How AI is changing weather forecasting

Summary

Catalyst with Shayle Kann

Episode Summary: "How AI is Changing Weather Forecasting"

Release Date: January 2, 2026
Host: Shayle Kann
Guest: Peter Battaglia, Senior Director at Google DeepMind

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Overview

In this episode, Shayle Kann explores the intersection of cutting-edge artificial intelligence and weather forecasting with guest Peter Battaglia from Google DeepMind. The discussion delves into how traditional weather forecasting works, the transformative role new AI approaches can play, key technical differences between old and new models, data challenges, and what the future might hold if AI lives up to its promise in this field.

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Key Discussion Points & Insights

1. The Foundations of Weather Forecasting

Timestamps: 03:27–10:05

• Brief History:

  • Forecasting moved from informal observations to systematic scientific collection (approx. 100–150 years ago) and then to major government bureaus like NOAA (US) and ECMWF (Europe) in the 1970s.
  • Weather forecasts are considered a public good, funded by taxpayers, and offer strong economic returns.

• Current Process:

  • The physical basis is fluid dynamics—the atmosphere is a fluid modeled by the Navier-Stokes equations, approximated with enormous supercomputing resources (a practice called "numerical weather prediction").
  • These models are run at global scale (often at coarse resolution), then specialized through downstream post-processing for specific uses (e.g., your phone's weather app).

• Data Collection as an Ongoing Challenge:

  • Estimating the current state of the weather (from satellites, ground stations, balloons) is as difficult and important as predicting the future.

Peter Battaglia [03:53]: "The first step in weather forecasting isn't actually predicting. It's taking all the satellite data and all the stations and all the different observations and estimating the current state of the weather across the earth."

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2. Limits and Progress of Traditional Approaches

Timestamps: 10:05–18:09

• Linear but Significant Progress:

  • Forecast accuracy has gradually improved, due to better data, more compute, and smarter model tweaks.
  • Forecasts now span up to two weeks ahead with reasonable confidence—unimaginable 100 years ago.

• Hard Problems Remain:

  • Predicting precipitation (because it changes at small scales), and fine spatial details (like thunderstorms) is still very hard, even with today's best approaches.
  • Temperature trends smoothly, but storms and wind patterns shift rapidly at local scales.

Peter Battaglia [18:42]: "What doesn't vary smoothly is precipitation... It's everything happening at a finer scale, finer scale than a lot of our models even capture."

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3. How 'AI' Enters the Forecast: Old and New

Timestamps: 21:12–31:08

• First Steps:

  • Early "AI" meant using statistical techniques or machine learning (ML) for post-processing applications (tweaking NOAA/ECMWF forecasts for local use).

• Modern AI/ML in Forecasts:

  • Now, researchers train large, supervised learning models (neural networks) to directly predict atmospheric states from data.
  • Transformers and graph neural networks, popular in language and vision, are reshaping weather forecasting too.

• What's New with Transformers:

  • Traditional models (like convolutional neural networks) handle local, hierarchical information transfer.
  • Transformers enable models to "connect" data from distant points directly, mapping complex long-range spatial interactions—essential in weather, where, for instance, a hurricane's influence spans hundreds of kilometers.

Peter Battaglia [27:33]: "We use transformers and graph neural networks to capture the short and long range spatial dependencies...those interactions between what's nearby and what's about to happen next are what determine weather."

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4. Comparing AI for Weather vs. Language Models

Timestamps: 31:08–34:04

• Key Differences:

  • Language models (LLMs) predict "next word" based on textual sequence; weather forecasting is about predicting physics-based, Markovian systems where the immediate past is most relevant.
  • Weather data is spatially and temporally distributed, and predictions must be grounded in physical reality (not just probability distributions of words).

• Uncertainty and Structure:

  • Physics is deterministic, but lack of precise information (e.g., not knowing where a "butterfly flapped its wings") makes accurate prediction uncertain—a case of "randomness from ignorance".
  • The challenge: Weather models must predict millions of variables simultaneously—much more complex than predicting a single next word.

Shayle Kann [31:08]: "On one hand, a much harder problem than an LLM because you've got the entire physical world and the data is sparse...On the other hand, it's determinative in a way that LLMs are not."

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5. Data: The Lifeblood and Limitation

Timestamps: 34:04–40:16

• Data Abundance—and Scarcity:
  • Weather’s historical datasets (like ECMWF's ERA5 reanalysis) are highly valuable but limited by changes in data collection quality over time.
  • There's a ceiling; "new weather" only arrives one day at a time, limiting the amount of training data.
  • Researchers are turning to unconventional data sources: IoT devices, video doorbells, car sensors, even social media reports—hoping for more granular or novel input.

Peter Battaglia [38:37]: "Cars, your car...it's got a thermometer in it. It's got your windshield wipers...I get very excited about the possibility of using all these kinds of things."

