The process of data assimilation, in which meteorological observations and weather forecasts are merged to provide an analysis field, has been largely studied by the scientific community and operational centers. The 3D-Variational (3D-Var) approach available in the Weather Research and Forecast (WRF) computer model is evaluated for data assimilation for the Terminal Control Area of Rio de Janeiro (TCA-RJ). The basic goal of any variational data assimilation system is to produce an optimal estimate of the atmospheric state at analysis time. The analysis field is estimated from a first guess (previous forecast) and an observation field, weighted by the error matrices. The WRF is designed for nowcasting (forecasts up to 6h) for the TCA-RJ through assimilation cycles using surface, sounding, and wind profile data. The preliminary results show the model sensibility for each observation type and encourage the use of this technique operationally for the support of the air traffic management controlled by the Brazilian Air Force.
The problem of parameter optimization for a feedforward artificial neural network (ANN) to determined its best architecture is addressed. A new metaheuristic called Multiple Particle Collision Algorithm (MPCA), introduced by Luz et al. [12], was applied to design an optimum architecture for two models of supervised neural network: the Multilayer Perceptron (MLP), and recurrent Elman network. The NN obtained using this approach is said to be self-configurable. In addition, two strategies are employed for calculating the connection weights to the MLP and Elman networks: MPCA, and backpropagation algorithm. The resulting ANNs were applied to predict the monthly mesoscale climate for the precipitation field. The comparison is performed between the ANN configuration obtained by automatic process and another configuration proposed by a human specialist.
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