用于评估物理一致性的基于机器学习的天气模型的空间泛化测试
Spatial Generalization Tests for Machine Learning-based Weather Models to Assess Physical Consistency
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中文总结 AI 辅助
研究基于机器学习的天气模型在空间泛化方面的问题，提出通过反转或旋转行星经纬度并调整边界条件等进行测试的方法，应用于GraphCast和NeuralGCM，发现其存在问题，强调模型应通过泛化测试防过拟合。
中文摘要 AI 辅助
基于机器学习的天气预报正通过从当前气候的天气数据中学习来革新天气预报。然而，推广到其他气候仍然是一个重大挑战。随着海冰融化、土地利用变化和海洋温度上升，边界条件正在改变。因此，时间上的泛化取决于空间上的泛化。在此，我们提出三个测试案例来评估基于机器学习的天气和气候模型在空间上是否能泛化，并将其应用于GraphCast和NeuralGCM。我们在模型坐标系下反转或旋转行星的经度或纬度，并相应地调整所有边界条件和强迫。基于物理的大气环流模型模拟旋转/反转的行星时只有舍入误差，但GraphCast和NeuralGCM未能通过这些测试。分析还揭示了基于相关性而非因果关系的非物理变量映射。我们认为基于机器学习的气候模型应设计为通过泛化测试，以防止过度拟合当前区域气候。
英文摘要
Machine learning-based weather prediction is revolutionizing weather forecasting by learning from weather data in present-day climate. However, generalization to other climates remains a major challenge. With melting sea ice, land-use change, and increasing ocean temperatures, boundary conditions are changing. Therefore, generalization in time depends on generalization in space. Here, we present three test cases to evaluate whether machine learning-based weather and climate models generalize in space and apply them to GraphCast and NeuralGCM. We reverse or rotate the planet in longitude or latitude under the model's coordinate system and adapt all boundary conditions and forcings accordingly. Physics-based general circulation models simulate a rotated/reversed planet with only rounding errors, but GraphCast and NeuralGCM fail these tests. The analyses furthermore revealed unphysical variable mappings based on correlation rather than causation. We argue that machine learning-based climate models should be designed to pass generalization tests to prevent overfitting on present-day regional climate.
