U-Net Kalman Filter (UNetKF): An Example of Machine Learning-assisted Ensemble Data Assimilation
<|reference_start|>U-Net Kalman Filter (UNetKF): An Example of Machine Learning-assisted Ensemble Data Assimilation: Machine learning techniques have seen a tremendous rise in popularity in weather and climate sciences. Data assimilation (DA), which combines observations and numerical models, has great potential to incorporate machine learning and artificial intelligence (ML/AI) techniques. In this paper, we use U-Net, a type of convolutional neutral network (CNN), to predict the localized ensemble covariances for the Ensemble Kalman Filter (EnKF) algorithm. Using a 2-layer quasi-geostrophic model, U-Nets are trained using data from EnKF DA experiments. The trained U-Nets are then used to predict the flow-dependent localized error covariance matrices in U-Net Kalman Filter (UNetKF) experiments, which are compared to traditional 3-dimensional variational (3DVar), ensemble 3DVar (En3DVar) and EnKF methods. The performance of UNetKF can match or exceed that of 3DVar, En3DVar or EnKF. We also demonstrate that trained U-Nets can be transferred to a higher-resolution model for UNetKF implementation, which again performs competitively to 3DVar and EnKF, particularly for small ensemble sizes.<|reference_end|>
@article{lu2024u-net,
    title={U-Net Kalman Filter (UNetKF): An Example of Machine Learning-assisted
  Ensemble Data Assimilation},
    author={Feiyu Lu},
    journal={arXiv preprint arXiv:2403.12366},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12366},
    primaryClass={cs.LG physics.ao-ph}
}