Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts

Wang, Yi; Wang, Jun; Zhou, Meng; Henze, Daven K.; Ge, Cui; Wang, Wei

doi:https://doi.org/10.5194/acp-20-6651-2020

Articles | Volume 20, issue 11

https://doi.org/10.5194/acp-20-6651-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-20-6651-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 11

Research article

|

05 Jun 2020

Research article |

| 05 Jun 2020

Inverse modeling of SO₂ and NO_x emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts

Yi Wang, Jun Wang, Meng Zhou, Daven K. Henze, Cui Ge, and Wei Wang

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Final revised paper (published on 05 Jun 2020)
Supplement to the final revised paper
Preprint (discussion started on 12 Nov 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'acp-2019-880 review', Anonymous Referee #1, 11 Dec 2019
- AC1: 'replies', Jun Wang, 14 Mar 2020
RC2: 'Review', Anonymous Referee #2, 02 Jan 2020
- AC2: 'Replies', Jun Wang, 14 Mar 2020

Peer-review completion

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

AR by Jun Wang on behalf of the Authors (14 Mar 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (24 Mar 2020) by Leiming Zhang

RR by Anonymous Referee #1 (12 Apr 2020)

ED: Publish as is (14 Apr 2020) by Leiming Zhang

AR by Jun Wang on behalf of the Authors (15 Apr 2020) Manuscript

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Short summary

We developed four different methods to downscale SO₂ and NO₂ emissions derived from OMPS satellite observations (in Part 1) for regional air quality modeling at a spatial resolution that is finer than satellite observations. The VIIRS (city lights), TROPOMI, and OMI satellite data as well as surface data are used to evaluate the model. The method of using the top-down emissions from the past month for the air quality forecast in the present month is also shown to have practical merit.