Predicting the weather is dependent on the initial states specified in the computer model used to make the prediction. The data assimilation (DA) schemes are state-estimation techniques to generate an appropriated initial states for numerical models. DA deals with observations and data from the nonlinear dynamical models, both data set are very large in use on operational weather centers. The output from the DA procedure is called analysis. Some DA techniques become computationally intensive.

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... e artificial neural networks (NN) can be employed to improve the computational performance. Two DA schemes are analized here: the Local Ensemble Transform Kalman Filter [17] , and a version of a variational assimilation method [2] named the representer method. The EnKF was applied to a 3D atmospheric global spectral model (SPEEDY model), while the representer scheme was applied to the 2D shallow-water modelfor simulating the ocean circulation. These DA techniques were emulated by multilayer perpectron neural network (MLP-NN). The goal of this paper is to show the speed up for the DA computer performance in comparison to the methods emulated. The data assimilation process by NN preserves the analysis quality of the former DA techniques. In our experiments, the NN applied to DA on the SPEEDY model was 75 times faster than EnKF.