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ANGEO | Articles | Volume 37, issue 1
Ann. Geophys., 37, 89–100, 2019
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...

Ann. Geophys., 37, 89–100, 2019
https://doi.org/10.5194/angeo-37-89-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Regular paper 01 Feb 2019

Regular paper | 01 Feb 2019

An improved pixel-based water vapor tomography model

Yibin Yao et al.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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 Anna Mirena Feist-Polner on behalf of the Authors (24 Sep 2018)  Author's response    Manuscript
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
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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.
In this paper, we propose an improved pixel-based water vapor tomography model, which uses...
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