  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.
