Rotating or reversing the simulated planet reveals that GraphCast and NeuralGCM encode present-day geography rather than spatially invariant physics, while a traditional GCM passes the same tests to numerical precision.
URL https://joss.theoj.org/papers/10.21105/joss.06323
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Spatial Generalization Tests for Machine Learning-based Weather Models to Assess Physical Consistency
Rotating or reversing the simulated planet reveals that GraphCast and NeuralGCM encode present-day geography rather than spatially invariant physics, while a traditional GCM passes the same tests to numerical precision.