the data or you just want to kind of see the icon you can see the forecast and we were talking about all of these extreme weather events right of hurricanes and cyclones and so on is weather prediction becoming more urgent as time goes on weather is becoming more extreme as time goes on That's pretty clear.

10:56The real reason is because weather is energy. I mean, you have like, you know, the sun is electromagnetic radiation. The wind is mechanical energy. Temperature is kinetic energy. When you have a warmer Earth, you have more energy. And so the weather will be more intense. And that intensity also may at best is less predictable, at least by historical standards, because it's sort of changing weather patterns. And we're seeing that there's warmer temperatures globally. I think last year or the year before was the hottest year on record. And if you look back over the last 10, 15 years, all of the hottest years on record have basically been occurring in the last few decades.

11:36And so I think we see much more wildfires, other types of flooding and things like this. So I think as we see AI advance, its calling is there to try to apply it to some of the challenges that are also becoming more difficult and important.

11:50Hannah Fry:I can imagine there's some people listening to this who are like, look, why is it so hot? We've done amazing, phenomenal things with science. Why can't you tell me whether it's going to rain next Wednesday? You know? What makes weather forecasting hard is that little things can have big effects. So this is like the butterfly effect. If a butterfly flaps its wings or it doesn't, you know, this can have actually an impact on what large scale, if it's a storm or it's clear or what. Well, we can't observe every butterfly. So there's a fundamental limit to what we can observe. And that then translates to a fundamental limit in what we can predict.

12:32But that doesn't mean that, again, on the basis of all the evidence we've ever seen, we can't detect subtle little patterns that maybe have gone unnoticed historically with previous methods. And as technology advances and gets better, we're always going to see an increase in our ability to predict things more accurately. That's the reason it's hard. It's hard because we don't observe important features that drive it. But they do leave little hints and breadcrumbs that we can capture with statistical learning.

13:03Hannah Fry:Because when people talk about the butterfly effect, I mean, it's sort of not meant metaphorically, right? It's like, it's sort of quite literally true, right? That in theory, a butterfly could cause a hurricane. I'm not sure like how many butterflies have actually caused hurricanes. Like I couldn't say. But the point is that the atmosphere is a fluid. And a fluid has a particular type of physical characteristics that mean that little things can make big, you know, impact. So, again, even if you drop like a small rock into a pond, you might see a little ring or a little wave or a little sort of, you know, splash of droplet of water.

13:41And if you can capture that and understand that that indicates that there's probably something happened that will cause a sort of, you know, concentric circle to grow, you can take advantage of that. So this is where I think AI and machine learning are really how they work. They're able to, you know, realize, wait a second, I've seen droplets before that got splashed in this little ring over here. And then what happens is you get a big ring of, you know, little waves that hit the other shore of the pond.

14:08Hannah Fry:When you put it like that, it sort of does make sense that this might be an AI problem because it's pattern recognition in a lot of ways. But why was an AI lab like DeepMind? Why did you guys get involved in weather forecasting in the first place? What was the motivation? I think there was two main reasons. So one is because weather is very important. It's like probably the oldest prediction problem, one of the oldest problems in science. And, you know, part of our mission at Will DeepMind is to try to solve major scientific challenges that humanity faces. You know, one of the goals of AI is to be able to, you know, increase our ability to solve major scientific challenges that we face.

14:47Weather itself is a challenge, but it's not an abstract problem. Literally everybody knows a lot about weather, and it impacts everyone's lives every single day. For me more personally, I had been working on machine learning for simulation for many, many years, even before this. And we had even been working on using machine learning to simulate fluids. So several teams at DeepMind came together. Folks were interested in weather, folks were interested in simulation, and we realized that the methods that we had probably could apply to some of the methods, some of the problems in weather. And then one other thing that actually helped us, helped push us over to work on weather was that there was amazing data sets on weather that were already available.

15:33So the ECMWF, the European Center for Media Range Weather Forecasting, had been building these records of weather on Earth that like spanned decades. So we see this and we're like, this is perfect for machine learning. Like this is, we're so fortunate to be able to build on this. And it's no surprise that they also have very powerful, good AI weather models as well. So, you know, when you have, when the time is right, you have the right sort of raw materials. And then you have like the teams that can put this together and it's aligned with your mission. It's something, it's a good choice to work on it.

16:03Hannah Fry:We should probably describe actually what the more traditional numerical method is. What was happening before? How would we solve the weather before? Yeah, so one of the dominant methods of forecasting the weather is called numerical weather prediction. So the way this works is you have a supercomputer, and it runs an algorithm that takes in the current estimate of the weather on the Earth. And then it makes a prediction about the weather in the next hour or six hours. And then it takes its own prediction, and it feeds it back in, and it makes another prediction for a few hours later. Sure. And it's all based on essentially physics equations.

