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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-20
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
02 Mar 2018
Review status
This discussion paper is a preprint. A revision of the manuscript was accepted for the journal Geoscientific Model Development (GMD).
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 Fasoli1, John C. Lin1, David R. Bowling2, Logan Mitchell1, and Daniel Mendoza1,3 1Department of Atmospheric Sciences, University of Utah, Salt Lake City, 84112, USA
2Department of Biology, University of Utah, Salt Lake City, 84112, USA
3Division of Pulmonary Medicine, School of Medicine, University of Utah, Salt Lake City, 84112, USA
Abstract. The Stochastic Time-Inverted Lagrangian Transport (STILT) model is comprised of a compiled Fortran executable that carries out advection and dispersion calculations as well as a higher level code layer for simulation control and user interaction, written in the open source data analysis language R. We introduce modifications to the STILT-R codebase with the aim to improve the model's applicability to fine-scale (< 1 km) trace gas measurement studies. The changes facilitate placement of spatially distributed receptors and provide high level methods for single and multi-node parallelism. We present a kernel density estimator to calculate influence footprints and demonstrate improvements over prior methods. Vertical dilution in the hyper near-field is calculated using the Lagrangian decorrelation timescale and vertical turbulence to approximate the effective mixing depth. This framework provides a central source repository to reduce code fragmentation between STILT user groups as well as a systematic, well documented workflow for users. We apply the modified STILT-R to light-rail measurements in Salt Lake City, Utah, United States and discuss how results from our analyses can inform future fine-scale measurement approaches and modeling efforts.
Citation: Fasoli, B., Lin, J. C., Bowling, D. R., Mitchell, L., and Mendoza, D.: Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model's R interface (STILT-R version 2), Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-20, in review, 2018.
Benjamin Fasoli et al.
Benjamin Fasoli et al.
Benjamin Fasoli et al.

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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, 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 on-board a Salt Lake City, Utah, USA light-rail train.
The Stochastic Time-Inverted Lagrangian Transport (STILT) model is used to determine the area...
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