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# GraphCast

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  1. arXiv

    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.

    Simon Toedtli（First author）
    GraphCastData AssimilationCovariance EstimationMachine learning weather forecasting
  2. researchsquare.com

    An AI weather model reads the lower stratosphere: wiping eddies above 150 hPa shifts northern Z500 by 55 m at 120 hours

    Whether machine-learning weather models such as GraphCast actually use lower-stratospheric and moisture information had lacked controlled input interventions. An audit over 72 ten-day ERA5 forecasts from 2016 to 2021 replaced the atmosphere above 150 hPa with its zonal mean, shifting Northern Hemisphere Z500 by 55.2 m at 120 hours, 1.49 times the response to random-phase noise with identical spectra; global humidity scaling moved 30–70°N Z500 eddy variance monotonically, down to 0.661 of control under complete drying.

    Yi Zou（First author, Corresponding author）
    GraphCastInformation-use auditStratosphere–troposphere couplingGlobal humidity scalingMachine-learning weather prediction

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