Although it is trivial to develop an ensemble data assimilation facility for an atmospheric prediction model, the standard ensemble algorithms have a number of shortcomings. They are subject to most error sources that impact more traditional assimilation methods and also to sampling error from small ensemble sizes. A general purpose ensemble facility must provide additional adjunct algorithms that can deal adaptively with these errors. DART includes a wide range of novel algorithms. To deal with sampling error, DART includes a hierarchical Bayesian algorithm that can automatically recommend a multi-variate, spatially anisotropic localization. Hierarchical Bayesian algorithms for spatially- and temporally-varying inflation are also included. A methodology for adaptive thinning is available to efficiently assimilate observations where they are dense. Both stochastic and deterministic ensemble filters, as well as novel hybrid particle/ensemble filter algorithms are available in the facility. The DART facility includes a generic highly-scalable parallel algorithm that has been ported to a number of parallel architectures. DART also provides tutorials and exercises appropriate for undergraduate and graduate instruction about ensemble filtering. This poster provides an overview of the DART algorithms and examples of their application in global and regional NWP.