  Rejuvenation in particle filters is necessary to prevent the collapse of the
weights when the number of particles is insufficient to sample the high
probability regions of the state space. Rejuvenation is often implemented in a
heuristic manner by the addition of stochastic samples that widen the support
of the ensemble. This work aims at improving canonical rejuvenation methodology
by the introduction of additional prior information obtained from
climatological samples; the dynamical particles used for importance sampling
are augmented with samples obtained from stochastic covariance shrinkage. The
ensemble transport particle filter, and its second order variant, are extended
with the proposed rejuvenation approach. Numerical experiments show that
modified filters significantly improve the analyses for low dynamical ensemble
sizes.
