On Ensemble and Particle Filters for Large-Scale Data Assimilation

Ensemble data assimilation (EDA) techniques are of rapidly growing importance. Ensemble techniques allow to describe and forecast uncertainty of the analysis, but they also improve the assimilation result itself, by allowing estimates of the covariance or, more general, the prior and posterior probability distribution of atmospheric states. For our operational system we are now using an EDA based on the Local Ensemble Transform Kalman Filter (LETKF) in combination with
variational techniques, which together build the Ensemble-Var (EnVar)  system.
In our talk, we will first give a survey about recent activities of the German Meteorological Service DWD. Then, we present recent work on the further development of the ensemble data assimilation towards a particle filter for large-scale atmospheric systems, which keeps the advantages of the LETKF, but overcomes some of its limitations