16:41So yeah, the algorithm itself, it's an approximation to the solution to the equations of fluid motion. So again, the atmosphere is a fluid. But if you say, I only know the variables of the present, and I want to solve for the variables of the future, I need to solve that somehow. Now, again, because of the butterfly effect and because there's coupling across scales, it's very difficult to solve these equations exactly because you'd have to know very, very detailed information to be able to solve for the whole thing. And it's a very big problem. So instead, they approximate this. And they approximate it by simulating the weather at a sort of coarse, blurry scale.

17:24And they find approximate solutions to those equations. and I mean really in practice this has been a sort of triumph of science and engineering for decades right the fact that you can like know what's going to happen in a week and a half or two weeks like what's the sky is you know whether there's rain doesn't fall or something is amazing really like this was never historically this was never possible maybe a day or two in advance but it just sort of shows that through decades of you know physics and engineering and computing and data collection. This has already been a huge triumph for humanity and an amazing demonstration of human ingenuity and intelligence.

18:01Hannah Fry:But then how does Google DeepMind's approach differ from everything that went before it? So these AI weather forecasts that we're building are based on looking at a history of weather and how it unfolds in time, and then learning the statistical patterns and on the basis of the past patterns, making predictions about the future in the same way. So in the same way that you might fit a line to a trend of numbers that are increasing, here we fit a very complicated line to a very complicated set of weather from the past and then extrapolate it out into the future. Talk me through the lineage then of AI and weather forecasting.

18:40Hannah Fry:How has it progressed over time? So the earliest use of machine learning and AI and weather forecasting was usually to help traditional numerical models fill in the blanks, especially in the finer scale structure that they weren't explicitly simulating. Then we saw as the image modeling work in machine learning evolved in the last maybe eight, 10 years, a new type of weather, AI weather model that was predicting, say, the rain over a country for 90 minutes or directly trying to predict the wind velocities in a small region. They weren't using numerical weather prediction, but they also weren't simulating all of the weather over the earth.

19:30They were just trying to basically do image or video modeling, but with weather images and video. And this third phase we're in now is simulating all the weather over the Earth in the same way that the numerical weather prediction methods are also simulating them. So they're trying to predict what's happening on the surface and in the atmosphere at all parts of the globe. And in principle, these are able to capture everything about the weather, at least at the resolution they're modeling, the same way that numerical weather prediction does.

20:04Hannah Fry:That thing you're describing, that's GraphCast, essentially, is it? Yeah, GraphCast was one of the first models in phase three, where they were taking all the weather on the Earth and then simulating a forecast out to 10 days. So, GraphCast, the way it works is it takes the full state of the weather over the globe. And similar to other AI and machine learning method, it runs the same local operation or learned function everywhere. And this roughly reflects that physics is the same everywhere. And then it processes this into a larger set of representations that cover the whole globe. And then it takes that and then predicts back down to what will happen locally again.

20:51Hannah Fry:What will happen in London? Does this make sense across the entire globe all at once and back down to what's going to happen in London? And you carry on going up and down and up and down. That's right. And the reason that's helpful, so numerical weather prediction doesn't do that. It just makes predictions at a very local scale. But statistical learning methods are able to learn large-scale structure of weather. So, for example, a very large hurricane can span, you know, 100 or more kilometers. And there's a lot of structure there. So, even the west side of a hurricane can tell you a lot about the east side of the hurricane.

21:27So by having the model able to represent the whole hurricane in one representation, it can better inform the local properties of what's going to happen next. And this is one big difference between AI weather models and traditional numerical weather prediction. That is probably why the AI models are so effective.

21:45Hannah Fry:Because they can see the whole globe at once. They can see large scale patterns as well and learn that structure and exploit that as well as the local structure. That makes sense. At what point did these models become probabilistic? Probabilistic means probability or probably. So a deterministic model always makes exactly one guess based on its inputs. A probabilistic model makes many guesses because this could happen or that could happen. And this allows a model to express the range of scenarios, most likely, and even some of the more unlikely but still possible events. So in the field of weather forecasting, they've been moving toward probabilistic forecasts for a while.

22:22So even traditional numerical weather forecasting has been using probabilistic models. And they just roll out different scenarios that represent different possible futures. We started working on probabilistic weather models just after GraphCast. So this was around 2023. And the first model we had in that was called GenCast. So it was in many ways a follow-on to GraphCast. It changed a bunch of things. but the biggest difference was instead of trying to predict the average weather that was expected to happen it predicted many different scenarios that were likely to happen and this is much more useful for extreme events and for any kind of decision making scenario where if you have you know extreme things or rare things that matter a lot you really want to kind of know even if it's only a 5 % or 10 % chance of happening, you probably want to prepare for it.