Data assimilation by artificial neural networks for the global FSU atmospheric model: Surface pressure

Abstract

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 model (AGCM) for the Florida State University in USA. The Local Ensemble Transform Kalman filter (LETKF) is implemented with Florida State University Global Spectral Model (FSUGSM). LETKF is a version of Kalman filter with Monte-Carlo ensembles of short-term forecasts to solve the data assimilation problem. FSUGSM is a multilevel spectral primitive equation model with a vertical sigma coordinate, at resolution T63L27. The LETKF data assimilation experiments are based in simulated observations data. For the NN data assimilation scheme, we use Multilayer Perceptron (MLP-DA) with supervised training algorithm where NN receives input vectors with their corresponding response from LETKF scheme. The surface pressure results are presented. An self-configuration method finds the optimal NN and configures the MLP-DA in this experiment. The NNs were trained with data from each month of 2001, 2002 and 2003. A experiment for data assimilation cycle using MLP-DA was performed with simulated observations for January of 2004. The results demonstrate the effectiveness of the ANN technique for atmospheric data assimilation, with similar quality to LETKF analyses.