An improved pixel-based water vapor tomography model

Yao, Yibin; Xin, Linyang; Zhao, Qingzhi

doi:https://doi.org/10.5194/angeo-37-89-2019

Articles | Volume 37, issue 1

https://doi.org/10.5194/angeo-37-89-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Advanced Global Navigation Satellite Systems tropospheric...

https://doi.org/10.5194/angeo-37-89-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 37, issue 1

Regular paper

|

01 Feb 2019

Regular paper |

| 01 Feb 2019

An improved pixel-based water vapor tomography model

Yibin Yao, Linyang Xin, and Qingzhi Zhao

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Final revised paper (published on 01 Feb 2019)
Preprint (discussion started on 23 May 2018)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Comments', Anonymous Referee #1, 10 Jul 2018
- AC1: 'Responses to Referee #1', Yibin Yao, 12 Sep 2018
- AC4: 'Revised manuscript with changes highlighted', Yibin Yao, 12 Sep 2018
RC2: 'A new tropospheric tomography model combining the pixel-based and function-based models', Anonymous Referee #2, 07 Aug 2018
- AC2: 'Responses to Referee #2', Yibin Yao, 12 Sep 2018
- AC3: 'Revised manuscript with changes highlighted', Yibin Yao, 12 Sep 2018

Peer-review completion

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

ED: Publish subject to revisions (further review by editor and referees) (19 Sep 2018) by Petr Pisoft

AR by Yibin Yao on behalf of the Authors (19 Sep 2018)

ED: Referee Nomination & Report Request started (24 Sep 2018) by Petr Pisoft

RR by Anonymous Referee #1 (20 Oct 2018)

RR by Anonymous Referee #2 (21 Oct 2018)

RR by Anonymous Referee #3 (12 Nov 2018)

ED: Publish subject to minor revisions (review by editor) (29 Nov 2018) by Petr Pisoft

AR by Yibin Yao on behalf of the Authors (12 Dec 2018) Author's response Manuscript

ED: Publish as is (22 Dec 2018) by Petr Pisoft

AR by Yibin Yao on behalf of the Authors (23 Dec 2018)

Short summary

In this paper, we propose an improved pixel-based water vapor tomography model, which uses layered optimal polynomial functions by adaptive training for water vapor retrieval. Under different scenarios, tomography results show that the new model outperforms the traditional one by reducing the root-mean-square error (RMSE), and this improvement is more pronounced, at 5.88 % in voxels without the penetration of GNSS rays. The improved model also has advantages in more convenient expression.