Loïk Berre, Météo-France/CNRS (CNRM)

Simulation of error cycling and covariance filtering using ensemble data assimilation and innovations


Ensemble data assimilation methods based on observation and model perturbations are powerful approaches in order to simulate observation and model error contributions to background errors and to the associated error cycling. This relies partly on the use of innovation-based diagnostics for the estimation of space- and/or time-averaged estimates of observation and background error covariances. The ensemble approach can be seen as a way to provide associated space- and time-dependent background error covariance estimates. As will be shown formally and experimentally, innovation-based estimates of background error covariances can also be compared with ensemble data assimilation estimates, in order to deduce some model error estimates.

Another major aspect is the application of spatial filtering methods to ensemble estimates of covariances. This will be shown to be important not only for correlations, but also for variances. Filtering methods based on either localisation or convolution-like approaches will be discussed in particular. Four-dimensional aspects of covariance filtering will also be tackled possibly during the talk.

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