GraphCast takes over only ensemble perturbations: a month of real-observation cycling keeps background errors at least level with a physics model
Placing the machine-learning weather model GraphCast inside a real data-assimilation cycle, with the mean still supplied by the physics-based MPAS model and GraphCast evolving only ensemble perturbations, gives ensemble spread and covariance structure comparable to an all-physics baseline over one month; the EnVar background RMSE against GFS analyses is consistently lower below model level 40, while degradation appears near the model top from interpolation and extrapolation.