@article{Reich2012,
  author    = {Reich, Sebastian},
  title     = {A Gaussian-mixture ensemble transform filter},
  series = {Quarterly journal of the Royal Meteorological Society},
  volume    = {138},
  journal   = {Quarterly journal of the Royal Meteorological Society},
  number    = {662},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0035-9009},
  doi       = {10.1002/qj.898},
  pages     = {222 -- 233},
  year      = {2012},
  abstract  = {We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior distribution can take the form of a Gaussian mixture or a Gaussian kernel density estimator. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian-mixture filter (EGMF). The EGMF is implemented for three simple test problems (Brownian dynamics in one dimension, Langevin dynamics in two dimensions and the three-dimensional Lorenz-63 model). It is demonstrated that the EGMF is capable of tracking systems with non-Gaussian uni- and multimodal ensemble distributions.},
  language  = {en}
}