  The choice of the prior model can have a large impact on the ability to
assimilate data. In standard applications of ensemble-based data assimilation,
all realizations in the initial ensemble are generated from the same covariance
matrix with the implicit assumption that this covariance is appropriate for the
problem. In a hierarchical approach, the parameters of the covariance function,
for example the variance, the orientation of the anisotropy and the ranges in
two principal directions, may all be uncertain. Thus, the hierarchical approach
is much more robust against model misspecification. In this paper, three
approaches to sampling from the posterior for hierarchical parameterizations
are discussed: an optimization-based sampling approach (RML), an iterative
ensemble smoother (IES), and a novel hybrid of the previous two approaches
(hybrid IES). The three approximate sampling methods are applied to a
linear-Gaussian inverse problem for which it is possible to compare results
with an exact "marginal-then-conditional" approach. Additionally, the IES and
the hybrid IES methods are tested on a two-dimensional flow problem with
uncertain anisotropy in the prior covariance. The standard IES method is shown
to perform poorly in the flow examples because of the poor representation of
the local sensitivity matrix by the ensemble-based method. The hybrid method,
however, samples well even with a relatively small ensemble size.
