Implementation of various ensemble Kalman Filter data assimilation methods in Julia
Author Alexander-Barth
20 Stars
Updated Last
11 Months Ago
Started In
November 2016


Build Status Linux and macOS Build Status Windows

Coverage Status

documentation latest

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.


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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