Climate Modeling Platforms

NVIDIA just open-sourced a whole family of weather and climate ai models, and I think this is the moment serious #forecasting stops being something only national weather agencies can do. #Earth2 isn't one model, it's a stack. #Atlas does 15-day global forecasts and beats GenCast on benchmarks. #StormScope is the first AI to outperform physics-based systems on storm dynamics. #HealDA spins up initial atmospheric conditions in seconds on a GPU, the kind of step that used to take hours on a supercomputer. #CorrDiff downscales 500x faster while using 10,000x less energy. what gets me excited isn't any one of these though, it's that the whole pipeline is just sitting on #HuggingFace and #GitHub now. running weather ai used to mean physics models, now a small team in any country can fine-tune these and run them on their own hardware. bigger models are interesting, but this feels more important to me, taking something that lived behind a national lab's firewall and just handing it out. Link to Earth-2: https://lnkd.in/exReKuTV #climateAI #remotesensing #foundationmodel #weather #climate

🌍 Biggest Earth release of the year just dropped courtesy of NVIDIA 🌍 Production-grade weather prediction just became open and landed on Hugging Face. + Did I mention it's full stack? Meet the new Earth-2 models: → Nowcasting: km-scale severe weather prediction → Medium-range: 15-day forecasts → Global Data Assimilation: accurate initial conditions The best part is that it's incredibly accessible. NVIDIA shipped two frameworks to get developers up and running really fast: 🛠️ Earth2Studio - build inference pipelines with just a few lines of Python 🛠️ PhysicsNemo (for earth) - train new models with your custom data If you want all the gory details, there are three research papers and a comprehensive blog post to keep you busy (which I'll link in the comments). This is going to fundamentally change disaster prediction and response, transform agricultural planning, ++ hopefully give the commercial weather providers a serious run for their money, which should enfranchise lower resource regions to also predict and safeguard their climates. Open source for the win! 🤗

Precipitation is one of the most challenging variables to accurately simulate in global climate models as it depends on small-scale physical processes. In our latest research published in 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 𝘈𝘥𝘷𝘢𝘯𝘤𝘦𝘴, we describe an advancement in our hybrid atmospheric model, NeuralGCM, which now leverages AI trained directly on NASA satellite observations to improve global precipitation simulations. Key results of this work: 👉 Physics-AI Integration: The model combines a traditional fluid dynamics solver for large-scale processes with AI neural networks that learn to account for the effects of small-scale physics, specifically precipitation. 👉 Improved Extremes: NeuralGCM demonstrates significant improvements in capturing the intensity of the top 0.1% of extreme rainfall events, better representing heavy precipitation than many traditional models. 👉 Long-Term Accuracy: In multi-year simulations, the model achieved a 40% average error reduction over land compared to leading atmospheric models used in the latest Intergovernmental Panel on Climate Change (IPCC) report. 👉 Daily Patterns: It more accurately reproduces the timing of peak daily precipitation, which is critical for hydrology and agricultural planning. We are already seeing the value of this approach in the field. A partnership between the University of Chicago and the Indian Ministry of Agriculture recently used NeuralGCM in a pilot program to help predict the onset of the monsoon season. NeuralGCM is part of our Earth AI program to better understand the physical earth in ways that benefit society. We have made the code and model checkpoints openly available to the community. Read the full details on the Google Research blog by Janni Yuval: goo.gle/4qH63sU Paper: https://lnkd.in/d7E4US4W

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

AAAS: “High-resolution climate model forecasts a wet, turbulent future.” Let me start with a story I heard yesterday. One of my writing groups meets remotely, weekly, including 2 participants who live in eastern Florida + in Barbados, about 1600 miles apart. Both were almost giddy in recounting stories of recent intense rain events + flooding; for example Barbados recently experienced 17″ of rain over 2 days, with 1 drowning death. Now let’s discuss advanced climate modeling. “To simulate hundreds of years in a manageable amount of computer time, [conventional] models divide the atmosphere into the equivalent of coarse pixels, 100 kilometers across, before solving the equations of fluid dynamics for each one.” But this coarseness leads to inaccuracies in the predictions, especially when it comes to patchy phenomena such as heat waves and downpours, which are heavily influenced by what happens at a finer scale.  “A new high-resolution modeling project called MESACLIP, run at great computational expense over the past 5 years, is putting Earth’s future into sharper focus by simulating the churning of the atmosphere and ocean at a level of detail similar to the scale of weather forecasts.” Unfortunately, the project reveals heightened risks for regions like the Gulf Coast and coastal California, where extreme rainfall could occur far more often than traditionally projected. “MESACLIP divides the atmosphere into 25-km boxes and the upper layer of the ocean into a 10-km grid…running global simulations that began in 1900 and looked ahead to 2100 for multiple greenhouse gas emission scenarios.”  The results better match the historic records of ocean and air temperature, which many climate models have long struggled to do. “They also better capture cold tongues of upwelling water and swirling eddies in the ocean, which are thought to play an important role in modulating wind patterns…they mimic the extreme rainfall events observed today far more accurately.” After 900 days of computing time + 4500 yrs of simulation, 6 petabytes of open data will prove to be a gold mine for climate scientists everywhere. Note a petabyte is a unit of digital information storage equal to one quadrillion (1,000,000,000,000,000) bytes, or ~ 1,000 terabytes (TB). So—keep your shoes dry.

