Ep 378: How AI is Transforming Climate Forecasting and Weather Prediction

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The weather and climate is something that impacts all of our lives, especially here. Recently in the US we've had a. A few devastating storms that have really caused a lot of havoc. So can generative AI help us better prepare and predict for these storms? And how can it improve our climate in the long run? Well, that's what we're going to be talking about today on Everyday AI. I'm very excited to have a leading expert from Nvidia to help us go over all of this very timely and very important topic. All right, well, what's going on, y'?

All?

My name is Jordan Wilson and welcome to Everyday AI. Before we get started, I have to give a shout out to our partners at Microsoft. So the Work Lab podcast from Microsoft is made for leaders who want to understand the future of work. It offers expert insights on everything from how to approach digital transformation to what it takes to thrive in the AI area. That's W, R, K, L, A B, no spaces, available wherever you get your podcasts. Speaking of podcasts, well, you're listening to it right now. Welcome to Everyday AI. This is for you. It's for me, it's for all of us, to help us better understand generative AI. So if you haven't already, if you're listening, whether on the podcast, the live stream, or reading about this on the newsletter, well, please go read that newsletter, go to your everydayai.com and sign up for that free daily newsletter. All right, so I am super excited to talk about how AI and generative AI is impacting the weather and climate. But first let's go over quickly started how we do every single day by going over the AI news. So Microsoft has introduced new AI tools to help revolutionize healthcare efficiency. So Microsoft has unveiled a series of advanced healthcare data and artificial intelligence tools at improving workflows for clinicians and reducing burnout in the industry. So the new offerings include a collection of open source multimodal AI models that can analyze diverse data types such as medical images, clinical records and genomic data, allowing healthcare organizations to develop tailored applications more efficiently. So Microsoft has indicated that many of those tools are still in early development stages or available for preview testing, emphasizing the importance of validation by health care organizations before wider rollout. Hey, speaking of Nvidia, Nvidia, their stock is surging after Black. Their new Blackwell GPU has reportedly sold out for a year. So Nvidia's stock is experiencing a significant uptick following the announcement that demand for its upcoming Blackwell GPU has exceeded supply, resulting in a complete sellout for the next months, according to reports. So this surge in interest obviously reflects the growing appetite and advanced GPU demand in various sectors. So this announcement has led to a noticeable increase in Nvidia's stock price where it is near an all time high. And keep an eye out whether today or to today or early next week where, hey, Nvidia might be passing Apple to become the most valuable company in terms of market cap. To reclaim that title, we always cover who's, who's the most valuable, you know, company in the world here on the show. So our last piece of AI news. Tesla has unveiled a new line of AI powered vehicles last night at their We Robot event. So they announced the Cyber Cab. And the Cyber Cab is designed to operate entirely without human intervention, showcasing Tesla's advancements in AI and self driving capabilities. So this vehicle aims to create a new standard for ride sharing services. So Elon Musk revealed the Cyber Cab and that it will cost reportedly below $30,000 and is expected to launch by 2026 cyber. So this price point could make autonomous transportation more accessible to a broader audience, potentially transforming how people commute. They also announced the Robo Van, a larger autonomous vehicle capable of seating 20 people, which can be adopted for various uses like a school bus, an RV or for cargo. All right, there's a lot more where that came from, but make sure to go check out the newsletter for that. All right, I'm super excited for our guest today. I think the topic of weather and climate is very important for us all to pay attention to. Not only that, but how can generative AI actually change it? So I'm very excited to have on the show. Please help me in welcoming. There we go, we got him, Mike Pritchard. And Mike is the Director of Climate Simulation Research at Nvidia. Mike, thank you so much for joining the everyday AI show.

Thanks so much for having me.

All right, hey, and we're, we gotta shout out those of us that, you know, join at like 5:30am West coast time. So, you know, Mike, thank you for that. So, so real quick, kind of before we dive deeper into this topic, can you tell us a little bit about what your role entails there at Nvidia?

