Rosangela S. Cintra

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Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modelled system as its initial condition. This paper shows the results of a data assimilation technique using artificial neural networks (NN) to obtain the initial condition to the atmospheric general circulation(More)
An Artificial Neural Network (ANN) is designed to investigate its application for data assimilation. This procedure provides an appropriated initial condition to the atmosphere to weather forecasting. Data assimilation is a method to insert observational information into a physical-mathematical model. The goal here is the process for assimilating(More)
To generate reliable forecasts, we need good estimates of both the current system state and the model parameters. Numerical weather prediction (NWP) uses atmospheric general circulation models (AGCMs) to predict weather based on current weather conditions. The process of entering observation data into mathematical model to generate the accurate initial(More)
This paper presents an approach for employing an artificial neural network (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data(More)
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