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

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 Traj...

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Main Authors: Helge Simon, Jannik Heusinger, Tim Sinsel, Stephan Weber, Michael Bruse
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Atmosphere
Subjects:
Online Access:https://www.mdpi.com/2073-4433/12/8/977
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author Helge Simon
Jannik Heusinger
Tim Sinsel
Stephan Weber
Michael Bruse
author_facet Helge Simon
Jannik Heusinger
Tim Sinsel
Stephan Weber
Michael Bruse
author_sort Helge Simon
collection DOAJ
description 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.
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spelling doaj.art-c3f19f1f697e4d1a85907f476e6721332023-11-22T06:47:15ZengMDPI AGAtmosphere2073-44332021-07-0112897710.3390/atmos12080977Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-MetHelge Simon0Jannik Heusinger1Tim Sinsel2Stephan Weber3Michael Bruse4Department of Geography, Johannes Gutenberg University Mainz, 55099 Mainz, GermanyClimatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, 38106 Braunschweig, GermanyDepartment of Geography, Johannes Gutenberg University Mainz, 55099 Mainz, GermanyClimatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, 38106 Braunschweig, GermanyDepartment of Geography, Johannes Gutenberg University Mainz, 55099 Mainz, GermanyThe 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.https://www.mdpi.com/2073-4433/12/8/977RANS-model post-processingmulticore 3D footprint calculationbackwards trajectoriesparticle model
spellingShingle Helge Simon
Jannik Heusinger
Tim Sinsel
Stephan Weber
Michael Bruse
Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
Atmosphere
RANS-model post-processing
multicore 3D footprint calculation
backwards trajectories
particle model
title Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
title_full Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
title_fullStr Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
title_full_unstemmed Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
title_short Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
title_sort implementation of a lagrangian stochastic particle trajectory model lasttram to simulate concentration and flux footprints using the microclimate model envi met
topic RANS-model post-processing
multicore 3D footprint calculation
backwards trajectories
particle model
url https://www.mdpi.com/2073-4433/12/8/977
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