面向全球AI天气预报的域自适应数据同化
Domain-Adaptive Data Assimilation for Global AI Weather Forecasting
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中文总结 AI 辅助
针对AI天气预报模型因训练与业务初始条件不匹配导致的技能损失，提出观测引导的域自适应数据同化框架，仅优化初始扰动，在五个全球模型上显著减少短时预报误差。
中文摘要 AI 辅助
AI天气预报模型通常基于ERA5再分析资料进行训练，而ERA5无法实时获取。因此，业务化部署依赖于由数值或AI分析系统产生的初始条件，这些初始条件与训练时遇到的条件不同。这种不匹配会降低预报技能，而为每个分析系统重新训练模型则代价高昂。在此，我们提出了域自适应数据同化（DADA），一种观测引导的框架，可将外部分析资料适配到预训练的AI天气预报模型。从背景状态出发，DADA仅优化初始状态扰动，同时保持预报模型冻结。扰动状态通过模型传播，其短时轨迹通过学习的观测算子受真实世界观测约束。由此产生的初始条件同时受观测约束和目标模型学习的动力学影响。我们使用来自全球预报系统（GFS）和基于AI的HealDA的背景场，在五个全球AI天气预报模型上评估了DADA。在确定性和概率性预报中，DADA显著减少了由初始条件来源变化引起的短时技能损失。更广泛地，DADA将观测转化为独立开发的分析与预报系统之间的通用接口，使预训练的AI天气预报模型能够适应不断变化的业务初始条件，而无需重建ERA5或重新训练预报模型。
英文摘要
AI weather forecasting models are commonly trained on the ERA5 reanalysis, which is unavailable in real time. Operational deployment therefore relies on initial conditions produced by numerical or AI analysis systems that differ from those encountered during training. This mismatch can degrade forecast skill, while retraining for every analysis system is costly. Here, we present Domain-Adaptive Data Assimilation (DADA), an observation-guided framework that adapts external analyses to pretrained AI weather models. Starting from a background state, DADA optimizes only an initial-state perturbation while keeping the forecast model frozen. The perturbed state is propagated through the model, and its short-range trajectory is constrained by real-world observations through a learned observation operator. The resulting initial condition is shaped jointly by observational constraints and the dynamics learned by the target model. We evaluate DADA across five global AI weather models using backgrounds from the Global Forecast System and the AI-based HealDA. Across deterministic and probabilistic forecasts, DADA substantially reduces short-range skill loss caused by changes in the initial-condition source. More broadly, DADA turns observations into a common interface between independently developed analysis and forecasting systems, enabling pretrained AI weather models to accommodate evolving operational initial conditions without reconstructing ERA5 or retraining the forecast model.
发表机构
- Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
- NVIDIA（英伟达）
- National Institute of Meteorological Sciences (NIMS)（国家气象科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
