Articles | Volume 11, issue 7
Geosci. Model Dev., 11, 2813–2824, 2018
https://doi.org/10.5194/gmd-11-2813-2018
Geosci. Model Dev., 11, 2813–2824, 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 et al.

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

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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.