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

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Helge Simon
  • Jannik Heusinger
  • Tim Sinsel
  • Stephan Weber
  • Michael Bruse
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000688855800001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.3390/atmos12080977
eISSN
2073-4433
Externe Identifier
  • Clarivate Analytics Document Solution ID: UF8ZG
Ausgabe der Veröffentlichung
8
Zeitschrift
ATMOSPHERE
Schlüsselwörter
  • RANS-model post-processing
  • multicore 3D footprint calculation
  • backwards trajectories
  • particle model
Artikelnummer
ARTN 977
Datum der Veröffentlichung
2021
Status
Published
Titel
Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
Sub types
  • Article
Ausgabe der Zeitschrift
12

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:p>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.</jats:p>
Autoren
  • Helge Simon
  • Jannik Heusinger
  • Tim Sinsel
  • Stephan Weber
  • Michael Bruse
DOI
10.3390/atmos12080977
eISSN
2073-4433
Ausgabe der Veröffentlichung
8
Zeitschrift
Atmosphere
Sprache
en
Online publication date
2021
Paginierung
977 - 977
Status
Published online
Herausgeber
MDPI AG
Herausgeber URL
http://dx.doi.org/10.3390/atmos12080977
Datum der Datenerfassung
2021
Titel
Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met
Ausgabe der Zeitschrift
12

Datenquelle: Crossref

Author's licence
CC-BY
Autoren
  • Helge Simon
  • Jannik Heusinger
  • Tim Sinsel
  • Stephan Weber
  • Michael Bruse
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • JGU-Publikationen
Resource version
Published version
DOI
10.3390/atmos12080977
Funding acknowledgements
  • Open Access-Publizieren Universität Mainz / Universitätsmedizin Mainz
File(s) embargoed
false
Open access
true
ISSN
2073-4433
Ausgabe der Veröffentlichung
8
Zeitschrift
Atmosphere
Schlüsselwörter
  • 550 Geowissenschaften
  • 550 Earth sciences
Sprache
eng
Open access status
Open Access
Paginierung
977
Datum der Veröffentlichung
2021
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/6522
Herausgeber
MDPI AG
Herausgeber URL
https://doi.org/10.3390/atmos12080977
Datum der Datenerfassung
2021
Datum, an dem der Datensatz öffentlich gemacht wurde
2021
Zugang
Public
Titel
Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to simulate concentration and flux footprints using the microclimate model ENVI-Met
Ausgabe der Zeitschrift
12

Files

simon_helge-implementation-20211115120308724.pdf

Datenquelle: OPENSCIENCE.UB

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