@InProceedings{CintraVelh:2012:GlDaAs,
author = "Cintra, Rosangela. S. and Velho, Haroldo F. de Campos",
affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Global Data Assimilation Using Artificial Neural Networks in
Speedy Model",
booktitle = "Proceedings...",
year = "2012",
pages = "648--654",
organization = "International Symposium on Uncertainty Quantification and
Stochastic Modeling, 1.",
keywords = "data assimilation, artificial neural network, ensemble kalman
Filter, numerical weather forecasting.",
abstract = "Weather forecasting systems require a model for the time evolution
and an estimate of the current state of the system. Data
assimilation provides such an initial estimate of the atmosphere
where it combines information from observations and from a prior
short-term forecast producing an current state estimate. An
Artificial Neural Network (ANN) is designed for data assimilation.
The use of observations from the earth-orbiting satellites in
operational numerical prediction models is performed for improving
weather forecasts. The data related to atmospheric, oceanic, and
land surface state from satellites provides increasingly large
volumes. However, the use of this amount of data increases the
computational effort. The goal here is to simulate the process for
assimilating temperature data computed from satellite radiances.
The numerical experiment is carried out with the global model
Simplified parameterizations, primitive-Equation Dynamics (SPEEDY
) with simplified physical processes of an atmospheric general
circulation in tri-dimensional coordinates. For the data
assimilation scheme was applied an ANN: a Multilayer
Perceptron(MLP) with supervised training. The MLP-ANN is able to
emulate the analysis from the Local Ensemble Transform Kalman
Filter(LETKF). LETKF is a version of Kalman Filter with
Monte-Carlo ensembles of short-term forecasts. In this experiment,
the MLP-ANN was trained with supervision from first six months
considering the years 1982, 1983, and 1984. A hindcasting
experiment for data assimilation performed a cycle for january of
1985 with MLP-NN, LETKF and SPEEDY model. The synthetic
temperature observations were used. The numerical results
demonstrate the effectiveness of this ANN technique on atmospheric
data assimilation. The results for analysis with ANN are very
close with the results from LETKF data assimilation. The
simulations show that the major advantage of using MLP-NN is the
better computational performance, with similar quality of
analysis. The CPU-time assimilation with MLP-NN is 75% less than
LETKF with the same observations. Actually, considering the
supervised ANN for data assimilation, the most relevant issue is
the computational speed-up for computing the analyzed initial
condition for state model that accelerates the whole process of
numerical weather prediction.",
conference-location = "S{\~a}o Sebasti{\~a}o, SP",
conference-year = "Feb. 26th to Mar. 2nd, 2012",
issn = "2238-1007",
targetfile = "106RCintra.pdf",
urlaccessdate = "29 jun. 2024"
}