A New Unconstrained Approach to GNSS Atmospheric Water Vapor Tomography
Pedro Miranda, Pedro Mateus
Abstract
Abstract A new atmospheric tomographic model totally based on Global Navigation Satellite System (GNSS) observations is proposed and tested against field observations. The method does not require a first guess, does not contain specific constraints on the variability of water vapor density inside the tomographic domain, and is able to produce reasonable results at 6 km horizontal and 500 m vertical resolutions, from short (30 min) GNSS data samples. The inversion method uses the Moore‐Penrose pseudoinverse, which is made possible by increasing the rank of the design matrix through angular interpolation and extrapolation of the observations. Comparisons against 30 consecutive 4‐h radiosonde observations and model simulations suggest the ability of the method to detect inversions and local maxima aloft, and behave sensibly in the far‐field. Further improvements from this method may be expected from higher density and multi‐constellation networks.