  This paper presents an algorithm for Monte Carlo fixed-lag smoothing in
state-space models defined by a diffusion process observed through noisy
discrete-time measurements. Based on a particles approximation of the filtering
and smoothing distributions, the method relies on a simulation technique of
conditioned diffusions. The proposed sequential smoother can be applied to
general non linear and multidimensional models, like the ones used in
environmental applications. The smoothing of a turbulent flow in a
high-dimensional context is given as a practical example.
