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 repre...

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Autores principales: Pulido, Manuel, Rosso, Osvaldo A.
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
PARAMETRIZACION
TEORIA DE LA INFORMACION
MODELOS MATEMATICOS
MODELOS CLIMATICOS
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1710
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1710
record_format dspace
spelling I32-R138-123456789-17102022-12-07T13:07:02Z Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations Pulido, Manuel Rosso, Osvaldo A. PARAMETRIZACION TEORIA DE LA INFORMACION MODELOS MATEMATICOS MODELOS CLIMATICOS "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 subgrid-scale effects pose a big challenge. For climate studies, the parameters or parameterizations are usually selected according to a root-mean-square error criterion that measures the differences between the model-state evolution and observations along the trajectory. However, inaccurate initial conditions and systematic model errors contaminate root-mean-square error measures. In this work, information theory quantifiers, in particular Shannon entropy, statistical complexity, and Jensen–Shannon divergence, are evaluated as measures of the model dynamics. An ordinal analysis is conducted using the Bandt–Pompe symbolic data reduction in the signals. The proposed ordinal information measures are examined in the two-scale Lorenz-96 system. By comparing the two-scale Lorenz-96 system signals with a one-scale Lorenz-96 system with deterministic and stochastic parameterizations, the study shows that information measures are able to select the correct model and to distinguish the parameterizations, including the degree of stochasticity that results in the closest model dynamics to the two-scale Lorenz-96 system." 2019-08-14T19:20:45Z 2019-08-14T19:20:45Z 2017 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 0022-4928 http://ri.itba.edu.ar/handle/123456789/1710 en info:eu-repo/semantics/altIdentifier/doi/10.1175/JAS-D-16-0340.1 info:eu-repo/grantAgreement/ANPCyT/PICT/2015-2368/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/CONICET/PIP/11220120100414CO/AR. Ciudad Autónoma de Buenos Aires application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic PARAMETRIZACION
TEORIA DE LA INFORMACION
MODELOS MATEMATICOS
MODELOS CLIMATICOS
spellingShingle PARAMETRIZACION
TEORIA DE LA INFORMACION
MODELOS MATEMATICOS
MODELOS CLIMATICOS
Pulido, Manuel
Rosso, Osvaldo A.
Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
topic_facet PARAMETRIZACION
TEORIA DE LA INFORMACION
MODELOS MATEMATICOS
MODELOS CLIMATICOS
description "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 subgrid-scale effects pose a big challenge. For climate studies, the parameters or parameterizations are usually selected according to a root-mean-square error criterion that measures the differences between the model-state evolution and observations along the trajectory. However, inaccurate initial conditions and systematic model errors contaminate root-mean-square error measures. In this work, information theory quantifiers, in particular Shannon entropy, statistical complexity, and Jensen–Shannon divergence, are evaluated as measures of the model dynamics. An ordinal analysis is conducted using the Bandt–Pompe symbolic data reduction in the signals. The proposed ordinal information measures are examined in the two-scale Lorenz-96 system. By comparing the two-scale Lorenz-96 system signals with a one-scale Lorenz-96 system with deterministic and stochastic parameterizations, the study shows that information measures are able to select the correct model and to distinguish the parameterizations, including the degree of stochasticity that results in the closest model dynamics to the two-scale Lorenz-96 system."
format Artículos de Publicaciones Periódicas
acceptedVersion
author Pulido, Manuel
Rosso, Osvaldo A.
author_facet Pulido, Manuel
Rosso, Osvaldo A.
author_sort Pulido, Manuel
title Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
title_short Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
title_full Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
title_fullStr Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
title_full_unstemmed Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
title_sort model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1710
work_keys_str_mv AT pulidomanuel modelselectionusinginformationmeasuresfromordinalsymbolicanalysistoselectmodelsubgridscaleparameterizations
AT rossoosvaldoa modelselectionusinginformationmeasuresfromordinalsymbolicanalysistoselectmodelsubgridscaleparameterizations
_version_ 1765660909440598016

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