Sure, yeah. I'm, I'm the Director of Climate Simulation Research. It's my great privilege to have been brought over from academia to Nvidia to lead a team of four or five AI researchers and climate domain experts who are working as part of a broader initiative called Earth2 to try to do fundamental research where AI has the potential to transform the Earth system modeling stack from weather to climate, from near term to future.

Yeah, and I was at, you know, partnered with Nvidia for the GTC conference and I was on the floor when, you know, they put Earth 2 up on the screen and I was like, mind blown by that. But, you know, maybe. Can you just explain, Mike, a little bit? Because I think a lot of times people think Nvidia, right? And they think maybe traditional, like traditionally gaming, right. GPUs for gaming. And now, you know, everyone knows Nvidia for, you know, providing those chips that power everyone else's generative AI systems. But how does, how did Nvidia kind of get involved in weather and climate and, you know, what is really your goal in that space?

Oh, what a great question. You know, I had the same impression. I'm an academic. I've been a professor of climate science since 2013. And my first experience of Nvidia was through using their supercomputers, which changed my life. You know, a GPU powered supercomputer that the Department of Energy bought eventually allowed me to do climate simulations 100 times more ambitious than I was used to. But what I've come to discover since actually joining the company is there's a research organization that just has fundamental AI research on cutting edge generative AI, including on some applications that are important to the company. And it's Jensen Huang, our CEO, that's declared climate is something very important to the company. He actually gave a great keynote at the Berlin Climate Summit last July. I encourage anyone to watch if they're interested in our positioning. There's three fundamental missions of Earth 2. The first is to accelerate global cloud resolving modeling technology so that we can perform higher resolution simulations of the Earth's climate than has ever been possible. And using AI to enable more interactive experiences of the output and then also to produce stunning visualizations. And I guess you saw one at the GTC conference in March. I hope that gives you a taste. I should also say Nvidia's got a long history of fundamentally accelerating weather and climate codes that goes back at least a decade. And I've been amazed to discover how, how Nvidia engineers have been inside many of the code bases I was familiar with in academia.

Yeah, and you know, I'm sure there's going to be people tuning in today who have experience in this field. But I think for the most of us, when we think weather, right. We just, you know, turn on the weather channel. Or maybe, maybe that's aging me, right? Like, I always like listening to my local meteorologists here in Chicago or, you know, opening up apps. But maybe. Mike, can you just talk in general? Right, so even, you know, outside of Nvidia or not. But how does the weather actually work before we can actually understand how generative AI and all the research that you're doing impacts it? Like, how do, like how do we get all this information? And then we'll go a little bit into how Nvidia is changing that.

Sure. Yeah. Great. Yeah. Yeah. Behind your phone app, there's an incredible enterprise going on. You know, every, it begins with observations. Every six hours, there's a coordinated launch of balloons worldwide that sample the atmosphere. And these data streams, along with satellites and aircraft sensors and ground based sensors are fused into a best estimate of the initial condition of the atmosphere. That's used to produce predictions to launch predictions from. And the predictions are done by physics models, you know, computational models that solve equations like Newton's law, F equals MA on a rotating sphere for an incompressible gas. And these predictions into the future, they're chaotic, they're stochastic because of the butterfly effect. So you have to do many predictions of six hours from now because things could turn out differently. But it's a very computationally intensive exercise. And your average meteorologist is looking at the results from a half dozen to a dozen different national and international weather models and making their own nuanced decision about what to tell the public based on their familiarity with those models and their biases and their behaviors and their observations from the sensor network of what's going on. So there's a lot going on behind the scenes of your phone.

Okay, that's super helpful. So essentially, you know, all the meteorologists and you know, these, these organizations that help us better understand the current weather, they're looking at many different models, I guess is, does Nvidia have one of those models that everyone else looks at or is maybe Nvidia's technology helping all of those other kind of companies improve their respective models? I hope that makes sen. But you know, I, I love weather and you know, I'm, I'm trying to really explain it here for everyone else to see how Nvidia's work is currently, you know, helping and how it will change?

