INVESTIGADORES
RUIZ Juan Jose
artículos
Título:
Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem
Autor/es:
METREF, S.; HANNART, A.; RUIZ, J.; BOCQUET, M.; CARRASSI, A.; GHIL, M.
Revista:
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Editorial:
JOHN WILEY & SONS LTD
Referencias:
Año: 2019
ISSN:
0035-9009
Resumen:
In recent years, there has been a growing interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. In this setting, [Carrassi et al. (2017)] introduced the contextual formulation of model evidence (CME) and showed that CME can be efficiently computed using a hierarchy of ensemble‐based DA procedures. Although [Carrassi et al. (2017)] analyzed the DA methods most commonly used for operational atmospheric and oceanic prediction worldwide, they did not study these methods in conjunction with localization to a specific domain. Yet any application of ensemble DA methods to realistic, very high‐dimensional geophysical models requires the implementation of some form of localization. The present study extends CME estimation to ensemble DA methods with domain localization. Domain‐localized CME (DL‐CME) developed herein is tested for model selection with two models: (i) the Lorenz 40‐variable mid‐latitude atmospheric dynamics model (L95); and (ii) the simplified global atmospheric SPEEDY model. CME is compared to the root‐mean‐square error (RMSE) as a metric for model selection. The experiments show that CME outperforms systematically RMSE in model selection skill, and that this skill improvement is further enhanced by applying localization in the estimate of CME, using DL‐CME. The potential use and range of applications of CME and DL‐CME as a model selection metric are also discussed.