用物理引导的融合方法，让机器学习直接从多样观测数据预报全球天气。
XiChen: A global weather observation-to-forecast machine learning system via four-dimensional variational gradient-guided flexible assimilation
- 基于四维变分梯度构建统一状态空间，灵活融合不同来源观测数据。
- 在多个指标上达到与业务数值预报系统相当的预测精度。
- 适合需要应对多源、动态变化观测数据的气象模型研发者使用。
机器学习在天气预报中展现巨大潜力，但多数系统仍依赖数值天气预报（NWP）生成的初始条件。端到端的机器学习模型虽试图摆脱这一依赖，却常需为特定观测设计编码器，且当观测源改变时需重新设计或训练，限制了实际应用的鲁棒性。本文提出XiChen，一种基于四维变分（4DVar）梯度引导的柔性同化机制的全球气象观测到预报机器学习系统。我们证明，4DVar代价函数的梯度可作为物理一致的接口，将异构观测映射至统一状态空间。该新范式使XiChen能够灵活同化常规及原始卫星观测，同时保持物理一致性。实验表明，该系统在预报性能上与现有业务NWP系统相当。本工作为实现具有异构和动态观测能力的实用化机器学习全球天气预报系统提供了可行且物理一致的路径。
原文摘要 · Abstract (English)
Machine Learning (ML) has shown great promise in revolutionizing weather forecasting, yet most ML systems still rely on initial conditions generated by Numerical Weather Prediction (NWP) systems. End-to-end ML models aim to eliminate this dependency, but they often rely on observation-specific encoders and require redesign or retraining when observation sources change, thereby limiting their operational robustness. Here, we introduce XiChen, a global weather observation-to-forecast ML system via four-dimensional variational (4DVar) gradient-guided flexible assimilation. We demonstrate that the gradient of the 4DVar cost function serves as a physically grounded interface that maps heterogeneous observations into a common state space. This novel formulation enables XiChen to flexibly assimilate diverse conventional and raw satellite observations while preserving physical consistency. Experiments show that the system achieves forecasting metrics competitive with operational NWP systems. This work provides a practical and physically consistent route toward operational ML-based global weather forecasting systems with heterogeneous and evolving observations.
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