The definition of mesoscale selective forecast error covariances for a limited area variational analysis

The paper deals with an alternative formulation of the so-called NMC (National Meteorological Center, now National Centers for Environmental Prediction) statistics to compute the background error covariance matrix to be used in a mesoscale variational analysis. While the standard method uses differences of forecasts valid for the same time, but starting from different analysis times, the new formulation required the recomputation of the short-term forecast with the initial and lateral boundary data that come from the long-term run. In the frame of a limited-area model, this approach forces the error variances at large scales to decrease drastically, because those scales are controlled by the (constant data) lateral boundary coupling. As a result, the background cost function acts more scale selectively, with an emphasis on medium scales. The analysis increments obtained from the 3D-VAR system show that the analysis increments are sharper and more concentrated with the new formulation, both in single observation and in full observation experiments. This work is part of a wider project for building a variational assimilation system inside the ALADIN model. The complete system should concentrate on mesoscale features and it should not reanalyse those scales that were already treated by the global model (ARPEGE). Some difficulties and perspectives are drawn in the concluding discussion.