Interpretability of AI weather models via intermediate decoding
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.
ProofPaper ↗
Key points
- AI weather prediction models are often regarded as black boxes.
- In this approach, latent states from intermediate depths are decoded back into meteorological fields.
- Since latent states at different depths do not share a consistent basis, we train affine transformations that align intermediate latent states with the decoder's expected input space.
- Third, by examining increments of individual processor blocks across model depth, we show that all three models progressively refine the forecast from coarse to finer spatial scales.
Sources (1)
- [1]Interpretability of AI weather models via intermediate decodingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 06:36 PM
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.
AI weather prediction models are often regarded as black boxes.
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