Abstract
A rapid tropospheric tomography system was developed by using algebraicreconstruction technique. Influences of different factors on the tomographic results, including theground meteorological data, the multi-Global Navigation Satellite System (GNSS) observations, theground station distribution and the tomographic horizontal resolution, were systematicallyinvestigated. In order to exclude the impacts from discrepancies of water vapor informationbetween input observations and references on the tomographic results, the latest reanalysisproducts, ERA5, which were taken as references for result evaluations, were used to simulate slantwet delay (SWD) observations at GNSS stations. Besides, the slant delays derived from GNSSprocessing were also used to evaluate the reliability of simulated observations. Tomography resultsshow that the input both SWD and ground meteorological data could improve the tomographicresults where SWD mainly improve the results at middle layers (500 to 5000m, namely 2 to 16 layer)and ground meteorological data could improve the humidity fields at bottom layers further (0 to500m, namely 0 to 2 layer). Compared to the usage of Global Positioning System (GPS) only SWD,the inclusion of multi-GNSS SWD does not significantly improve the tomographic results at alllayers due to the almost unchanged dispersion of puncture points of GNSS signals. However,increases in the ground GNSS stations can benefit the tomography, with improvements of morethan 10% at bottom and middle layers. Higher tomographic horizontal resolution can furtherslightly improve the tomographic results (about 3-6% from 0.5 to 0.25 degrees), which, however,will also increase the computational burden at the same time.
Highlights
As the most abundant greenhouse gas, atmospheric water vapor plays an important role in the climate and weather system
The discrepancy between the simulated slant tropospheric delay (STD) and Global Navigation Satellite System (GNSS)-derived STD has no impacts on the tomographic results, but through this kind of comparisons, we can learn the accuracy of simulated slant wet delay (SWD) and STD based on ERA5, and provide a meaningful reference for other studies
Water vapor is a precursor of cloud, rain, snow, sleet, hail, and other precipitations, all of which provide useful information to weather phenomena study and navigation and positioning, but it faces challenges in rank-deficit issue and massive parameter rapid estimation which were solved by using numerical weather prediction (NWP) forecasting products and algebraic reconstruction technique (ART) technique in this study
Summary
As the most abundant greenhouse gas, atmospheric water vapor plays an important role in the climate and weather system. Due to the geometric distribution of the ground-based GNSS station and the geometry of the satellite constellation, some voxels in the tomographic region cannot be touched by any GNSS signal ray paths, resulting in a rank-deficient issue in the tropospheric tomography. This issue can be solved by introducing additional constraints, for example, by adding the vertical constraints [16], or taking numerical weather prediction (NWP) model as initial values [17].
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