Artificial intelligence (AI)-based weather prediction research is growing rapidly and has shown to be competitive with the advanced dynamic numerical weather prediction models. However, research combining AI-based weather prediction models with data assimilation remains limited partially because long-term sequential data assimilation cycles are required to evaluate data assimilation systems. This study explores integrating the local ensemble transform Kalman filter (LETKF) with an AI-based weather prediction model ClimaX. Our experiments demonstrated that the ensemble data assimilation cycled stably for the AI-based weather prediction model using covariance inflation and localization techniques inside the LETKF. While ClimaX showed some limitations in capturing flow-dependent error covariance compared to dynamical models, the AI-based ensemble forecasts provided reasonable and beneficial error covariance in sparsely observed regions. These findings highlight the potential of AI models in weather forecasting and the importance of physical consistency and accurate error growth representation in improving ensemble data assimilation.

翻译：基于人工智能的天气预测研究正迅速发展，并已展现出与先进动力数值天气预报模型相竞争的能力。然而，将基于AI的天气预测模型与数据同化相结合的研究仍然有限，部分原因在于评估数据同化系统需要长期连续的数据同化循环。本研究探索了将局部集合变换卡尔曼滤波与基于AI的天气预测模型ClimaX相集成。实验表明，通过在LETKF内部采用协方差膨胀和局地化技术，集合数据同化能够在基于AI的天气预测模型中稳定循环运行。尽管与动力模型相比，ClimaX在捕捉流依赖误差协方差方面存在一定局限性，但基于AI的集合预报在稀疏观测区域提供了合理且有益的误差协方差。这些发现凸显了AI模型在天气预报中的潜力，以及物理一致性和准确误差增长表征在改进集合数据同化中的重要性。