Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions

arXiv:2608.30795 · physics.ao-ph, cs.LG · Submitted 2026-08-31 · Read on arXiv

physics.ao-ph, cs.LG

Submitted: 2026-08-31

Updated: 2026-08-31

Comments: 17 pages, 7 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 17× in this paper · explained in 7 episodes
• Verification — 13× in this paper · explained in 2 episodes
• Uncertainty Quantification — 7× in this paper · explained in 8 episodes
• Epistemic Uncertainty — 6× in this paper · explained in 9 episodes
• Aleatoric Uncertainty — 5× in this paper · explained in 4 episodes
• Digital Twin — 4× in this paper · explained in 4 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 3× in this paper
• Monte Carlo dropout — 3× in this paper · explained in 2 episodes
• AI Weather Models — 2× in this paper · explained in 1 episode
• Ensemble of Data Assimilations — 2× in this paper
• Uncertainty Decomposition — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast
• GraphCast: Learning skillful medium-range global weather forecasting
• FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead
• Aardvark weather: end-to-end data-driven weather forecasting
• GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations
• GenCast: Diffusion-based ensemble forecasting for medium-range weather
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
• Uncertainty quantification for data-driven weather models
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
• Bayesian Active Learning for Classification and Preference Learning
• Understanding Measures of Uncertainty for Adversarial Example Detection
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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