Why Deterministic AI Weather Models Fail at Extremes and Physical Conservation: From the Perspective of Probabilistic Mean Field
This paper argues that deterministic AI weather models fail to capture extremes and conserve physical laws because minimizing mean-square error forces them to predict conditional means rather than full transition distributions, a limitation that probabilistic generative approaches can overcome by better representing intrinsic atmospheric variability.