The OpenDA data-assimilation toolbox

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The OpenDA data-assimilation toolbox

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.


Release of version 2.2

OpenDA version 2.2 is released in March 2015. OpenDA is available through There are binary versions available for Linux (64 bit) and Windows (64 bit). The Linux distributions also contain precompiled native components for Mac.


Features of OpenDA

Currently available methods

  • Data-assimilation methods
    • Ensemble KF (EnKF)
    • Ensemble SquareRoot KF (EnSR)
    • Steady State KF
    • Particle Filter
    • 3DVar
    • DudEnKF (still under research)
    • DudEnSR (still under research)
  • Parameter estimation (calibration) methods:
    • Dud
    • Sparse Dud
    • Simplex
    • Powell
    • Gridded full search
    • Shuffled Comples Evolution (SCE)
    • Generalized Likelihood Uncertainty Estimation (GLUE)
    • (L)BFGS
    • Conjugate Gradient: Fleetjer-Reeves, Polak-Ribiere, Steepest Descent

Language interfaces

  • C/C++
  • Java
  • Fortran77/90

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