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

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

doi:https://doi.org/10.5194/gmd-11-2813-2018

Articles | Volume 11, issue 7

https://doi.org/10.5194/gmd-11-2813-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-11-2813-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 7

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

Model code and software

Stochastic Time Inverted Lagrangian Transport Model B. Fasoli, J. C. Lin, D. R. Bowling, L. Mitchell, and D. Mendoza https://doi.org/10.5281/zenodo.1238047

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.