Why Big Tech is Building AI Weather Models

You might have seen news from our Google DeepMind colleagues lately on GenCast, which is changing the game of weather forecasting by building state-of-the-art weather models using AI. Some of our teams started to wonder – can we apply similar techniques to the notoriously compute-intensive challenge of climate modeling? General circulation models (GCMs) are a critical part of climate modeling, focused on the physical aspects of the climate system, such as temperature, pressure, wind, and ocean currents. Traditional GCMs, while powerful, can struggle with precipitation – and our teams wanted to see if AI could help. Our team released a paper and data on our AI-based GCM, building on our Nature paper from last year - specifically, now predicting precipitation with greater accuracy than prior state of the art. The new paper on NeuralGCM introduces 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗿𝗮𝗶𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀. Kudos to Janni Yuval, Ian Langmore, Dmitrii Kochkov, and Stephan Hoyer! Here's why this is a big deal: 𝗟𝗲𝘀𝘀 𝗕𝗶𝗮𝘀, 𝗠𝗼𝗿𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: These new models have less bias, meaning they align more closely with actual observations – and we see this both for forecasts up to 15 days, and also for 20-year projections (in which sea surface temperatures and sea ice were fixed at historical values, since we don’t yet have an ocean model). NeuralGCM forecasts are especially performant around extremes, which are especially important in understanding climate anomalies, and can predict rain patterns throughout the day with better precision. 𝗖𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗔𝗜, 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗜𝗺𝗮𝗴𝗲𝗿𝘆, 𝗮𝗻𝗱 𝗣𝗵𝘆𝘀𝗶𝗰𝘀: The model combines a learned physics model with a dynamic differentiable core to leverage both physics and AI methods, with the model trained directly on satellite-based precipitation observations. 𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲! This is perhaps the most exciting news! The team has made their pre-trained NeuralGCM model checkpoints (including their awesome new precipitation models) available under a CC BY-SA 4.0 license. Anyone can use and build upon this cutting-edge technology! https://lnkd.in/gfmAx_Ju 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Accurate predictions of precipitation are crucial for everything from water resource management and flood mitigation to understanding the impacts of climate change on agriculture and ecosystems. Check out the paper to learn more:  https://lnkd.in/geqaNTRP

🌩️ We Are Revolutionizing Weather AI 🚀 AI weather models are only as good as the data they train on. With Tomorrow.io’s new global Microwave Sounder satellite data (beautiful data sample attached), we’re reshaping AI-driven weather forecasting through better training, real-time inference, and reinforcement learning. Here's how: ✅ Training: Multi-altitude data (temperature, humidity, and more) helps models learn complex atmospheric interactions, improving predictions for rain, storms, and heatwaves. ✅ Inference: Real-time satellite streams provide live, high-fidelity data—closing gaps where ground-based observations are missing, ensuring more accurate forecasts globally. ✅ Reinforcement Learning: Continuous satellite feedback allows models to adapt and self-correct. AI can "learn" from past mistakes, refining forecasts for events like floods or heavy rainfall. 🌍 From emergency response and agriculture to aviation and renewable energy, this is the next leap in weather prediction.

Another new milestone is broken by AI. Just two years after producing a better short-term forecast model, a recent model has now comprehensively beaten the existing weather models much, much less compute power. 💥 But how its beaten it, and when, is where the interesting part begins. .. A new model from Google's Deepming ('GenCast') has beaten our best models in forecasting weather events. This is a big deal for two reasons: 1. Climate is changing, and bigger storms, and bigger economic events are happening. Accurate and fast weather predictions are crucial to mitigating the economic impact of extreme conditions. Faster, more precise forecasts can help governments, businesses, and communities act sooner—reducing the damage from floods, heatwaves, and other disruptive events. The forecast of weather, specifically winds, is also critical for our use of wind energy, and turbine management. To avoid damaging infrastructure, and optimally switch these on and off for example (Which I'm told is extremely expensive to do regularly). 2. Traditionally, weather prediction relies on numerical weather prediction (NWP) models, which simulate complex atmospheric processes using physical equations. While powerful, NWPs require enormous computational resources and often struggle to deliver high-resolution forecasts quickly enough, particularly for extreme conditions. So importantly, we are not talking about the 'middle' of the distribution, but rather around the edges, and unusual events. GenCast, by contrast, is a diffusion model that generates highly accurate forecasts at lower computational costs and with faster turnaround times. Fourty years of training, it generates many scenarios in parallel, effectively creating distributions of uncertainty. This approach enhances the prediction of severe weather events, allowing for more timely warnings and better preparation. Importantly, and again, the advent of machine learning means we are allowing data and processes to learn patterns (potentially not linked or driven by our understanding of the underlying causal processes). This should make us nervous, because again, 'it just works'. The paper: https://lnkd.in/gavxZvjX (they open sourced the model!) What are your thoughts on the role of AI in tackling climate risks? Bureau of Meteorology Andrew Huang, CFA Well done the team at: Google DeepMind #climate #esg #ai #artificialintelligence #genai Marcos Lopez de Prado