That's a great question. Yeah, definitely the latter. You know, in the research Org, we're trying to seed Technologies that we hope will transform the business of weather prediction that will improve it. And we're already seeing some, some uptake. It's fascinating. You know, I'm a climate scientist, so I've mostly been concerned about 20, 20 to 100 years from now. But when I joined Nvidia is when I discovered what was going on with the AI revolution in weather. And, you know, Nvidia is proud to have built one of the first global AI weather models that could feed off the native resolution of one of the highest quality data sets worth training on. And I think it's amazing to look back two years later, this seed technology has now disrupted the world's premier weather prediction agency, the European center for Medium Range Weather Forecasting, who has developed an AI companion model for their classical physics model that's starting to beat their classical physics model and is being served to the public alongside their standard physical predictions. So this is happening all around the world in the private sector, in the public center, large meteorological agencies are taking note of the potential of AI to beat physics at predicting the weather.

Wow. To me, that's wild, right? But can you even maybe explain specifically how does generative AI change this? Right, Because Nvidia is one of the companies leading the generative AI revolution in many different ways. Not just from a hardware perspective, but, you know, the infrastructure that you help provide other companies as well. But how does generative AI change what can be done with, you know, climate, you know, climate prediction, weather? How does generative AI change at all?

Oh, thank you so much. You're gonna have to keep me on the rails, Jordan. Case I nerd out too hard here.

Hey, that's, that's what this is for, where like we're here to nerd out a little bit. Right?

Okay, good. Okay. To me, as a physicist, generative AI is so important because it can produce stochastic predictions. They can produce an ensemble of probabilistic outcomes and, you know, so, so for the topic of weather prediction, we're limited by the data. And you mentioned Stormcast. This is our research paper. I'm so excited about. A challenge we faced in building Stormcast, which is trying to become a high resolution national U.S. weather model, is that the training data is available only at low frequency in time, so about an hour. That might seem high frequency, but your average storm, an individual cumulus cloud, lives for only an hour or two. So that's not a lot of data to understand the process that's going on inside the storm. And especially if you're predicting those high resolution Physics on an hour timescale, there's many possible outcomes of the next hour ahead. And so generative AI is a way to respect the fact that the physics are stochastic, that the prediction is probabilistic, and to produce an ensemble of equivalently plausible outcomes for an hour ahead. And that's deeply important to the business of weather and climate prediction, this ability to do probabilistic prediction.

Okay, so you've kind of mentioned Stormcast a little bit. Can you explain this piece of research by Nvidia Idea? I'm personally fascinated by it. So yeah, can you explain to us what, what is a stormcast?

Okay, thanks. Yeah, so, so I mentioned that there's many different meteorological models out there in the world that are served to you and there's different flavors of them. So there are planetary scale models that cover the whole planet with 25 kilometer resolution, you know, about the scale of a county. And then there's national models. Countries that can afford it, like the US have high resolution national models where the resolution is on the order of a kilometer and it's too computationally expensive to cover the whole planet, that much resolution. But countries cover their own domains with that much resolution. And for two or three years now, these global models, these course resolution models, AI has completely disrupted them. And I've been waiting for this moment when AI might also disrupt the business of these high resolution models where the physics are, are quite different. The physics of thunderstorms, the physics of cloud formation, the physics of turbulence. And so that's what Stormcast is. It's a solution to trying to build an emulator, an AI emulator of the US National Weather Model. As a proof of concept, we only study a patch of atmosphere in the central U.S. 900 by 900 kilometers. But it performs predictions in real time that you can compare the scale of these predictions against the US national weather model. And it's got as much, if not sometimes more skill, despite being a prototype in its infancy as the National Oceanographic and Atmospheric Administration's high resolution rapid refresh model, which is what served to you if you use Windy, for instance, on your phone to get your aviation outlook. So that's quite exciting. It's a milestone in AI at this high resolution prediction scale.

