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Abstract:

In recent years, there has been increased interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. In this setting, previous studies introduced the contextual formulation of model evidence, or contextual model evidence (CME), and showed that CME can be efficiently computed using a hierarchy of ensemble-based DA procedures. Although these studies analysed 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 in this article is tested for model selection with two models: (a) the Lorenz 40-variable midlatitude atmospheric dynamics model (Lorenz-95); and (b) 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 systematically outperforms RMSE in model selection skill, and that this skill improvement is further enhanced by applying localization to the CME estimate using DL-CME. The potential use and range of applications of CME and DL-CME as a model selection metric are also discussed. © 2019 Royal Meteorological Society

Registro:

Documento: Artículo
Título:Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem
Autor:Metref, S.; Hannart, A.; Ruiz, J.; Bocquet, M.; Carrassi, A.; Ghil, M.
Filiación:IFAECI, CNRS-CONICET-UBA, Buenos Aires, Argentina
CIMA-CONICET, University of Buenos Aires, Buenos Aires, Argentina
CEREA, Joint Laboratory École des Ponts ParisTech and EDF R&D, Université Paris-Est, Champs-sur-Marne, France
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL), École Normale Supérieure and PSL Research University, Paris, France
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, Afghanistan
Palabras clave:contextual model evidence; detection and attribution; ensemble Kalman filter; localization; parameter estimation; Earth atmosphere; Meteorology; Parameter estimation; Atmospheric dynamics; Contextual modeling; Detection and attributions; Ensemble based data assimilation; Ensemble Kalman Filter; localization; Model selection problem; Root mean square errors; Mean square error
Año:2019
DOI: http://dx.doi.org/10.1002/qj.3513
Título revista:Quarterly Journal of the Royal Meteorological Society
Título revista abreviado:Q. J. R. Meteorol. Soc.
ISSN:00359009
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00359009_v_n_p_Metref

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Citas:

---------- APA ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A. & Ghil, M. (2019) . Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem. Quarterly Journal of the Royal Meteorological Society.
http://dx.doi.org/10.1002/qj.3513
---------- CHICAGO ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A., Ghil, M. "Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem" . Quarterly Journal of the Royal Meteorological Society (2019).
http://dx.doi.org/10.1002/qj.3513
---------- MLA ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A., Ghil, M. "Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem" . Quarterly Journal of the Royal Meteorological Society, 2019.
http://dx.doi.org/10.1002/qj.3513
---------- VANCOUVER ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A., Ghil, M. Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem. Q. J. R. Meteorol. Soc. 2019.
http://dx.doi.org/10.1002/qj.3513