Different Schemes for Data Assimilation for the Lorenz

Data assimilation is a step for improving forecasting process by means of a weighted combination between observational and data from a mathetical model. This procedure is essential for operational prediction centers for weather, ocean circulation, and atmospheric pollution. The goal here is to compare four schemes for data assimilation: Kalman filter [1, 2, 4, 5], optimal interpolation [1, 2, 4], variational approach [1, 2, 4], and artificial neural networks (ANN) [3, 5]. The multilayer perceptron is the approach used to implement the ANN. The assimilation techniques are tested on the Lorenz dynamical system. It is imorportant to note that ANN is a method recently developed, and its application is under study. However, all thecniques tested presented a good performance, depending on the ratio of sampling of the observations. From computational point of view, the ANN presented a best perfoamance, considering only a trained ANN