• Universal Truth in AI:
  • "You're always data poor," says Battaglia: More data always enables better models, and this lesson has repeatedly proven itself after initial skepticism.

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6. The Future: What Improved AI Weather Forecasts Could Unlock

Timestamps: 40:16–43:53

• Potential Impact Areas:
  • Consumer Guidance: More precise, tailored forecasts could inform individual choices—what you wear, what you buy, when you travel.
  • Energy Sector: From solar and wind forecasting for grid stability, to better demand management as extreme events (like heatwaves and storms) occur with increased frequency.
  • Supply Chains and Logistics: Improved planning and operations.
  • Disaster Preparedness: Better and earlier warnings for cyclones, wildfires, floods—potentially enabling earlier, more targeted intervention.
  • Overall: Technology-driven progress could leave the next generation with a safer, more predictable world.

Peter Battaglia [42:30]: "I have a feeling that there's a lot of headroom...in how we, you know, plan how to operate our electrical grids, how we predict what the electrical demand is going to be...I don't think we've even begun to get into this."

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Notable Quotes & Memorable Moments

• On Fluid Dynamics and Complexity:

  "It's hard to even imagine, like, the scale of that type of predictive accuracy was...unimaginable 100 years ago."
  — Peter Battaglia [09:45]

• On the Data Bottleneck:

  "You're always data poor, right? Like, that's sort of the...story of modern AI is you're basically always kind of data poor because...just how well it scales with data. More data just means better models."
  — Peter Battaglia [39:42]

• On Why AI Can Outperform Traditional Models:

  "AI models...are capable of treating the hurricane as almost like a large macroscopic scale object that is moving because they can see all the structure of the hurricane..."
  — Peter Battaglia [27:33]

• On the Promise and Optimism of AI for Weather:

  "My team and I really believe in the power of technology that can have a lot of positive benefits. So we often are trying to look for ways that we can put technology to best use and leave our kids with a world that's better than the one that we grew up in."
  — Peter Battaglia [43:06]

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Structure & Flow

• 0:00–01:54 — Introduction & Sponsors (Skipped)
• 01:54–03:27 — Shayle frames the challenge and introduces Peter Battaglia
• 03:27–13:18 — Walkthrough of traditional weather forecasting: history, physics, and current challenges
• 13:18–21:12 — Discussion of improvement drivers and ongoing ML tweaks
• 21:12–31:08 — How AI/ML is now being applied, technical deep-dive into model architectures (transformers, GNNs)
• 31:08–34:04 — Comparing weather models to LLMs, physical determinism vs statistical prediction
• 34:04–40:16 — Data richness, its constraints, and the search for better input sources
• 40:16–43:53 — Forward-looking speculation: what improved forecasts could mean for society
• 43:53–End — Closing remarks

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Takeaways for the Uninitiated

• AI is not replacing fundamental physics, but augmenting and optimizing it—unlocking spatial awareness and efficiency unimaginable for traditional models.
• Weather forecasting will never be perfect, due to both inherent chaos and data collection limits—but each leap forward in model structure, compute, and (especially) data translates into societal benefits: resilience, safety, efficiency.
• The field is highly interdisciplinary, drawing in methods and lessons from language, vision, and even unexpected data sources; collaboration and data-sharing will underlie the next big breakthroughs.
• Optimism is high that AI will yield not only better forecasts but also a wide array of societal benefits—energy savings, improved disaster response, and even daily life conveniences.

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For more on this topic, explore Latitude Media’s coverage and follow the developing work on AI-driven weather forecasting from agencies and tech leaders like Google DeepMind.

Transcript

[A]

Latitude Media covering the new frontiers of the energy transition.

[B]

I'm Shayl Khan and this is Catalyst.

[C]

We don't really understand how the AI models forecast it, but they are capable of treating the hurricane as almost like a large macroscopic scale object that is moving.

[B]

They have like spatial awareness in a way that the old models didn't.

[C]

Yeah, that's. It's a really interesting area, I would say, of like sort of the science of how AI works to understand exactly how they see the world in that sense.

[B]

Coming up, where the winds are blowing. Using AI for weather forecasting.

[A]

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[D]

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[B]

I'm Sheil Khan. I lead the early stage investing practice at Energy Impact Partners. Welcome. All right, so here's a statement that I suspect would be pretty non controversial. AI will improve weather forecasting. It's obvious, right? And it seems like it must be true. I certainly would have agreed with that statement had you asked me before this conversation you're about to listen to. But to me the interesting question is why exactly like through what mechanism can AI improve weather forecasting? For that matter, how do we actually do weather forecasting today? And if it does get better, what are some of the likely outcomes that it will enable? It's an interesting set of questions for me for two reasons. First, weather forecasting itself is important to a whole host of other categories I care about, obviously resilience, but also energy and a variety of others, agriculture, et cetera. But also it's interesting because I think it's exemplary of a whole host of next wave applications for AI. LLMs are of course finding their way through everything that requires language. Now there are world models starting to show up to try to revolutionize robotics and things in the physical world. But what about things like weather, where we have used some machine learning historically, but can we do better with transformers and the new architecture of AI that we're seeing in other categories? Let's find out. My guest today is Peter Battaglia. He's a senior director at Google DeepMind, where he is leveraging the big brain inside the DeepMind to improve weather forecasting. Here's Peter. Peter, welcome.

