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 (http://data-assimilation.net/).

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.