Stochastic trajectory modelling of atmospheric dispersion

The stochastic trajectory-based (Lagrangian) approach has gained increasing importance and sophistication in atmospheric transport and dispersion modelling over the last few decades. State-of-the-art Lagrangian particle dispersion model (LPDMs) are used to compute trajectories of a large number of ‘marked’ particles and numerically simulate the dispersion of a pollutant (passive tracer) in the turbulent atmosphere. In this thesis, we mainly investigate the stochastic formulation and behaviour of LPDMs in the context of the turbulent atmospheric boundary layer (ABL). A random flight model (RFM) is a type of LPDM that describes the paths of particles of an air pollutant in a turbulent flow, given a statistical knowledge of the random velocity field. Operational RFMs such as FLEXPART have not taken advantage of modern developments of numerical methods for stochastic differential equations. Chapter 2 of this thesis aims to determine whether current numerical schemes used in operational atmospheric dispersion modelling can be improved. Several commonly used numerical schemes are investigated in a simple one-dimensional dispersion model describing the vertical turbulence in the ABL. Eulerian FokkerPlanck equation (FPE) solutions with the required level of accuracy are used to validate the performance of the RFM numerical schemes. The results allow for optimal time-step selection and recommendations to be made for use in operational models.