Model selection: Using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations

The use of information measures for model selection in geophysical models with subgrid parameterizations is examined. Although the resolved dynamical equations of atmospheric or oceanic global numerical models are well established, the development and evaluation of parameterizations that represent s...

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Detalles Bibliográficos
Autores principales: Pulido, M., Rosso, O.A.
Formato: JOUR
Materias:
Data assimilation
Lyapunov vectors
Optimization
Subgrid-scale processes
Errors
Information theory
Mean square error
Parameterization
Stochastic models
Stochastic systems
Systematic errors
Turbulent flow
Information measures
Jensen-Shannon divergence
Root mean square errors
Statistical complexity
Sub-grid scale process
Subgrid-scale effects
Climate models
atmospheric modeling
data assimilation
numerical model
optimization
parameterization
vector
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00224928_v74_n10_p3253_Pulido
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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