Diagnosis and impacts of non-Gaussianity of innovations in data assimilation

Carlos Pires (1) , Olivier Talagrand (2) , Marc Bocquet (3, 4)

1 Instituto Dom Luiz
2 LMD - Laboratoire de Météorologie Dynamique (UMR 8539)
3 CEREA - Centre d'Enseignement et de Recherche en Environnement Atmosphérique
4 Clime - Coupling environmental data and simulation models for software integration

The Best Linear Unbiased Estimator (BLUE) has widely been used in atmospheric and oceanic data assimilation (DA). However, when the errors from data (observations and background forecasts) have non-Gaussian probability density functions (pdfs), the BLUE diers from the absolute Minimum Variance Unbiased Estimator (MVUE), minimizing the mean square a posteriori error. The non-Gaussianity of errors can be due to the inherent statistical skewness and positiveness of some physical observables (e.g. moisture, chemical species) or because of the nonlinearity of the data assimilation models and observation operators acting on Gaussian errors. Non-Gaussianity of assimilated data errors can be justied from a priori hypotheses or inferred from statistical diagnostics of innovations (observation minus background). ollowing this rationale, we compute measures of innovation non-Gaussianity, namely its skewness and kurtosis, relating it to: a) the non-Gaussianity of the individual error themselves, b) the correlation between nonlinear functions of errors, and c) the heteroscedasticity of errors within diagnostic samples. Those relationships impose bounds for skewness and kurtosis of errors which are critically dependent on the error variances, thus leading to a necessary tuning of error variances in order to accomplish consistency with innovations. We evaluate the sub-optimality of the BLUE as compared to the MVUE, in terms of excess of error variance, under the presence of non-Gaussian errors. The error pdfs are obtained by the maximum entropy method constrained by error moments up to fourth order, from which the Bayesian probability density function and the MVUE are computed. The impact is higher for skewed extreme innovations and grows in average with the skewness of data errors, especially if those skewnesses have the same sign. Application has been performed to the quality-accepted ECMWF innovations of brightness temperatures of a set of High Resolution Infrared Sounder (HIRS) channels. In this context, the MVUE has led in some extreme cases to a potential reduction of 20-60% error variance as compared to the BLUE.

Mots clés: Non-Gaussianity; Data assimilation; Innovation statistics; Maximum entropy method

Domaines: Modélisation et simulation

Carlos Pires, Olivier Talagrand, Marc Bocquet. Diagnosis and impacts of non-Gaussianity of innovations in data assimilation. Physica D: Nonlinear Phenomena, 2010, 239, ⟨10.1016/j.physd.2010.05.006⟩. ⟨inria-00538662⟩