mlsanityData to 7 Oct 2026

A Foundation Model for the Earth System

arXiv 2405.13063 ↗First posted 20 May 2024physics.ao-ph cs.LGcited by 23

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Abstract (arXiv)

Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction accuracy and computational efficiency in this field, however this remains underexplored in many domains. Here we introduce Aurora, a large-scale foundation model for the Earth system trained on over a million hours of diverse data. Aurora outperforms operational forecasts for air quality, ocean waves, tropical cyclone tracks, and high-resolution weather forecasting at orders of magnitude smaller computational expense than dedicated existing systems. With the ability to fine-tune Aurora to diverse application domains at only modest computational cost, Aurora represents significant progress in making actionable Earth system predictions accessible to anyone.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 1; 2026: 16.

23 papers.

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  1. 2024WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learningcited by 12
  2. 2024DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuningcited by 11
  3. 2026Autonomous Research Agents: A Survey of AI Scientists and the Verification Gapcited by 6
  4. 2024OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observationscited by 5
  5. 2024MATEY: multiscale adaptive foundation models for spatiotemporal physical systemscited by 4
  6. 2026Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoningcited by 3
  7. 2024Towards Knowledge Guided Pretraining Approaches for Multimodal Foundation Models: Applications in Remote Sensingcited by 2
  8. 2024WxC-Bench: A Novel Dataset for Weather and Climate Downstream Taskscited by 2
  9. 2026AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecastingcited by 1
  10. 2026HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RLcited by 1
  11. 2026The physics of AI weather modelscited by 1
  12. 2026Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernelscited by 1
11 more
  1. 2026Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metricscited by 1
  2. 2026VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecastingcited by 1
  3. 2025GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reductioncited by 0
  4. 2026Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulationscited by 0
  5. 2026MR-GNF: Multi-Resolution Graph Neural Forecasting on Ellipsoidal Meshes for Efficient Regional Weather Predictioncited by 0
  6. 2026Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Modelcited by 0
  7. 2026Nipping the Butterfly Effect in the Bud: Self-Output Fine-Tuning for Autoregressive Weather Predictioncited by 0
  8. 2026Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscalingcited by 0
  9. 2026Catching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature Permutationscited by 0
  10. 2026Tracing the Unlabeled Storm: Cross-Variable Transfer in a Lagrangian Atmospheric JEPA Frameworkcited by 0
  11. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applicationscited by 0