首个纯数据驱动的气象集合预报系统，实现多年稳定运行。
Bridging Artificial Intelligence and Data Assimilation: The Data-driven Ensemble Forecasting System ClimaX-LETKF
- 用数据驱动方法融合观测数据，构建独立于传统模型的预报系统。
- 放松到先验扰动比放松到先验偏差更稳定且准确，优于传统方法。
- 为机器学习气象预报提升提供关键洞见，适合气象与AI交叉研究者。
尽管基于机器学习的天气预测（MLWP）已取得显著进展，但针对在MLWP模型中同化真实观测或集合预报的研究仍有限。我们提出ClimaX-LETKF，首个完全数据驱动的机器学习集合天气预报系统。该系统在多年尺度上稳定运行，无需依赖数值天气预报（NWP）模型，通过同化美国国家环境预报中心（NCEP）全球高空和地面观测数据。实验表明，相比放松到先验偏差（RTPS），使用放松到先验扰动（RTPP）时系统更具稳定性和准确性；而传统NWP模型则在RTPS下表现更优。RTPP将分析扰动替换为分析与背景扰动的加权组合，而RTPS仅缩放分析扰动。结果还显示，与NWP模型相比，MLWP模型恢复大气状态至其吸引子的能力较弱。本研究为提升MLWP集合预报系统的性能提供了宝贵见解，并推动其向实际应用迈出重要一步。
原文摘要 · Abstract (English)
While machine learning-based weather prediction (MLWP) has achieved significant advancements, research on assimilating real observations or ensemble forecasts within MLWP models remains limited. We introduce ClimaX-LETKF, the first purely data-driven ML-based ensemble weather forecasting system. It operates stably over multiple years, independently of numerical weather prediction (NWP) models, by assimilating the NCEP ADP Global Upper Air and Surface Weather Observations. The system demonstrates greater stability and accuracy with relaxation to prior perturbation (RTPP) than with relaxation to prior spread (RTPS), while NWP models tend to be more stable with RTPS. RTPP replaces an analysis perturbation with a weighted blend of analysis and background perturbations, whereas RTPS simply rescales the analysis perturbation. Our experiments reveal that MLWP models are less capable of restoring the atmospheric field to its attractor than NWP models. This work provides valuable insights for enhancing MLWP ensemble forecasting systems and represents a substantial step toward their practical applications.
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