DART/CAM spaghetti plot 00Z 01 Feb 2003. 20 of 80 member ensemble. T85 CAM GPH at 500hPa. Sensitiviy Analysis on Hurricane Katrina Ensembles allow exploration of circulation on hurricane position. GPS RO observations Rapid support for new observation types. Chemical Weather Forecasting and Analysis CAM-Chem, WRF-Chem, MOPITT, MODIS DART, WRF, and MARS   CAM inflation values for U Wind after 1 month of adaptive inflation. CAM inflation values for U Wind after 1 month of DAMPED adaptive inflation. DART/CMAQ CO assimilation   Planetary Boundary Layer Probabilistic nowcasting with a single column model. A good rank histogram Lorenz '96 - the true state is as likely as any of the ensemble members. A bad rank histogram (filter divergence) Lorenz '96 - the true state generally falls outside the ensemble members. Tracer advection One of the tutorial examples explores sources/sinks. Impact of PS observations on V winds DART has novel algorithms to explore localization. Ensemble Trajectories DART has routines to explore and diagnose. Ensemble Trajectories Explore the impact of the increments. time series of 'total error' Quantify the effects of observations or parameter settings. CAM@T85 observation space performance DART natively supports direct comparisons with observations. CAM@T85 assimilation performance See if all the observations are being used.

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 (Try that with a variational system!). 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 several (many?) Fortran 90 compilers and run on linux compute-servers, linux clusters, OSX laptops/desktops, SGI Altix clusters, supercomputers running AIX ... a pretty broad range, really.

The Data Assimilation Research Section (DAReS)

from left to right: Tim Hoar, Jeff Anderson, Hui Liu,
Kevin Raeder, Nancy Collins, Silvia Gentile
(David Dowell 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.

DAReS Staff

• Jeff Anderson, Scientist
• Nancy Collins, Software Engineer
• David Dowell, Scientist
• Glen Romine, Project Scientist
• Silvia Gentile, Administrative Assistant
• Tim Hoar, Associate Scientist
• Hui Liu, Project Scientist
• Kevin Raeder, Associate Scientist

Affiliates

• Chris Snyder, Scientist III, MMM
• Joe Tribbia, Senior Scientist, CGD
• Doug Nychka, Senior Scientist, IMAGe

Our central email address is dart@ucar.edu, 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.

Shipping information:

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

This is the DART view of ensemble data assimilation for models that run as separate executables. Starting at the top and working clockwise: Everything is driven by a Fortran namelist and the presence or absence of observations. A Fortran executable named 'filter' reads a namelist, an initial state for the ensemble, and a file containing observations and goes to work. Given the observations and an initial state, 'filter' assimilates the observations and then determines how far to advance the model (using information from the namelist and the observation file). 'filter' forks a shell script to the system and it is this shell script that is responsible for three things: 1) for converting the DART state vectors and 'advance_to_time' to the format required by the underlying model, 2) advancing the model, and 3) converting the model output into a form suitable for 'filter'. [The script is responsible for the lower portion of the diagram.] The model advances each ensemble member (either in turn or all-at-once) and the model output is converted to the input format expected by 'filter'. The shell script finishes and signals 'filter' to continue. We are now back at the beginning and the cycle continues as long as there are observations to assimilate or until the control information in the Fortran namelist is met. When that happens, a set of restart files is written (suitable to continue an experiment with more observations) and diagnostic files are written. These diagnostic files allow for the exploration of the assimilation before and after each assimilation step and for exploration of the assimilation in 'observation space'; each real observation is paired with the estimates of the observation from all of the ensemble members (if desired). Minimally, the ensemble mean estimate of the observation and the ensemble spread of the estimates is recorded.

DART code distributions

DART is distributed through an anonymous-access readonly Subversion (SVN) repository. This makes updates and comparisons between your sandbox and the latest, greatest version of the code trivially easy. The same cannot be said for a TAR file. If you are not familiar with SVN (the client application of subversion), you should take a stroll through my svn primer. If you cannot use svn (e.g. are firewalled out), please let me (Tim) know and I'll send you a tarfile as a last resort. See contact info for email addresses.

For several years we've gotten away from a strategy of official code releases and corresponding versions. We would like to change that and have released the Kodiak version on 30 Jun 2011. There have been more than 2000 (that's TWO THOUSAND) revisions to DART since the Jamaica release. The vast majority of those changes are well-tested enhancements, bugfixes, and documentation; some are new modules/models and diagnostics. As the facility matures, we resolve to be better about tagging new versions.

Since the DART software is still an area of active research, we'd like to be able to contact people to inform them of any bugs or major updates. (This includes local users, BTW!) This is a very low-traffic commitment - perhaps 4 emails per year - so PLEASE use a real email address when signing up. I solemnly swear to protect your email address like it is my own!

download instructions

Please suggest ways for us to improve DART.