Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?

arXiv:2608.25835 · physics.ao-ph, cs.AI · Submitted 2026-08-26 · Read on arXiv

physics.ao-ph, cs.AI

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/HartmutBorth/PLASIM

Terminology

• Transformer — 51× in this paper · explained in 2 episodes
• Accuracy — 25× in this paper · explained in 1 episode
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Loss Function — 8× in this paper · explained in 2 episodes
• Adam — 6× in this paper · explained in 2 episodes
• AI Weather Models — 5× in this paper · explained in 1 episode
• Chaotic Dynamics — 5× in this paper · explained in 2 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Scaling Laws — 3× in this paper · explained in 4 episodes
• AdamW Optimizer — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Curse of Dimensionality — 2× in this paper · explained in 6 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Approximation Bound — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Center and Data Science Institute — 1× in this paper
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Assimilation — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• European Centre for MediumRange Weather Forecasts — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Ornstein–Uhlenbeck Process — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
• Decision-oriented benchmarking to transform AI weather forecast access: Application to the Indian monsoon
• Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0
• Designing probabilistic AI monsoon forecasts to inform agricultural decision-making
• AIMIP Phase 1: systematic evaluations of AI weather and climate models
• Predicting Beyond Training Data via Extrapolation versus Translocation: AI Weather Models and Dubai's Unprecedented 2024 Rainfall
• Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
• The physics of AI weather models
• HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model
• Scaling Laws of Global Weather Models
• On Neural Scaling Laws for Weather Emulation through Continual Training
• Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere
• Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
• Physics constraints and response validation in discrete-time reduced-order modeling: from idealized turbulent systems to climate dynamics

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