P1.10
A General-Purpose Ensemble Assimilation Facility: DART
Jeffrey Anderson, NCAR, Boulder, CO; and T. Hoar, N. Collins, K. Raeder, and H. Liu
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. 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.
Poster Session 1, IOAS Poster Session I: Data Assimilation and Impact Studies
Monday, 21 January 2008, 2:30 PM-4:00 PM, Exhibit Hall B
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