[C]

Thanks. Happy to be here.

[B]

All right, let's talk weather forecasting. I want to start maybe by having you school me a bit on something that I realize I don't know, which is how do we do weather forecasting currently? And maybe a little bit of history. Have there been major shifts technologically in how we forecast weather historically? So maybe walk me through the history, such as it is, of how we forecast weather and then what do we actually do today?

[C]

Yeah, so I have to admit, I'm actually a relative newcomer to the area of weather forecasting myself. So we, we had gotten involved in this several years ago and it was sort of built out of a research program that was trying to model complex simulations, including fluids. And the Earth's atmosphere is a fluid. And one of the big challenges that we were sort of interested in exploring was modeling the atmospheric fluid, which is weather forecasting. So I should say that I sort of have gone through this journey of learning about weather forecasting. So the stuff that I'll say, hopefully it's accurate, but forgive me if I make mistakes. So I think my understanding about the field is that really a lot of the historically weather forecasting was very important for agriculture and other use cases that were very important for day to day life. But I think it was about maybe 100, 150 years ago that you had agencies or bureaus that were starting to do like marine forecasting or like kind of more systematic, like collecting observations systematically and treating it as a science. But then probably about 50 years ago or so, you started to see the emergence of like large government public weather agencies. So I think NOAA in the US was formed in the 70s. I think ECMWF, the European center for Medium Range Forecasting, was also formed in the 70s. And these are two of the big prominent weather bureaus. But most governments have a weather bureau and weather has traditionally been viewed as a public good. So this is something that they collect tax money and then fund their weather service. And the idea is that a lot of the, not only is forecasting the weather useful, just again, like, what are you going to have an umbrella or wear a coat. But for things that are more like there's a dangerous storm coming, flood, extreme heat, extreme cold, those types of things. And also a lot of sort of decision making like agriculture, energy, transportation. And I think in general, weather forecasting has traditionally been understood to be something that's a very good investment on a dollar. So the public tax money that's invested in the national weather bureaus has significant economic returns on those investments. So that's kind of, I think my understanding of the history of the kind of standardized or official weather forecasting business in a sense, maybe one more thing I can say about this, the way to think about the industry, I think my understanding is you can kind of think about the weather forecasting industry as divided into. It's almost like a pipeline really. There's the sort of government official weather bureaus that are issuing these like global forecasts that are predicting all sorts of weather variables, but typically more at like a coarser spatial resolution. And then you have this full, this like big post processing chain where they take the like sort of base forecasts and then they specialize them for different use cases. So for example, when you look at the app on your phone and you see the chance of precipitation that's not coming directly from noaa, that's coming from other intermediaries that are taking local weather station data and other historical information and trying to tweak it and improve it and make it especially useful for your use case. And you see that sort of in energy and all sorts of other applications of weather forecasting.

[B]

Is there a corollary to how that has worked historically to what's happening with LLMs today? Not to jump into the AI stuff too early, but just in the sense of is the global forecast, let's say Noah's forecast, is that a big mega model that spits out this one big forecast. And then what people are doing in the post processing world is saying, okay, I'm going to take that model, but then I'm going to. Fork. It is the wrong word, but I'm going to fork it and add a bunch of additional data into it to try to make it better at a smaller spatial resolution. I'm just trying to picture what it actually is.

[C]

Yeah, so I mean I didn't say much about where your actual weather forecast comes from in terms of like technically. So maybe if I say that then maybe it will sort of open the answer to that question. So again, fluids, like the atmosphere is a fluid and in physics we have fluid equations called the Navier Stokes equations. And they govern fluids at all scales, like from the largest scale structure of the universe, which actually turns. Turns out to also be a fluid, down to what's happening in your blood. Basically, there's turbulence that determines how your blood flows. And it has kind of important implications. Now, all things in between that, you have weather and stream flow and other types of things like that, all fluids. And what happens, engineers have figured out that, well, so the fluids are very complicated to simulate. So in order to simulate them accurately, they need to approximate the solutions so they can run them very large computers, because they're so complicated, they would never run on a computer natively. You have to sort of break up the computation and approximate certain things in order to actually model everything that's happening, for example, in the atmosphere. So that's called numerical weather prediction. The numerical is just saying that they're making a numerical approximation to these Navier Stokes equations. And traditionally, it's been run on supercomputers. So, like, a lot of the big supercomputer centers have either do a lot of weather forecasting or even built to do weather forecasting. And in many ways, it's been sort of a triumph of science and engineering that we've been able to, like, decade on decade, predict not one day, two days, but like, 10, 12, 15 days into the future. It's hard to even sort of imagine, like, the scale of that type of predictive accuracy was sort of unimaginable 100 years ago. People just didn't think, like, in two weeks, we can kind of know what the weather's going to be. That's crazy. That relies on knowing, like, what's happening on the other side of the Earth as the sort of prevailing winds carry the moisture and the temperature and all that kind of stuff.