We know the Earth is getting warmer, but not what it means specifically for different regions. To figure this out, scientists do climate modelling. 🔎 🌍 , Google Research has published groundbreaking advancements in climate prediction using the power of #AI! Typically, researchers use "climate modelling" to understand the regional impacts of climate change, but current approaches have large uncertainty. Introducing NeuralGCM: a new atmospheric model that outperforms existing models by combining AI with physics-based modelling for improved accuracy and efficiency. Here’s why it stands out: ✅ More Accurate Simulations When predicting global temperatures and humidity for 2020, NeuralGCM had 15-50% less error than the state-of-the-art model "X-SHiELD". ✅ Faster Results NeuralGCM is 3,500 times quicker than X-SHiELD. If researchers simulated a year of the Earth's atmosphere with X-SHiELD, it would take 20 days to complete —  whereas NeuralGCM achieves this in just 8 minutes. ✅ Greater Accessibility Google Research has made NeuralGCM openly available on GitHub for non-commercial use, allowing researchers to explore, test ideas, and improve the model’s functionality. The research showcases AI’s ability to help deliver more accurate, efficient, and accessible climate predictions, which is critical to navigating a changing global climate. Read more about the team’s groundbreaking research in   Nature Portfolio’s  latest article! → https://lnkd.in/e-Etb_x4 #AIforClimateAction #Sustainability #AI

AI has the potential to bring new waves of innovation, social and economic progress on a scale we’ve not seen before - including supercharging scientific progress. This week, Google published NeuralGCM: an openly available tool for fast, accurate climate modelling - critical to a changing global climate. We know that the Earth is getting warmer, but it’s hard to predict what that means for each different region. To figure this out, scientists use climate modelling. But current approaches have large uncertainty, including systematic errors - like forecasting extreme rain that is only half as intense as what scientists actually observe. That’s where NeuralGCM comes in. It combines physics-based modelling and AI to simulate the Earth’s atmosphere - making it faster and more accurate than existing climate models. For scientists exploring how to build better weather and climate models, it should make a huge difference in helping them understand the effects of the climate crisis on our world - and it could also be great for meteorologists making predictions about our daily weather! Interested in learning more? Read all about it here and watch the video below ⬇️ https://lnkd.in/e_bCuAhq 

AI-Powered Weather Forecasting: The GraphCast Innovation In the quest to enhance weather forecasting and provide early warnings for extreme events like hurricanes, artificial intelligence has emerged as a potent tool. Traditional forecasting systems have undoubtedly improved over the years, but AI's ability to swiftly analyze historical data and make predictions is transforming the field. Google #DeepMind's innovative AI tool, #GraphCast, has demonstrated its potential by outperforming conventional models and significantly expediting forecast delivery. Weather forecasts serve a crucial purpose beyond helping us decide our daily attire; they offer a lifeline in anticipating and preparing for severe weather events such as storms, floods, and heatwaves. However, traditional weather forecasting demands immense computational power. It involves processing hundreds of variables across various atmospheric layers worldwide. GraphCast takes a fundamentally different approach. Instead of attempting to model intricate atmospheric processes, it leverages machine learning to analyze extensive historical weather data, including output from the European Centre for Medium-Range Weather Forecasts (ECMRWF) model, to understand the evolution of weather patterns. This AI-driven approach enables it to predict how current conditions are likely to change in the future, with remarkable precision. GraphCast has demonstrated exceptional accuracy, outperforming traditional models on more than 90% of the factors crucial for weather forecasting. Moreover, it produces forecasts in under a minute, utilizing only a fraction of the computing power required by traditional numerical weather prediction (NWP) models. An illustrative example of its success is its prediction of Hurricane Lee's landfall in Canada in September. The AI tool accurately forecasted the storm's path nine days in advance, surpassing the ECMRWF's six-day prediction window. This extended lead time for forecasting can be pivotal in preparing for extreme weather events, potentially saving lives and mitigating property damage. Crucially, AI models like GraphCast do not supplant traditional weather forecasts but complement them. These AI models rely on data generated by traditional approaches, emphasizing the symbiotic relationship between AI and traditional meteorological methods. Despite advances, climate change brings unpredictable weather extremes, challenging AI models with data quality issues. Rising ocean temperatures introduce a previously unseen variable that can accelerate storm intensification — like Hurricane Otis's swift escalation from a tropical storm to a Category 5 hurricane within 24 hours. GraphCast by Google DeepMind represents a significant advancement in weather forecasting. As climate change continues to reshape weather patterns, AI's role in forecasting becomes increasingly crucial in safeguarding communities worldwide. #JordiPlusJavis Note: This is an #AI generated image

🌦️ GenAI in Weather Forecasting: Decoding Unseen Patterns 🌦️ Imagine a world where weather predictions are so accurate, they can anticipate even the most subtle changes in the atmosphere. This is not science fiction—it's the power of Generative AI (GenAI) in weather forecasting. Why GenAI? 1. Decoding Satellite Images: Traditional weather forecasting relies heavily on interpreting satellite images. GenAI can process these images with unparalleled precision, identifying patterns and anomalies that human eyes might miss. 2. Unseen Patterns: The true strength of GenAI lies in its ability to detect unseen patterns in vast datasets. By analyzing historical and real-time data, it can predict weather events with greater accuracy. How Does It Work? - Data Processing: GenAI processes massive amounts of data from satellites, sensors, and historical records. - Pattern Recognition: It uses advanced algorithms to recognize patterns that indicate specific weather conditions. - Predictive Modeling: The AI generates predictive models that can forecast weather events with higher precision than ever before. The Impact 🌪️ Disaster Preparedness: More accurate predictions mean better preparation for natural disasters, potentially saving lives and reducing economic losses. 🚜 Agricultural Benefits: Farmers can make more informed decisions about planting and harvesting, leading to better yields and more sustainable practices. ✈️ Aviation Safety: Improved forecasts can enhance flight safety and efficiency, reducing delays and optimizing routes. The Future The integration of GenAI in weather forecasting is just the beginning. As technology evolves, we can expect even more refined and accurate predictions, leading to a safer and more efficient world. 🔍 Curious about the future of weather forecasting with GenAI? Let's explore it together! P.S. Have you experienced the benefits of advanced weather forecasting in your field? Share your story below! 🌍