检验机器学习天气模型能否用于数据同化，发现其存在物理不一致问题。
Exploring the Use of Machine Learning Weather Models in Data Assimilation
- 构建图神经网络与神经气候模型的切线与伴随模型，评估其在四维变分同化中的适用性
- 对比发现两类模型伴随响应存在垂直方向噪声，物理一致性不足
- 结果警示：此类模型或影响集合同化系统性能，需改进后才能投入业务应用
机器学习（ML）模型在气象学中备受关注，因其有望提升天气预报的效率与精度。GraphCast 和 NeuralGCM 是两种领先的基于 ML 的天气模型，但它们在数据同化（DA）系统，尤其是四维变分（4DVar）DA 中的适用性仍待探索。本研究评估了 GraphCast 与 NeuralGCM 的切线线性（TL）和伴随（AD）模型，将其与成熟数值天气预测模型 MPAS-A 的结果进行对比，重点考察其对扰动的物理一致性与可靠性。尽管两者的伴随结果在部分区域与 MPAS-A 相似，但在多个垂直层次上仍表现出非物理噪声，提示其在业务化数据同化系统中稳健性存疑。该问题不仅影响 4DVar，还可能导致误差协方差估计偏差和集合预报不可靠，进而降低集合类数据同化系统的整体性能。解决这些挑战对于实现 GraphCast、NeuralGCM 等 ML 模型在业务数据同化系统中的有效集成至关重要，为更精准高效的天气预报铺平道路。
原文摘要 · Abstract (English)
The use of machine learning (ML) models in meteorology has attracted significant attention for their potential to improve weather forecasting efficiency and accuracy. GraphCast and NeuralGCM, two promising ML-based weather models, are at the forefront of this innovation. However, their suitability for data assimilation (DA) systems, particularly for four-dimensional variational (4DVar) DA, remains under-explored. This study evaluates the tangent linear (TL) and adjoint (AD) models of both GraphCast and NeuralGCM to assess their viability for integration into a DA framework. We compare the TL/AD results of GraphCast and NeuralGCM with those of the Model for Prediction Across Scales - Atmosphere (MPAS-A), a well-established numerical weather prediction (NWP) model. The comparison focuses on the physical consistency and reliability of TL/AD responses to perturbations. While the adjoint results of both GraphCast and NeuralGCM show some similarity to those of MPAS-A, they also exhibit unphysical noise at various vertical levels, raising concerns about their robustness for operational DA systems. The implications of this study extend beyond 4DVar applications. Unphysical behavior and noise in ML-derived TL/AD models could lead to inaccurate error covariances and unreliable ensemble forecasts, potentially degrading the overall performance of ensemble-based DA systems, as well. Addressing these challenges is critical to ensuring that ML models, such as GraphCast and NeuralGCM, can be effectively integrated into operational DA systems, paving the way for more accurate and efficient weather predictions.
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