Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment

Oceanic and atmospheric global numerical models represent explicitly the large-scale dynamics while the smaller-scale processes are not resolved, so that their effects in the large-scale dynamics are included through subgrid-scale parametrizations. These parametrizations represent small-scale effect...

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Autores principales: Pulido, Manuel Arturo, Scheffler, Guillermo, Ruiz, Juan José, Lucini, María Magdalena, Tandeo, Pierre
Formato: Artículo
Lenguaje:Inglés
Publicado: Royal Meteorological Society 2021
Materias:
EnKF
Parameter estimation
Subgrid-scale schemes
Lorenz ’96 system
Parametrization
Acceso en línea:http://repositorio.unne.edu.ar/handle/123456789/30326
Aporte de:
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste
id I48-R184-123456789-30326
record_format dspace
spelling I48-R184-123456789-303262025-03-06T10:58:32Z Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment Pulido, Manuel Arturo Scheffler, Guillermo Ruiz, Juan José Lucini, María Magdalena Tandeo, Pierre EnKF Parameter estimation Subgrid-scale schemes Lorenz ’96 system Parametrization Oceanic and atmospheric global numerical models represent explicitly the large-scale dynamics while the smaller-scale processes are not resolved, so that their effects in the large-scale dynamics are included through subgrid-scale parametrizations. These parametrizations represent small-scale effects as a function of the resolved variables. In this work, data assimilation principles are used not only to estimate the parameters of subgrid-scale parametrizations but also to uncover the functional dependencies of subgridscale processes as a function of large-scale variables. Two data assimilation methods based on the ensemble transform Kalman filter (ETKF) are evaluated in the two-scale Lorenz ’96 system scenario. The first method is an online estimation which uses the ETKF with an augmented space state composed of the model large-scale variables and a set of unknown global parameters from the parametrization. The second method is an offline estimation which uses the ETKF to estimate an augmented space state composed of the large-scale variables and by a space-dependentmodel error term. Then a polynomial regression is used to fit the estimated model error as a function of the large-scale model variables in order to develop a parametrization of small-scale dynamics. The online estimation shows a Good performancewhen the parameter-state relationship is assumed to be a quadratic polynomial function. The offline estimation captures better some of the highly nonlinear functional dependencies found in the subgrid-scale processes. The nonlinear and non-local dependence found in an experiment with shear-generated small-scale dynamics is also recovered by the offline estimation method. Therefore, the combination of these two methods could be a useful tool for the estimation of the functional form of subgrid-scale parametrizations. 2021-12-09T15:31:07Z 2021-12-09T15:31:07Z 2016 Artículo Pulido, Manuel Arturo, et. al., 2016. Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment. Quarterly Journal of the Royal Meteorological Society. Londres: Royal Meteorological Society, vol. 142, p. 2974–2984. ISSN 0035-9009. 0035-9009 http://repositorio.unne.edu.ar/handle/123456789/30326 eng openAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf application/pdf Royal Meteorological Society Quarterly Journal of the Royal Meteorological Society, 2016, vol. 142, p. 2974–2984.
institution Universidad Nacional del Nordeste
institution_str I-48
repository_str R-184
collection RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
language Inglés
topic EnKF
Parameter estimation
Subgrid-scale schemes
Lorenz ’96 system
Parametrization
spellingShingle EnKF
Parameter estimation
Subgrid-scale schemes
Lorenz ’96 system
Parametrization
Pulido, Manuel Arturo
Scheffler, Guillermo
Ruiz, Juan José
Lucini, María Magdalena
Tandeo, Pierre
Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment
topic_facet EnKF
Parameter estimation
Subgrid-scale schemes
Lorenz ’96 system
Parametrization
description Oceanic and atmospheric global numerical models represent explicitly the large-scale dynamics while the smaller-scale processes are not resolved, so that their effects in the large-scale dynamics are included through subgrid-scale parametrizations. These parametrizations represent small-scale effects as a function of the resolved variables. In this work, data assimilation principles are used not only to estimate the parameters of subgrid-scale parametrizations but also to uncover the functional dependencies of subgridscale processes as a function of large-scale variables. Two data assimilation methods based on the ensemble transform Kalman filter (ETKF) are evaluated in the two-scale Lorenz ’96 system scenario. The first method is an online estimation which uses the ETKF with an augmented space state composed of the model large-scale variables and a set of unknown global parameters from the parametrization. The second method is an offline estimation which uses the ETKF to estimate an augmented space state composed of the large-scale variables and by a space-dependentmodel error term. Then a polynomial regression is used to fit the estimated model error as a function of the large-scale model variables in order to develop a parametrization of small-scale dynamics. The online estimation shows a Good performancewhen the parameter-state relationship is assumed to be a quadratic polynomial function. The offline estimation captures better some of the highly nonlinear functional dependencies found in the subgrid-scale processes. The nonlinear and non-local dependence found in an experiment with shear-generated small-scale dynamics is also recovered by the offline estimation method. Therefore, the combination of these two methods could be a useful tool for the estimation of the functional form of subgrid-scale parametrizations.
format Artículo
author Pulido, Manuel Arturo
Scheffler, Guillermo
Ruiz, Juan José
Lucini, María Magdalena
Tandeo, Pierre
author_facet Pulido, Manuel Arturo
Scheffler, Guillermo
Ruiz, Juan José
Lucini, María Magdalena
Tandeo, Pierre
author_sort Pulido, Manuel Arturo
title Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment
title_short Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment
title_full Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment
title_fullStr Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment
title_full_unstemmed Estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment
title_sort estimation of the functional form of subgrid-scale parametrizations using ensemble-based data assimilation : a simplemodel experiment
publisher Royal Meteorological Society
publishDate 2021
url http://repositorio.unne.edu.ar/handle/123456789/30326
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AT lucinimariamagdalena estimationofthefunctionalformofsubgridscaleparametrizationsusingensemblebaseddataassimilationasimplemodelexperiment
AT tandeopierre estimationofthefunctionalformofsubgridscaleparametrizationsusingensemblebaseddataassimilationasimplemodelexperiment
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