Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem
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...
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2019
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00359009_v_n_p_Metref http://hdl.handle.net/20.500.12110/paper_00359009_v_n_p_Metref |
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paper:paper_00359009_v_n_p_Metref2023-06-08T15:01:52Z Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem 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 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 2019 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00359009_v_n_p_Metref http://hdl.handle.net/20.500.12110/paper_00359009_v_n_p_Metref |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
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 |
| spellingShingle |
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 Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem |
| topic_facet |
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 |
| description |
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 |
| title |
Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem |
| title_short |
Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem |
| title_full |
Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem |
| title_fullStr |
Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem |
| title_full_unstemmed |
Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem |
| title_sort |
estimating model evidence using ensemble-based data assimilation with localization – the model selection problem |
| publishDate |
2019 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00359009_v_n_p_Metref http://hdl.handle.net/20.500.12110/paper_00359009_v_n_p_Metref |
| _version_ |
1768544994466988032 |