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.

翻译：机器学习天气预报模型在实现出色预测性能的同时，仅需传统基于物理方法所需计算成本的一小部分。然而，这类模型主要（1）依赖数据驱动，且（2）采用像素级误差指标（如均方根误差）进行评估，因此无法保证其预测结果符合已知物理定律。我们提出PhysMetrics.Weather评估框架，该框架通过三类指标（守恒性、谱特性与动力特性）对机器学习天气预报模型的物理真实性进行量化评估。该工具通过量化物理真实性，可指导物理信息架构的发展，并帮助判断机器学习天气预报模型是否适用于业务化运行。本框架已在GitHub（https://github.com/Emmakast/PhysMetrics.Weather）上开源。