[B]

And I imagine there's like an exponential increase in complexity the further out into the future you get, just because there are a variety of possibilities of what actually happens today. And each one of those needs to be taken into account, account when I'm trying to predict what's going to happen tomorrow and so on and so forth as you move into the future.

[C]

That's the butterfly effect, right? It's that a butterfly might or might not flap its wings, and then that will determine, like, a week later whether there's a hurricane or not, right? So the idea is that little tiny changes or little tiny effects or lacks, you know, missing effects will cause, could cause huge changes in weather over time. And that's exactly right. So the fluid, the atmosphere of fluid is thought to be chaotic, which means that that's sort of the definition of chaoticity. It's that little tiny changes can have huge large impacts later. That's what makes it so hard. And there's coupling across scales. Again, the butterfly flaps its wings. But then you have like up in the top of the atmosphere stuff is happening. Now that is exactly why it's very, very difficult to sort of, you know, find solutions to the exact equations that govern the atmospheric fluid. So we have to make approximations and we use supercomputers and we have all kinds of tricks. I should also say, like, another important thing to recognize is that when a weather forecast has been generated, the actual prediction of the future is only half of the process. It's the second half. The first half of the process is figuring out what the weather currently is. So if you. We have satellites and we have weather stations and balloons and ships and all sorts of information that are taking measurements of like what the weather is all over the earth. But again, using the butterfly as an example, you would have to know like where every butterfly is in principle to act to perfectly forecast the weather. So if you sort of think that through, you realize that it's not really weather forecast. Weather forecasting is always going to be fundamentally uncertain to some level. We're never going to be able to make perfect observations of the weather everywhere on earth with the precision required to perfectly predict the weather a week out. And so when weather forecasting, that's why you have a chance of rain versus like it's definitely going to rain, right? And you have like a range of temperatures, especially as you go out in time. That's again what makes weather forecasting so hard. The first step in weather forecasting isn't actually predicting. It's taking all the satellite data and all the stations and all the different observations and estimating the current state of the weather across the earth. Once we have that estimate, then we can make the prediction with the supercomputer. That second part is what our team and a lot of the teams in the field who are working on AI based weather forecasting have been especially focused on. But my guess is that over time we're going to other parts of the weather forecasting process, having more and more AI methods that are coming in and trying to advance them.

[B]

Before we get into the AI methods, it seems like we have generally, even pre AI, we've been getting, I mean, you tell me if the curve has been linear or exponential or flat, but it seems like there's been, I Don't know, fairly linear improvement in our weather forecasting ability for decades. Like we're getting more precise. We are also getting better at predicting further out into the future, as you said, like a week, two weeks, et cetera. To the extent that that's true, I'm sure it's all these things. But how much of the improvement that we have seen historically has come from, I don't know. A, as you said, just having better ground truth data on the current state of the weather. B, more compute, as you said, has been running in supercomputers. So we get more and more powerful computers, we can just run more and more complicated Navier Stokes equations or C, additional tricks basically that allow you to do better predictions without adding more compute.

[C]

Yeah, that's a great question. So I'll just admit I don't know the answer to that. I think all three contribute. So I can say on the first one, data, yeah, there's better satellites that are flown and there's more better systems for collecting balloon observations or these different sort of things. So we definitely are getting better data and we know that that improves the quality of the forecast. We are also getting better models. That's definitely true as well. We're building bigger supercomputers. They can operate at finer resolution. Just I think in the last less than 10 years, the ECMWF, which has the best weather forecast, they increased the resolution, meaning that they had finer detail and space in their forecasts and that allowed the forecast to be more accurate. So you see both like adding just raw compute power, but also improving the quality of the models and the approximations has also made, I think, a pretty dramatic impact. And I think that sort of blurs into your third category of like other tricks. I think in general you have without getting into the details of how the numerical models work, you can kind of think about them as a backbone that's making a sort of general prediction at a coarse scale. And then you have a lot of other parameterizations and trickery sort of under the hood in making finer and finer grained predictions and also updating the backbone to be consistent with the fine grain. And those are all being advanced sort of in parallel. And like the teams, these engineering teams and scientific teams are sort of working together to make these better. The last thing I would also say too is again, going back to that post processing part of the weather forecasting pipeline, it's not just again that these large NOAA and ECMWF and these other large agencies, it's not just that they are improving the forecast, it's that other parts downstream and post processing are improving what they're doing. So actually the first advent of like AI and machine learning in weather forecasting, or at least some of the earliest, was not trying to overhaul the whole weather forecast process itself, but using more and more statistical methods and linear regression and nonlinear regression, neural networks and other types of earlier machine learning techniques to improve not the base forecast, but the specific application. So maybe we can calibrate your chance of rain better if we have a slightly better downstream model.

