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Rejuvenation in particle filters is necessary to prevent the collapse of theweights when the number of particles is insufficient to sample the highprobability regions of the state space. Rejuvenation is often implemented in aheuristic manner by the addition of stochastic samples that widen the supportof the ensemble. This work aims at improving canonical rejuvenation methodologyby the introduction of additional prior information obtained fromclimatological samples; the dynamical particles used for importance samplingare augmented with samples obtained from stochastic covariance shrinkage. Theensemble transport particle filter, and its second order variant, are extendedwith the proposed rejuvenation approach. Numerical experiments show thatmodified filters significantly improve the analyses for low dynamical ensemblesizes.

A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter