Comparison of static mesoscale background‐error covariances estimated by three different ensemble data assimilation techniques

Abstract

AbstractThree ensemble‐based analysis‐error representation techniques are applied and compared with the aim of simulating background errors of the mesoscale limited‐area model (LAM) Aire Limitée Adaptation Dynamique Développement International (ALADIN) and thus computing appropriate mesoscale static background‐error covariances for its three‐dimensional variational (3DVAR) data assimilation system. Real data assimilation experiments are performed for the simulation of the background errors, where uncertainties of the initial conditions and lateral boundary conditions are represented, while model errors are not accounted for. The three analysis error‐simulation techniques compared are as follows: (i) the downscaling of the analysis error represented by the stochastic ensemble data assimilation (EDA) system of a global model (DSC EDA); (ii) the representation of the analysis error by a LAM stochastic ensemble data assimilation (LAM EDA) system; and (iii) the representation of the analysis error by a LAM ensemble transform Kalman filter (LAM ETKF) system. Spectral diagnostics have been developed in the study, allowing an examination of the simulated background errors at different spatial scales. It is shown that LAM EDA provides more realistic simulated background errors than DSC EDA, particularly at mesoscales. This is also partly the case for LAM ETKF at mesoscales (although to a lesser extent than for LAM EDA). The error variance is underestimated by LAM ETKF, which suggests that the applied inflation technique could be improved. Analysis and forecast impact studies using background‐error covariance matrices based on the DSC EDA and LAM EDA error samples confirm the superiority of the latter simulation technique.