[A]

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[D]

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[B]

So, okay, so we've been improving. We've been, you know, in more recent years, applying sort of the earlier versions of ML to continually improve. I guess I'm curious from your perspective, what the biggest gaps are. I mean, like, obviously we don't, we don't have the ability today to generate a perfect forecast three months into the future. Like it could always get better. But apart from just that element of it, are there any areas where you feel like actually there's like a real. It's really hard to do X. Is it like precipitation is a bugaboo or, you know, something else? Right?

[C]

Yeah, I mean, I think there's sort of two ways to answer that. You're always going to be limited by the quality of your data. So if you don't have good data about something, you're just garbage in, garbage out sort of thing. Right? So these models take an estimate of the state of the current weather and then predict what's going to happen. If your estimate isn't very good because your raw observations weren't very good, you're not going to get a very good forecast. So improving, just collecting more data and using the data you have collected to form a better estimate of the current weather, that's definitely going to always improve things. So that's, it's sort of a known gap right now. We don't know exactly what the ceiling is. We don't know, like, if we do this satellite or that station observation, how's it going to improve things? We might have an idea, but we don't always know. And sometimes we have to just test it out. But the other thing I would say is that you, you have different features of weather which are harder or easier to predict. So an obvious one is temperature. So temperature sort of very smoothly. Like if you look at a map of the temperature across the earth, it's sort of, you know, it's not, you know, up a mountain, it's me colder. And like in a valley, it'll be, you know, different. But it sort of varies smoothly. What doesn't vary smoothly is precipitation. So like a rap, you know, a violent thunderstorm that sort of emerges out of nowhere and there's like high wind and low pressure and all this kind of stuff that the, like, where exactly that front will be, like, where exactly the precipitation will happen. What exactly? The wind and these kinds of things are much, much harder because the, the detail of, like, it's, everything's happening at a finer scale. Like where, you know, even if you look at a radar map, you can see this. It's not like precipitation sort of very smoothly over the earth. You see, like, there's a little, you know, thunderstorm right there, or like there's a, you know, rain and it's. And then, you know, a few miles over nothing. So that type of very high resolution, complex, you know, patterns of precipitation, for example, wind as well, those are much harder to predict because you're effectively predicting a lot more information. You can't just sort of summarize it by saying, oh, every, you know, 25 kilometers or whatever, the temperature is this. And then everything else is just kind of interpolated in between. And you have a lot of stuff happening at a much finer scale, finer scale than a lot of our models even capture. And then we have to do secondary steps to try to resolve those finer details.

[B]

All right, so let's talk about AI then. I mean, you mentioned this is one thing, right? Whenever we Talk about AI quote fingers applied here, right? Like there is ML as a subset of AI as a related entity. We've been doing ML already. So I guess the first question that I have is, as you think about leveraging AI now and into the future for weather forecasting, what version of AI are we talking about? Like what version or versions are you actually, what is the, what are the actual capabilities and or model structures that are interesting here?

[C]

Yeah, that's a good question. So yeah, these days, I mean AI is a pretty catch all term. I find myself just using the word AI just to mean a lot of different things because I think it's kind of easier and usually people kind of know. But the way I would say it is the difference between AI and. So machine learning is sort of the statistical inner core of AI. It's trying to capture, taking data and trying to capture the patterns through a training process and then kind of use some inductive assumption like what we've seen in the past is going to be similar to what we see in the future. Modern AI I think is a broader family of things. It sort of involves agents and your interactions with them. And a lot of language models are often sort of associated with AI. What we use in our weather forecasting models and a lot of folks out in the community are using. As this field is advancing and this AI based weather forecasting is developing, we're still mostly using fairly traditional machine learning, supervised learning. So supervised learning just means you take a data set that has a pair of examples, an input example and a target example, and you train a model to try to take input examples and accurately predict the target examples. And so if you think about weather as again, like I said before, you're estimating the current state of the weather and then the next step is to predict what's going to happen next. A supervised learning method can be trained to do that. And that's what we're doing in our models and like most folks that I see are doing as well. And then the only extra step is that just makes one prediction, but then we feed the output of the model back into itself and then we have it make another prediction. So the output becomes the input and then it generates another output. And if you just sort of chain those steps together, you get your first input and then you get a sequence of outputs that represent future steps in time. So supervised learning we use a lot of these days in terms of AI architectures. Transformers and graph neural networks are what we use. People use convolutional neural networks, but I don't feel that neural network architectures these days tend to be the sort of exciting part. It's usually more of, like, the training and the sort of data, how you handle the data and that kind of thing.

