A number of ensemble filter methods for data assimilation have been developed during the past decade. The theoretical simplicity and ease of development of these algorithms makes it possible to consider developing generic filtering algorithms that can be applied efficiently to a wide array of models and data streams. A filtering data assimilation facility has been developed as part of NCAR's new data assimilation initiative. This facility, the Data Assimilation Research Testbed, has developed ensemble filter assimilation capabilities for a hierarchy of models including low order models, a regional prediction model (WRF), several climate AGCMs (the GFDL AM-model and the NCAR CAM model), and an operational global prediction model (NCEP's GFS model). A survey of the capabilities of ensemble filter assimilations will be presented, beginning with their ability to extract vast amounts of information from highly indirect observations in synthetic observation experiments. Results from experiments with the operational NCEP global model and data streams will point out the relative capabilities of current ensemble filters and operational 3D-variational schemes. A closing discussion will point out remaining difficulties and evaluate the potential for using ensemble filters in operational atmospheric prediction systems.