A GPS water vapour tomography method based on a genetic algorithm

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

doi:https://doi.org/10.5194/amt-13-355-2020

Articles | Volume 13, issue 1

https://doi.org/10.5194/amt-13-355-2020

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

https://doi.org/10.5194/amt-13-355-2020

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

Articles | Volume 13, issue 1

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

Download

Final revised paper (published on 31 Jan 2020)
Preprint (discussion started on 04 Apr 2019)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Further revision required', Anonymous Referee #1, 01 Jul 2019
- AC1: 'Response to referee #1', Fei Yang, 13 Sep 2019
RC2: 'AMT-2019-95: A GPS water vapor tomography method based on a genetic algorithm, by Fei Yang, Jiming Guo, Junbo Shi, Xiaolin Meng, Yinzhi Zhao, Lv Zhou, and Di Zhang', Anonymous Referee #2, 19 Aug 2019
- AC2: 'Response to referee #2', Fei Yang, 13 Sep 2019

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)

Suggestions for revision or reasons for rejection

Comment:
It is not fully clear to me the implementation of the Genetic Algorithm. Namely the selection of the parameters settings and the sensitivity of the algorithm concerning these settings. The setup of the stopping criteria or calculation time.

Major recommendations:

I recommend a more effective and clear description concerning my previous comment.

P1L14-16: “By using the proposed approach, it is not necessary to perform the matrix inversion process, and the water vapor tomography is no longer dependent on excessive constraints, a priori information and external data, which give rise to many limitations and difficulties.”
I think this is a very strong sentence. The ART algorithms are iterative processes; they also do not need to perform the matrix inversion process. I use SIRT algorithms to do GNSS tomography and I do not use constraints. Please note that the tomographic solution is not tightly constraint to the a priori field. It uses the a priori field as a first guess to start the iteration, but this value can be much different from the value that the tomography will converge to in each voxel. The used term “no longer” may indicate that all the other methodologies besides the one presented here, use excessive constraints. I advice to rephrase the sentence.

P1L18-21: Please add some numerical information (values of agreement). How significant are the “high levels of agreement”, some overall numbers concerning the comparative results.

P3L18-21: “The mandatory use of excessive constraints”. Please read my comments in P1L14-16. It also depends on the density and configuration of the GNSS network, data quality, etc. I am able to get nice tomographic results with good convergence behaviour without constraints.

Referee Report: PDF

Hide

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

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.