A Hybrid 3DVar-EnKF Data Assimilation Approach Based on Multilayer Perceptron

The quality and accuracy of Numerical Weather Prediction (NWP) is based on its initial conditions (ICs), boundary conditions and forecast models. Data assimilation (DA) is a crucial procedure to optimally estimate the actual atmospheric state (known as the analysis field) as ICs for NWP by integrating available information, including the observation and the background field. Instead of only focusing on the speed-up for DA in virtue of the customized neural networks, this paper exploratively introduces the spatial-temporal peculiarities to construct a new hybrid data assimilation approach based on multilayer perceptron (MLP); and, its effectiveness and validity are verified in two classical nonlinear dynamic models. The results of experiments demonstrate that the Cache-MLP generally produces similar or smaller root mean square errors (RMSE) with much less time consuming, compared to the conventional 3D-Var and EnKF DA methods, and noticeably, the Cache-MLP has a more robust representation of turning points in the trajectories of the state variables. The final Backtracked-MLP learns a propriate weight matrix to couple previous two traditional DA methods and increases the accuracy by 10.32% in the Lorenz-63 system while 14.03% in the Lorenz-96 system, in comparison with the empirical hybrid DA method. To some extent, this method could be a reference to further researches to optimize the quality of the analysis field, in the meantime, saving significant computing time and resources by deep learning.