[B]

So. But you mentioned transformers, because, I guess, like, if we had been having this conversation five years ago. Right. I imagine that you still would have told me about supervised learning, for example. Right. Like, that wasn't. That's not new. Transformers had been invented by that point, but, like, had not been, you know, broadly applied the way that they are today. So what is it that, like, this new wave of AI unlocked by things like, by transformers and convolutional neural networks and so on, like, what does that enable above and beyond what you would have been able to do five years ago?

[C]

Yeah, that's a good question. So the way I look at it is, so transformers are very similar to graph neural networks. Both of them are. So actually, let's take this back. We used to use often convolutional neural networks, and the idea here is it learns a little function that's sort of local in an image, and then it sort of applies that same function everywhere. And then you stack up sequences of these layers, and that eventually lets you, like, the information on one side of an image communicate with the information on the other side of the image. Because the. It's a hierarchy, a transformer architecture allows you to make a direct connection between the information on one side of the image and the other side of the image, the same way the graph neural network does. And I like to think about it like graph neural networks, because what it's like saying is, well, in a graph, you have nodes and you have edges or connections between the nodes. And a longer connection between nodes is for nodes that are farther away, and shorter connections are for nodes that are closer. So if you use the graph neural network analogy to describe the older convolutional networks, it's like the graphs are all small. They're all kind of. Everything's kind of close. It's like nearby in an image. Graph neural networks allow you to choose how far away you want information to interact. And in transformers, it can be understood as a graph that has connectivity across any spatial scale. So in language models, the way a transformer works is it says, when I want to make predictions about the next word, I want to be informed by the most recent word, but also every word that has happened in the text before. And that's important because in language, the next word is not predicted by just the previous Word, it actually is predicted by stuff that happened earlier in the sentence or in the paragraph or in the book. And so the ability to make, to have information across large spatial scales that interact with one another, that ability allows you to, it opens sort of new patterns of computation and allows you to represent functions that have traditionally been harder to represent. But it allows you to make better predictions of the next word or the next. In our case, you can kind of think about as the next spatial point or a far away spatial point. And that allows the model to be more flexible and capture richer functions.

[B]

There's a good comparison there. What do you think of as being. I guess you just described something that is similar about what you can do in weather forecasting thanks to a transformer architecture to what you can do with large language models, which is what most people are going to be most familiar with in the new wave of AI. What's different?

[C]

So that's a great question. I think what's interesting is that the way that so in language the text is understood to be is treated as a sequence. It's like token, token, token. We are also modeling sequences in weather, but we're not allowing our models to look too far back in time because weather is actually different from text in a fundamental way. In fact, most physical processes are. They are what's called Markov in that the most recent state of the system determines the subsequent state. Like I said in text, that's not the case right now. I'll just pause. You didn't know what word I was going to say next. It depends on the context, a bunch of words behind it or earlier. With weather forecasting in principle, if you know exactly what's happening right now, you can fully predict what's going to happen next. You don't need to look further back in the past. So we actually use transformers to not to model the spatial, the interactions in weather over time, like the sequence of text, but in space. So in text you actually don't have a sense of spatial structure, right? You just have one sequence of text. It's just word, word, word. And when you read you just see word, word, word. In weather you have spatial structure. You have weather all over the earth at the same time. And it's all, especially the close weather sort of determines and can be used to predict what's happening next. Our current location, we use transformers and graph neural networks to capture the short and long range spatial dependencies. Those interactions between what's nearby and what's about to happen next are what determine weather. And that's how we make these predictions. But one thing I should also add is that, similar to how I was saying earlier that it's impossible to measure everything that's happening on the Earth in the fine detail in weather. You have to make approximations. These models do too. And this actually brings us to a very fundamental difference between how AI models are making their predictions and how traditional models are. So AI models can take the statistical structure of weather patterns. So, for example, if I'm looking at a hurricane that's traveling over the Earth in a traditional model, the way it simulates that is it in very fine detail. It kind of figures out, like, what's the pressure and the temperature and the wind and the moisture and what are those things. What's going to happen next is determined strictly locally. AI models, because they can look at a much larger spatial range, they can use what traditional methods use, or they can use other approaches. Because when you look at a hurricane, it almost looks like an object sliding over a globe. Right. That's not how a traditional model models it. And we don't really understand how the AI models forecast it. But they are capable of treating the hurricane as almost like a large macroscopic scale object that is moving because they can see all the structure of the hurricane and they can see sort of what's happening in the recent past.

[B]

They have spatial awareness in a way that the old models didn't.

[C]

Yeah. And we don't know. It's a really interesting area, I would say, of like, sort of the science of how AI works to understand exactly how they see the world. In that sense, it seems like to.

