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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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GMD | Articles | 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 Fasoli et al.

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Cited articles

Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W., Lunden, M. M., Marshall, J. D., Portier, C. J., Vermeulen, R. C., and Hamburg, S. P.: High-resolution air pollution mapping with Google street view cars: exploiting big data, Environ. Sci. Technol., 51, 6999–7008, https://doi.org/10.1021/acs.est.7b00891, 2017. a
Bush, S. E., Hopkins, F. M., Randerson, J. T., Lai, C.-T., and Ehleringer, J. R.: Design and application of a mobile ground-based observatory for continuous measurements of atmospheric trace gas and criteria pollutant species, Atmos. Meas. Tech., 8, 3481–3492, https://doi.org/10.5194/amt-8-3481-2015, 2015. a
Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B., Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillett, N. P., Houghton, R. A., and Marland, G.: Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks, P. Natl. Acad. Sci. USA, 104, 18866–18870, https://doi.org/10.1073/pnas.0702737104, 2007. a
Draxler, R. R. and Hess, G. D.: An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition, Aust. Meteorol. Mag., 47, 295–308, 1998. a
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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...
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