Comment by counters

It doesn't take care of the errors. They still "accumulate" over time, leading to the same divergence that traditional physics-based weather models experience. In fact, the hallmark that these AI models are _doing things right_ is they show realistic modes of error growth when compared with those physics-based models - and there is already early peer-reviewed literature suggesting this is the case.

This _class_ of models (not Aurora, or Silurian's model specifically) can potentially improve on this a bit by incorporating forecast error at longer lead times in their core training loss. This is already done in practice for some major models like GraphCast and Stormer. But these models are almost certainly not a magical silver bullet for 10x'ing forecast accuracy.