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

JUAN RUIZ; MANUEL PULIDO

Monthly Weather Review

AMER METEOROLOGICAL SOC

Lugar: Boston; Año: 2015 vol. 143 p. 1568 - 1582

1520-0493

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 withmodel error treatment techniques,multiplicative and additive combination or a bias correction technique, parameter estimation produces a further improvement of analysis quality and mediumrange forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation ismainly 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.