Machine learning–driven advances in geophysical data assimilation

Machine learning (ML), particularly deep learning (DL), is becoming increasingly central to geophysical data assimilation (DA), serving to enhance classical methods, complement them, or potentially replace parts of the DA cycle altogether. This talk reviews recent developments and outlines promising directions for integrating ML into DA, and ultimately improve forecasting in the geosciences. For instance, ML can be used to develop auto-differentiable emulators for dynamics, parametrisations, or model-error corrections, which can be seamlessly incorporated into variational DA frameworks. ML also enables adaptive and efficient exchanges of information among the state, observation, and latent spaces in which DA analysis computations occur. In ensemble DA, ML can improve forecast ensemble generation and facilitate the efficient tuning of hyperparameters through auto-differentiable DA implementations. Moreover, ML opens the possibility of learning and replacing the analysis step, or even the full DA and forecast cycle, in an end-to-end manner. I will illustrate these opportunities with two examples: one in which DL is used to discover new and efficient analysis operators, and another in which generative AI is embedded within classical DA schemes.