Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met

Abstract

The number of studies evaluating flux or concentration footprints has grown considerably in recent years. These footprints are vital to understand surface–atmosphere flux measurements, for example by eddy covariance. The newly developed backwards trajectory model LaStTraM (Lagrangian Stochastic Trajectory Model) is a post-processing tool, which uses simulation results of the holistic 3D microclimate model ENVI-met as input. The probability distribution of the particles is calculated using the Lagrangian Stochastic method. Combining LaStTraM with ENVI-met should allow us to simulate flux and concentration footprints in complex urban environments. Applications and evaluations were conducted through a comparison with the commonly used 2D models Kormann Meixner and Flux Footprint Predictions in two different meteorological cases (stable, unstable) and in three different detector heights. LaStTraM is capable of reproducing the results of the commonly used 2D models with high accuracy. In addition to the comparison with common footprint models, studies with a simple heterogeneous and a realistic, more complex model domain are presented. All examples show plausible results, thus demonstrating LaStTraM’s potential for the reliable calculation of footprints in homogeneous and heterogenous areas.

Keywords

• RANS-model post-processing
• multicore 3D footprint calculation
• backwards trajectories
• particle model