Data assimilation in variable dimension dispersion models using particle filters

Abstract

Data assimilation in the context of puff based dispersion models is studied. A representative two dimensional Gaussian puff atmospheric dispersion model is used for the purpose of testing and comparing several data assimilation techniques. A continuous nonlinear observation model, and a quantized probabilistic nonlinear observation model, are used to simulate the measurements. The quantized model is used to simulate bar sensor readings of the concentration. Dispersion models usually lead to high dimensional space-gridded state space models. In the case of puff based dispersion models, this may be avoided by using puff parameters themselves as the states, but at the cost of introducing nonlinearity and variable dimensionality. The potential of sampling based techniques is discussed in this context, with a particular focus on the particle filter approach, for which variable state dimensionality creates no difficulties. The performance of particle filter is compared with that of the extended Kalman filter, and its advantages and limitations are illustrated.