41 research outputs found
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
A new gravitational N-body simulation algorithm for investigation of cosmological chaotic advection
Recently alternative approaches in cosmology seeks to explain the nature of
dark matter as a direct result of the non-linear spacetime curvature due to
different types of deformation potentials. In this context, a key test for this
hypothesis is to examine the effects of deformation on the evolution of large
scales structures. An important requirement for the fine analysis of this pure
gravitational signature (without dark matter elements) is to characterize the
position of a galaxy during its trajectory to the gravitational collapse of
super clusters at low redshifts. In this context, each element in an
gravitational N-body simulation behaves as a tracer of collapse governed by the
process known as chaotic advection (or lagrangian turbulence). In order to
develop a detailed study of this new approach we develop the COsmic LAgrangian
TUrbulence Simulator (COLATUS) to perform gravitational N-body simulations
based on Compute Unified Device Architecture (CUDA) for graphics processing
units (GPUs). In this paper we report the first robust results obtained from
COLATUS.Comment: Proceedings of Sixth International School on Field Theory and
Gravitation-2012 - by American Institute of Physic
Heisenberg.s Turbulent Spectral Theory Determining the Filtering Procedure in les Models
Heisenbergs turbulent spectral theory determining thefiltering procedure in LES model