Improved GNSS Water Vapor Tomography With Modified Mapping Functions

The use of optimized GNSS mapping functions is here shown to lead to significant improvements in the performance of a water vapor tomographic model, totally driven by GNSS observations. The method improves a recent proposal for unconstrained tomographic inversions and is developed and validated with...

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Bibliographic Details
Published inGeophysical research letters Vol. 49; no. 18
Main Authors Miranda, P. M. A., Mateus, P.
Format Journal Article
LanguageEnglish
Published Washington John Wiley & Sons, Inc 28.09.2022
Wiley
Subjects
Distribution
Geodesy
Global navigation satellite system
Global positioning systems
GNSS meteorology
GPS
Inversions
Mapping
mapping function
Navigation
Navigation satellites
Navigation systems
Navigational satellites
Optimization
Parameters
Radiosondes
Satellite navigation systems
Satellite observation
Statistical methods
Synoptic conditions
Tomography
Vapor density
Vertical distribution
Water vapor
Water vapour
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Summary:The use of optimized GNSS mapping functions is here shown to lead to significant improvements in the performance of a water vapor tomographic model, totally driven by GNSS observations. The method improves a recent proposal for unconstrained tomographic inversions and is developed and validated with data from the Manaus dense GNSS network and in‐situ radiosondes, covering different seasons and synoptic conditions. The optimization uses a Monte Carlo technique to find the minimum root‐mean‐square error in a two‐dimensional parameter space. A set of fixed parameters, computed by optimizing over a small subset of cases, is found to lead to results that are overall significantly better than those produced by the three more common mapping functions used for geodesy applications. The quality of inversions is, however, found to have significant spread, an indication of the impact of varying GNSS data quality and a reminder of the need of further improvements in the method. Plain Language Summary The three‐dimensional distribution of water vapor can be obtained by the tomographic inversion of global navigation satellite systems data, such as GPS. Here we show that the method can be substantially improved, and work without any extra information, by fitting the data to an optimized mapping function. The optimization is done by searching for the minimum root mean square error of the vertical distribution of water vapor density, observed by radiosondes, with a Monte Carlo technique. Key Points An algorithm for GNSS water vapor tomography is significantly improved with an objectively optimized mapping function The new mapping function is found to outperform the three main competing formulations, including the preferred geodetic function The improved model is found to behave consistently in a several months continuous experiment in the equatorial climate
ISSN:0094-8276
1944-8007
DOI:10.1029/2022GL100140