  This paper presents the application of a particle filter for data
assimilation in the context of puff-based dispersion models. Particle filters
provide estimates of the higher moments, and are well suited for strongly
nonlinear and/or non-Gaussian models. The Gaussian puff model SCIPUFF, is used
in predicting the chemical concentration field after a chemical incident. This
model is highly nonlinear and evolves with variable state dimension and, after
sufficient time, high dimensionality. While the particle filter formalism
naturally supports variable state dimensionality high dimensionality represents
a challenge in selecting an adequate number of particles, especially for the
Bootstrap version. We present an implementation of the Bootstrap particle
filter and compare its performance with the SCIPUFF predictions. Both the model
and the Particle Filter are evaluated on the Dipole Pride 26 experimental data.
Since there is no available ground truth, the data has been divided in two
sets: training and testing. We show that even with a modest number of
particles, the Bootstrap particle filter provides better estimates of the
concentration field compared with the process model, without excessive increase
in computational complexity.
