Ground-Based GPS Tomography of Water Vapor: Analysis of Simulated and Real Data

Abstract

We present the concept, some of the approaches used, and the capabilities of the technique referred to as GPS tomography. It is used for retrieval of the 3-dimensional distribution of the refractivity due to atmospheric water vapor. We discuss the presently used methods for retrieval of the primary observable in the GPS tomography, the slant path delay, as well as their shortcomings. Comparisons of GPS slant delays to independent data from a microwave radiometer are included. From a tomographic point of view we concentrate on the capabilities to retrieve the vertical structure of the wet refractivity. For this purpose we present and apply two methods for tomographic inversion. Both are based on the Kalman filtering technique, where the expected statistical behavior of the refractivity is utilized. The difference between the two is in the way the covariance matrix of the Kalman filter is constructed. We base our study on simulated and real data from the ground network of 8 GPS receivers operating in Göteborg, Sweden. The results demonstrate that at present the limitations of the GPS tomographic technique are errors in the retrieved wet slant delays and their poor geometric distribution.

New parameterized model for GPS water vapor tomography

“…The methods for solving the abovementioned problems can be broadly divided into four categories: (1) enhancement of the precision of SWD using the "zero differences" (ZDs) technique (Alber et al, 2000;Seko et al, 2004); (2) addition of constraint conditions to tomographic models, e.g., horizontal, vertical, and boundary constraint conditions (Flores et al, 2000;Hirahara, 2000;Perler, 2011;Rohm and Bosy, 2009;Seko et al, 2000;Song et al, 2006); (3) usage of additional extra observations through RINEX met files, zenith wet delay, WVR, RS, and voxel-optimized regional water vapor tomography (Bi et al, 2006;Chen and Liu, 2014;Jiang et al, 2014;Rocken et al, 1993;Rohm et al, 2014;Yao et al, 2016); and (4) new algorithms to improve inversion quality, such as singular value decomposition (SVD), the wet refractivity Kalman filter (KF), algebraic reconstruction techniques (ARTs), and the parameterization of voxels (volumetric pixels) based on trilinear and spline functions (Bender et al, 2011;Flores et al, 2001;Gradinarsky, 2002;Gradinarsky and Jarlemark, 2004;Nilsson and Gradinarsky, 2006;Rohm et al, 2013;Shangguan et al, 2013). At present, we are focused on replacing divided voxel-based traditional methods with new, parameterized approaches.…”

Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong

“…Finally, assuming the time evolution of humidity values is a random walk stochastic process, we apply a standard Kalman filtering (Herring et al, 1990;Gradinarsky and Jarlemark, 2004) to identify the time variation of the studied humidity field. The Kalman filtering method has the advantage of obtaining a 4-D humidity field.…”

GNSS Signals: A Powerful Source for Atmosphere and Earth’s Surface Monitoring

“…After that, several methods were applied to different kind of real or simulated GPS observables (obtained by more or less dense receiver networks), demonstrating the effectiveness of water vapour field reconstructions on different atmospheric volume sizes, with different resolutions, against radiosonde data, Numerical Weather Prediction models or other independent water vapour dataset. Some reference papers (the list is not exhaustive) are that of Hirahara (2000), Gradinarsky and Jarlemark (2004), Champollion (2005Champollion ( , 2009), Bi et al (2006), Troller et al (2006), Nilsson and Gradinarsky (2006).…”