Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models
Read the original on arXiv Machine Learning →The paper examines kinetic energy (KE) spectra and KE transfer across spatial scales in four leading probabilistic machine learning weather prediction models—NeuralGCM-ENS, FourCastNet 3, AIFS-ENS, and GenCast—by comparing them to the physics‑based IFS‑ENS model. NeuralGCM‑ENS captures the expected upscale KE transfer but underestimates mesoscale KE due to noise injection at its encoder, while AIFS‑ENS, GenCast, and FourCastNet 3 produce realistic KE magnitudes yet fail to show the expected upscale transfer, with AIFS‑ENS and GenCast accumulating KE at high wavenumbers because of spatially uncorrelated stochastic perturbations. All models display an upscale shift of the difference KE (DKE) spectral peak over time, indicating error growth, but none reproduce the rapid initial growth of ensemble spread at small scales associated with the butterfly effect, highlighting a misrepresentation of kinetic energy scale transfer despite overall skillful forecasts.
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