Implementation of various ensemble Kalman Filter data assimilation methods in Julia
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Updated Last
10 Months Ago
Started In
November 2016


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The packages implements various data assimilation methods:

  • (Extended) Kalman Filter
  • Incremental 4D-Var
  • Ensemble Square Root Filter (EnSRF)
  • Ensemble Square Root Filter with serial processing of the observations (serialEnSRF)
  • Ensemble Transform Kalman Filter (ETKF)
  • Ensemble Transform Kalman Filter (EAKF)
  • Singular Evolutive Interpolated Kalman filter (SEIK)
  • Error-subspace Transform Kalman Filter (ESTKF)
  • Ensemble Kalman Filter (EnKF)

The Julia code is ported from the Matlab/Octave code generated in the frame of the Sangoma project (


An example of using to package is available as a jupyter-notebook.

Kalman Filter

The example below is the result of the Kalman Filter. The red elipse corresponds to the model forecast (Gaussian probability density function), the blue elipse corresponds to the observations and the purple elipse is the analysis (after assimilation).


Ensemble Transform Kalman Filter

The example below is the result of the Ensemble Transform Kalman Filter. The red dots corresponds to the model ensemble, the blue elipse is the Gaussian probability density function of the observations and the purple dots is the model ensemble after analysis.



Most of the algorithms are described in the review article:

Sanita Vetra-Carvalho, Peter Jan van Leeuwen, Lars Nerger, Alexander Barth, M. Umer Altaf, Pierre Brasseur, Paul Kirchgessner, and Jean-Marie Beckers. State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems. Tellus A: Dynamic Meteorology and Oceanography, 70(1):1445364, 2018. doi: 10.1080/16000870.2018.1445364.

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