- Jeff Anderson, Scientist
- Nancy Collins, Software Engineer
- Helen Kershaw, Software Engineer
- Glen Romine, Project Scientist
- Silvia Gentile, Administrative Assistant
- Tim Hoar, Associate Scientist
- Kevin Raeder, Associate Scientist
- Chris Snyder, Scientist III, MMM
- Joe Tribbia, Senior Scientist, CGD
- Doug Nychka, Senior Scientist, IMAGe
- Hui Liu, Project Scientist
- David Dowell, NOAA Scientist
Sensitiviy Analysis on Hurricane Katrina
Ensembles allow exploration of circulation on hurricane position.
A bad rank histogram (filter divergence)
Lorenz '96 - the true state generally falls outside the ensemble members.
Welcome to the Data Assimilation Research Testbed - DART
DART is a community facility for ensemble DA developed and maintained by the Data Assimilation Research Section (DAReS) at the National Center for Atmospheric Research (NCAR). DART provides modelers, observational scientists, and geophysicists with powerful, flexible DA tools that are easy to implement and use and can be customized to support efficient operational DA applications. DART is a software environment that makes it easy to explore a variety of data assimiliation methods and observations with different numerical models and is designed to facilitate the combination of assimilation algorithms, models, and real (as well as synthetic) observations to allow increased understanding of all three. DART includes extensive documentation, a comprehensive tutorial, and a variety of models and observation sets that can be used to introduce new users or graduate students to ensemble DA. DART also provides a framework for developing, testing, and distributing advances in ensemble DA to a broad community of users by removing the implementation-specific peculiarities of one-off DA systems.
DART employs a modular programming approach to apply an Ensemble Kalman Filter which nudges the underlying models toward a state that is more consistent with information from a set of observations. Models may be swapped in and out, as can different algorithms in the Ensemble Kalman Filter. The method requires running multiple instances of a model to generate an ensemble of states. A forward operator appropriate for the type of observation being assimilated is applied to each of the states to generate the model's estimate of the observation.
The DART algorithms are designed so that incorporating new models and new observation types requires minimal coding of a small set of interface routines, and does not require modification of the existing model code. Several comprehensive atmosphere and ocean general circulation models (GCMs) have been added to DART by modelers from outside of NCAR, in some cases with less than one person-month of development effort. Forward operators for new observation types can be created in a fashion that is nearly independent of the forecast model, many of the standard operators are available 'out of the box' and will work with no additional coding. DART has been through the crucible of many compilers and platforms. It is ready for friendly use and has been used in several field programs requiring real-time forecasting. The DART programs have been compiled with many Fortran 90 compilers and have run on linux compute-servers, linux clusters, OSX laptops/desktops, SGI Altix clusters, IBM supercomputers based on both Power and Intel CPUs, and Cray supercomputers.
The Data Assimilation Research Section (DAReS)
from left to right: Tim Hoar, Jeff Anderson, Hui Liu,
Kevin Raeder, Nancy Collins, Silvia Gentile
(Helen Kershaw and Glen Romine are not pictured)
Our small group is comprised of experts in software design, algorithm development, large-model implementation and execution, observations and observation operators, and hardware/software portability. We have given many presentations on DART - our software facility for ensemble data assimilation, and have held several workshops for young researchers interested in DA.
Our central email address is firstname.lastname@example.org, which
will hit 'everyone' and find its way to the best person.
The categories that follow are not set in stone, everyone has some expertise in all areas.
|Jeff Anderson||Tim Hoar||Nancy Collins||Helen Kershaw||Kevin Raeder||Glen Romine|
|jla @ ucar . edu||thoar @ ucar . edu||nancy @ ucar . edu||hkershaw @ ucar . edu||raeder @ ucar . edu||romine @ ucar . edu|
|postal address||"overnight" deliveries||electronic methods|
|Silvia Gentile||Silvia Gentile||sgentile @ ucar . edu|
|NCAR||NCAR||303 497 2480|
|P.O. Box 3000||1850 Table Mesa Dr.||303 497 2483 (FAX)|
|Boulder, CO 80307-3000||Boulder, CO 80305|
What is Data Assimilation ... "DA"?
