Abstract
In most previous studies of tropospheric tomography, water vapor is assumed to have a homogeneous distribution within each voxel. The parameterization of voxels can mitigate the negative effects of the improper assumption to the tomographic solution. An improved parameterized algorithm is proposed for determining the water vapor distribution by Global Navigation Satellite System (GNSS) tomography. Within a voxel, a generic point is determined via horizontal inverse distance weighted (IDW) interpolation and vertical exponential interpolation from the wet refractivities at the eight surrounding voxel nodes. The parameters involved in exponential and IDW interpolation are dynamically estimated for each tomography by using the refractivity field of the last process. By considering the quasi-exponential behavior of the wet refractivity profile, an optimal algorithm is proposed to discretize the vertical layers of the tomographic model. The improved parameterization algorithm is validated with the observational data collected over a 1-month period from 124 Global Positioning System (GPS) stations of Hunan Province, China. Assessments by GPS, radiosonde, and European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5) data, demonstrate that the improved model outperforms the traditional nonparametric model and the parameterized model using trilinear interpolation. In the assessment by GPS data, the improved model performs better than the traditional model and the trilinear parameterized model by 54% and 10%, respectively. Such improvements are 31% and 10% in the validation by radiosonde profiles. In comparison with the ERA5 reanalysis, the improved model yields a minimum overall root mean square (RMS) error of 8.94 mm/km, while those of the traditional and trilinear parametrized models are 10.79 and 9.73 mm/km, respectively. The RMS errors vertically decrease from ~20 mm/km at the bottom to ~5 mm/km at the top layer.
Highlights
Water vapor in the troposphere represents a mere fraction of the total atmospheric volume but is strongly associated with climate change, atmospheric radiation, weather pattern, and hydrologic cycle [1–4]
Accurate information on water vapor leads to a better understanding of the aforementioned fields and to an enhanced natural hazard mitigation because water vapor observations are crucial for initializing the numerical weather prediction (NWP) models [5–7]
The global navigation satellite system (GNSS)-based tropospheric tomography has become a powerful technique for retrieving the water vapor fields with both high spatial and temporal resolutions owing to the rapid development of the GNSS [11–16]
Summary
Water vapor in the troposphere represents a mere fraction of the total atmospheric volume but is strongly associated with climate change, atmospheric radiation, weather pattern, and hydrologic cycle [1–4]. The first research work was carried out by Flores et al.; they reconstructed the 3D wet refractivity fields with the tomographic method by using rays from a global positioning system (GPS) network in Hawaii, USA [11] After this successful trial, a number of significant studies have been performed in terms of the theoretical models and experimental analysis for GNSS-based tropospheric tomography [5,16–22]. The model space is discretized into many voxels to reconstruct the wet refractivity field from the massive SWDs interweaving in the troposphere across different directions (Figure 1). The water vapor distribution is generally assumed to be homogeneous for each voxel over the reconstruction period In this case, each SWD is approximately equal to the sum of the product of wet refractivity and the length of the ray path crossing each voxel. The wet refractivity of a generic point is equal to that of its located voxel
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