Sondergaard, T. and P.F.J. Lermusiaux, 2013b. Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II: Applications. Monthly Weather Review, 141, 6, 1761-1785, doi:10.1175/MWR-D-11-00296.1.
The properties and capabilities of the GMM-DO filter are assessed and exemplified by applications
to two dynamical systems: (1) the Double Well Diffusion and (2) Sudden Expansion flows; both
of which admit far-from-Gaussian statistics. The former test case, or twin experiment, validates
the use of the EM algorithm and Bayesian Information Criterion with Gaussian Mixture Models
in a filtering context; the latter further exemplifies its ability to efficiently handle state vectors of
non-trivial dimensionality and dynamics with jets and eddies. For each test case, qualitative and
quantitative comparisons are made with contemporary filters. The sensitivity to input parameters
is illustrated and discussed. Properties of the filter are examined and its estimates are described,
including: the equation-based and adaptive prediction of the probability densities; the evolution
of the mean field, stochastic subspace modes and stochastic coefficients; the fitting of Gaussian
Mixture Models; and, the efficient and analytical Bayesian updates at assimilation times and the
corresponding data impacts. The advantages of respecting nonlinear dynamics and preserving
non-Gaussian statistics are brought to light. For realistic test cases admitting complex distributions
and with sparse or noisy measurements, the GMM-DO filter is shown to fundamentally improve the
filtering skill, outperforming simpler schemes invoking the Gaussian parametric distribution.