Articles | Volume 11, issue 7
https://doi.org/10.5194/gmd-11-2813-2018
https://doi.org/10.5194/gmd-11-2813-2018
Model description paper
 | 
13 Jul 2018
Model description paper |  | 13 Jul 2018

Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model's R interface (STILT-R version 2)

Benjamin Fasoli, John C. Lin, David R. Bowling, Logan Mitchell, and Daniel Mendoza

Viewed

Total article views: 5,343 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,691 1,574 78 5,343 124 119
  • HTML: 3,691
  • PDF: 1,574
  • XML: 78
  • Total: 5,343
  • BibTeX: 124
  • EndNote: 119
Views and downloads (calculated since 02 Mar 2018)
Cumulative views and downloads (calculated since 02 Mar 2018)

Viewed (geographical distribution)

Total article views: 5,343 (including HTML, PDF, and XML) Thereof 4,922 with geography defined and 421 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Jun 2024
Download
Short summary
The Stochastic Time-Inverted Lagrangian Transport (STILT) model is used to determine the area upstream that influences the air arriving at a given location. We introduce a new framework that makes the STILT model faster and easier to deploy and improves results. We also show how the model can be applied to spatially complex measurement strategies using trace gas observations collected onboard a Salt Lake City, Utah, USA, light-rail train.