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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: regular paper 03 Jul 2019

Submitted as: regular paper | 03 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Annales Geophysicae (ANGEO).

Observing geometry effects on a GNSS based water vapor tomography solved by Least Squares and by Compressive Sensing

Marion Heublein, Patrick Erik Bradley, and Stefan Hinz Marion Heublein et al.
  • Karlsruhe Institute of Technology, Institute of Photogrammetry and Remote Sensing, 76128 Karlsruhe, Germany

Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized Least Squares (LSQ) and a Compressive Sensing (CS) approach for neutrospheric water vapor tomography is compared based on synthetic Global Navigation Satellite System (GNSS) Slant Wet Delay (SWD) estimates. In this context, the term observing geometry mainly refers to the number of GNSS sites situated within a specific study area subdivided into a certain number of volumetric pixels (voxels) and to the number of signal directions available at each GNSS site. The novelties of this research are 1) the comparison of the observing geometry’s effects on the tomographic reconstruction accuracy when using LSQ resp. CS for the solution of the tomographic system and 2) the investigation of the effect of the signal directions’ variability on the tomographic reconstruction. The tomographic reconstruction is performed based on synthetic SWD data sets generated, for many samples of various observing geometry settings, based on wet refractivity information from the Weather Research and Forecasting (WRF) model. The validation of the achieved results focuses on a comparison of the refractivity estimates with the input WRF refractivities. The results show that the recommendation of Champollion et al. (2004) to discretize the analyzed study area into voxels with horizontal sizes comparable to the mean GNSS inter site distance represents a good rule of thumb for both LSQ and CS based tomography solutions. In addition, this research shows that CS needs a variety of at least 15 signal directions per site in order to estimate the refractivity field more accurately and more precisely than LSQ. Therefore, the use of CS is particularly recommended for water vapor tomography applications for which a high number of multi-GNSS SWD estimates are available.

Marion Heublein et al.
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Status: final response (author comments only)
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Marion Heublein et al.
Marion Heublein et al.
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