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dc.creatorSaher Cintra, Rosangela
dc.creatorDe Campos Velho, Haroldo F.
dc.date.accessioned2021-01-20T20:29:13Z
dc.date.available2021-01-20T20:29:13Z
dc.date.created2018-02-28
dc.identifier.otherhttps://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/data-assimilation-by-artificial-neural-networks-for-an-atmospheric-general-circulation-model
dc.identifier.urihttp://hdl.handle.net/20.500.12010/16800
dc.format.extent23 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherIntechOpenspa
dc.subjectRedes neuronales artificialesspa
dc.titleData Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Modelspa
dc.subject.lembPredicción meteorológicaspa
dc.subject.lembCampos meteorológicosspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doi10.5772/intechopen.70791
dc.relation.referencesRosangela Saher Cintra and Haroldo F. de Campos Velho (February 28th 2018). Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model, Advanced Applications for Artificial Neural Networks, Adel El-Shahat, IntechOpen, DOI: 10.5772/intechopen.70791.spa
dc.description.abstractenglishNumerical 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.spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by-nc/4.0/legalcode


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