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    NCAR's Data Assimilation Research Section

    The NCAR Data Assimilation Initiative was founded to create and to then lead a research community for data assimilation where individuals benefit from sharing ideas, methodologies, and software tools as well as access to a data assimilation testbed. NCAR has a large number of researchers for whom data assimilation is an essential part of their ongoing or planned research. New developments in theoretical data assimilation and in software engineering are making collaborations between data assimilation experts, modelers, observational specialists and statisticians easier and more productive than was possible in the past. The maturation of the Initiative resulted in the Data Assimilation Research Section (DAReS): a component of the Institute for Mathematics Applied to Geosciences. The primary goal of DAReS is to continue to advance the theory and practice of ensemble data assimilation. Also, DAReS accelerates the progress of many other NCAR projects by providing a centralized data assimilation expertise which can be coordinated with existing observational and modeling expertise.

    Ensemble Filters for Geophysical Data Assimilation

    6 frame animation demonstrating the assimilation schematic

    Click on Image to Download DART

    dart download icon

    The Data Assimilation Research Testbed Facility : DART

    The Data Assimilation Research Testbed (DART), is a software environment for making it easy to match a variety of data assimiliation methods to different numerical models and different kinds of observations. 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 source code is distributed through our anonymous subversion server (meaning you don't need an account on our machines) after you fill out a trivial form asking for your name, email address, and institution. We need to gather summary statistics to maintain an email list should there be an important update and to help us quantify our impact on the community. This is an ultra-low-traffic email list. We have never sent an email to the entire list! Please be assured we will keep your email address confidential.

    The "Lanai" version of DART was released in December 2013 and contains many stable bugfixes and improvements over the Kodiak version. The DART source code and documentation can be downloaded at www.image.ucar.edu/DAReS/DART/DART_download. The most current version of the documentation is (always) distributed with the code, but if you want to take a look before downloading, visit the Lanai release document.

    The most current version of the documentation is distributed with the code, but if you want an idea of what it takes to build DART and run experiments before downloading, look at the Getting Started page.

    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.

    We regularly give tutorials on Ensemble Data Assimilation

    IMAGe Theme of the Year (2015):

    The main page for the Workshop and Tutorial is available [here].

    WRF/DART Tutorial at NCAR: 22 Jan 2014

    A WRF/DART Ensemble Data Assimilation Tutorial was taught on 22 January 2014 at NCAR in Boulder, Colorado.

    It covered:

    1. Introduction to ensemble DA
    2. Using DART with simple models
    3. Running the WRF/DART assimilation system
    4. Using WRF/DART analysis tools
    5. Examples of WRF/DART ensemble analyses and forecasts

    The main page for the WRF/DART tutorial, plus pointers to the general WRF Tutorial is available [here], and the tutorial materials for students in the workshop is available [here].

    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.

    algorithms diagnostics platforms/mpi CAM WRF
    Jeff Anderson Tim Hoar Nancy Collins Kevin Raeder Glen Romine
    jla @ ucar . edu  thoar @ ucar . edu  nancy @ ucar . edu  raeder @ ucar . edu  romine @ ucar . edu 

    Shipping information:

    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