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: open (until 09 Aug 2022)
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RC1: 'Comment on essd-2022-57', Anonymous Referee #1, 02 Jul 2022
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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?
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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