Voxel Nodes Model Parameterization for GPS Water Vapor Tomography

Abstract

Water vapor is the basic parameter to describe atmospheric conditions and the content of it in the atmosphere is rare for water circulation system, but it is the most active element with quick space-time change. GPS tomography is a powerful way to provide high spatiotemporal resolution of water vapor density. In general, water vapor tomography utilizes slant wet delay information from ground-based GPS network to reconstruct the humidity field. Space at the zenith directions of ground-based GPS is discretized into voxel both at horizontal and vertical direction; setting up tomographic equations by slant delay observations can work out vapor parameter in voxel. In this paper, spatial structure model of humidity field is constructed by voxel nodes, and new parameterizations for acquiring data of water vapor in the troposphere by GPS are proposed based on inverse distance weighted (IDW) interpolation in horizontal and vertical interpolation function in vertical. Unlike the water vapor density is constant within a voxel; the density at a certain point is determined by new parameterizations. This algorithm avoids using horizontal constraint to smooth some voxels that not be crossed by satellite rays. Grouping and sorting access order scheme is introduced to minimize correlation between SWV observations. Three experimental schemes for GPS tomography are carried out using 7 days of Hong Kong Satellite Positioning Reference Station Network (SatRef). The results indicate that water vapor density derived from 4 nodes parameterization are most robust than 8 nodes and 12 nodes.