A modified ensemble Kalman filter (KF) is proposed which can enhance performance for highly non-linear prognostic models. The algorithm differs from the traditional ensemble KF by the addition of an expectation maximization step, which estimates the parameters of a Gaussian mixture model for the ensemble of forecast states. The algorithm is tested in twin experiments using a simple phytoplankton—zooplankton model.

Smith, K.W. (0AD) ‘Cluster ensemble Kalman filter’, Tellus A: Dynamic Meteorology and Oceanography, {volume}({issue}), p. {page}. Available at: https://doi.org/10.1111/j.1600-0870.2007.00246.x.

Smith, K W. “Cluster ensemble Kalman filter”. Tellus A: Dynamic Meteorology and Oceanography, vol. {volume}, no. {issue}, 0AD, p. {page}, doi:10.1111/j.1600-0870.2007.00246.x.

Smith, K. W. (0AD). Cluster ensemble Kalman filter. Tellus A: Dynamic Meteorology and Oceanography, {volume}({issue}), {page}. https://doi.org/10.1111/j.1600-0870.2007.00246.x

Smith, Keston W.. 0AD. “Cluster Ensemble Kalman Filter”. Tellus A: Dynamic Meteorology and Oceanography {volume} ({issue}): {page}. https://doi.org/10.1111/j.1600-0870.2007.00246.x.

1. Smith KW. Cluster ensemble Kalman filter. Tellus A: Dynamic Meteorology and Oceanography. 0AD;{volume}({issue}):{page}. DOI: https://doi.org/10.1111/j.1600-0870.2007.00246.x

1. Smith KW. Cluster ensemble Kalman filter. Tellus A: Dynamic Meteorology and Oceanography. 0AD;{volume}({issue}):{page}. doi:10.1111/j.1600-0870.2007.00246.x

Smith KW, ‘Cluster Ensemble Kalman Filter’ (0AD) {volume} Tellus A: Dynamic Meteorology and Oceanography {page} DOI: https://doi.org/10.1111/j.1600-0870.2007.00246.x

[1]K. W. Smith, “Cluster ensemble Kalman filter”, Tellus A: Dynamic Meteorology and Oceanography, vol. {volume}, no. {issue}, p. {page}, 0AD, doi: 10.1111/j.1600-0870.2007.00246.x.

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Original Research Papers

Cluster ensemble Kalman filter

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