The benefits of using DART¶
A common pitfall for graduate students and professionals alike is to look at the simplicity of data assimilation, in particular ensemble data assimilation, and decide they can easily write their own DA system. Indeed, this is true. After learning of the core algorithms, a talented programmer using their favorite language could write a functional DA system in a manner of weeks if not days. However, he or she will soon find that while the core of DA systems are easy to write, the more “real” the system needs to be, the more complex it will become. Writing a parallel DA system that can efficiently utilize multiple cores with MPI is not straight-forward, and adding covariance localization, observation operators, multiple models, and auxiliary tools such as quality control and pre-processing will quickly dwarf the amount of core DA code, not to mention the headaches involved in supporting multiple computing environments, compilers, etc.
DART employs a modular programming approach to apply an algorithm to move 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 DA algorithms. 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.
DART remains the top choice for scientists, educators, and mathematicians seeking mature and robust ensemble DA solutions without reinventing the wheel. Here are some of the many benefits of using DART:
DART is freely available, open source, and released under the Apache 2.0 License . In short this means that you are granted a copyright license stating you are free to use, modify, and redistribute any derivative works derived from the DART system provided that you maintain the license and copyright information. Of course, we also ask that you credit DART in your publications, and kindly ask that you contribute your modifications so that other users may benefit. See How should I cite DART? and How can I contribute to DART? for more information.
DART is fully parallel and carefully engineered to run on systems ranging from single-core research computers to the top performing multicore supercomputers in the world. Writing scalable parallel code is arguably the most difficult and time-consuming task in scientific computing today, but DART has already carefully implemented and tested this project, and the code is available for you to use out-of-the-box. For more information on how DART was written (and continues to be developed), see DART’s design philosophy.
DART contains numerous tools that accelerate getting started on both research and “real-world” problems. Multiple rigorously tested inflation, localization, perturbation, and other auxiliary data assimilation algorithms are available for immediate use and testing. See Important capabilities of DART for more information.
DART makes adding a new model straightforward. A new model only needs to implement a list of (at most) 18 core functions or use the default behavior if applicable to take advantage of DART’s mature and robust DA algorithms. A basic data assimilation system for a large model can be built in person-weeks, and comprehensive systems have been built in a few months. See How do I run DART with my model? for more information.
DART makes it easy to add new observations in order to test their potential beneficial impact. Incorporating new observation types only requires creating a forward operator that computes the expected value of an observation given a model’s state. See How do I add my observations to DART? for more information.
DART can be used to test new DA algorithms. Many such algorithms have been successfully implemented, tested, and published using DART. This is not covered in this getting started guide as this is an “advanced user” functionality, so for this purpose it is best to first get in touch with the DART team at dart @ ucar.edu to make the process as smooth as possible.
Finally, and perhaps most importantly, DART has world-class support available from the DART team at NCAR. A talented team of dedicated software engineers and data assimilation scientists work together to continually improve DART and support user needs. See the About page for more information about the DART team.