Comparison of SCIPUFF Plume Prediction with Particle Filter Assimilated Prediction for Dipole Pride 26 Data

TL;DR

This study compares the performance of a Bootstrap particle filter with the SCIPUFF Gaussian puff model in predicting chemical dispersion, demonstrating improved estimates with modest computational cost on Dipole Pride 26 data.

Contribution

It introduces an implementation of the Bootstrap particle filter for nonlinear dispersion modeling and evaluates its effectiveness against SCIPUFF using experimental data.

Findings

Particle filter outperforms SCIPUFF in concentration estimates.

Modest number of particles yields significant improvement.

Particle filter maintains computational efficiency.

Abstract

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.