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. The Data Assimilation

Research Testbed facility developed at NCAR 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. This poster will provide an overview of these

algorithms and examples of their application in global NWP assimilations.