Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment

In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state...

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Autores principales: Ruiz, J.J., Pulido, M., Miyoshi, T.
Formato: JOUR
Materias:
Data assimilation
Ensemble kalman filter
Error covariance
Parameter estimation
atmospheric general circulation model
covariance analysis
data assimilation
ensemble forecasting
error analysis
estimation method
Kalman filter
numerical model
uncertainty analysis
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00261165_v91_n4_p453_Ruiz
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
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record_format dspace
spelling todo:paper_00261165_v91_n4_p453_Ruiz2023-10-03T14:36:52Z Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment Ruiz, J.J. Pulido, M. Miyoshi, T. Data assimilation Ensemble kalman filter Error covariance Parameter estimation atmospheric general circulation model covariance analysis data assimilation ensemble forecasting error analysis estimation method Kalman filter numerical model uncertainty analysis In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state variables and the parameters: one using a single ensemble for the simultaneous estimation of model state and parameters, and the other using two separate ensembles; for the initial conditions and for the parameters. It is found that the method which uses two ensembles produces a more accurate representation of the covariances between observed variables and parameters, although this does not produce an improvement of the parameter or state estimation. The experiments show that the former method with a single ensemble is more efficient and produces results as accurate as the ones obtained with the two separate ensembles method. The impact of parameter ensemble spread upon the parameter estimation and its associated analysis is also investigated. A new approach to the optimization of the estimated parameter ensemble spread (EPES) is proposed in this work. This approach preserves the structure of the analysis error covariance matrix of the augmented state vector. Results indicate that the new approach determines the value of the parameter ensemble spread that produces the lowest errors in the analysis and in the estimated parameters. A simple low-resolution atmospheric general circulation model known as SPEEDY is used for the evaluation of the different parameter estimation techniques. © 2013, Meteorological Society of Japan. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00261165_v91_n4_p453_Ruiz
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Data assimilation
Ensemble kalman filter
Error covariance
Parameter estimation
atmospheric general circulation model
covariance analysis
data assimilation
ensemble forecasting
error analysis
estimation method
Kalman filter
numerical model
uncertainty analysis
spellingShingle Data assimilation
Ensemble kalman filter
Error covariance
Parameter estimation
atmospheric general circulation model
covariance analysis
data assimilation
ensemble forecasting
error analysis
estimation method
Kalman filter
numerical model
uncertainty analysis
Ruiz, J.J.
Pulido, M.
Miyoshi, T.
Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment
topic_facet Data assimilation
Ensemble kalman filter
Error covariance
Parameter estimation
atmospheric general circulation model
covariance analysis
data assimilation
ensemble forecasting
error analysis
estimation method
Kalman filter
numerical model
uncertainty analysis
description In this work, various methods for the estimation of the parameter uncertainty and the covariance between the parameters and the state variables are investigated using the local ensemble transform Kalman filter (LETKF). Two methods are compared for the estimation of the covariances between the state variables and the parameters: one using a single ensemble for the simultaneous estimation of model state and parameters, and the other using two separate ensembles; for the initial conditions and for the parameters. It is found that the method which uses two ensembles produces a more accurate representation of the covariances between observed variables and parameters, although this does not produce an improvement of the parameter or state estimation. The experiments show that the former method with a single ensemble is more efficient and produces results as accurate as the ones obtained with the two separate ensembles method. The impact of parameter ensemble spread upon the parameter estimation and its associated analysis is also investigated. A new approach to the optimization of the estimated parameter ensemble spread (EPES) is proposed in this work. This approach preserves the structure of the analysis error covariance matrix of the augmented state vector. Results indicate that the new approach determines the value of the parameter ensemble spread that produces the lowest errors in the analysis and in the estimated parameters. A simple low-resolution atmospheric general circulation model known as SPEEDY is used for the evaluation of the different parameter estimation techniques. © 2013, Meteorological Society of Japan.
format JOUR
author Ruiz, J.J.
Pulido, M.
Miyoshi, T.
author_facet Ruiz, J.J.
Pulido, M.
Miyoshi, T.
author_sort Ruiz, J.J.
title Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment
title_short Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment
title_full Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment
title_fullStr Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment
title_full_unstemmed Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment
title_sort estimating model parameters with ensemble-based data assimilation: parameter covariance treatment
url http://hdl.handle.net/20.500.12110/paper_00261165_v91_n4_p453_Ruiz
work_keys_str_mv AT ruizjj estimatingmodelparameterswithensemblebaseddataassimilationparametercovariancetreatment
AT pulidom estimatingmodelparameterswithensemblebaseddataassimilationparametercovariancetreatment
AT miyoshit estimatingmodelparameterswithensemblebaseddataassimilationparametercovariancetreatment
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