Loosely speaking, data assimilation is any method of making models utilize the
information from observations of the system being modeled. Good assimilations
make the modeled state more consistent with the observations; particularly future
observations. Effective data assimilation systems tend to make forecasts more
accurate - within the ability of the model, naturally - and tend to make 'hindcasts'
(the model state immediately after the observations have been assimilated) more
accurately reflect the state of the system.
The low-order models (Lorenz '63, '96, etc.) are a great place to start learning about data assimilation. These dynamical models were created as simple analogues to chaotic systems. Once you get comfortable running and exploring assimilations with the low-order models, you are well on your way to understanding assimilations with high-order (more realistic) models.
Schematic of Ensemble Data Assimilation - from the DAReS Perspective
DART code distributions
The DART code is distributed via a Subversion (SVN) repository. Anonymous access is allowed, and the repository code is read-only for everyone except the DART development team. DART is distributed as source code, so you must be prepared to build the parts of the system you need before you can run it.
Using subversion makes it easy to update and compare your checked-out version of the code with the latest repository version of the code. If you are not familiar with the svn command (the client application of subversion), you should take a stroll through Tim's svn primer. Or there are several GUI programs that help you manage, check out, and merge subversion distributions. If you cannot use svn (e.g. because you are behind a firewall that does not permit subversion access), please email the DART team and we may be able to send you a tar file as a last resort.
The DART development team keeps released versions of the code which are stable (don't change) except for bug fixes. Generally we recommend users check out one of these versions. The DART development team makes frequent updates to the trunk version of the code as new features are developed. Those users who want to use recently added features are welcome to check out the trunk, but they should be prepared to work around possible non-backwards compatible changes and more lightly tested code than the released versions.
DART continues to evolve. We request that you register using this web page and afterwards you will be redirected to instructions on how to download a version of the DART code. Registration helps us track how many people are using our code, and allows us to contact current users in case of bugs or major updates. The DART mailing list is a very low-traffic list -- perhaps 4 emails per year -- so PLEASE use a real email address when signing up. We solemnly swear to protect your email address like it is our own! Even local NCAR users or users who have registered in the past are encouraged to reregister when downloading new versions. Thank you for your understanding.
|version||date||instructions||most noteable change(s)|
|lanai||13 Dec 2013||[doc]||Support for the CESM climate components under the CESM framework;
the MPAS models, the NOAH land model, the GITM ionosphere model,
the NOGAPS atmosphere model, the NAAPS aerosol model,
and the SQG surface quasi-geostrophic model.
Support for many new chemistry and aerosol observation types,
support for many new observations sources, many new diagnostic
routines, and new utilities.
|trunk||today||included in distrib.||varies, use svn log --revision ####:HEAD to see log messages about changes since revision ####.|
|kodiak||30 Jun 2011||[doc]||Damped Inflation, Sampling Error Correction, Boxcar
Kernel Filter option, support for new models, new observation types, new
diagnostics, new utilities.
|jamaica||12 Apr 2007||[doc]||vertical localization, extensive testing of MPI implementation,
full documentation for new algorithms, new tutorial sections
|pre_j||02 Oct 2006||[doc]||contains an updated scalable filter algorithm
|post_iceland||20 Jun 2006||[doc]||observation-space adaptive inflation, bug fixes,
obs_sequence_tool support ...
|iceland||23 Nov 2005||[doc]||huge expansion of real observation capability
better namelist processing, PBL_1d available.
|pre_iceland||20 Oct 2005||for developers only||huge expansion of real observation capability|
|DA workshop 2005||13 Jun 2005||docs included in distrib.||tutorial directory in distribution, observation preprocessing|
|hawaii||28 Feb 2005||[doc]||new filtering algorithms|
|pre-hawaii||20 Dec 2004||[doc]||new filtering algorithms|
|guam||12 Aug 2004||[doc]||new observation modules, removing autopromotion|
|fiji||29 Apr 2004||[doc]||enhanced portability, CAM, WRF|
|easter||8 March 2004||[doc]||initial release|