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Master's Dissertation
DOI
https://doi.org/10.11606/D.45.2012.tde-25052012-170217
Document
Author
Full name
Jessé Américo Gomes de Lima
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2012
Supervisor
Committee
Birgin, Ernesto Julian Goldberg (President)
Biloti, Ricardo Caetano Azevedo
Stern, Julio Michael
Title in Portuguese
Otimização em Meteorologia: cálculo de perturbações condicionais não-lineares ótimas
Keywords in Portuguese
CNOPs
estabilidade
Meteorologia
modelo tangente linear
previsibilidade
sensibilidade
SPG
Abstract in Portuguese
Neste trabalho estudamos as aplicações do método do Gradiente Espectral Projetado (SPG) em Meteorologia nos campos de previsibilidade, estabilidade e sensibilidade. Inicialmente revisamos os Vetores Singulares Lineares (LSVs) e em seguida apresentamos a teoria das Perturbações Condicionais Não-Lineares Ótimas (CNOPs). Enquanto os métodos clássicos estão baseados no Modelo Tangente Linear, as CNOPs são uma formulação do mesmo problema baseado em Programação Não-Linear. As CNOPs são descritas na literatura como responsáveis por melhorias em relação aos métodos anteriores. Finalmente analisamos três exemplos de aplicação do método à problemas de previsibilidade, estabilidade e sensibilidade.
Title in English
Optimization in Meteorology: computation of conditional nonlinear optimal perturbations
Keywords in English
CNOP
meteorology
predictability
sensibility
SPG
stability
tangent linear model
Abstract in English
A revision about applications of Spectral Projected Gradient (SPG) in meteorology is done in the fields of predictability, stability and sensitivity. Initially we review about Linear Singular Vectos (LSVs) and we present the Conditional Nonlinear Optimal perturbations (CNOPs). While the classic methods are based on the Tangent Linear Model, CNOPs are another formulation of the problem based on Nonlinear Programming. CNOPs are described in bibliography as responsible by better results than older methods. Finally we analyze three applications in predictability, stability and sensibility.
 
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Publishing Date
2012-05-29
 
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