  The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for
high-dimensional meteorological problems. On the other hand, the EnKF can be
interpreted as a particle filter, and particle filters collapse in
high-dimensional problems. We explain that these seemingly contradictory
statements offer insights about how particle filters function in certain
high-dimensional problems, and in particular support recent efforts in
meteorology to "localize" particle filters, i.e., to restrict the influence of
an observation to its neighborhood.
