The Data Assimilation Research Testbed: A Community Facility

Jeffrey Anderson
Search for other papers by Jeffrey Anderson in
Current site
Google Scholar
PubMed
Close
,
Tim Hoar
Search for other papers by Tim Hoar in
Current site
Google Scholar
PubMed
Close
,
Kevin Raeder
Search for other papers by Kevin Raeder in
Current site
Google Scholar
PubMed
Close
,
Hui Liu
Search for other papers by Hui Liu in
Current site
Google Scholar
PubMed
Close
,
Nancy Collins
Search for other papers by Nancy Collins in
Current site
Google Scholar
PubMed
Close
,
Ryan Torn
Search for other papers by Ryan Torn in
Current site
Google Scholar
PubMed
Close
, and
Avelino Avellano
Search for other papers by Avelino Avellano in
Current site
Google Scholar
PubMed
Close
Full access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

The Data Assimilation Research Testbed (DART) is an open-source community facility for data assimilation education, research, and development. DART's ensemble data assimilation algorithms, careful software engineering, and diagnostic tools allow atmospheric scientists, oceanographers, hydrologists, chemists, and other geophysicists to build state-of-the-art data assimilation systems with unprecedented ease. For global numerical weather prediction, DART produces ensemble-mean analyses comparable to analyses from major centers while also providing initial conditions for ensemble predictions. In addition, DART supports more novel assimilation applications like parameter estimation, sensitivity analysis, observing system design, and smoothing. Implementing basic systems for large models requires only a few person-weeks; comprehensive systems have been built in a few months. Incorporating new observation types is also straightforward, requiring only a forward operator mapping between a model's state and an observation's expected value. Forward operators for standard, in situ observations and novel types, like GPS radio occultation soundings, are available. DART algorithms scale well on a variety of parallel architectures, allowing large data assimilation problems to be studied. DART also includes many low-order models and an ensemble assimilation tutorial appropriate for undergraduate and graduate instruction.

NCAR* Data Assimilation Research Section, Boulder, Colorado

Department of Earth and Atmospheric Sciences, University at Albany, State University of New York, Albany, New York

NCAR Atmospheric Chemistry Division, Boulder, Colorado

*The National Center for Atmospheric Research is sponsored by the National Science Foundation

CORRESPONDING AUTHOR: Jeffrey Anderson, NCAR, P.O. Box 3000, Boulder, C O 80307-3000, E-mail: jla@ucar.edu

The Data Assimilation Research Testbed (DART) is an open-source community facility for data assimilation education, research, and development. DART's ensemble data assimilation algorithms, careful software engineering, and diagnostic tools allow atmospheric scientists, oceanographers, hydrologists, chemists, and other geophysicists to build state-of-the-art data assimilation systems with unprecedented ease. For global numerical weather prediction, DART produces ensemble-mean analyses comparable to analyses from major centers while also providing initial conditions for ensemble predictions. In addition, DART supports more novel assimilation applications like parameter estimation, sensitivity analysis, observing system design, and smoothing. Implementing basic systems for large models requires only a few person-weeks; comprehensive systems have been built in a few months. Incorporating new observation types is also straightforward, requiring only a forward operator mapping between a model's state and an observation's expected value. Forward operators for standard, in situ observations and novel types, like GPS radio occultation soundings, are available. DART algorithms scale well on a variety of parallel architectures, allowing large data assimilation problems to be studied. DART also includes many low-order models and an ensemble assimilation tutorial appropriate for undergraduate and graduate instruction.

NCAR* Data Assimilation Research Section, Boulder, Colorado

Department of Earth and Atmospheric Sciences, University at Albany, State University of New York, Albany, New York

NCAR Atmospheric Chemistry Division, Boulder, Colorado

*The National Center for Atmospheric Research is sponsored by the National Science Foundation

CORRESPONDING AUTHOR: Jeffrey Anderson, NCAR, P.O. Box 3000, Boulder, C O 80307-3000, E-mail: jla@ucar.edu
Save