🎙️ Episode 21 — AtmoRep: A Stochastic Model of Atmospheric Dynamics Using Large-Scale Representation Learning

This week, we explore AtmoRep, a novel task-independent AI model for simulating atmospheric dynamics. Built on large-scale representation learning and trained on ERA5 reanalysis data, AtmoRep delivers strong performance across a variety of tasks—without needing task-specific training.

🔍 Highlights from the episode:

• Introduction to AtmoRep, a stochastic computer model leveraging AI to simulate the atmosphere.

• Zero-shot capabilities for nowcasting, temporal interpolation, model correction, and generating counterfactuals.

• Outperforms or matches state-of-the-art models like Pangu-Weather and even ECMWF's IFS at short forecast horizons.

• Fine-tuning with additional data, like radar observations, enhances performance—especially for precipitation forecasts.

• Offers a computationally efficient alternative to traditional numerical models, with potential for broader scientific and societal applications.

📚 Read the paper: https://doi.org/10.48550/arXiv.2308.13280

✍️ Citation:
Lessig, Christian, et al. "AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning." arXiv:2308.13280 (2023)