Climate models have long struggled with coarse resolution, limiting precise climate risk insights. But AI-driven methods are now changing this, unlocking more detailed intelligence than traditional physics-based approaches. I recently spoke with a research scientist at Google Research who highlighted a promising new hybrid approach. This method combines physics-based General Circulation Models (GCMs) with AI refinement, significantly improving resolution. The process starts with Regional Climate Models (RCMs) anchoring physical consistency at ~45 km resolution. Then, it uses a diffusion model, R2-D2, to enhance output resolution to 9 km, making estimates more suitable for projecting extreme climate events. 🔥 About R2-D2 R2‑D2 (Regional Residual Diffusion-based Downscaling) is a diffusion model trained on residuals between RCM outputs and high-resolution targets. Conditioned on physical inputs like coarse climate fields and terrain, it rapidly generates high-res climate maps (~800 fields/hour on GPUs), complete with uncertainty estimates. ✅ Why this matters - Offers detailed projections of extreme climate events for precise risk quantification. - Delivers probabilistic forecasts, improving risk modeling and scenario planning. - Provides another high-resolution modeling approach, enriching ensemble strategies for climate risk projections. 👉 Read the full paper: https://lnkd.in/gU6qmZTR 👉 An excellent explainer blog: https://lnkd.in/gAEJFEV2 If your work involves climate risk assessment, adaptation planning, or quantitative modeling, how are you leveraging high-resolution risk projections?

NVIDIA Revolutionizes Climate Tech with ‘Earth-2’: The World’s First Fully Open Accelerated AI Weather Stack In a move that democratizes climate science, NVIDIA unveiled 3 groundbreaking new models powered by novel architectures: Atlas, StormScope, and HealDA. These tools promise to accelerate forecasting speeds by orders of magnitude while delivering accuracy that rivals or exceeds traditional methods. The suite includes three new breakthrough models: Earth-2 Medium Range: High-accuracy 15-day forecasts across 70+ variables.  Earth-2 Nowcasting: Generative AI that delivers kilometer-scale storm predictions in minutes.  Earth-2 Global Data Assimilation: Real-time snapshots of global atmospheric conditions. Full analysis: https://lnkd.in/gt_BugDZ Model weight: https://lnkd.in/gkUVqH5E Paper [Earth-2 Medium Range]: https://lnkd.in/gTf-f_Gd Paper [Earth-2 Nowcasting]: https://lnkd.in/gQf7muqz Paper [Earth-2 Global Data Assimilation]: https://lnkd.in/gu_-eZsn Technical details: https://lnkd.in/gPQ66Me2 NVIDIA NVIDIA AI

AI is completely rewriting the rules of weather forecasting, and this video from NVIDIA is a perfect example of how fast things are moving. In just under 5 minutes, the video demonstrates Earth-2, a platform that allows you to run global weather forecasts in mere seconds using just a few lines of Python. You can seamlessly switch between data sources (like ERA5, GFS, IFS) and even swap out entire AI models (like FourCastNet, GraphCast, or Aurora) with a single line of code. But NVIDIA isn’t alone. We are witnessing an arms race among big tech to solve weather prediction: - Google DeepMind has GraphCast and NeuralGCM, which have already outperformed gold-standard physical models in many metrics. - Microsoft released Aurora, a foundation model trained on over a million hours of data, claiming to be 5000x faster than traditional numerical systems. - IBM & NASA recently open-sourced Prithvi, a "geospatial foundation model" designed not just for weather, but to be fine-tuned for specific climate applications. - Huawei has Pangu-Weather, which famously predicted the path of a typhoon more accurately than traditional methods. Why is this happening? - Compute: Traditional Numerical Weather Prediction (NWP) solves complex physics equations requiring massive supercomputers. AI models, once trained, infer results in seconds on a few GPUs. - Ensemble Forecasting: Because they are so cheap to run, we can generate thousands of scenarios (ensembles) instead of just a few. This is a game changer for predicting low probability extreme weather events. - Data Fusion: These models are proving incredibly good at learning patterns from historical data that pure physics equations might miss. For the geospatial practice, this is a big change. Weather is moving from a static dataset we download to a dynamic capability we run. You no longer need a supercomputer to generate high-resolution forecasts; you just need a GPU and a Python script. We may soon see fine-tuned weather models for specific geospatial use cases like hyper local wind for drones, precise precip for agriculture, or cloud cover for satellite tasking. The latency between data in and forecast out is shrinking to near zero, enabling true real time geospatial intelligence. Have you tried any of these models? What are your thoughts? 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc

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