Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator

Hanna, Natalia; Trzcina, Estera; Möller, Gregor; Rohm, Witold; Weber, Robert

doi:https://doi.org/10.5194/amt-12-4829-2019

Articles | Volume 12, issue 9

https://doi.org/10.5194/amt-12-4829-2019

© 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/amt-12-4829-2019

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

Articles | Volume 12, issue 9

Research article

|

10 Sep 2019

Research article |

| 10 Sep 2019

Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator

Natalia Hanna, Estera Trzcina, Gregor Möller, Witold Rohm, and Robert Weber

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Final revised paper (published on 10 Sep 2019)
Preprint (discussion started on 12 Feb 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of the manuscript', Anonymous Referee #1, 15 Mar 2019
- AC1: 'Review1 - Answers', Natalia Hanna, 11 Jun 2019
RC2: 'General Review Comments', Anonymous Referee #3, 14 May 2019
- AC2: 'Review2 - answers', Natalia Hanna, 11 Jun 2019

Peer-review completion

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

AR by Natalia Hanna on behalf of the Authors (18 Jun 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (11 Jul 2019) by Eric Pottiaux

RR by Anonymous Referee #1 (29 Jul 2019)

RR by Anonymous Referee #3 (13 Aug 2019)

Suggestions for revision or reasons for rejection

Minor revisions and comments
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• Page 5, lines 7-8: The authors answered my question why they projected in the direction of both GPS and GLONASS satellites while Dousa et al. only processed GPS observations. However, the manuscript does not mention it. Please mention at least the fact that Dousa et al. have processed only GPS observations but that you can project in the direction of all satellites from both constellation because you expect that the error will not be significant.
• Page 10, line 9: “This information exchange between the grids in both directions” --> this sentence is not correct, do you mean something like “The latest allow information exchange between the grids in both directions”?
• Page 15, line 15: “larger standard deviations” --> too vague, please be more precise, e.g. “larger standard deviations up to …”.
• Page 17, line 17: “horizontally interpolated” --> by which method, please be concrete.
• Page 18, Table 4 (recommendation): it would have been good to provide information about the extremes values. This could be easily added with a box plot graph (for ALL) for each 3 comparisons.

Technical comments
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• Page 2, line 16: “numerical weather prediction (NWP) models” --> to make the text consistent throughout the manuscript, the first letters of any acronym description should be capitalized, i.e. “Numerical Weather Prediction (NWP) models”.
• Page 2, lines 21-22: Please order the citation by year.
• Page 2, line 28: “horizontal structures in troposphere” should be “horizontal structures in the troposphere”.
• Page 2, line 33: “are considered in reconstruction” should be “are considered in the reconstruction”.
• Page 4, line 11: “Near-Real-Time” should be “Near Real-Time”.
• Page 4, line 28: “GNSS4SWEC” --> the acronym was never defined before, so please add the full title at first instance of GNSS4SWEC + reference to the website/final report.
• Page 4, line 28: “COST action ES1206” should be “COST Action ES1206”.
• Page 4, line 29: “(1h ZTDs and 6h horizontal gradients)” should be “(with time resolution of 1h for ZTDs and 6h for horizontal gradients)”.
• Page 5, line 20: “we retrived hydrostatic gradients” should be “we retrieved hydrostatic gradients”.
• Page 6, line 8: “(see section 3.1” should be “(see Section 3.1”.
• Page 7, line 6: “truncated singular value decomposition (TSVD)” should be “Truncated Singular Value Decomposition (TSVD)”.
• Page 7, line 18: “3.1 Least-square solution” --> some occurrences in the manuscript are written “least-square” (with a stripe), some are written “least square” (without stripe). Please harmonize the text.
• Page 9, line 17: “numerical weather prediction” --> “NWP”.
• Page 10, line 4: “A coarse grid” should be “The coarse grid”.
• Page 10, line 16: “These data are available on …” --> “These data are available at …”.
• Page 18: The caption label “Figure 4” should be “Figure 6”.
• Page 18, line 6: “GNSS derived” should be “GNSS-derived”.
• Page 20: The caption label “Figure 5” should be “Figure 7”.
• Page 21: The caption label “Figure 6” should be “Figure 8”.
• Page 22: The caption label “Figure 7” should be “Figure 9”.
• Page 23: The caption label “Figure 8” should be “Figure 10”.
• Page 23, Figure 8 caption: “Number of successfully assimilated observations for” should be “Percentage of observations successfully assimilated for”.
• Page 25: The caption label “Figure 9” should be “Figure 11”.
• Page 29: The caption label “Figure 10” should be “Figure 14”.
• Page 29, Figure 10 caption: “The difference between” should be “Absolute bias and standard deviation of the differences between”.
• Page 30, line 2: “can play the key role in” should be “can play a key role in”.

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ED: Publish subject to minor revisions (review by editor) (13 Aug 2019) by Eric Pottiaux

AR by Natalia Hanna on behalf of the Authors (16 Aug 2019) Author's response Manuscript

ED: Publish as is (21 Aug 2019) by Eric Pottiaux

Short summary

In the study, the potential of GNSS tomography as an important supplementary data source for numerical weather prediction models was examined. We used two GNSS tomography models (TUW, WUELS) in different configurations. The GNSS tomography outputs were assimilated into the WRF model using a radio occultation observations operator (non-standard approach). Promising results show improvement in the weather forecasting of relative humidity and temperature during heavy-precipitation events.