Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.252 IF 4.252
  • IF 5-year value: 4.890 IF 5-year 4.890
  • CiteScore value: 4.49 CiteScore 4.49
  • SNIP value: 1.539 SNIP 1.539
  • SJR value: 2.404 SJR 2.404
  • IPP value: 4.28 IPP 4.28
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 51 Scimago H index 51
Volume 11, issue 7
Geosci. Model Dev., 11, 2813-2824, 2018
https://doi.org/10.5194/gmd-11-2813-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 2813-2824, 2018
https://doi.org/10.5194/gmd-11-2813-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 Fasoli1, John C. Lin1, David R. Bowling2, Logan Mitchell1, and Daniel Mendoza1,3 Benjamin Fasoli et al.
  • 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 code base with the aim to improve the model's applicability to fine-scale ( < 1km) 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 among 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.

Publications Copernicus
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.
The Stochastic Time-Inverted Lagrangian Transport (STILT) model is used to determine the area...
Citation
Share