Testing Predictive Climate Software

In this week's column, I look at NVIDIA's new generative foundation model that it says enables simulations of Earth’s global climate with an unprecedented level of resolution. As is so often the case with powerful new technology, however, the question is what else humans will do with it. The company expects that climate researchers will build on top of its new AI-powered model to make climate predictions that focus on five-kilometer areas. Previous leading-edge global climate models typically don’t drill below 25 to 100 kilometers. Researchers using the new model may be able to predict conditions decades into the future with a new level of precision, providing information that could help efforts to mitigate climate change or its effects. A 5-kilometer resolution may help capture vertical movements of air in the lower atmosphere that can lead to certain kinds of thunderstorms, for example, and that might be missed with other models. And to the extent that high-resolution near-term forecasts are more accurate, the accuracy of longer-term climate forecasts will improve in turn, because the accuracy of such predictions compounds over time. The model, branded by Nvidia as cBottle for “Climate in a Bottle,” compresses the scale of Earth observation data 3,000 times and transforms it into ultra-high-resolution, queryable and interactive climate simulations, according to Dion Harris, senior director of high-performance computing and AI factory solutions at Nvidia. It was trained on high-resolution physical climate simulations and estimates of observed atmospheric states over the past 50 years. It will take years, of course, to know just how accurate the model’s long-term predictions turn out to be. The The Alan Turing Institute of AI and the Max Planck Institute of Meteorology, are actively exploring the new model, Nvidia said Tuesday at the ISC 2025 computing conference in Hamburg. Bjorn Stevens, director of the Planck Institute, said it “represents a transformative leap in our ability to understand, predict and adapt to the world around us.” The Earth-2 platform is in various states of deployment at weather agencies from NOAA: National Oceanic & Atmospheric Administration in the U.S. to G42, an Abu Dhabi-based holding company focused on AI, and the National Science and Technology Center for Disaster Reduction in Taiwan. Spire Global, a provider of data analytics in areas such as climate and global security, has used Earth-2 to help improve its weather forecasts by three orders of magnitude with regards to speed and cost over the last three or four years, according to Peter Platzer, co-founder and executive chairman.

Nature has just published Microsoft Research's Aurora, the first foundation #model of the #earth system. Aurora outperforms operational #forecasts in predicting #air quality, #ocean waves, tropical #cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. Aurora first learns how to generate forecasts through training on #weather patterns from over one #million hours of data. These data are derived from satellites, radar and weather stations, simulations, and forecasts. The model can then be fine-tuned to perform a variety of specific tasks such as predicting wave height or air quality. When #Typhoon Doksuri hit the Philippines in July 2023, the damage was devastating. As reported in Nature, Aurora accurately predicts Typhoon Doksuri’s landfall in the Philippines using measurements from four days in advance of the event (image below). Official predictions at that time mistakenly placed the storm off the coast of Northern Taiwan. Results like this show how #AI is paving the way toward democratizing high-quality climate and weather prediction.  Learn more here: https://lnkd.in/gNiM5tsQ Try it here: https://lnkd.in/gn9DZsry

IBM & NASA - National Aeronautics and Space Administration just dropped an open-source AI model for weather & climate on Hugging Face that could revolutionize modeling in these fields. It’s far beyond your average forecasting tool—it handles everything from short-term weather to long-term climate projections. Some cool features: - Pre-trained on 40 years of NASA Earth observation data - Can be fine-tuned to global, regional, and local scales - Two fine-tuned versions available: climate and weather data downscaling, and gravity wave modeling Potential applications: - Creating targeted forecasts based on local observations, - Detecting and predicting severe weather patterns, - Improving the spatial resolution of global climate simulations, - Enhancing how physical processes are represented in numerical weather and climate models Oh, and they tested it on Hurricane Ida's formation, path, and intensification. The accuracy is wild! 🌪️ 👉 Grab the model here: Foundation model and the gravity wave parameterization model: https://lnkd.in/e-VpRcKw Downscaling model: https://lnkd.in/e-t7576k Who's planning to play with this? What other use cases can you think of? #NASA #WeatherAI #ClimateScience #OpenSource

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-Powered Climate Prediction: LSTM for Multi-Model Ensemble (MME) 🌍📊 Accurate climate forecasting requires integrating multiple General Circulation Models (GCMs). Using LSTM-based Multi-Model Ensemble (LSTM-MME), we enhance prediction accuracy by capturing temporal dependencies in climate data. 🚀 Why LSTM for Climate Modeling? ✅ Learns long-term patterns from multiple GCMs ✅ Handles nonlinear relationships in climate variables ✅ Improves forecast precision with dynamic adjustments 📊 Methodology: 🔹 Inputs: 10 GCM models 🔹 Target: Observed climate data 🔹 Architecture: LSTM layers + Dropout for stability 🔹 Training: 50 epochs, Adam optimizer, MSE loss 📈 Results: Predictions align closely with observed data, improving climate risk assessment and policy planning. 💡 Next Steps: Explore hybrid models (CNN-LSTM, Transformers) for regional downscaling. How do you see AI transforming climate science? Let’s connect! 🔗 #ClimateTech #AI #DeepLearning Medium story:https://lnkd.in/dDXGwVm7