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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Royal Meteorological Society
2021
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| Acceso en línea: | http://repositorio.unne.edu.ar/handle/123456789/30326 |
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I48-R184-123456789-30326 |
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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 |
| work_keys_str_mv |
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1832343975019675648 |