arXiv Machine Learning September 17, 2026 By Younes Elberkennou, Dmitri Demler, Thierry Meier, Luca Rispoli, Fanny Lehmann, Joel Oskarsson

Every Fixed Metric Has a Blind Spot: A Learned Atmospheric Critic for Scoring Forecast Realism

Read the original on arXiv Machine Learning →

The paper introduces a learned atmospheric critic that discriminates between real weather data and model outputs to produce a realism score. Unlike fixed metrics, the discriminator adapts to the specific failure modes of a given model, effectively detecting various synthetic corruptions in ERA5 data. Experiments show the learned critic outperforms existing metrics and reveals that realism decreases with longer forecast lead times, favoring numerical over machine‑learning models.

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