Abstract
Abstract. In this work, the effect of the observing geometry on the tomographic reconstruction quality of both a regularized least squares (LSQ) approach and a compressive sensing (CS) approach for 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 or 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 intersite 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.
Highlights
In this paper, we intend to determine the three-dimensional (3-D) atmospheric water vapor distribution for each point in time
4 Results For the most humid acquisition date (27 June 2005) for which Weather Research and Forecasting (WRF) simulations were provided for this research and for an exemplary voxel in the lower middle of the lowest voxel layer, Fig. 3 shows that variations in the slant wet delay (SWD) signal directions available within the tomographic system cause variations in the estimated refractivities
When investigating the observing geometry’s effect on the quality of the tomographic reconstruction, the chosen solution strategy as well as the effect of varying signal directions should be taken into account
Summary
We intend to determine the three-dimensional (3-D) atmospheric water vapor distribution for each point in time. This adds further essential information to the spatiotemporal analyses of two-dimensional (2-D) water vapor fields commonly used in weather forecasting and climate research. Atmospheric water vapor delays the microwave signal propagation within the atmosphere and represents an error source in, e.g., Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) observations. A precise knowledge of the water vapor field, for example, is required for accurate deformation monitoring using InSAR (Hansen and Yu, 2001). Several approaches exist for reconstructing the 3-D tomographic water vapor reconstruction using one-dimensional (1-D) GNSS slant wet delays (SWDs); see Sect. Several approaches exist for reconstructing the 3-D tomographic water vapor reconstruction using one-dimensional (1-D) GNSS slant wet delays (SWDs); see Sect. 2
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
