Interpretability of AI weather models via intermediate decoding
AI weather prediction models are often regarded as black boxes. Despite their impressive skill, there is little understanding of how the input data is processed during inference to produce the forecast. This limitation arises from the use of high-dimensional latent representations in which individual dimensions are no longer directly interpretable in physical terms. To gain insight into how forecasts are constructed, we propose the method of intermediate decoding for AI weather models and apply it to GraphCast, Aurora and ArchesWeather. In this approach, latent states from intermediate depths are decoded back into meteorological fields. To do this, we use the decoder that has been trained...
| Authors | Matthias Beylich, Kirsten I. Tempest, Tobias Selz et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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