the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Tropospheric water vapor: A comprehensive high resolution data collection for the transnational Upper Rhine Graben region
- 1Karlsruhe Institute of Technology, Campus Alpin (IMK-IFU), Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
- 2University of Augsburg, Institute of Geography (IGUA), Alter Postweg 118, 86159 Augsburg, Germany
- 3Karlsruhe Institute of Technology, Institute of Photogrammetry and Remote Sensing (IPF), Englerstr. 7, 76131 Karlsruhe, Germany
- 4Karlsruhe Institute of Technology, Geodetic Institute (GIK), Englerstr. 7, 76131 Karlsruhe, Germany
- 5ETH Zurich, Institute of Geodesy and Photogrammetry, Robert-Gnehm-Weg 15, 8093, Zurich, Switzerland
- 1Karlsruhe Institute of Technology, Campus Alpin (IMK-IFU), Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
- 2University of Augsburg, Institute of Geography (IGUA), Alter Postweg 118, 86159 Augsburg, Germany
- 3Karlsruhe Institute of Technology, Institute of Photogrammetry and Remote Sensing (IPF), Englerstr. 7, 76131 Karlsruhe, Germany
- 4Karlsruhe Institute of Technology, Geodetic Institute (GIK), Englerstr. 7, 76131 Karlsruhe, Germany
- 5ETH Zurich, Institute of Geodesy and Photogrammetry, Robert-Gnehm-Weg 15, 8093, Zurich, Switzerland
Abstract. Tropospheric water vapor is among the most important trace gases of the Earth’s climate system and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the non-linear relation of air temperature and saturated vapor pressure, it is highly variable which hampers the development of high resolution and three-dimensional maps of regional extent. As a complement to the sparsely distributed radio sounding observation network, GNSS meteorology and interferometric radar satellite remote sensing can assist with their complementary high temporal or spatial resolution. In addition, data fusion with collocation and tomography methods enables the construction of detailed maps in either two or three dimensions. By assimilation of these observation based datasets into regional dynamic atmospheric models the optimal state of the tropospheric water vapor conditions can be guessed. In the following, a collection of basic and processed datasets, obtained with the above listed methods, is presented that describes the state and course of atmospheric water vapor within the range of the GNSS Upper Rhine Graben Network (GURN) region.
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Benjamin Fersch et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2022-57', Anonymous Referee #1, 02 Jul 2022
Review of Fersch et al.: Tropospheric water vapor: A comprehensive high resolution data collection of the transnational Upper Rhine Graben region
Water vapor is a crucial constituent of the atmosphere, not least because of its importance for severe weather events and climate change. The authors describe GNSS and InSAR datasets as input for assimilation in atmospheric models, along with the applied methods for merging. The datasets encompass the Upper Rhine Graben Region. The data are valuable and an interesting contribution for the scientific community.
The article is not always easy to read, but I understand that this is due to the fact that different communities (GNSS, InSAR, WRF, ..) are coming here together for this joint work. Moreover, some abbreviations are not understandable at first reading. There is an appendix with the explanations, but it would be appreciated if more explanations are added in the text.
Other comments:
line 12: What is meant with 2.5 mm global mean water equivalent? Average precipitable water? If yes, I would have expected a larger value.
185: are applied
Equation 1: I suggest adding the gradient mapping function to grad(a,e)
277: Where is Figure S4?
Equation 3: is there a certain reason to use * instead of . ?
466: derived
Figure captions 9 and 10, and others: please provide all the information in the figure caption, which is necessary to understand the figure.
493: datasets
Equation A1, and other equations in the appendix: please add units
Equations A8 and A9 denote the ZWD delay as a pure "wet" delay. On the other hand, A3 refer to a non-hydrostatic delay (not wet in the strict sense). Does this (small) difference cause any inconsistencies?
- AC1: 'Reply on RC1', Benjamin Fersch, 04 Aug 2022
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RC2: 'Comment on essd-2022-57', Minyan Wang, 14 Jul 2022
The authors introduce the process of how to get tropospheric IWV products based on occultation and satellite remote sensing synthetic aperture radar data, using data fusion and data assimilation methods in WRF, and describes the preliminarily evaluations of the quality in this paper.
Major comments.
1. In the introduction, it should be included the previous research on using similar methods and data to get the product. It is suggested to add.
2. At present, the contents before page 7 are far more than expectation. Many of them have nothing to do with the research itself, but are related to the fundamental data and methods. It is suggested that only the contents closely related to the study are kept. The structure of the paper needs to be greatly adjusted. The second section and the first section should be merged, and there is no need for subtitles. The second section is not an introduction to the data and research methods used in this paper, but is more like the expression in the master's or doctoral thesis technical document. It is not suitable in the scientific research paper. Pages 2 to 7 is suggested to be shortened to 3 pages at most. It is assumed that the reader is engaged in parts of research in this field. The third section should not be a description of the data set, but an introduction to the specific input data used in my research and the methods. You can also introduce the horizontal spacing and vertical resolution of observation points actually used in the research. It seems that the focus of input data and output products of this study is not prominent. The IWV product is in the form of grid data, right?
3. The difficulty of this study is the treatment in the case of clear/cloudy circumstances, or precipitation, when the variation of water vapor in the lower atmosphere is more complex.
4ï¼What are the thresholds of spatial and temporal matching during evaluation or colocation? The distance in space, and the time difference.
The evaluation results are insufficient. What is the variation of time series? What is the seasonal change from 2015 to 2019? And what is the difference with those in ERA5? In ERA5, GNSS bending angles and water vapor information from radiosonde in different height of the atmospheric are assimilated. InSAR data are not assimilated. Is IWV integration of GNSS assimilated? From the results, it is found that local water vapor field is more reasonable after assimilation of new observations like InSAR.
Minor comments.
1. The temporal and spatial range of the product should be stated in the abstract and summary. In the conclusion, we should summarize the above and data quality of this product, briefly explain the input data and methods.
2. Abstract. Among should be changed, like one of. Guess should be changed, like estimated or other word. Add the physical quantity IWV in the abstract, which is very important. Add the time period from 2015 to 2019 (or others?), and the quantitative results like 0.98.
3. L82. Delete the citation of the extreme weather events.
4. L267 and 338, etc., no details like these are required.
5. Figure 1 can be deleted. Please consider whether to delete section 3.5.
6. Figure 2 and Figure 4 (not 4S? Put Section 3.3 instead of section 3.5) should be placed in earlier pages.
7. L345. There is a logic problem. Generally, the citations should not be this section itself. It is section 3.
8. Figure 5. What are the first seven in the y vertical axis?
9. Figure 6. In the caption and text, = is inappropriate. The relationship between GSI and GNSS should be expressed in many words to prevent ambiguity. GSI is not referred to the NCEP assimilation system (Gridpoint Statistical Interpolation)?
10. Figure 10. More scales should be added in y axis. The longitude and latitude of Figures 4 and 11 are irregular, while those in Figure 2 are standard.
Benjamin Fersch et al.
Data sets
A comprehensive high resolution data collection for tropospheric water vapor assessment for the Upper Rhine Graben, Germany. Fersch, Benjamin; Kamm, Bettina; Shehaj, Endrit; Wagner, Andreas; Yuan, Peng; Möller, Gregor; Schenk, Andreas; Geiger, Alain; Hinz, Stefan; Kutterer, Hansjörg; Kunstmann, Harald https://www.pangaea.de/tok/e11e2f371fb3b638563ed6b6e3128564ba9ba845
Benjamin Fersch et al.
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