Articles | Volume 13, issue 1
https://doi.org/10.5194/amt-13-355-2020
https://doi.org/10.5194/amt-13-355-2020
Research article
 | 
31 Jan 2020
Research article |  | 31 Jan 2020

A GPS water vapour tomography method based on a genetic algorithm

Fei Yang, Jiming Guo, Junbo Shi, Xiaolin Meng, Yinzhi Zhao, Lv Zhou, and Di Zhang

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Fei Yang on behalf of the Authors (13 Sep 2019)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (26 Sep 2019) by Roeland Van Malderen
RR by Andre Sa (26 Oct 2019)
RR by Anonymous Referee #1 (18 Nov 2019)
ED: Publish subject to minor revisions (review by editor) (21 Nov 2019) by Roeland Van Malderen
AR by Fei Yang on behalf of the Authors (29 Nov 2019)  Author's response 
ED: Publish subject to minor revisions (review by editor) (20 Dec 2019) by Roeland Van Malderen
AR by Fei Yang on behalf of the Authors (28 Dec 2019)  Author's response   Manuscript 
ED: Publish as is (06 Jan 2020) by Roeland Van Malderen
AR by Fei Yang on behalf of the Authors (07 Jan 2020)
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Short summary
The development of GPS station networks that provide rich data sources containing atmospheric information will enable more GPS applications in the field of meteorology. This study describes a genetic algorithm for the water vapour tomography; overcomes the ill-conditioned problem; and eliminates the reliance on excessive constraints, priori information, and external data. It is proven in the paper that accurate 3-D water vapour distribution can be provided by this study for atmospheric research.