Can you. Because I'm still trying to wrap my, my brain around this, right? How, right? How can you know, this piece of research from, you know, a private company already be comparable to these models that have been used, I think, for decades and you know, that are overseen by the government? Like, how is that Even possible. And what does that also mean for the future of weather prediction?

Well, those are deep questions. I'm sure I won't have the answers to all of them. It's important to recognize that a big part of how is the huge investment governments have made in producing very high quality, quality controlled data sets that are in the public domain. And so we should all be grateful that in the US our national agencies like NASA and NOAA have a mission to serve high quality data to the public in the open domain. And so that's essential. I mean, the training data, everything flows from the training data. But a big part of the how I've realized, especially coming from academia and my former life, are the personnel I've never met. People like I've met at Nvidia, you know, professional deep engineers, professional gen AI research researchers. The people, the sort of people that can take a massive data set and unclog all the bottlenecks that can prevent you from training efficiently on it at scale and mining its essence. And it turns out it is remarkable. You're right to point it out. You know, teams of five or six people in a few separate companies, Huawei and Google and DeepMind Nvidia, have managed to produce convincing AI weather simulations that have competitive skill with these huge government institutions. Yeah, so it says something deep and gosh, I can't wait to see what it's going to look like in five years. Once the government institutions with all of their domain expertise and history and institutional knowledge of the available data streams also embrace this technology, it's beginning to happen.

Wow. Okay, so I, I want to dive in a little bit deeper on that. But just real quick, we have to give a quick shout out to our partners at Microsoft. So the Work Lab podcast from Microsoft is made for leaders who want to understand how work is changing. Effective leaders adapt. They stay on top of trends. They embrace any edge that they can get. Effective leaders also know that the key to understanding artificial intelligence is to get better at understanding human intelligence. So for real world lessons and actionable insights to help you stay ahead, check out the Work Lab podcast. That's Work Lab, no spaces available, wherever you get your podcasts. All right, so thank you to our partners there at Work Lab. So let me just get right back to this. So you said, Mike, you, you kind of said, hey, can't wait to see what's going to happen in five years. Well, what does that mean? Right? So I know for people like you, researchers, people who are working in and around the field, I'm sure it Means one thing, what does this mean for everyone else? Right. Like is every single, you know, you mentioned, you know, meteorologists, they use all these models. Well, essentially the stormcast research just help all these models become so much better. Or how does this actually impact, you know, the average person when it comes to weather and climate?

Oh wow. Yeah, there's a, there's a few future. I mean we're daydreaming now here. If we're entitled to daydream, there's a few scenarios.

Yeah, that's what we're here for.

Yeah, but I mean what one possibility is that AI so disrupts the business of data assimilation and the initialization of weather models and all the coordinated mechanisms. I mentioned that weather forecasting becomes more on demand, that you know, users can launch their own forecasts at any time they'd like rather than waiting for the institutions to launch their forecast on the hour or every six hours. Another possibility is that there, there becomes a biodiverse universe of weather models that are niche that are fine tuned to individual stakeholders. You know, people care about the weather for different reasons and in different regions. And weather models are not necessarily calibrated to individual stakeholders needs. So there could become a.

A larger.

