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

• Autores: Pulido, Manuel Arturo; Scheffler, Guillermo; Ruiz, Juan José; Lucini, María Magdalena; Tandeo, Pierre
• Año de publicación: 2016
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Quarterly Journal of the Royal Meteorological Society, 2016, vol. 142, p. 2974–2984.
• Materia: EnKF; Parameter estimation; Subgrid-scale schemes; Lorenz ’96 system; Parametrization
• Institución: Universidad Nacional del Nordeste

Fil: Pulido, Manuel Arturo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina.
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Franco-Argentino de Estudios sobre el Clima y sus Impactos; Argetina.
Fil: Scheffler, Guillermo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina.
Fil: Scheffler, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Ruiz, Juan José. Universidad de Buenos Aires. Centro de Investigaciones del Mar y la Atmósfera; Argentina.
Fil: Ruiz, Juan José. Advanced Institute for Computational Science, Kobe; Japón.
Fil: Ruiz, Juan José. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Franco-Argentino de Estudios sobre el Clima y sus Impactos; Argentina.
Fil: Lucini, María Magdalena. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura; Argentina.
Fil: Lucini, María Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Tandeo, Pierre. Laboratoire des Sciences et Techniques de l'information de la Communication et de la Connaissance; Francia.
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.