[B]

Me, on one hand, a much harder problem than an LLM because you've got the entire physical world and the data is sparse, as you said, and there's lots of complex interactions. On the other hand, it's determinative in a way that LLMs are not right. Like, there is no correct next word necessarily right. There's like a. It's a best guess as to what the best next word should be. But in the case of weather forecasting, there is a correct, correct prediction to make, and there is a universe of historical data that you can draw upon to do it. So I'm back and forth on whether this is a harder problem or an easier problem than making a really, really good LLM.

[C]

Yeah, I think you'd have. I think depending on where your allegiances lie, that'll probably be. I think it's probably more of like an opinion question. But yeah, I think you're absolutely so the way I think so. It's the one thing I just would like, I might say it a different way, is that it's still, like I said before, it still is fundamentally uncertain from the standpoint of the information that's available to the model. Now, yes, the physics is truly deterministic underneath, but because the model doesn't again, see the butterflies or see the little fine scale stuff, from its perspective, it actually is a random process because if it doesn't know whether the butterfly flapped its wings or not, how could it know whether the hurricane is going to form or not right now? So from the perspective of the information available to the model, it is also an uncertain random process to some extent. But yeah, I think what you're saying is exactly right. So I think the underlying structure of text is random in different ways. Again, I can. I'm going to pause and then I'm going to say a word. Furniture.

[B]

Right.

[C]

Like, weather doesn't work like that. Right? Like it doesn't, it doesn't just have something pop into its head that's completely different than what's historic, like, present in the historical record, like you pointed out. So I think that the structure of the uncertainty is different in weather. It's. It's more constrained in a way. So in text, it's. You can imagine, you know, if you're. Same thing with like video, you're watching a movie and someone's going to come through the door, you have no idea what they're going to be wearing as a shirt it could be wearing. Blue, red, anything. There's no way to predict it. In weather. You can always have some idea. You just don't know the fine details. On the flip side, weather is an extremely complicated process. This fluid, chaotic, fluid system has interactions from small scales to large scales and is happening all over the Earth at once. So in some sense, instead of just predicting the next word, you're predicting millions of variables at once. So I think you can kind of like, it's probably better to have this as a debate over beers with your friends in their LLM lab rather than something that can be adjudicated just on the basis of these things.

[B]

I guess there's one other question on this sort of comparison to LLM world. Notoriously, the big LLMs are trained on the Internet, right? Your training dataset is all words on the Internet. And so that's one of the reasons that they've been among the first sort of major AI models in this new wave to commercialize is because there is this gigantic body of training Data that you can draw upon. Now we're hearing lots of folks who are in robotics world, for example, facing the challenge of there just isn't an equivalent data set. You can try to train on YouTube videos or whatever, but it's not quite the same thing. In the case of weather forecasting, it seems to me like in theory you have an incredible historical, you can look at every historical weather measurement, right. If you had access to that data, if NOAA does, right. Like every input data point they ever took historically and then the subsequent next measurement which dictated what happened after that, I would think that would be an incredibly rich training data set. Am I. Well, two questions. Am I right about that and is that actually available?

[C]

Yeah, that's a good question. So the first thing I would say is actually it's not just language, right? Like the first big visual neural networks were built came out of after imagenet, a big corpus of image data, the first language models. Like even a decade ago they were starting to build large text databases, protein folding, big databases of proteins. So actually you see like AI and machine learning are still very, very sensitive to the availability of high quality data and large amounts of it. And you kind of like, you know, one of the best ways to advance the field is to go collect high quality data and make it sort of standardized and available. So for weather I think that there's, there's sort of good things, there's good news and bad news. So for one thing is we, we were very fortunate when we started. I think all the, all the folks who are working on AI weather forecasting have benefited tremendously from work that was done by the ecmwf, the European center for Medium Range Forecasting. They built this Data set called Era 5 and they'd been building these. Era 5, I think was the fifth generation of the Era data set. It was a record of Earth's weather going back for decades. And I think it was originally released going back into the 1979 and then they actually opened it back into the 60s and they didn't design this data set to be supporting machine learning. I think it was more to just have an authoritative record of the climate on Earth over year on year. And it's at a six hour resolution in 25 kilometers or spatial resolution. So it's very, very rich. It just happened to be perfect for machine learning, for weather. And it was just a really well curated data set. The folks at ECMWF are just brilliant and sort of organized and systematic and they had made this available and it allowed a lot of people to sort of stand on their shoulders and build great new AI methods. Now one thing is though, because we had different satellites and different stations over time, it's not actually all the data set is standardized, but it's not derived from the same underlying observations. So the quality going back in time actually gets worse in terms of it's not as accurate of a record of weather just because again, as I said before, the input data wasn't as good. Now the other thing that's not great about weather is that weather takes a while to happen. So we have to sort of just wait for more weather data to like, for more weather to happen, to get more data. Right. Like the weather data we have so far, we're sort of stuck with it now. Like tomorrow we're going to have one more day of weather data. But like when our models are taking six hour steps, we have to just kind of wait. So you're sort of like, we got a lot of data, but at the same time there's not much room to get more of the same kind of data. And I think we and a lot of others are now looking to more unusual or underexplored sources of data to support building richer, better models.

