  The high dimensionality and computational constraints associated with
filtering problems in large-scale geophysical applications are particularly
challenging for the Particle Filter (PF). Approximate but efficient methods
such as the Ensemble Kalman Filter (EnKF) are therefore usually preferred. A
key element of these approximate methods is localization, which is in principle
a general technique to avoid the curse of dimensionality and consists in
limiting the influence of observations to neighboring sites. However, while it
works effectively with the EnKF, localization introduces harmful
discontinuities in the estimated physical fields when applied blindly to the
PF. In the present paper, we explore two possible local algorithms based on the
EnKPF, a hybrid method combining the EnKF and the PF. A simulation study in a
conjugate normal setup allows to highlight the trade-offs involved when
applying localization to PF type of algorithms in the high-dimensional setting.
Experiments with the Lorenz96 model demonstrate the ability of the local EnKPF
algorithms to perform well even with a small number of particles compared to
the problem size.

Computation;Chaotic Dynamics;Atmospheric and Oceanic Physics