Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model's R interface (STILT-R version 2)

Fasoli, Benjamin; Lin, John C.; Bowling, David R.; Mitchell, Logan; Mendoza, Daniel

doi:https://doi.org/10.5194/gmd-11-2813-2018

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.

https://doi.org/10.5194/gmd-11-2813-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 7

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, John C. Lin, David R. Bowling, Logan Mitchell, and Daniel Mendoza

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Final revised paper (published on 13 Jul 2018)
Preprint (discussion started on 02 Mar 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'Executive Editor's comment', Julia Hargreaves, 12 Mar 2018
- SC2: 'Code version bookmark', Benjamin Fasoli, 13 Mar 2018
RC1: 'Review of Fasoli et al.', Anonymous Referee #1, 29 Mar 2018
- AC1: 'Response to Reviewer 1', Benjamin Fasoli, 20 May 2018
RC2: 'Review comments on "Simulating atmospheric tracer concentrations for spatially distributed receptors"', Anonymous Referee #2, 09 Apr 2018
- AC2: 'Response to Reviewer 2', Benjamin Fasoli, 20 May 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Benjamin Fasoli on behalf of the Authors (20 May 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (23 May 2018) by Alex B. Guenther

RR by Anonymous Referee #2 (11 Jun 2018)

ED: Publish as is (19 Jun 2018) by Alex B. Guenther

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.