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

RUIZ J J; PULIDO M

MONTHLY ENERGY REVIEW

AMER METEOROLOGICAL SOC

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

0027-0644

This work explores the potential of on-line parameter estimation as a technique for model7 error treatment under an imperfect model scenario, in an ensemble-based data assimilation8 system, using a simple atmospheric general circulation model and an observing system simu-9 lation experiment (OSSE) approach. The synthetic observations are obtained from a model10 integration. Model error is introduced in the imperfect model scenario by changing the value11 of the parameters associated with different schemes. The parameters of the moist convec-12 tion scheme are the only ones to be estimated in the data assimilation system. In this work,13 parameter estimation is compared and combined with techniques that account for the lack14 of ensemble spread and for the systematic model error. The OSSE experiments show that15 when parameter estimation is combined with either multiplicative and additive inflation or a16 bias correction technique, parameter estimation produces a further improvement of analysis17 quality and medium-range forecast skill. The improvement produced by parameter estima-18 tion is mainly a consequence of the optimization of the parameter values. The estimated19 parameters do not converge to the value used to generate the observations in the imperfect20 model scenario, however the analysis error is reduced and the forecast skill is improved.