Graphical Abstract

Tsuyuki, T., and R. Tamura, 2022: Nonlinear data assimilation by deep learning embedded in an ensemble Kalman filter. J. Meteor. Soc. Japan, 100, 533-553.
Graphical Abstract Published


Plain Language Summary: As an alternative to the particle filter for high-dimensional systems, a nonlinear data assimilation method based on deep learning is proposed, in which deep neural networks (DNNs) are locally embedded in the ensemble Kalman filter (EnKF). This method is named the deep learning-ensemble Kalman filter (DL-EnKF). Results of data assimilation experiments using three versions of the Lorenz 96 model and an EnKF with a small ensemble show that the DL-EnKF is superior to the EnKF in terms of accuracy in strongly nonlinear regimes.