Earthly Machine Learning

• FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale

  FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, and Alexander Keller• FourCastNet 3 (FCN3) introduces a pioneering geometric machine learning approach for probabilistic ensemble weather forecasting. It is designed to respect spherical geometry and accurately model the spatially correlated probabilistic nature of weather, resulting in stable spectra and realistic dynamics across multiple scales. The architecture is a purely convolutional neural network tailored for spherical geometry.• Achieves superior forecasting accuracy and speed, surpassing leading conventional ensemble models and rivaling the best diffusion-based ML methods. FCN3 produces forecasts 8 to 60 times faster than these approaches; for instance, a 60-day global forecast at 0.25°, 6-hourly resolution is generated in under 4 minutes on a single GPU.• Demonstrates exceptional physical fidelity and long-term stability, maintaining excellent probabilistic calibration and realistic spectra even at extended lead times of up to 60 days. This crucial achievement mitigates issues like blurring and the build-up of small-scale noise, which challenge other machine learning models, paving the way for physically faithful data-driven probabilistic weather models.• Enables scalable and efficient operations through a novel training paradigm that combines model- and data-parallelism, allowing large-scale training on 1024 GPUs and more. All key components, including training and inference code, are fully open-source, providing transparent and reproducible tools for meteorological forecasting and atmospheric science research.

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• Can AI weather models predict out-of-distribution gray swan tropical cyclones?

  Can AI weather models predict out-of-distribution gray swan tropical cyclones?by Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand, Ashesh Chattopadhyay, Jonathan Weare, and Dorian S. AbbotInability to Extrapolate to Gray Swans Globally: AI weather models like FourCastNet struggle to predict "gray swan" tropical cyclones (TCs), which are rare, strong, and absent from training data. When Category 3-5 TCs are entirely removed from the global training dataset, the model cannot extrapolate from weaker storms (Category 1-2) to accurately forecast these stronger, unseen events, often leading to dangerous "false negative" predictions. This limitation persists even if the training data includes strong extratropical cyclones, as their dynamics differ from TCs.Limited Generalization Across Basins for Dynamically Similar Events: Despite the global extrapolation challenge, FourCastNet can demonstrate some ability to generalize learning across tropical basins for dynamically similar strong storms. This means that if the model has seen strong TCs in one ocean basin, it can apply that learned knowledge to forecast similar strong TCs in another basin, even if those specific events were excluded from the training data for that particular region.Lack of Physical Consistency and Masked Performance: Current AI weather models, including FourCastNet, fail to reproduce key physical balances like the gradient-wind balance that TCs obey in real-world data, regardless of whether they were trained on full or reduced datasets. Furthermore, common evaluation metrics (e.g., anomaly correlation coefficient or root-mean-square error) can obscure these critical shortcomings by showing similar overall performance for general weather or less extreme events, highlighting the need for specialized tests for gray swans.Implications and Future Directions: This research suggests that current AI weather models may provide unreliable early warnings for unprecedented extreme weather events, potentially leading to serious societal risks. It also indicates that AI climate emulators might mischaracterize extreme weather statistics for gray swans. The study emphasizes the urgent need for novel learning strategies (such as incorporating physics-based synthetic data or rare-event sampling algorithms) and rigorous testing methodologies to improve and reliably validate AI models for these high-impact, out-of-distribution events.

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• Probabilistic Emulation of a Global Climate Model with Spherical DYffusion

  Probabilistic Emulation of a Global Climate Model with Spherical DYffusionby Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose YuThe paper introduces Spherical DYffusion, the first conditional generative model for probabilistic emulation of a realistic global climate model, offering efficient and accurate climate ensemble simulations.It demonstrates that weather forecasting performance is not a strong indicator of long-term climate performance, a crucial insight for developing climate models.Spherical DYffusion significantly reduces climate biases compared to existing baselines like ACE and DYffusion, achieving errors often closer to the reference simulation's "noise floor".The model generates stable, 10-year-long probabilistic predictions with minimal computational overhead, being more than 25 times faster than the physics-based FV3GFS model it emulates, while also reproducing consistent climate variability.

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• Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

  "Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán" By Andrew J. Charlton-Perez, Helen F. Dacre, Simon Driscoll, Suzanne L. Gray, Ben Harvey, Natalie J. Harvey, Kieran M. R. Hunt, Robert W. Lee, Ranjini Swaminathan, Remy Vandaele & Ambrogio Volonté. Published in partnership with CECCR at King Abdulaziz University, Nature, DOI: 10.1038/s41612-024-00638-w.Here are the main takeaways from the paper:• AI models (FourCastNet, Pangu-Weather, GraphCast, FourCastNet-v2) demonstrate strong capabilities in capturing large-scale dynamical drivers vital for rapid storm development, such as the storm's position relative to upper-level jets. They also accurately reproduce the larger synoptic-scale structure of cyclones like Storm Ciarán, including the cloud head's position and the warm sector's shape. Despite these strengths, AI models consistently underestimate the peak amplitude of winds, both at the surface and in the free atmosphere, associated with storms. They also struggle to resolve detailed structures crucial for issuing severe weather warnings, such as sharp bent-back warm frontal gradients, and show variable success in capturing warm core seclusion. The underestimation of strong winds is not a consequence of the AI models' output resolution or their training data. This discrepancy persists even when compared against ERA5 (on which these models were trained) and numerical weather prediction (NWP) models of similar resolution, suggesting a more fundamental limitation in their ability to represent intense wind features.The case study of Storm Ciarán highlights the pressing need for a more comprehensive assessment of machine learning weather forecasts. Moving beyond isolated error metrics to evaluate all relevant spatio-temporal features of physical phenomena is essential for identifying specific areas for improvement and fostering rapid advancements in this new and potentially transformative forecasting tool.

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• Early Warning of Complex Climate Risk with Integrated Artificial Intelligence

  🧠 Abstract:Climate change is increasing the frequency and severity of disasters, demanding more effective Early Warning Systems (EWS). While current systems face hurdles in forecasting, communication, and decision-making, this episode examines how integrated Artificial Intelligence (AI) can revolutionize risk detection and response.📌 Bullet points summary:Current EWS struggle with forecasting accuracy, impact prediction across diverse contexts, and effective communication with affected communities.Integrated AI and Foundation Models (FMs) enhance EWS by improving forecast precision, offering impact-specific alerts, and utilizing diverse data sources—from weather to social media.Foundation Models for geospatial and meteorological data, combined with natural language processing, pave the way for user-adaptive, intuitive warning systems, including chatbots and realistic visualizations.Ensuring equity and effectiveness in AI-driven EWS requires addressing data bias, robustness, ownership issues, and power dynamics—guided by FATES principles and supported by open-source tools, global cooperation, and digital inclusivity.💡 The Big Idea:Integrated AI holds the key to transforming climate early warning—from hazard alerts to adaptive, inclusive, and impact-driven systems that empower communities worldwide.📖 Citation:Reichstein, Markus, et al. "Early warning of complex climate risk with integrated artificial intelligence." Nature Communications 16.1 (2025): 2564. https://doi.org/10.1038/s41467-025-57640-w

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