Computationally efficient air quality forecasting tool: implementation of
STOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust

Jeon, Wonbae; Choi, Yunsoo; Percell, Peter; Souri, Amir Hossein; Song, Chang-Keun; Kim, Soon-Tae; Kim, Jhoon

doi:https://doi.org/10.5194/gmd-9-3671-2016

Articles | Volume 9, issue 10

https://doi.org/10.5194/gmd-9-3671-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-3671-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 10

Model evaluation paper

|

17 Oct 2016

Model evaluation paper |

| 17 Oct 2016

Computationally efficient air quality forecasting tool: implementation of STOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust

Wonbae Jeon, Yunsoo Choi, Peter Percell, Amir Hossein Souri, Chang-Keun Song, Soon-Tae Kim, and Jhoon Kim

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Final revised paper (published on 17 Oct 2016)
Supplement to the final revised paper
Preprint (discussion started on 21 Jul 2016)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Reviewing “The new implementation of a computationally efficient modeling tool into CMAQ and its application for a more accurate prediction of Asian Dust”', Anonymous Referee #1, 19 Aug 2016
- AC1: 'thre response to the first reviewer', Y. Choi, 18 Sep 2016
RC2: 'Review of the manuscript titled “The new implementation of a computationally efficient modeling tool (STOPS v1.5) into CMAQ v5.0.2 and its application for a more accurate prediction of Asian dust”', Anonymous Referee #2, 06 Sep 2016
- AC2: 'the response to the second reviewer', Y. Choi, 18 Sep 2016

Peer-review completion

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

AR by Yunsoo Choi on behalf of the Authors (20 Sep 2016) Author's response Manuscript

ED: Publish subject to technical corrections (26 Sep 2016) by Alex B. Guenther

Short summary

This study suggests a new hybrid Lagrangian–Eulerian modeling tool (the Screening Trajectory Ozone Prediction System, STOPS) for an accurate/fast prediction of Asian dust events. The STOPS is a moving nest (Lagrangian approach) between the source and the receptor inside Eulerian model. We run STOPS, instead of running a time-consuming Eulerian model, using constrained PM concentration from remote sensing aerosol optical depth, reflecting real-time dust particles. STOPS is for unexpected events.