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

Pulido, Manuel; Rosso, Osvaldo A.

artículo de publicación periódica.page.titleprefix
Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations

dc.contributor.author	Pulido, Manuel
dc.contributor.author	Rosso, Osvaldo A.
dc.date.accessioned	2019-08-14T19:20:45Z
dc.date.available	2019-08-14T19:20:45Z
dc.date.issued	2017
dc.description.abstract	"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."	en
dc.identifier.issn	0022-4928
dc.identifier.uri	http://ri.itba.edu.ar/handle/123456789/1710
dc.language.iso	en	en
dc.relation	info:eu-repo/semantics/altIdentifier/doi/10.1175/JAS-D-16-0340.1
dc.relation	info:eu-repo/grantAgreement/ANPCyT/PICT/2015-2368/AR. Ciudad Autónoma de Buenos Aires
dc.relation	info:eu-repo/grantAgreement/CONICET/PIP/11220120100414CO/AR. Ciudad Autónoma de Buenos Aires
dc.subject	PARAMETRIZACION	es
dc.subject	TEORIA DE LA INFORMACION	es
dc.subject	MODELOS MATEMATICOS	es
dc.subject	MODELOS CLIMATICOS	es
dc.title	Model selection: using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations	en
dc.type	Artículos de Publicaciones Periódicas	es
dc.type	info:eu-repo/semantics/acceptedVersion
dspace.entity.type	Artículo de Publicación Periódica
itba.description.filiation	Fil: Pulido, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiation	Fil: Pulido, Manuel. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina.
itba.description.filiation	Fil: Rosso, Osvaldo A. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiation	Fil: Rosso, Osvaldo A. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiation	Fil: Rosso, Osvaldo A. Universidad de los Andes; Chile.
itba.description.filiation	Fil: Rosso, Osvaldo A. Universidade Federal de Alagoas; Brasil.