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Parameter Estimation Using Ensemble-Based Data Assimilation in the Presence of Model Error

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  • 1 Centro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET-UBA), DCAO/FCEyN-Universidad de Buenos Aires, UMI-IFAECI/CNRS, Buenos Aires, Argentina, and AICS/RIKEN, Kobe, Japan
  • | 2 Department of Physics, Universidad Nacional del Nordeste, IMIT (UNNE-CONICET), Corrientes, and UMI-IFAECI/CNRS, Buenos Aires, Argentina
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Abstract

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.

Corresponding author address: Juan Ruiz, CIMA (CONICET-Universidad de Buenos Aires), Ciudad Universitaria, Buenos Aires, CABA C1428EGA, Argentina. E-mail: jruiz@cima.fcen.uba.ar

Abstract

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.

Corresponding author address: Juan Ruiz, CIMA (CONICET-Universidad de Buenos Aires), Ciudad Universitaria, Buenos Aires, CABA C1428EGA, Argentina. E-mail: jruiz@cima.fcen.uba.ar
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