Ensemble data assimilation to diagnose AI-based weather prediction model: A case with ClimaX version 0.3.1
This work addresses the limited research on data assimilation for AI weather models, offering a diagnostic tool for model properties, though it is incremental in applying existing ensemble methods to a new AI model.
The study tackled the challenge of integrating AI-based weather prediction models with data assimilation by implementing an ensemble Kalman filter with ClimaX, demonstrating stable cycling and revealing weaker error growth compared to dynamical models, requiring higher inflation factors.
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 proposes using ensemble data assimilation for diagnosing AI-based weather prediction models, and marked the first successful implementation of ensemble Kalman filter with AI-based weather prediction models. Our experiments with an AI-based model ClimaX demonstrated that the ensemble data assimilation cycled stably for the AI-based weather prediction model using covariance inflation and localization techniques within the ensemble Kalman filter. 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. In addition, ensemble data assimilation revealed that error growth based on ensemble ClimaX predictions was weaker than that of dynamical NWP models, leading to higher inflation factors. A series of experiments demonstrated that ensemble data assimilation can be used to diagnose properties of AI weather prediction models such as physical consistency and accurate error growth representation.