Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation

This paper presents an approach for employing artificial neural networks (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 simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses is of order 0.02. The simulations show that the major advantage of using the MLP-NN is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN is 90 times faster than cycle assimilation with LETKF for the numerical experiment.Comment: 17 pages, 16 figures, monthly weather revie

Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model

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 conditions is called data assimilation (DA). It combines observations, forecasting, and filtering step. This paper presents an approach for employing artificial neural networks (NNs) to emulate the local ensemble transform Kalman filter (LETKF) as a method of data assimilation. This assimilation experiment tests the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localizations of meteorological balloons. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLPs) networks are applied. After the training process, the method, forehead-calling MLP-DA, is seen as a function of data assimilation. The NNs were trained with data from first 3 months of 1982, 1983, and 1984. The experiment is performed for January 1985, one data assimilation cycle using MLP-DA with synthetic observations. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses are of order 10–2. The simulations show that the major advantage of using the MLP-DA is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-DA analyses is 90 times faster than LETKF cycle assimilation with the mean analyses used to run the forecast experiment

Neuro-estimador do Ciclo Diurno de Co2V

The emission rate of minority atmospheric gases is inferred bya new approach based on artificial neural network (ANN) multilayerperceptron (MLP). Synthetic data are used for training the ANN. Theinverse solution is obtained by applying the ANN to identify the diurnalcycle for the rate of carbon dioxide on an area with different vegetationcovering: pasture and rainforest.A taxa de emissão dos gases minoritários da atmosfera é estimadapor uma nova abordagem baseada na rede neural artificial (RNA)multilayer perceptron (MLP). Dados sintéticos são usados para treinar arede. A solução inversa é obtida com aplicação da RNA para identificar ataxa do ciclo diurno do dióxido de carbono em uma área com coberturavegetal variável: pastagem e floresta tropical

Kalman Filtering in the Air Quality Monitoring

Data assimilation is a process where an improved prediction is obtained from a weighted combination between experimental measurements and mathematical model data. In the present work this procedure is applied to pollutant atmospheric dispersion by using a Kalman filter (KF). This is interesting approach, because the KF gives an output in which the balance between the data from the diffusion model and the experimental data is done automaticaly, through the Kalman gain. In addition, the Kalman filter computes the propagation of the error

Nocturnal Jet Simulation Under Neutral Conditions by Theoretical Model

O jato noturno, ou jato de baixos níveis, ocorre normalmente emnoites de céu claro, i.e., sob condições estáveis. Aqui é analisada a ocorrênciado jato noturno em condições neutras por meio de um modelo teórico

Heisenberg.s Turbulent Spectral Theory Determining the Filtering Procedure in les Models

Heisenberg’s turbulent spectral theory determining thefiltering procedure in LES model

Sistema de Previsão da Qualidade do Ar para o Vale do Paraíba

Sistema de previsão da qualidade do ar para oVale do Paraíb

Convective Boundary Layer Growth: Analytical and Numerical Approaches

The transition phase from neutral boundary layer to convectiveboundary layer (CBL) is the focus of this paper. The characteristics ofthe turbulent kinetic energy (TKE) during the growth phase of the CBLare analyzed with the help of two analytical spectral models. The TKEevolutions generated by the analytical models agree fairly well with theresults of Large Eddy Simulation for different vertical levels.A fase de transição de uma camada limite neutra para uma camadalimite convectiva (CLC) é tópico do presente artigo. As característicasda energia cinética turbulenta (ECT) durante a fase de crescimentoda CLC são analisadas com o uso de dois modelos espectrais analíticos.A evolução gerada pelos modelos analíticos concorda bem com os resultadosde simulação de grandes turbilhões para diferentes níveis verticais