Number of weather models that have ever existed before if the technology becomes fine tunable and differentiated the way some large language models have for instance and. But yeah, I think mostly the biggest thing though is the speed. So Once trained, these AI weather models are 1000 times faster than the physics calculations and that changes everything, you know, for the history of weather prediction. There's been this tension between how much resolution can I put into my weather forecast? I'd like more because hurricane forecasts are more realistic when there's more resolution versus how many ensemble members can I simulate? I'd love to simulate a thousand realizations of the hurricane to know what the worst possible one is to hope for the best and plan for the worst. But those things are intentioned because they trade off against a computational budget. And when you unthold the compute by a thousand with AI, then you can afford massive ensembles. And I mentioned the stormcast technology for the regional scale. And the high resolution is new, but what's more mature now are the global AI emulators for the world. There's another couple of research papers that we've done with UC Berkeley that I'm really proud of that's proved this massive ensemble capability where you can do 20,000 realizations of last summer, 2023 to get counterfactuals on a warm summer to understand the statistical drivers of low likelihood, high impact extreme events, which are so important to calibrating our risk to extreme events and to understand the hazard of future events by knowing what our exposure is today. So these are some of the ways that I think just the compute can be transformative.

Yeah, so, so Mike, at the very beginning of that answer there, and I want to make sure that I fully understand this and our audience does too. So, you know, you said that kind of the, the research that Nvidia has done with Stormcast could enable, you know, weather forecasting to become much more on demand. So you said right, now these forecasts, you know, that all meteorologists and everyone else use come out every hour. Right. And now it could be on demand or people could fine tune these, you know, future models to, you know, be more personalized for them. Is. Is that correct? And then if so. Right. How does that actually change? Right. You know, I kind of started the show by, by talking about some of this devastating weather that we've seen recently with, with the hurricanes and the tropical storms, especially in the southern US Here. How does that change things in the future? Is it just much more accurate and faster weather predictions?

Great questions. Yes. So I think we're all hoping that eventually there'll be more accuracy for extreme events like hurricanes. You know, there's already a glimmer of hope that the, the global course resolution emulators are producing more skillful tropical cy tracks than conventional physics calculations. So that's quite encouraging. The holy grail is the intensification. I mean, we've seen just like how perilously the latest hurricane intensified over the warm Gulf of Mexico. So that's a ways off and there's a lot of research to do. But to the extent that, you know, in physics calculations, we have to cope with a lot of problems of how we formulate the grid and how we represent unresolved processes that can lead to intensification. The ability of AI to be completely agnostic to that and just feed off data and nonetheless be skillful is just a philosophical reason to hope that we might penetrate the skill barrier in intensity forecasting. And that's just so important to everybody that wants to plan for an incoming hurricane to get an accurate, reliable intensity forecast in terms of the on demand nature. Again, we were daydreaming and painting into the future here. You know, Stormcast has proved the prediction problem can be tackled with AI, that we can get competitive storm skill, convection skill, compared to the national weather model. The initialization problem is a different beast, but it's the marriage of the two. And there's people working on this and working on it in Nvidia too, trying to use AI to make the process of how you initialize these weather models more seamless, in addition to changing the prediction from physics to data.

Yeah, and it's fascinating to hear about Mike, and to have you explain it in such a simple way. You know, my, my brain's churning and I'm sure everyone else's is as well. You know, even if we zoom out, right. If we talk a little bit more broadly about the climate.

Right.

So you helped us better understand, you know, Stormcast and how that can help with more real time and accurate weather predictions. But what if we zoom out just to the climate in general? How is generative AI and the work that you're doing at Nvidia helping us look at climate a little differently? And what does that mean for everyone?

