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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Jul 2018
Model description paper |
| 13 Jul 2018
| 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
CORRESPONDING AUTHOR
Department of Atmospheric Sciences, University of Utah, Salt Lake City, 84112, USA
John C. Lin
Department of Atmospheric Sciences, University of Utah, Salt Lake City, 84112, USA
David R. Bowling
Department of Biology, University of Utah, Salt Lake City, 84112, USA
Logan Mitchell
Department of Atmospheric Sciences, University of Utah, Salt Lake City, 84112, USA
Daniel Mendoza
Department of Atmospheric Sciences, University of Utah, Salt Lake City, 84112, USA
Division of Pulmonary Medicine, School of Medicine, University of Utah, Salt Lake City, 84112, USA
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- Wildfire activity is driving summertime air quality degradation across the western US: a model-based attribution to smoke source regions T. Wilmot et al. 10.1088/1748-9326/ac9a5d
- Mars Methane Sources in Northwestern Gale Crater Inferred From Back Trajectory Modeling Y. Luo et al. 10.1029/2021EA001915
- Dairy Methane Emissions in California's San Joaquin Valley Inferred With Ground‐Based Remote Sensing Observations in the Summer and Winter S. Heerah et al. 10.1029/2021JD034785
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Latest update: 23 Nov 2024
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.
The Stochastic Time-Inverted Lagrangian Transport (STILT) model is used to determine the area...
