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arXiv · 2606.10642

PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models

Emma Kasteleyn·Timo Maier·Axel Lauer·Veronika Eyring·Pierre Gentine·Ana Lucic
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Abstract

Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs required for traditional physics-based methods. However, they are primarily (1) data-driven and (2) evaluated using pixel-wide error metrics (e.g., RMSE), so there are no guarantees that their forecasts are consistent with known physical laws. We introduce PhysMetrics$.$Weather, an evaluation framework that assesses the physical realism of MLWP models across three types of metrics: conservation, spectral, and dynamical. By quantifying physical realism, this tool guides the development of physics-informed architectures and helps evaluate whether MLWP models are reliable for operational use. Our framework is available on Github at https://github.com/Emmakast/PhysMetrics.Weather.

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Emma Kasteleyn, Timo Maier, Axel Lauer, Veronika Eyring, Pierre Gentine, Ana Lucic. 2026-06-09. PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models. https://arxiv.org/abs/2606.10642

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