Well, thank you so much. Yes, yes. I'm a climate scientist. I've spent my career trying to investigate algorithms with breakthrough potential for climate simulation. Because it's a horsepower, limited problem, simulating the whole planet for 100 years hundreds of times to look across what if scenarios of what emissions might do. That's just a gargantuan computational problem. And you have to make compromises that feel unsatisfying in the number of processes you're resolving. And so AI could short circuit Moore's law. It could. You know, when you double the resolution of an atmospheric model, Jordan, the computational intensity goes up by eight because you have to double, you know, multiple dimensions of space. And then also the time step has to shrink to keep the simulation stable. So getting high resolution simulations is actually really, really difficult. And if AI, if you can just outsource the high resolution physics to AI, it changes everything. It means that you can, you can afford high resolution physics today. So I worry about clouds a lot when it comes to climate. I live in Southern California. I drive by the stratocumulus cloud. We call it the marine layer. It looks like a little gray haze on the horizon when you're at the beach. If it comes in, you're annoyed because it makes you cold. But it's just the edge of a massive mirror of clouds that you can see them out your window if you fly from San Diego to Hawaii for half the trip, because a massive sheet of clouds, they reflect a lot of watts from the planet that keep the planet cooler than it would be otherwise, like ice sheets. And if those clouds shrink like ice sheets and dissipate, revealing darker, more absorptive surfaces. It amplifies global warming. It amplifies all the hazards that go with global warming. If they thicken up and become brighter, which they might, unlike ice sheets, they could counteract the hazard. And we actually don't know the answer to that question with high confidence because high resolution is the key to simulating those clouds faithfully. So the same, you know, the same business of AI short circuiting Moore's law, removing tensions that have existed computationally in weather and climate simulation is the reason to be excited for the climate problem. Now I didn't mention generative AI there where generative AI is proving very helpful in work with Nvidia for climate is for downscaling and that's for turning low resolution predictions, which we have a lot of today from the world's climate climate modeling agencies into high resolution actionable predictions. So you can just like super resolution in images, you can take these low resolution data and turn them into high resolution impacts, impacts, relevant metrics.

So you know, Mike, we've gone over so much here. So you know, as an example, you mentioned in the beginning, you know, Earth 2, which you know, I think is just kind of like a digital twin of the Earth, right? We talked about Stormcast, you know, you talked about all, all this great research that, that you and your colleagues are doing that can help us better understand, you know, Earth's climate. You know, all of these things happening at once and then throw in obviously generative AI and you know, all of the compute resources that you have at Nvidia. What is your mind focused on? Right? Like, like what are you thinking? Okay, hey, once we, you know, figure this piece out, then this happens next, right? Like what is that next thing, thing that you're looking at when it comes to this, you know, this intersection of, you know, weather, climate and AI?

I'm afraid there's no one thing, Jordan, because there's a few fronts of work that we need to progress. But on the weather side, I think the frontiers are sub seasonal to seasonal prediction. Evolving these AI weather models that have proven to be good at atmospheric prediction to also to be good at ocean prediction and coupled ocean atmosphere prediction. The ocean moves slower than the atmosphere. So when you want the predictions to roll out beyond the timescales of weather into climate, beyond weeks, to months, to years, then you need to start worrying about simulating the ocean with AI, simulating the coupled interactions between the ocean and atmosphere with AI, and then especially simulating the whole system's response to changes that humans are making, like turning up the CO2 and changing the opacity of the atmosphere, or changing land use, or changing the emissions of soot particles that can interact with clouds and change their brightness. So each of those things I mentioned are frontiers where researchers are working actively within Nvidia, across Nvidia and other institutions, with our collaborators to try to see can the same gains that are being made for the atmosphere, for the weather timescale, really fundamentally disrupt the climate timescale, where these coupled interactions become really important. But there's a very complicated physics and multi scale physics. It's going to take a lot of work to see if it can happen. And then I think the other front that I'm really passionate about though is downscaling. You know, there's so much impact to be had from taking all the wonderful predictions that we already have from the world's investment in physics based climate prediction and making them relatable, making them high enough resolution for us to understand their implications for the average person to interact with them and to just like AI, weather can create massive ensembles of weather, to create massive ensembles of these climate projections so that we can hope for the best, but plan for the worst and understand how extreme events, you know, including the most extreme events, may change in the future to build resilient infrastructure. Those are the two fronts that I'm most passionate about.

So, you know, Mike, I know this is an area of research and the work that you're doing that doesn't end right? But you know, if all of you know, this, this research that, you know, we talked about, this Earth simulation on better understanding the climate, if all of this works, right, works and continues to improve with generative AI and everyone's models get better and we better understand extreme weather and climate. Like what does that mean, right? Like what does it actually mean for all of us? Is it just okay, well now we're safer and we just have a better idea of the earth around us. But what does this ultimately mean? Like bigger picture as generative AI, you know, really just changes, you know, what humans are capable of.

