U‐Net Kalman Filter (UNetKF): An example of machine learning‐assisted data assimilation

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 improve the localized error 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 successfully implemented in U‐Net Kalman Filter (UNetKF) experiments to predict localized error covariances that possess adaptive localization and some state‐dependent features of the model error covariances. UNetKF is compared to traditional 3‐dimensional variational (3DVar), ensemble 3DVar (En3DVar) and EnKF methods.