Li Zongheng, Peng Jun, Zhang Lifeng, Wu Hanyan, Zhang Wei, Zhu Juan
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China.
Zhangzhou Base Preparation Office, National Ocean Technology Center, Xiamen, China.
Sci Rep. 2025 May 3;15(1):15504. doi: 10.1038/s41598-025-99815-x.
It is an urgent need to understand the ability of current artificial intelligence (AI) models in simulating atmospheric mesoscale aspects. This paper compares mesoscale kinetic energy spectra from an 11-day experiment simulated by a novel AI-based model (Pangu) and a physics-based model (MPAS), using ERA5 reanalysis as a reference. Based on the commonly used evaluation metrics of latitude weighted root mean square error (RMSE) and anomaly correlation coefficient (ACC), the AI-based model has better short to medium-range weather forecasting skill compared to the physics-based model. However, the AI-based model cannot replicate the mesoscale - 5/3 spectral slope and underestimates the mesoscale energy at wavelength smaller than 1000 km. As altitude increases and scale decreases, the deviation of the AI-based model from the reanalysis significantly increases. These features prove that the AI-based model has the lower effective resolution compared to the physics-based model with the close nominal resolution. Compared to the physics-based simulations, AI-based model has stronger downscale energy flux at larger mesoscales, which is dominated by divergent kinetic energy flux. But it rapidly becomes the weakest at smaller mesoscales. The diagnosed vertical velocity of AI-based model and its related budget terms are closest to those of the reanalysis at large scales. Overall, the AI-based model Pangu shows closer agreement with ERA5 at large scales, likely due to its use of the latter as training data, but significantly underestimates mesoscale kinetic energy compared to the physics-based model MPAS. Note that these findings are specific to the models and configurations used and should be interpreted with caution.
了解当前人工智能（AI）模型在模拟大气中尺度方面的能力迫在眉睫。本文以ERA5再分析为参考，比较了基于新型人工智能模型（盘古）和基于物理模型（MPAS）模拟的11天实验中的中尺度动能谱。基于常用的纬度加权均方根误差（RMSE）和异常相关系数（ACC）评估指标，与基于物理的模型相比，基于人工智能的模型具有更好的中短期天气预报技能。然而，基于人工智能的模型无法复制中尺度-5/3谱斜率，并且低估了波长小于1000公里的中尺度能量。随着海拔升高和尺度减小，基于人工智能的模型与再分析的偏差显著增加。这些特征证明，与具有相近标称分辨率的基于物理的模型相比，基于人工智能的模型具有较低的有效分辨率。与基于物理的模拟相比，基于人工智能的模型在较大中尺度上具有更强的降尺度能量通量，这主要由发散动能通量主导。但在较小中尺度上它很快变得最弱。基于人工智能的模型的诊断垂直速度及其相关的收支项在大尺度上最接近再分析结果。总体而言，基于人工智能的盘古模型在大尺度上与ERA5的一致性更高，这可能是由于其使用后者作为训练数据，但与基于物理的MPAS模型相比，中尺度动能明显被低估。请注意，这些发现特定于所使用的模型和配置，应谨慎解读。