Well, more reliable predictions, more accurate predictions are fundamental to planning and we've got a lot of planning to do. You know, generative AI is not going to erase the fact that, you know, there's been 200 years of emissions that have changed the composition of the atmosphere for including a molecule that has 100 year lifetime and therefore will, will be remaining in the atmosphere for a long time. But, but as the, as the weather changes, as the climate warms, I think that the ability to sidestep Physics and to not have to rely on human assumptions about how physics we cannot afford to represent numerically, but instead rely on data and AI to learn relationships. It has the capacity to extrapolate beyond the boundaries of the observed record enough for us to get some benefits in extreme weather prediction that I think could be helpful to planning. You know, can I nerd out for one more moment, Jordan?

Yeah, please. This is good. This is good.

Okay. This seeming act of video prediction in AI for weather, where you're trying to predict the frame ahead not of an image, not of three channels RGB pixels, but rather of, you know, hundreds of channels, temperatures, winds at different altitudes. It can appear like an imitation of video generation, but it's actually deeply physical. What's learned, what's trained along the way, is a huge AI model that's learned physics. Now, I was skeptical of this two years ago as a physicist and an ML Luddite compared to all my Nvidia colleagues. But I'm convinced now. And the reason I'm convinced is because a wonderful atmospheric science professor at the University of Washington took one of these trained AI models and then poked it and exposed it to clean conditions that were never in the training data, the noisy data of real weather, but analogous to the kinds of pen and pencil problems we work on in grad school and atmospheric science, where you examine the force balances in response to idealized conditions. And he proved the AI model could produce beautiful, clean solutions to these beautiful canonical problems. And it's very convincing evidence that there's learned physics. And when there's learned physics, then there's the hope of generalization. And. Yeah, so I really, I think we don't know the limits of that generalization, but it could, it could really benefit some of the existential problems we have in simulating the climate deterministically with physics.

So much, so much to wrap our heads around here, Micah. It's, it's. It's early in the morning for me. I'm a little bit tired, but this is waking my brain up. But, you know, as we wrap up here, because we've covered a lot in a very short amount of time, maybe what is the one most important takeaway? And I know that's hard because we covered so much, but what is the one most important, important takeaway that you want our listeners and viewers to understand when it comes to climate forecasting and weather prediction and how generative AI is changing all of that?

Great. I would say that AI for weather prediction is here. The skill is unequivocal. Then the largest meteorological agencies are responding. The future of AI will include or the future of weather prediction will include AI. So stay tuned and watch when it appears on your, on your app that you like to look at eventually. And that AI for climate prediction is coming and that multiple world leading climate models are being infused with AI subcomponents. And there's an enterprise of people working wonderfully in a collaborative sphere and across the private and public domain in this sector. A problem that's really important for humanity. And there's some applications of AI that give people pause. This is one I think we can all feel really good about. It's authentically quite important to Micah's future and it's a great privilege to get to work on it with such fantastic people at Nvidia and really appreciate your interest.

All right, well, hey, I think this was an important one. Timely, you know, very timely for us to better understand with all the extreme weather we've seen recently, how generative AI and all the research that you're doing can really help us prepare for, you know, hopefully safer, you know, safer day to day dealing with the weather. So, Mike, thank you so much for joining the Everyday AI Show. We really appreciate your time.

Thanks, Jordan.

And hey, as a reminder, y', all, there was a lot there. Make sure if you haven't already, please go to your everydayai.com Sign up for the free daily newsletter. Mike just dropped so many. He was just making it rain facts and figures on us. So we're going to be recapping the most important takeaways in the newsletter. So if you haven't, please go to your everyday AI dot com. Thank you for tuning in. Please join us next time and every day for more Everyday AI. Thanks, y'.

All.

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