No abstract

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. The 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.

Abstract. The goal of the present work is to employ artificial neural networks as a data assimilation method applied to shallow water equation. This model is used to represent ocean dynamics. Data assimilation is a computational procedure to combine observation data with model data for identifying the best initial condition (analysis) to an operational prediction system. Here we compare two techniques: representer method and artificial neural network.

The LAG-Clima project leads with web-computing environment for data-grid and processing-grid. The goal is to establish a computer network linking institutions on South America for climate prediction on meso-scale, and to share and analyze data. Software platforms: BRAMS meteorological meso-scale code, OurGrid middleware (grid computing), OAR system for managing the jobs. Resumo: O projeto LAG-Clima quer estabelecer um ambiente de computação em grade de processamento e compartilhamento de dados. O objetivo é manter uma rede de interconexão entre instituições na América do Sul para previsão climática em meso-escala e disponibilizar dados. Plataforma de softwares: BRAMS (código meteorológico de meso-escala), OurGrid (computação em grade), sistema OAR para gerencia de tarefas.

The turbulent kinetic energy (TKE) evolution is analyzed during the morning transition from the neutral stratification to fully convective boundary layer, using analytical spectral models.Parameterizations for wind variance are introduced into 3D spectral models. The TKE for these models has good agree agreement with LES. RESUMOA evolução da energia cinética turbulenta (ECT) é analisada durante a fase de transição matinal, usando modelos espectrais analíticos. Distintas parametrizações para a variância do vento são introduzidas nos modelos espectrais. A evolução da ECT para os modelos espectrais tem boa concordância com resultados da simulação de grandes vortices.