Modeling of the Background Error Statistics

The specification of model background error statistics is a key component to data assimilation since it conditions the impact observations will have on the analysis. While Ensemble Kalman Filter (EnKF) algorithms rely on an ensemble of model forecasts to implicitly convey the background error covariances, variational methods represent them through a statistical model. The model for the WRF data assimilation estimates separately the variances, vertical auto-correlations via Empirical Orthogonal Functions (EOFs), horizontal auto-correlations via Recursive Filters and cross-covariances via linear regressions.

Recent work to include new variables in the analysis such as cloud parameters and chemical species have required to fundamentally restructure the software interface to allow for a simpler, flexible, robust, community oriented framework. We will present the advantages of this new design for the data assimilation community and demonstrate some of the new features on data assimilation experiments.