Atmospheric Dispersion Modeling Using a Stochastic Wind Model

Abstract

In this work, we propose a stochastic wind field based on the Bayesian dynamic linear model to account for the wind flow field in the transient advection-diffusion partial differential equation (PDE). The resulting dispersion model accounts for the time variation in the wind field and meteorological variables, allowing the simulation of a transient regime. The main advantage of using such a wind field model over a Fourier series to fit wind time series is its potential to make predictions. In addition, a suitable methodology is necessary to solve the resulting dispersion model. In this work, we use a finite element formulation appropriate to solve transient advection-diffusion PDEs. We verify the accuracy of the proposed methodology by reproducing a case study considering a field tracer experiment. The model evaluation against experimental data shows the good performance of the proposed dispersion model.