# A Foundation Model for the Earth System

arXiv: https://arxiv.org/abs/2405.13063 · HTML: https://mlsanity.com/paper/2405.13063/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2024.

## Built on by (most cited first, 25 of 25)

- [WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning](https://mlsanity.com/paper/2411.05420/) (2024)
- [DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning](https://mlsanity.com/paper/2411.07239/) (2024)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations](https://mlsanity.com/paper/2412.18239/) (2024)
- [MATEY: multiscale adaptive foundation models for spatiotemporal physical systems](https://mlsanity.com/paper/2412.20601/) (2024)
- [Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning](https://mlsanity.com/paper/2604.18715/) (2026)
- [Towards Knowledge Guided Pretraining Approaches for Multimodal Foundation Models: Applications in Remote Sensing](https://mlsanity.com/paper/2407.19660/) (2024)
- [WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks](https://mlsanity.com/paper/2412.02780/) (2024)
- [The physics of AI weather models](https://mlsanity.com/paper/2605.23778/) (2026)
- [Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics](https://mlsanity.com/paper/2607.01966/) (2026)
- [AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting](https://mlsanity.com/paper/2603.17529/) (2026)
- [HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL](https://mlsanity.com/paper/2605.17792/) (2026)
- [Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels](https://mlsanity.com/paper/2606.02886/) (2026)
- [VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting](https://mlsanity.com/paper/2608.09286/) (2026)
- [Tracing the Unlabeled Storm: Cross-Variable Transfer in a Lagrangian Atmospheric JEPA Framework](https://mlsanity.com/paper/2608.22358/) (2026)
- [LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications](https://mlsanity.com/paper/2608.23058/) (2026)
- [GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction](https://mlsanity.com/paper/2509.10308/) (2025)
- [Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulations](https://mlsanity.com/paper/2601.12614/) (2026)
- [MR-GNF: Multi-Resolution Graph Neural Forecasting on Ellipsoidal Meshes for Efficient Regional Weather Prediction](https://mlsanity.com/paper/2603.13563/) (2026)
- [Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model](https://mlsanity.com/paper/2604.25159/) (2026)
- [Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions](https://mlsanity.com/paper/2605.09870/) (2026)
- [Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems](https://mlsanity.com/paper/2605.19366/) (2026)
- [Nipping the Butterfly Effect in the Bud: Self-Output Fine-Tuning for Autoregressive Weather Prediction](https://mlsanity.com/paper/2607.21080/) (2026)
- [Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling](https://mlsanity.com/paper/2608.12271/) (2026)
- [Catching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature Permutations](https://mlsanity.com/paper/2608.14372/) (2026)

## 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.