Parameter estimation using ensemble-based data assimilation in the presence of model error
This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approa...
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00270644_v143_n5_p1568_Ruiz http://hdl.handle.net/20.500.12110/paper_00270644_v143_n5_p1568_Ruiz |
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paper:paper_00270644_v143_n5_p1568_Ruiz2023-06-08T14:54:09Z Parameter estimation using ensemble-based data assimilation in the presence of model error Ruiz, Juan Jose Bias Data assimilation Kalman filters Model errors Numerical weather prediction/forecasting Optimization Earth atmosphere Errors Forecasting Kalman filters Optimization Quality control Weather forecasting Atmospheric general circulation models Bias Data assimilation Ensemble based data assimilation Model errors Numerical weather prediction/forecasting Observing system simulation experiments On-line parameter estimations Parameter estimation atmospheric general circulation model data assimilation ensemble forecasting error analysis Kalman filter numerical model optimization weather forecasting This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved. © 2015 American Meteorological Society. Fil:Ruiz, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2015 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00270644_v143_n5_p1568_Ruiz http://hdl.handle.net/20.500.12110/paper_00270644_v143_n5_p1568_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 |
Bias Data assimilation Kalman filters Model errors Numerical weather prediction/forecasting Optimization Earth atmosphere Errors Forecasting Kalman filters Optimization Quality control Weather forecasting Atmospheric general circulation models Bias Data assimilation Ensemble based data assimilation Model errors Numerical weather prediction/forecasting Observing system simulation experiments On-line parameter estimations Parameter estimation atmospheric general circulation model data assimilation ensemble forecasting error analysis Kalman filter numerical model optimization weather forecasting |
| spellingShingle |
Bias Data assimilation Kalman filters Model errors Numerical weather prediction/forecasting Optimization Earth atmosphere Errors Forecasting Kalman filters Optimization Quality control Weather forecasting Atmospheric general circulation models Bias Data assimilation Ensemble based data assimilation Model errors Numerical weather prediction/forecasting Observing system simulation experiments On-line parameter estimations Parameter estimation atmospheric general circulation model data assimilation ensemble forecasting error analysis Kalman filter numerical model optimization weather forecasting Ruiz, Juan Jose Parameter estimation using ensemble-based data assimilation in the presence of model error |
| topic_facet |
Bias Data assimilation Kalman filters Model errors Numerical weather prediction/forecasting Optimization Earth atmosphere Errors Forecasting Kalman filters Optimization Quality control Weather forecasting Atmospheric general circulation models Bias Data assimilation Ensemble based data assimilation Model errors Numerical weather prediction/forecasting Observing system simulation experiments On-line parameter estimations Parameter estimation atmospheric general circulation model data assimilation ensemble forecasting error analysis Kalman filter numerical model optimization weather forecasting |
| description |
This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved. © 2015 American Meteorological Society. |
| author |
Ruiz, Juan Jose |
| author_facet |
Ruiz, Juan Jose |
| author_sort |
Ruiz, Juan Jose |
| title |
Parameter estimation using ensemble-based data assimilation in the presence of model error |
| title_short |
Parameter estimation using ensemble-based data assimilation in the presence of model error |
| title_full |
Parameter estimation using ensemble-based data assimilation in the presence of model error |
| title_fullStr |
Parameter estimation using ensemble-based data assimilation in the presence of model error |
| title_full_unstemmed |
Parameter estimation using ensemble-based data assimilation in the presence of model error |
| title_sort |
parameter estimation using ensemble-based data assimilation in the presence of model error |
| publishDate |
2015 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00270644_v143_n5_p1568_Ruiz http://hdl.handle.net/20.500.12110/paper_00270644_v143_n5_p1568_Ruiz |
| work_keys_str_mv |
AT ruizjuanjose parameterestimationusingensemblebaseddataassimilationinthepresenceofmodelerror |
| _version_ |
1768543980419547136 |