OpenDA: Integrating models and observations
OpenDA is an open interface standard for (and free implementation of) a set of tools to quickly implement data-assimilation and calibration for arbitrary numerical models. OpenDA wants to stimulate the use of data-assimilation and calibration by lowering the implementation costs and enhancing the exchange of software among researchers and end-users.
A model that conforms to the OpenDA standard can use all the tools that are available in OpenDA. This allows experimentation with data-assimilation/calibration methods without the need for extensive programming. Reversely, developers of data-assimilation/calibration software that make their implementations compatible with the OpenDA interface will make their new methods usable for all OpenDA users (either for free or on a commercial basis).
OpenDA has been designed for high performance. Hence, even large-scale models can use it. Also, OpenDA allows users to optimize the interaction between their model and the data-assimilation/calibration methods. Hence, data-assimilation with OpenDA can be as efficient as with custom-made implementations of data-assimilation methods.
OpenDA is an Open Source project. Contributions are welcome from anyone wishing to participate in the further development of the OpenDA toolset.
In summary, OpenDA:
- Provides researchers with a tool for experimentation with data-assimilation/calibration methods without the need for extensive programming;
- Enables a quick implementation of data-assimilation and calibration for arbitrary numerical models;
- Can be used without the need to learn and implement many different data assimilation and calibration algorithms/methods;
- Has been successfully used in many applications and publications, for example:
- Clever Monitoring
Features of OpenDA
- Data-assimilation methods:
- Ensemble KF (EnKF)
- Ensemble SquareRoot KF (EnSR)
- Steady State KF
- Particle Filter
- DudEnKF (still under research)
- DudEnSR (still under research)
- Parameter estimation (calibration) methods:
- Sparse Dud
- Gridded full search
- Shuffled Comples Evolution (SCE)
- Generalized Likelihood Uncertainty Estimation (GLUE)
- Conjugate Gradient: Fleetjer-Reeves, Polak-Ribiere, Steepest Descent
- Uncertainty Analaysis methods