> Here, since weather certainly changes its fundamental patterns over time, there is no way of reliably predicting out-sample performance. This seems overly pe

> Here, since weather certainly changes its fundamental patterns over time, there is no way of reliably predicting out-sample performance. This seems overly pessimistic. The weather doesn't change its fundamental patterns over time; those are governed by the fluid dynamics of the atmosphere, and are largely forced (to first order) by things like the amount of solar radiation received by the sun and the the configuration of the continents - neither of which change much at all on timescales of a few thousand or tens of thousands of years. The total manifold of "realistic" weather patterns - especially trajectories of weather patterns conditioned on one or more prior weather sates - is vastly smaller than it would seem at first glance (there's only so much variation in how a Rossby wave can evolve and there is clear asymptotic behavior with regards to amplitude). I think if you wanted an "ultimate stress test" of an AI weather forecasting system, you could run a a high-fidelity general circulation model equilibrated to the current climate freely for a several thousand year cycle, and randomly draw initial conditions to branch off a 10-day forecast. It shouldn't be _perfect_ (there is likely meaningful skew between the atmospheric state trajectories that arise in this free running simulation compared to reanalysis cycled over the historical observation record for a few decades), but the trends in error growth should be meaningful and directly interpretable in the context of a real-world forecast.