This study by Feiyu Lu incorporates deep learning methods to compliment and improve the ensemble Kalman filter data assimilation algorithm. More specifically, a convolutional neural network is trained to predict the error statistics of a model ensemble, which will reduce the computational costs of the ensemble Kalman filter. This approach is tested in a quasi-geostrophic dynamic model to demonstrate its feasibility in more advanced weather and climate applications.