Interpretability of AI weather models via intermediate decoding
Abstract
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 to output the forecast at the end of processing. 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. Applying this framework to the three models yields the following results. First, the skill of forecasts at intermediate stages of processing improves gradually through the processor layers. Second, bypassing all processor blocks shows that the encoder-decoder pair is not an identity mapping returning the input, but instead plays a significant role in the forecast construction. 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. Finally, we assess the physical consistency of the intermediate decoding forecasts and find that large scales remain physically balanced throughout processing, while small scales become more balanced with each additional processor block.
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BibTeX
@misc{beylich2026interpretability,
title = {{Interpretability of AI weather models via intermediate decoding}},
author = {Matthias Beylich and Kirsten I. Tempest and Tobias Selz and George C. Craig},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.24110},
eprint = {2610.24110},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.24110},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Interpretability of AI weather models via intermediate decoding
AU - Matthias Beylich
AU - Kirsten I. Tempest
AU - Tobias Selz
AU - George C. Craig
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.24110
UR - https://arxiv.org/abs/2610.24110
AB - 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 to output the forecast at the end of processing. 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. Applying this framework to the three models yields the following results. First, the skill of forecasts at intermediate stages of processing improves gradually through the processor layers. Second, bypassing all processor blocks shows that the encoder-decoder pair is not an identity mapping returning the input, but instead plays a significant role in the forecast construction. 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. Finally, we assess the physical consistency of the intermediate decoding forecasts and find that large scales remain physically balanced throughout processing, while small scales become more balanced with each additional processor block.
ER -