[B]

Right. Is there a distributed network data, in theory? If you had access to, I don't know, everybody's iPhone, there's probably a bunch of sensors in the iPhone. Presumably you could pull something from that that would have some signal for you.

[C]

Yeah, yeah, you can geek out on all these things. Like the way my favorite one is, like I have a video doorbell, it sits there and watches weather all day.

[B]

Right? Right.

[C]

Cars, your car. Right. It's got a thermometer in it. It's got your windshield wipers. Some of them now are, they're rain sensing. So it's sensing rain or it's like the lights go on automatically when it's dark, so they're sensing whether it's cloudy or these kinds of things. So I get very excited about the possibility of using all these kinds of things. The other one that's even weirder is people go on and they tweet about the weather or they talk about the weather on social media. And those types of observations, those are still observations. We don't know if they're very good, but there's actually a very wide range of pretty unusual underexplored data sources. But even before we get there, I think you can start to think about there's a lot of companies that are trying to build very cheap weather stations. People can put them on their roof. Like, these kinds of things could really help both with the kind of core weather forecast and probably a lot of the applications that people want to use weather for.

[B]

So my takeaway from that is that despite what I said, there being this, like, amazing historical record of every weather measurement that's ever been taken, you still feel kind of data poor, right? Or like training data poor.

[C]

I guess you're always data poor, right? Like, that's sort of the. The story of modern AI is you're basically always kind of data poor because. Because it's one of the most incredible facts about modern AI is just how well it scales with data. More data just means better models. And you know, I was a person who was very skeptical of this. I didn't think that it was going to scale like this, and I would make my sort of logical arguments, but it turns out I was wrong. And I think a lot of people were wrong. And the folks who really understood that data could really add value even at extremely large scales were right and pursued that course and brought us to where we are today.

[B]

Interesting. Okay, so I guess I want to finish by talking about what might come, like, if you draw a line forward a few years into the future, you pick your time, three years, five years, 10 years, whatever it is, and you and everybody else who's working on AI weather forecasting succeed. Where might we be? What might be possible in a few years that's not possible today?

[C]

Yeah, I mean, I think that. I think there's a lot. Weather affects everything and it has different things. Energy is obviously very sensitive to weather. Some things are only loosely affected by weather. One thing I would like to see, and I think is very exciting, is a wider range of use cases of weather. For example, we know that even people make different choices about what to put in their refrigerator or what clothes or whatever. These different choices are going to go on a trip, what they expect the weather to be. I think that you can start to make more subtle and informed guidance and suggestions for people on the basis of more accurate weather forecasts. And that's kind of like at the consumer level, but I also think that at the kind of, you know, industry level, there could be a huge opportunity. So, for example, in energy, you know, we see there's, you know, if you have a wind farm or a solar farm, you're making forecasts about the weather and then you're kind of using that to figure out, like, how, you know, if you're going to have energy to sell and how you price it. But I have A feeling that there's a lot of headroom, a lot more to be gained in how we, you know, plan how to operate our electrical grids, how we predict what the electrical demand is going to be. Is it going to be hot, is it going to be cold, is it going to be humid? Or the air is carrying more mass, which requires more energy to heat or cool. I think that we just haven't really scratched the surface of the opportunities. I think that supply chains and logistics and even just lots of choices that driving and these types of things I think really could be better informed by better weather forecasts. And I don't think we've even begun to get into this. And we need to see the quality of the forecast get better and more customized to these use cases to. To start unlocking that. The other one obviously is just better crisis. Handling crises, like some of our recent work on tropical cyclone forecasting, I'm very proud of. And we think hopefully with better forecasts, we can warn people earlier, more accurately. In some cases where there's a disaster like a wildfire, maybe we can even go and intervene and try to stop it before it gets completely out of control. So again, this is not something that we can do today. But I'm very optimistic that, and in general, maybe this is coming through, but my team and I really believe in the power of technology that can have a lot of positive benefits. So we often are trying to look for ways that we can put technology to best use and leave our kids with a world that's better than the one that we grew up in.

[B]

All right, Peter. This was super interesting and useful for me both in the context of thinking about like weather forecasting and just like understanding like the where AI, well, how AI is getting applied in various industries, what the challenges are, what the opportunities are. So really appreciate your time.

[C]

Sure. Yeah. It was great to have to be here. And your questions were great too, by the way. It was really fun.

[B]

Peter Battaglia is the Senior Director of research at Google DeepMind sustainability program. This show is a production of Latitude Media. You can head over to latitudemedia.com for links to today's topics. Latitude is supported by Prelude Ventures. This episode was produced by Daniel Waldorf. Mixing and theme song by Sean Marquand. Stephen Lacy is our Executive editor. I'm Shayl Khan and this is Catalyst.